Proj_1_CV.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5KIqzPY7g-i_",
        "colab_type": "text"
      },
      "source": [
        "# **Imports**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "au1RnENnrV83",
        "colab_type": "code",
        "outputId": "06f382dc-a884-4423-900a-baed526101b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#1\n",
        "#Check CPU and RAM specifications\n",
        "!cat /proc/cpuinfo\n",
        "!cat /proc/meminfo"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "processor\t: 0\n",
            "vendor_id\t: GenuineIntel\n",
            "cpu family\t: 6\n",
            "model\t\t: 63\n",
            "model name\t: Intel(R) Xeon(R) CPU @ 2.30GHz\n",
            "stepping\t: 0\n",
            "microcode\t: 0x1\n",
            "cpu MHz\t\t: 2300.000\n",
            "cache size\t: 46080 KB\n",
            "physical id\t: 0\n",
            "siblings\t: 2\n",
            "core id\t\t: 0\n",
            "cpu cores\t: 1\n",
            "apicid\t\t: 0\n",
            "initial apicid\t: 0\n",
            "fpu\t\t: yes\n",
            "fpu_exception\t: yes\n",
            "cpuid level\t: 13\n",
            "wp\t\t: yes\n",
            "flags\t\t: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid xsaveopt arat md_clear arch_capabilities\n",
            "bugs\t\t: cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs itlb_multihit\n",
            "bogomips\t: 4600.00\n",
            "clflush size\t: 64\n",
            "cache_alignment\t: 64\n",
            "address sizes\t: 46 bits physical, 48 bits virtual\n",
            "power management:\n",
            "\n",
            "processor\t: 1\n",
            "vendor_id\t: GenuineIntel\n",
            "cpu family\t: 6\n",
            "model\t\t: 63\n",
            "model name\t: Intel(R) Xeon(R) CPU @ 2.30GHz\n",
            "stepping\t: 0\n",
            "microcode\t: 0x1\n",
            "cpu MHz\t\t: 2300.000\n",
            "cache size\t: 46080 KB\n",
            "physical id\t: 0\n",
            "siblings\t: 2\n",
            "core id\t\t: 0\n",
            "cpu cores\t: 1\n",
            "apicid\t\t: 1\n",
            "initial apicid\t: 1\n",
            "fpu\t\t: yes\n",
            "fpu_exception\t: yes\n",
            "cpuid level\t: 13\n",
            "wp\t\t: yes\n",
            "flags\t\t: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid xsaveopt arat md_clear arch_capabilities\n",
            "bugs\t\t: cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs itlb_multihit\n",
            "bogomips\t: 4600.00\n",
            "clflush size\t: 64\n",
            "cache_alignment\t: 64\n",
            "address sizes\t: 46 bits physical, 48 bits virtual\n",
            "power management:\n",
            "\n",
            "MemTotal:       13333556 kB\n",
            "MemFree:         5626908 kB\n",
            "MemAvailable:   10670524 kB\n",
            "Buffers:           86260 kB\n",
            "Cached:          4171720 kB\n",
            "SwapCached:            0 kB\n",
            "Active:          2649448 kB\n",
            "Inactive:        4493084 kB\n",
            "Active(anon):    1917104 kB\n",
            "Inactive(anon):    20808 kB\n",
            "Active(file):     732344 kB\n",
            "Inactive(file):  4472276 kB\n",
            "Unevictable:           0 kB\n",
            "Mlocked:               0 kB\n",
            "SwapTotal:             0 kB\n",
            "SwapFree:              0 kB\n",
            "Dirty:               296 kB\n",
            "Writeback:             0 kB\n",
            "AnonPages:       2884628 kB\n",
            "Mapped:           824852 kB\n",
            "Shmem:             21412 kB\n",
            "Slab:             213736 kB\n",
            "SReclaimable:     159756 kB\n",
            "SUnreclaim:        53980 kB\n",
            "KernelStack:        4320 kB\n",
            "PageTables:        13144 kB\n",
            "NFS_Unstable:          0 kB\n",
            "Bounce:                0 kB\n",
            "WritebackTmp:          0 kB\n",
            "CommitLimit:     6666776 kB\n",
            "Committed_AS:    5493772 kB\n",
            "VmallocTotal:   34359738367 kB\n",
            "VmallocUsed:           0 kB\n",
            "VmallocChunk:          0 kB\n",
            "Percpu:              920 kB\n",
            "AnonHugePages:    552960 kB\n",
            "ShmemHugePages:        0 kB\n",
            "ShmemPmdMapped:        0 kB\n",
            "HugePages_Total:       0\n",
            "HugePages_Free:        0\n",
            "HugePages_Rsvd:        0\n",
            "HugePages_Surp:        0\n",
            "Hugepagesize:       2048 kB\n",
            "Hugetlb:               0 kB\n",
            "DirectMap4k:      296124 kB\n",
            "DirectMap2M:     8091648 kB\n",
            "DirectMap1G:     7340032 kB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jnT2f9QNraBk",
        "colab_type": "code",
        "outputId": "165eba98-8565-43e4-8a1f-387fcf5a9c7a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        }
      },
      "source": [
        "#2\n",
        "#Check GPU specifications\n",
        "from tensorflow.python.client import device_lib\n",
        "device_lib.list_local_devices()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[name: \"/device:CPU:0\"\n",
              " device_type: \"CPU\"\n",
              " memory_limit: 268435456\n",
              " locality {\n",
              " }\n",
              " incarnation: 10206111499572708790, name: \"/device:XLA_CPU:0\"\n",
              " device_type: \"XLA_CPU\"\n",
              " memory_limit: 17179869184\n",
              " locality {\n",
              " }\n",
              " incarnation: 3127188986815100851\n",
              " physical_device_desc: \"device: XLA_CPU device\", name: \"/device:XLA_GPU:0\"\n",
              " device_type: \"XLA_GPU\"\n",
              " memory_limit: 17179869184\n",
              " locality {\n",
              " }\n",
              " incarnation: 11725598480896674399\n",
              " physical_device_desc: \"device: XLA_GPU device\", name: \"/device:GPU:0\"\n",
              " device_type: \"GPU\"\n",
              " memory_limit: 14978973696\n",
              " locality {\n",
              "   bus_id: 1\n",
              "   links {\n",
              "   }\n",
              " }\n",
              " incarnation: 12111665464629010265\n",
              " physical_device_desc: \"device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0\"]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fbitiU5ztZs-",
        "colab_type": "code",
        "outputId": "7d3eaf77-418a-4e6f-9702-bd3759fb4ec2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#3\n",
        "!pip list"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Package                  Version        \n",
            "------------------------ ---------------\n",
            "absl-py                  0.9.0          \n",
            "alabaster                0.7.12         \n",
            "albumentations           0.1.12         \n",
            "altair                   4.1.0          \n",
            "asgiref                  3.2.7          \n",
            "astor                    0.8.1          \n",
            "astropy                  4.0.1.post1    \n",
            "astunparse               1.6.3          \n",
            "atari-py                 0.2.6          \n",
            "atomicwrites             1.3.0          \n",
            "attrs                    19.3.0         \n",
            "audioread                2.1.8          \n",
            "autograd                 1.3            \n",
            "Babel                    2.8.0          \n",
            "backcall                 0.1.0          \n",
            "beautifulsoup4           4.6.3          \n",
            "bleach                   3.1.4          \n",
            "blis                     0.4.1          \n",
            "bokeh                    1.4.0          \n",
            "boto                     2.49.0         \n",
            "boto3                    1.12.39        \n",
            "botocore                 1.15.39        \n",
            "Bottleneck               1.3.2          \n",
            "branca                   0.4.0          \n",
            "bs4                      0.0.1          \n",
            "CacheControl             0.12.6         \n",
            "cachetools               3.1.1          \n",
            "catalogue                1.0.0          \n",
            "certifi                  2020.4.5.1     \n",
            "cffi                     1.14.0         \n",
            "chainer                  6.5.0          \n",
            "chardet                  3.0.4          \n",
            "click                    7.1.1          \n",
            "cloudpickle              1.3.0          \n",
            "cmake                    3.12.0         \n",
            "cmdstanpy                0.4.0          \n",
            "colorlover               0.3.0          \n",
            "community                1.0.0b1        \n",
            "contextlib2              0.5.5          \n",
            "convertdate              2.2.0          \n",
            "coverage                 3.7.1          \n",
            "coveralls                0.5            \n",
            "crcmod                   1.7            \n",
            "cufflinks                0.17.3         \n",
            "cupy-cuda101             6.5.0          \n",
            "cvxopt                   1.2.4          \n",
            "cvxpy                    1.0.31         \n",
            "cycler                   0.10.0         \n",
            "cymem                    2.0.3          \n",
            "Cython                   0.29.16        \n",
            "daft                     0.0.4          \n",
            "dask                     2.12.0         \n",
            "dataclasses              0.7            \n",
            "datascience              0.10.6         \n",
            "decorator                4.4.2          \n",
            "defusedxml               0.6.0          \n",
            "descartes                1.1.0          \n",
            "dill                     0.3.1.1        \n",
            "distributed              1.25.3         \n",
            "Django                   3.0.5          \n",
            "dlib                     19.18.0        \n",
            "docopt                   0.6.2          \n",
            "docutils                 0.15.2         \n",
            "dopamine-rl              1.0.5          \n",
            "earthengine-api          0.1.217        \n",
            "easydict                 1.9            \n",
            "ecos                     2.0.7.post1    \n",
            "editdistance             0.5.3          \n",
            "en-core-web-sm           2.2.5          \n",
            "entrypoints              0.3            \n",
            "ephem                    3.7.7.1        \n",
            "et-xmlfile               1.0.1          \n",
            "fa2                      0.3.5          \n",
            "fancyimpute              0.4.3          \n",
            "fastai                   1.0.60         \n",
            "fastdtw                  0.3.4          \n",
            "fastprogress             0.2.3          \n",
            "fastrlock                0.4            \n",
            "fbprophet                0.6            \n",
            "feather-format           0.4.0          \n",
            "featuretools             0.4.1          \n",
            "filelock                 3.0.12         \n",
            "firebase-admin           4.0.1          \n",
            "fix-yahoo-finance        0.0.22         \n",
            "Flask                    1.1.2          \n",
            "folium                   0.8.3          \n",
            "fsspec                   0.7.2          \n",
            "future                   0.16.0         \n",
            "gast                     0.3.3          \n",
            "GDAL                     2.2.2          \n",
            "gdown                    3.6.4          \n",
            "gensim                   3.6.0          \n",
            "geographiclib            1.50           \n",
            "geopy                    1.17.0         \n",
            "gin-config               0.3.0          \n",
            "glob2                    0.7            \n",
            "google                   2.0.3          \n",
            "google-api-core          1.16.0         \n",
            "google-api-python-client 1.7.12         \n",
            "google-auth              1.7.2          \n",
            "google-auth-httplib2     0.0.3          \n",
            "google-auth-oauthlib     0.4.1          \n",
            "google-cloud-bigquery    1.21.0         \n",
            "google-cloud-core        1.0.3          \n",
            "google-cloud-datastore   1.8.0          \n",
            "google-cloud-firestore   1.6.2          \n",
            "google-cloud-language    1.2.0          \n",
            "google-cloud-storage     1.18.1         \n",
            "google-cloud-translate   1.5.0          \n",
            "google-colab             1.0.0          \n",
            "google-pasta             0.2.0          \n",
            "google-resumable-media   0.4.1          \n",
            "googleapis-common-protos 1.51.0         \n",
            "googledrivedownloader    0.4            \n",
            "graphviz                 0.10.1         \n",
            "grpcio                   1.28.1         \n",
            "gspread                  3.0.1          \n",
            "gspread-dataframe        3.0.5          \n",
            "gym                      0.17.1         \n",
            "h5py                     2.10.0         \n",
            "HeapDict                 1.0.1          \n",
            "holidays                 0.9.12         \n",
            "html5lib                 1.0.1          \n",
            "httpimport               0.5.18         \n",
            "httplib2                 0.17.2         \n",
            "httplib2shim             0.0.3          \n",
            "humanize                 0.5.1          \n",
            "hyperopt                 0.1.2          \n",
            "ideep4py                 2.0.0.post3    \n",
            "idna                     2.8            \n",
            "image                    1.5.30         \n",
            "imageio                  2.4.1          \n",
            "imagesize                1.2.0          \n",
            "imbalanced-learn         0.4.3          \n",
            "imblearn                 0.0            \n",
            "imgaug                   0.2.9          \n",
            "importlib-metadata       1.6.0          \n",
            "imutils                  0.5.3          \n",
            "inflect                  2.1.0          \n",
            "intel-openmp             2020.0.133     \n",
            "intervaltree             2.1.0          \n",
            "ipykernel                4.10.1         \n",
            "ipython                  5.5.0          \n",
            "ipython-genutils         0.2.0          \n",
            "ipython-sql              0.3.9          \n",
            "ipywidgets               7.5.1          \n",
            "itsdangerous             1.1.0          \n",
            "jax                      0.1.62         \n",
            "jaxlib                   0.1.42         \n",
            "jdcal                    1.4.1          \n",
            "jedi                     0.17.0         \n",
            "jieba                    0.42.1         \n",
            "Jinja2                   2.11.2         \n",
            "jmespath                 0.9.5          \n",
            "joblib                   0.14.1         \n",
            "jpeg4py                  0.1.4          \n",
            "jsonschema               2.6.0          \n",
            "jupyter                  1.0.0          \n",
            "jupyter-client           5.3.4          \n",
            "jupyter-console          5.2.0          \n",
            "jupyter-core             4.6.3          \n",
            "kaggle                   1.5.6          \n",
            "kapre                    0.1.3.1        \n",
            "Keras                    2.3.1          \n",
            "Keras-Applications       1.0.8          \n",
            "Keras-Preprocessing      1.1.0          \n",
            "keras-vis                0.4.1          \n",
            "kiwisolver               1.2.0          \n",
            "knnimpute                0.1.0          \n",
            "librosa                  0.6.3          \n",
            "lightgbm                 2.2.3          \n",
            "llvmlite                 0.31.0         \n",
            "lmdb                     0.98           \n",
            "lucid                    0.3.8          \n",
            "LunarCalendar            0.0.9          \n",
            "lxml                     4.2.6          \n",
            "Markdown                 3.2.1          \n",
            "MarkupSafe               1.1.1          \n",
            "matplotlib               3.2.1          \n",
            "matplotlib-venn          0.11.5         \n",
            "missingno                0.4.2          \n",
            "mistune                  0.8.4          \n",
            "mizani                   0.6.0          \n",
            "mkl                      2019.0         \n",
            "mlxtend                  0.14.0         \n",
            "more-itertools           8.2.0          \n",
            "moviepy                  0.2.3.5        \n",
            "mpmath                   1.1.0          \n",
            "msgpack                  1.0.0          \n",
            "multiprocess             0.70.9         \n",
            "multitasking             0.0.9          \n",
            "murmurhash               1.0.2          \n",
            "music21                  5.5.0          \n",
            "natsort                  5.5.0          \n",
            "nbconvert                5.6.1          \n",
            "nbformat                 5.0.5          \n",
            "networkx                 2.4            \n",
            "nibabel                  3.0.2          \n",
            "nltk                     3.2.5          \n",
            "notebook                 5.2.2          \n",
            "np-utils                 0.5.12.1       \n",
            "numba                    0.48.0         \n",
            "numexpr                  2.7.1          \n",
            "numpy                    1.18.2         \n",
            "nvidia-ml-py3            7.352.0        \n",
            "oauth2client             4.1.3          \n",
            "oauthlib                 3.1.0          \n",
            "okgrade                  0.4.3          \n",
            "opencv-contrib-python    4.1.2.30       \n",
            "opencv-python            4.1.2.30       \n",
            "openpyxl                 2.5.9          \n",
            "opt-einsum               3.2.0          \n",
            "osqp                     0.6.1          \n",
            "packaging                20.3           \n",
            "palettable               3.3.0          \n",
            "pandas                   1.0.3          \n",
            "pandas-datareader        0.8.1          \n",
            "pandas-gbq               0.11.0         \n",
            "pandas-profiling         1.4.1          \n",
            "pandocfilters            1.4.2          \n",
            "parso                    0.7.0          \n",
            "pathlib                  1.0.1          \n",
            "patsy                    0.5.1          \n",
            "pexpect                  4.8.0          \n",
            "pickleshare              0.7.5          \n",
            "Pillow                   7.0.0          \n",
            "pip                      19.3.1         \n",
            "pip-tools                4.5.1          \n",
            "pipreqs                  0.4.10         \n",
            "plac                     1.1.3          \n",
            "plotly                   4.4.1          \n",
            "plotnine                 0.6.0          \n",
            "pluggy                   0.7.1          \n",
            "portpicker               1.3.1          \n",
            "prefetch-generator       1.0.1          \n",
            "preshed                  3.0.2          \n",
            "prettytable              0.7.2          \n",
            "progressbar2             3.38.0         \n",
            "prometheus-client        0.7.1          \n",
            "promise                  2.3            \n",
            "prompt-toolkit           1.0.18         \n",
            "protobuf                 3.10.0         \n",
            "psutil                   5.4.8          \n",
            "psycopg2                 2.7.6.1        \n",
            "ptvsd                    5.0.0a12       \n",
            "ptyprocess               0.6.0          \n",
            "py                       1.8.1          \n",
            "pyarrow                  0.14.1         \n",
            "pyasn1                   0.4.8          \n",
            "pyasn1-modules           0.2.8          \n",
            "pycocotools              2.0.0          \n",
            "pycparser                2.20           \n",
            "pydata-google-auth       0.3.0          \n",
            "pydot                    1.3.0          \n",
            "pydot-ng                 2.0.0          \n",
            "pydotplus                2.0.2          \n",
            "PyDrive                  1.3.1          \n",
            "pyemd                    0.5.1          \n",
            "pyglet                   1.5.0          \n",
            "Pygments                 2.1.3          \n",
            "pygobject                3.26.1         \n",
            "pymc3                    3.7            \n",
            "PyMeeus                  0.3.7          \n",
            "pymongo                  3.10.1         \n",
            "pymystem3                0.2.0          \n",
            "PyOpenGL                 3.1.5          \n",
            "pyparsing                2.4.7          \n",
            "pyrsistent               0.16.0         \n",
            "pysndfile                1.3.8          \n",
            "PySocks                  1.7.1          \n",
            "pystan                   2.19.1.1       \n",
            "pytest                   3.6.4          \n",
            "python-apt               1.6.5+ubuntu0.2\n",
            "python-chess             0.23.11        \n",
            "python-dateutil          2.8.1          \n",
            "python-louvain           0.14           \n",
            "python-slugify           4.0.0          \n",
            "python-utils             2.4.0          \n",
            "pytz                     2018.9         \n",
            "PyWavelets               1.1.1          \n",
            "PyYAML                   3.13           \n",
            "pyzmq                    19.0.0         \n",
            "qtconsole                4.7.2          \n",
            "QtPy                     1.9.0          \n",
            "regex                    2019.12.20     \n",
            "requests                 2.21.0         \n",
            "requests-oauthlib        1.3.0          \n",
            "resampy                  0.2.2          \n",
            "retrying                 1.3.3          \n",
            "rpy2                     3.2.7          \n",
            "rsa                      4.0            \n",
            "s3fs                     0.4.2          \n",
            "s3transfer               0.3.3          \n",
            "scikit-image             0.16.2         \n",
            "scikit-learn             0.22.2.post1   \n",
            "scipy                    1.4.1          \n",
            "screen-resolution-extra  0.0.0          \n",
            "scs                      2.1.2          \n",
            "seaborn                  0.10.0         \n",
            "Send2Trash               1.5.0          \n",
            "setuptools               46.1.3         \n",
            "setuptools-git           1.2            \n",
            "Shapely                  1.7.0          \n",
            "simplegeneric            0.8.1          \n",
            "six                      1.12.0         \n",
            "sklearn                  0.0            \n",
            "sklearn-pandas           1.8.0          \n",
            "smart-open               1.11.1         \n",
            "snowballstemmer          2.0.0          \n",
            "sortedcontainers         2.1.0          \n",
            "spacy                    2.2.4          \n",
            "Sphinx                   1.8.5          \n",
            "sphinxcontrib-websupport 1.2.1          \n",
            "SQLAlchemy               1.3.16         \n",
            "sqlparse                 0.3.1          \n",
            "srsly                    1.0.2          \n",
            "statsmodels              0.10.2         \n",
            "sympy                    1.1.1          \n",
            "tables                   3.4.4          \n",
            "tabulate                 0.8.7          \n",
            "tbb                      2020.0.133     \n",
            "tblib                    1.6.0          \n",
            "tensorboard              2.2.0          \n",
            "tensorboard-plugin-wit   1.6.0.post3    \n",
            "tensorboardcolab         0.0.22         \n",
            "tensorflow               2.2.0rc3       \n",
            "tensorflow-addons        0.8.3          \n",
            "tensorflow-datasets      2.1.0          \n",
            "tensorflow-estimator     2.2.0rc0       \n",
            "tensorflow-gcs-config    2.1.8          \n",
            "tensorflow-hub           0.8.0          \n",
            "tensorflow-metadata      0.21.2         \n",
            "tensorflow-privacy       0.2.2          \n",
            "tensorflow-probability   0.9.0          \n",
            "termcolor                1.1.0          \n",
            "terminado                0.8.3          \n",
            "testpath                 0.4.4          \n",
            "text-unidecode           1.3            \n",
            "textblob                 0.15.3         \n",
            "textgenrnn               1.4.1          \n",
            "Theano                   1.0.4          \n",
            "thinc                    7.4.0          \n",
            "toolz                    0.10.0         \n",
            "torch                    1.4.0          \n",
            "torchsummary             1.5.1          \n",
            "torchtext                0.3.1          \n",
            "torchvision              0.5.0          \n",
            "tornado                  4.5.3          \n",
            "tqdm                     4.38.0         \n",
            "traitlets                4.3.3          \n",
            "tweepy                   3.6.0          \n",
            "typeguard                2.7.1          \n",
            "typing                   3.6.6          \n",
            "typing-extensions        3.6.6          \n",
            "tzlocal                  1.5.1          \n",
            "umap-learn               0.4.1          \n",
            "uritemplate              3.0.1          \n",
            "urllib3                  1.24.3         \n",
            "vega-datasets            0.8.0          \n",
            "wasabi                   0.6.0          \n",
            "wcwidth                  0.1.9          \n",
            "webencodings             0.5.1          \n",
            "Werkzeug                 1.0.1          \n",
            "wheel                    0.34.2         \n",
            "widgetsnbextension       3.5.1          \n",
            "wordcloud                1.5.0          \n",
            "wrapt                    1.12.1         \n",
            "xarray                   0.15.1         \n",
            "xgboost                  0.90           \n",
            "xkit                     0.0.0          \n",
            "xlrd                     1.1.0          \n",
            "xlwt                     1.3.0          \n",
            "yarg                     0.1.9          \n",
            "yellowbrick              0.9.1          \n",
            "zict                     2.0.0          \n",
            "zipp                     3.1.0          \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qfl1t3KfliTP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#4\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import numpy as np \n",
        "import torchvision\n",
        "from torchvision import datasets, models, transforms\n",
        "import matplotlib.pyplot as plt\n",
        "import time\n",
        "from google.colab import files, auth, drive\n",
        "import os\n",
        "import glob\n",
        "from shutil import copyfile\n",
        "import cv2 as cv\n",
        "from google.colab.patches import cv2_imshow\n",
        "import math\n",
        "import torch.nn.functional as F"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hONPjp_Pv1AK",
        "colab_type": "code",
        "outputId": "88fb88e4-c07c-4803-a8e0-25ea2a85bdd3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#5\n",
        "CUDA_LAUNCH_BLOCKING=1\n",
        "\n",
        "plt.ion()  \n",
        "\n",
        "use_gpu = torch.cuda.is_available()\n",
        "if use_gpu:\n",
        "    print(\"Using CUDA\")\n",
        "else:\n",
        "    print(\"Using CPU\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using CUDA\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eIt57Xefmf6V",
        "colab_type": "code",
        "outputId": "a27a58ea-a787-4c2a-f5c4-f5f5263ff8ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "#6\n",
        "# Mount\n",
        "drive.mount('/content/gdrive', force_remount=True)\n",
        "data_dir_drive ='/content/gdrive/My Drive/Colab Notebooks/CV/Proj1' #change to the project folder on the drive\n",
        "data_dir ='/content' # When read from unziped file"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jrn2KyVmmhGU",
        "colab_type": "code",
        "outputId": "acdc7ec7-d03f-463d-a99d-683ad1548e35",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "#7\n",
        "# Unzip dataset to /content\n",
        "start = time.time()\n",
        "\n",
        "!unzip -q '/content/gdrive/My Drive/Colab Notebooks/CV/Proj1/data/Project1.zip' -d '/content/gdrive/My Drive/Colab Notebooks/CV/Proj1/data'\n",
        "\n",
        "print('Took', (time.time() - start), ' secundes to unzip')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "warning:  stripped absolute path spec from /\n",
            "mapname:  conversion of  failed\n",
            "replace /content/gdrive/My Drive/Colab Notebooks/CV/Proj1/data/README.txt? [y]es, [n]o, [A]ll, [N]one, [r]ename: N\n",
            "Took 39.0506055355072  secundes to unzip\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UnxxQiYGwwJV",
        "colab_type": "code",
        "outputId": "3b9581fc-0e9d-4d11-931f-6a883c0ded2c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "#8\n",
        "!ls '/content/gdrive/My Drive/Colab Notebooks/CV/Proj1/data'"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "additional_data\t\t      hw_data.txt    project1.pdf  template.jpg\n",
            "digits\t\t\t      images\t     Project1.zip\n",
            "ground-truth-correct-answers  output_format  README.txt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rrtu-c9i-Mbh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#9\n",
        "folder_in = data_dir_drive + '/data/images/'\n",
        "folder_scanned_out = data_dir_drive + '/data/additional_data/1.scanned/'\n",
        "folder_rotated_and_perspective_out = data_dir_drive + '/data/additional_data/2.rotated+perspective/'\n",
        "folder_no_annotation_out = data_dir_drive + '/data/additional_data/3.no_annotation/'\n",
        "folder_handwritten_out = data_dir_drive + '/data/additional_data/4.handwritten/'\n",
        "folder_digits = data_dir_drive + '/data/digits/'\n",
        "folder_models = data_dir_drive + '/models/'\n",
        "folder_barem = data_dir_drive + '/data/ground-truth-correct-answers/'\n",
        "\n",
        "input_folder = data_dir_drive + '/input_data/'\n",
        "\n",
        "input_folder_scanned = input_folder + '1.scanned/'\n",
        "input_folder_rotated_and_perspective = input_folder + '2.rotated+perspective/'\n",
        "input_folder_no_annotation = input_folder + '3.no_annotation/'\n",
        "input_folder_handwritten = input_folder + '4.handwritten/'\n",
        "\n",
        "output_folder = data_dir_drive + '/output_data/'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mhaZRTJgHbXB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#10\n",
        "def make_additional_data_scanned():\n",
        "    if not os.path.exists(folder_scanned_out):\n",
        "        os.makedirs(folder_scanned_out)\n",
        "\n",
        "    for f in glob.iglob(folder_scanned_out+ '/*'):\n",
        "        os.remove(f)\n",
        "\n",
        "    for i in range(1, 151):\n",
        "        file = open(folder_in + 'image_' + str(i) +'.txt', 'r')\n",
        "        first_line = file.readline()\n",
        "        file.close() \n",
        "        first_line = first_line.replace('\\n', '')\n",
        "        info = first_line.split(' ')\n",
        "        copyfile(folder_in + 'image_' + str(i) +'.jpg', folder_scanned_out + str(i) + '_scanned_' + info[0] + info[1] +'.jpg')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HIIcDadDHhXn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#11\n",
        "def make_additional_data_rotated_and_perspective():\n",
        "    if not os.path.exists(folder_rotated_and_perspective_out):\n",
        "        os.makedirs(folder_rotated_and_perspective_out)\n",
        "\n",
        "    for f in glob.iglob(folder_rotated_and_perspective_out + '/*'):\n",
        "        os.remove(f)\n",
        "\n",
        "    for i in range(1, 151):\n",
        "        file = open(folder_in + 'image_' + str(i) +'.txt', 'r')\n",
        "        first_line = file.readline()\n",
        "        file.close() \n",
        "        first_line = first_line.replace('\\n', '')\n",
        "        info = first_line.split(' ')\n",
        "        copyfile(folder_in + 'perspective_' + str(i) +'.jpg', folder_rotated_and_perspective_out + str(i) + '_perspective_' + info[0] + info[1] +'.jpg')\n",
        "\n",
        "    for i in range(1, 151):\n",
        "        file = open(folder_in + 'image_' + str(i) +'.txt', 'r')\n",
        "        first_line = file.readline()\n",
        "        file.close() \n",
        "        first_line = first_line.replace('\\n', '')\n",
        "        info = first_line.split(' ')\n",
        "        copyfile(folder_in + 'rotation_' + str(i) +'.jpg', folder_rotated_and_perspective_out + str(i) + '_rotated_' + info[0] + info[1] +'.jpg')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VLrik-qwHmle",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#12\n",
        "def make_additional_data_no_annotation():\n",
        "    if not os.path.exists(folder_no_annotation_out):\n",
        "        os.makedirs(folder_no_annotation_out)\n",
        "\n",
        "    for f in glob.iglob(folder_no_annotation_out + '/*'):\n",
        "        os.remove(f)\n",
        "\n",
        "    for i in range(1, 151):\n",
        "        copyfile(folder_in + 'image_' + str(i) +'.jpg', folder_no_annotation_out + str(i) + '_scanned.jpg')\n",
        "        copyfile(folder_in + 'perspective_' + str(i) +'.jpg', folder_no_annotation_out + str(i) + '_perspective.jpg')\n",
        "        copyfile(folder_in + 'rotation_' + str(i) +'.jpg', folder_no_annotation_out + str(i) + '_rotated.jpg')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RElyS71vHnHS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#13\n",
        "def make_additional_handwritten():\n",
        "    pass"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bEJSt-E8wgCb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#14\n",
        "def make_additional_data():\n",
        "    make_additional_data_scanned()\n",
        "    make_additional_data_rotated_and_perspective()\n",
        "    make_additional_data_no_annotation()\n",
        "    make_additional_handwritten()\n",
        "\n",
        "#make_additional_data()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EiBXyPSzfZ85",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#15\n",
        "SHOW_INTERMEDIATE_RESULTS = True\n",
        "char_to_index = {'A': 0, 'B': 1, 'C': 2, 'D': 3}\n",
        "index_to_char = {0: 'A', 1: 'B', 2: 'C', 3: 'D'}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "djIiy0VmfTJP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#16\n",
        "def print_img(image):\n",
        "    if SHOW_INTERMEDIATE_RESULTS:\n",
        "        cv2_imshow(cv.resize(image, (0, 0), fx=0.15, fy=0.15))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CJOU9OOY9T13",
        "colab_type": "code",
        "outputId": "bcbdfb82-98d3-4641-a8d0-813fde4ecf26",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "#17\n",
        "ground_truth_hw = np.loadtxt(data_dir_drive + '/data/hw_data.txt', dtype=str)\n",
        "ground_truth_hw_dict = {}\n",
        "for [i, nr] in ground_truth_hw:\n",
        "    ground_truth_hw_dict[int(i)] = nr\n",
        "print(ground_truth_hw_dict)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{1: '4.9', 2: '6.7', 3: '5.8', 4: '8.5', 5: '4.9', 6: '6.7', 7: '9.1', 8: '3.7', 9: '5.5', 10: '5.5', 11: '4.6', 12: '6.7', 13: '7.3', 14: '6.4', 15: '7.0', 16: '4.6', 17: '7.6', 18: '6.1', 19: '4.9', 20: '7.6', 21: '6.4', 22: '5.5', 23: '6.4', 24: '7.3', 25: '7.9', 26: '8.5', 27: '5.5', 28: '8.8', 29: '6.1', 30: '7.9', 31: '4.9', 32: '5.2', 33: '7.6', 34: '7.0', 35: '7.0', 36: '6.4', 37: '8.8', 38: '6.7', 39: '7.0', 40: '5.8', 41: '7.6', 42: '9.7', 43: '6.7', 44: '6.4', 45: '5.5', 46: '7.3', 47: '8.8', 48: '6.4', 49: '5.8', 50: '7.0', 51: '8.2', 52: '4.9', 53: '4.3', 54: '8.8', 55: '8.2', 56: '5.2', 57: '5.2', 58: '5.5', 59: '7.0', 60: '7.9', 61: '4.9', 62: '4.6', 63: '5.2', 64: '7.6', 65: '7.0', 66: '9.4', 67: '7.3', 68: '4.6', 69: '4.3', 70: '4.0', 71: '6.4', 72: '4.9', 73: '5.5', 74: '9.4', 75: '4.0', 76: '6.1', 77: '7.3', 78: '4.0', 79: '5.5', 80: '5.5', 81: '8.5', 82: '4.9', 83: '6.7', 84: '5.2', 85: '7.6', 86: '4.9', 87: '5.5', 88: '9.1', 89: '6.4', 90: '6.1', 91: '6.1', 92: '5.5', 93: '4.6', 94: '7.9', 95: '4.6', 96: '6.7', 97: '5.5', 98: '5.5', 99: '7.3', 100: '4.9', 101: '4.9', 102: '5.2', 103: '5.5', 104: '5.2', 105: '4.0', 106: '6.4', 107: '9.7', 108: '8.8', 109: '5.8', 110: '7.3', 111: '6.1', 112: '4.3', 113: '6.1', 114: '6.4', 115: '5.2', 116: '6.4', 117: '4.9', 118: '3.7', 119: '5.8', 120: '4.6', 121: '6.7', 122: '5.5', 123: '4.0', 124: '8.8', 125: '6.4', 126: '4.3', 127: '7.3', 128: '8.8', 129: '6.4', 130: '4.3', 131: '6.4', 132: '4.3', 133: '3.7', 134: '6.4', 135: '7.6', 136: '5.8', 137: '5.8', 138: '6.7', 139: '4.6', 140: '7.3', 141: '5.5', 142: '4.3', 143: '6.4', 144: '8.8', 145: '6.7', 146: '4.3', 147: '5.8', 148: '7.3', 149: '7.6', 150: '6.4'}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mk1zunF2Pjwv",
        "colab_type": "text"
      },
      "source": [
        "# **Scenario 1 (real world)**\n",
        "You receive a test set containing 55 scanned images annotated with the option (F or I) and with the digit (1, 2, 3 or 4). For each image you have\n",
        "to output the corresponding grade. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oe1ZK3vQnvS0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#1\n",
        "def find_rows(grayscale_image):\n",
        "    # find the edges on the Y axis\n",
        "    # apply Sobel filter on the Y axis\n",
        "    edges_y = cv.Sobel(grayscale_image, ddepth = cv.CV_64F, dx = 0, dy = 1)\n",
        "    # normalize the output\n",
        "    edges_y = np.abs(edges_y)\n",
        "    edges_y = edges_y / edges_y.max()\n",
        "            \n",
        "    # convert to a black and white image\n",
        "    _, edges_y_th = cv.threshold(edges_y, 0.4, 255, cv.THRESH_BINARY_INV) # the second param is the threshold\n",
        "    #print_img(edges_y_th)\n",
        "        \n",
        "    # create a mask for black pixels\n",
        "    mask = (edges_y_th == 0) * 1\n",
        "    # find the rows from mask\n",
        "    all_lines = np.sum(mask, axis = 1)\n",
        "    all_lines = all_lines.argsort()\n",
        "\n",
        "    num_lines = min(len(all_lines), 150)\n",
        "    edges_y_th = np.dstack((edges_y_th, edges_y_th, edges_y_th))\n",
        "    lines = [] #  _ x \n",
        "    for i in range(num_lines):\n",
        "        cv.line(edges_y_th, (0, all_lines[-i]), (grayscale_image.shape[1], all_lines[-i]), (0, 0, 255), 2) \n",
        "        lines.append([(0, all_lines[-i]), (grayscale_image.shape[1], all_lines[-i])])\n",
        "        \n",
        "    #print_img(edges_y_th)\n",
        "    \n",
        "    # sort only on y\n",
        "    lines.sort(key=lambda coords: coords[0][1])\n",
        "    \n",
        "    threshold_same_line = 30\n",
        "    distict_lines = []   \n",
        "    distict_lines.append(lines[0])\n",
        "     \n",
        "    for line in lines:  \n",
        "        if line[0][1] - distict_lines[-1][0][1] > threshold_same_line:\n",
        "            distict_lines.append(line)   \n",
        "    \n",
        "    # take the last 16 lines\n",
        "    correct_lines = distict_lines[-16:]\n",
        "    color_image = np.dstack((grayscale_image, grayscale_image, grayscale_image))\n",
        "    for line in correct_lines: \n",
        "        cv.line(color_image, line[0], line[1], (255, 0, 0), 5) \n",
        "        \n",
        "    #print_img(color_image)\n",
        "    \n",
        "    return correct_lines"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BktBTzESnvS4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#2\n",
        "def find_columns(grayscale_image):\n",
        "    # find the edges on the X axis\n",
        "    edges_x = cv.Sobel(grayscale_image, ddepth = cv.CV_64F, dx = 1, dy = 0)\n",
        "    # normalize the output \n",
        "    edges_x = np.abs(edges_x)\n",
        "    edges_x = edges_x / edges_x.max()\n",
        "        \n",
        "    # convert to a black and white image \n",
        "    _, edges_x_th = cv.threshold(edges_x, 0.20, 255, cv.THRESH_BINARY_INV) # the second param is the threshold\n",
        "    #print_img(edges_x_th)\n",
        "        \n",
        "    # create a mask for black pixels\n",
        "    mask = (edges_x_th == 0) * 1\n",
        "    all_cols = np.sum(mask, axis = 0)\n",
        "    all_cols = all_cols.argsort()\n",
        "    num_cols = 60\n",
        "    edges_x_th = np.dstack((edges_x_th, edges_x_th, edges_x_th))\n",
        "    cols = [] #  _ x \n",
        "    for i in range(num_cols):\n",
        "        cv.line(edges_x_th, (all_cols[-i], 0), (all_cols[-i], grayscale_image.shape[0]), (0, 0, 255), 2)\n",
        "        cols.append([(all_cols[-i], 0), (all_cols[-i], grayscale_image.shape[0])])   \n",
        "        \n",
        "    #print_img(edges_x_th)\n",
        "    \n",
        "    # sort only on x0\n",
        "    cols.sort(key=lambda coords: coords[0][0])\n",
        "    threshold_same_column = 30\n",
        "    distinct_cols = []\n",
        "    distinct_cols.append(cols[0])\n",
        "    \n",
        "    for col in cols:  \n",
        "        if col[0][0] - distinct_cols[-1][0][0] > threshold_same_column:\n",
        "            distinct_cols.append(col)\n",
        "    \n",
        "    # take the last 5 cols\n",
        "    correct_cols = distinct_cols[-5:]\n",
        "    color_image = np.dstack((grayscale_image, grayscale_image, grayscale_image))\n",
        "    for col in correct_cols: \n",
        "        cv.line(color_image, col[0], col[1], (255, 0, 0), 5) \n",
        "    #print_img(color_image)\n",
        "    \n",
        "    return correct_cols"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tiozKMEwgmsW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#3\n",
        "def find_table(grayscale_image):\n",
        "    cols = find_columns(grayscale_image.copy())\n",
        "    rows = find_rows(grayscale_image.copy())\n",
        "    x_min = cols[0][0][0]\n",
        "    x_max = cols[-1][1][0]\n",
        "    y_min = rows[0][0][1]\n",
        "    y_max = rows[-1][1][1]\n",
        "    \n",
        "    table = grayscale_image[y_min:y_max, x_min:x_max] \n",
        "    image = np.dstack((grayscale_image, grayscale_image, grayscale_image))\n",
        "    \n",
        "    for i in range(min(5, len(cols))): \n",
        "        cv.line(image, cols[i][0], cols[i][1], (255, 0, 0), 5) \n",
        "    for i in range(min(16, len(rows))):  \n",
        "        cv.line(image, rows[i][0], rows[i][1], (0, 0, 255), 5) \n",
        "    #print_img(image)\n",
        "    #print_img(table)\n",
        "\n",
        "    return table, [x_min, y_min, x_max, y_max], cols, rows"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xIzjnEc31mF5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#4\n",
        "# write a function that plots the patches containing X with green\n",
        "# and patches containing blanks with red\n",
        "# use here your classifier (test mode)\n",
        "\n",
        "def find_x(grayscale_image, vertical_lines, horizontal_lines):\n",
        "    # grayscale_image - input image containing the frame\n",
        "    # vertical_lines - list with the vertical lines\n",
        "    # horizontal_lines - list with horizontal lines\n",
        "    # ground_truth - grounth truth content for a frame    \n",
        "    mean_x = []\n",
        "    mean_blank = [] \n",
        "    \n",
        "    image = np.dstack((grayscale_image, grayscale_image, grayscale_image))\n",
        "    x_color = (0, 255, 0)  # plot a patch containing an X with green color\n",
        "    blank_color = (0, 0, 255)  # plot a patch containing a blank with red color  \n",
        "\n",
        "    result = {}  \n",
        "            \n",
        "    # crop each patch and display it\n",
        "    for i in range(len(horizontal_lines) - 1):\n",
        "        mean_patches = []\n",
        "        for j in range(len(vertical_lines) - 1):\n",
        "            x_min = vertical_lines[j][0][0] + 15\n",
        "            x_max = vertical_lines[j + 1][1][0] - 5\n",
        "            y_min = horizontal_lines[i][0][1] + 15\n",
        "            y_max = horizontal_lines[i + 1][1][1] - 5\n",
        "            \n",
        "            # write your code here\n",
        "            patch = grayscale_image[y_min:y_max, x_min:x_max]\n",
        "\n",
        "            mean_patch = np.round(patch.mean())\n",
        "            mean_patches.append(mean_patch)\n",
        "\n",
        "        x_pos_j = np.argmin(mean_patches)\n",
        "        for j in range(len(vertical_lines) - 1):\n",
        "\n",
        "            x_min = vertical_lines[j][0][0] + 15\n",
        "            x_max = vertical_lines[j + 1][1][0] - 5\n",
        "            y_min = horizontal_lines[i][0][1] + 15\n",
        "            y_max = horizontal_lines[i + 1][1][1] - 5\n",
        "\n",
        "            if j == x_pos_j:\n",
        "                color = x_color\n",
        "                if i + 1 not in result.keys():\n",
        "                    result[i + 1] = [index_to_char[j]]\n",
        "                else:\n",
        "                    result[i + 1].append(index_to_char[j])\n",
        "            else:\n",
        "                color = blank_color\n",
        "            \n",
        "            cv.rectangle(image, (x_min, y_min), (x_max, y_max), color=color, thickness=5)\n",
        "            #cv.putText(image, str(mean_patch)[:3] ,(x_min + 10, y_min + 50), cv.FONT_HERSHEY_COMPLEX, 1, (0,0,0), 2) \n",
        "\n",
        "    #print_img(image)\n",
        "    return result, image"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FgZ7DXiQ1mF8",
        "colab_type": "code",
        "outputId": "83bca9d9-6786-4e83-99c6-66f5e039e964",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#5\n",
        "# test \n",
        "acc = 0\n",
        "for i in range(1, 151):\n",
        "    image_name = 'image_' + str(i) + '.jpg'\n",
        "   \n",
        "    print(image_name)\n",
        "    # load image\n",
        "\n",
        "    image = cv.imread(folder_in + image_name)\n",
        "    #print_img(image)\n",
        "\n",
        "    # blur the image using a 7 by 7 kernel\n",
        "    image = cv.GaussianBlur(image, (7, 7), 0)\n",
        "\n",
        "    # take the lower part of the image\n",
        "    orig_h, orig_w, _ = image.shape\n",
        "    image = image[int(orig_h * 0.41):int(orig_h * 0.88)]\n",
        "\n",
        "    # transform the image to grayscale\n",
        "    grayscale_image = cv.cvtColor(image, cv.COLOR_BGR2GRAY)\n",
        "\n",
        "    left_grayscale_image = grayscale_image[:, :orig_w//2]\n",
        "    right_grayscale_image = grayscale_image[:, orig_w//2:]\n",
        "\n",
        "    _, _, cols_l, rows_l = find_table(left_grayscale_image)\n",
        "    _, _, cols_r, rows_r = find_table(right_grayscale_image)\n",
        "\n",
        "    # load ground truth\n",
        "    ground_truth_content = np.loadtxt(folder_in + 'image_' + str(i) + '.txt', dtype=str)\n",
        "    ground_truth_left = ground_truth_content[1:16]\n",
        "    ground_truth_right = ground_truth_content[16:-1]\n",
        "\n",
        "    result_l, image_l = find_x(left_grayscale_image, cols_l, rows_l)\n",
        "    #print(result_l)\n",
        "    result_r, image_r = find_x(right_grayscale_image, cols_r, rows_r)\n",
        "    #print(result_r)\n",
        "\n",
        "    left_err = 0;\n",
        "    for r in range(15):\n",
        "        tr_ans = ground_truth_left[r][1]\n",
        "        if r + 1 not in result_l.keys():\n",
        "            print(\"LINIE NECOMPLETATA\")\n",
        "            left_err += 1\n",
        "        else:\n",
        "            if len(result_l[r+1]) > 1:\n",
        "                print(\"PREA MULTE RANDURI COMPLETATE\")\n",
        "                left_err += 1\n",
        "            else:\n",
        "                if result_l[r+1][0] != tr_ans:\n",
        "                    left_err = left_err + 1\n",
        "\n",
        "    if left_err:\n",
        "        print(\"GRESEALA PE STANGA\")\n",
        "        print_img(image_l)\n",
        "\n",
        "    right_err = 0;\n",
        "    for r in range(15):\n",
        "        tr_ans = ground_truth_right[r][1]\n",
        "        if r + 1 not in result_r.keys():\n",
        "            print(\"LINIE NECOMPLETATA\")\n",
        "            right_err += 1\n",
        "        else:\n",
        "            if len(result_r[r+1]) > 1:\n",
        "                print(\"PREA MULTE RANDURI COMPLETATE\")\n",
        "                right_err += 1\n",
        "            else:\n",
        "                if result_r[r+1][0] != tr_ans:\n",
        "                    right_err = right_err + 1\n",
        "\n",
        "    if right_err:\n",
        "        print(\"GRESEALA PE DREAPTA\")\n",
        "        print_img(image_r)\n",
        "\n",
        "    if left_err == 0 and right_err == 0:\n",
        "        print(\"OK\")\n",
        "\n",
        "    acc = acc + right_err + left_err"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "image_1.jpg\n",
            "OK\n",
            "image_2.jpg\n",
            "OK\n",
            "image_3.jpg\n",
            "OK\n",
            "image_4.jpg\n",
            "OK\n",
            "image_5.jpg\n",
            "OK\n",
            "image_6.jpg\n",
            "OK\n",
            "image_7.jpg\n",
            "OK\n",
            "image_8.jpg\n",
            "OK\n",
            "image_9.jpg\n",
            "OK\n",
            "image_10.jpg\n",
            "OK\n",
            "image_11.jpg\n",
            "OK\n",
            "image_12.jpg\n",
            "OK\n",
            "image_13.jpg\n",
            "OK\n",
            "image_14.jpg\n",
            "OK\n",
            "image_15.jpg\n",
            "OK\n",
            "image_16.jpg\n",
            "OK\n",
            "image_17.jpg\n",
            "OK\n",
            "image_18.jpg\n",
            "OK\n",
            "image_19.jpg\n",
            "OK\n",
            "image_20.jpg\n",
            "OK\n",
            "image_21.jpg\n",
            "OK\n",
            "image_22.jpg\n",
            "OK\n",
            "image_23.jpg\n",
            "OK\n",
            "image_24.jpg\n",
            "OK\n",
            "image_25.jpg\n",
            "OK\n",
            "image_26.jpg\n",
            "OK\n",
            "image_27.jpg\n",
            "OK\n",
            "image_28.jpg\n",
            "OK\n",
            "image_29.jpg\n",
            "OK\n",
            "image_30.jpg\n",
            "OK\n",
            "image_31.jpg\n",
            "OK\n",
            "image_32.jpg\n",
            "OK\n",
            "image_33.jpg\n",
            "OK\n",
            "image_34.jpg\n",
            "OK\n",
            "image_35.jpg\n",
            "OK\n",
            "image_36.jpg\n",
            "OK\n",
            "image_37.jpg\n",
            "OK\n",
            "image_38.jpg\n",
            "OK\n",
            "image_39.jpg\n",
            "OK\n",
            "image_40.jpg\n",
            "OK\n",
            "image_41.jpg\n",
            "OK\n",
            "image_42.jpg\n",
            "OK\n",
            "image_43.jpg\n",
            "OK\n",
            "image_44.jpg\n",
            "OK\n",
            "image_45.jpg\n",
            "OK\n",
            "image_46.jpg\n",
            "OK\n",
            "image_47.jpg\n",
            "OK\n",
            "image_48.jpg\n",
            "OK\n",
            "image_49.jpg\n",
            "OK\n",
            "image_50.jpg\n",
            "OK\n",
            "image_51.jpg\n",
            "OK\n",
            "image_52.jpg\n",
            "OK\n",
            "image_53.jpg\n",
            "OK\n",
            "image_54.jpg\n",
            "OK\n",
            "image_55.jpg\n",
            "OK\n",
            "image_56.jpg\n",
            "OK\n",
            "image_57.jpg\n",
            "OK\n",
            "image_58.jpg\n",
            "OK\n",
            "image_59.jpg\n",
            "OK\n",
            "image_60.jpg\n",
            "OK\n",
            "image_61.jpg\n",
            "OK\n",
            "image_62.jpg\n",
            "OK\n",
            "image_63.jpg\n",
            "OK\n",
            "image_64.jpg\n",
            "OK\n",
            "image_65.jpg\n",
            "OK\n",
            "image_66.jpg\n",
            "OK\n",
            "image_67.jpg\n",
            "OK\n",
            "image_68.jpg\n",
            "OK\n",
            "image_69.jpg\n",
            "OK\n",
            "image_70.jpg\n",
            "OK\n",
            "image_71.jpg\n",
            "OK\n",
            "image_72.jpg\n",
            "OK\n",
            "image_73.jpg\n",
            "OK\n",
            "image_74.jpg\n",
            "OK\n",
            "image_75.jpg\n",
            "OK\n",
            "image_76.jpg\n",
            "OK\n",
            "image_77.jpg\n",
            "OK\n",
            "image_78.jpg\n",
            "OK\n",
            "image_79.jpg\n",
            "OK\n",
            "image_80.jpg\n",
            "OK\n",
            "image_81.jpg\n",
            "OK\n",
            "image_82.jpg\n",
            "OK\n",
            "image_83.jpg\n",
            "OK\n",
            "image_84.jpg\n",
            "OK\n",
            "image_85.jpg\n",
            "OK\n",
            "image_86.jpg\n",
            "OK\n",
            "image_87.jpg\n",
            "OK\n",
            "image_88.jpg\n",
            "OK\n",
            "image_89.jpg\n",
            "OK\n",
            "image_90.jpg\n",
            "OK\n",
            "image_91.jpg\n",
            "OK\n",
            "image_92.jpg\n",
            "OK\n",
            "image_93.jpg\n",
            "OK\n",
            "image_94.jpg\n",
            "OK\n",
            "image_95.jpg\n",
            "OK\n",
            "image_96.jpg\n",
            "OK\n",
            "image_97.jpg\n",
            "OK\n",
            "image_98.jpg\n",
            "OK\n",
            "image_99.jpg\n",
            "OK\n",
            "image_100.jpg\n",
            "OK\n",
            "image_101.jpg\n",
            "OK\n",
            "image_102.jpg\n",
            "OK\n",
            "image_103.jpg\n",
            "OK\n",
            "image_104.jpg\n",
            "OK\n",
            "image_105.jpg\n",
            "OK\n",
            "image_106.jpg\n",
            "OK\n",
            "image_107.jpg\n",
            "OK\n",
            "image_108.jpg\n",
            "OK\n",
            "image_109.jpg\n",
            "OK\n",
            "image_110.jpg\n",
            "OK\n",
            "image_111.jpg\n",
            "OK\n",
            "image_112.jpg\n",
            "OK\n",
            "image_113.jpg\n",
            "OK\n",
            "image_114.jpg\n",
            "OK\n",
            "image_115.jpg\n",
            "OK\n",
            "image_116.jpg\n",
            "OK\n",
            "image_117.jpg\n",
            "OK\n",
            "image_118.jpg\n",
            "OK\n",
            "image_119.jpg\n",
            "OK\n",
            "image_120.jpg\n",
            "OK\n",
            "image_121.jpg\n",
            "OK\n",
            "image_122.jpg\n",
            "OK\n",
            "image_123.jpg\n",
            "OK\n",
            "image_124.jpg\n",
            "OK\n",
            "image_125.jpg\n",
            "OK\n",
            "image_126.jpg\n",
            "OK\n",
            "image_127.jpg\n",
            "OK\n",
            "image_128.jpg\n",
            "OK\n",
            "image_129.jpg\n",
            "OK\n",
            "image_130.jpg\n",
            "OK\n",
            "image_131.jpg\n",
            "OK\n",
            "image_132.jpg\n",
            "OK\n",
            "image_133.jpg\n",
            "OK\n",
            "image_134.jpg\n",
            "OK\n",
            "image_135.jpg\n",
            "OK\n",
            "image_136.jpg\n",
            "OK\n",
            "image_137.jpg\n",
            "OK\n",
            "image_138.jpg\n",
            "OK\n",
            "image_139.jpg\n",
            "OK\n",
            "image_140.jpg\n",
            "OK\n",
            "image_141.jpg\n",
            "OK\n",
            "image_142.jpg\n",
            "OK\n",
            "image_143.jpg\n",
            "OK\n",
            "image_144.jpg\n",
            "OK\n",
            "image_145.jpg\n",
            "OK\n",
            "image_146.jpg\n",
            "OK\n",
            "image_147.jpg\n",
            "OK\n",
            "image_148.jpg\n",
            "OK\n",
            "image_149.jpg\n",
            "OK\n",
            "image_150.jpg\n",
            "OK\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "woQ2hen3B_zX",
        "colab_type": "code",
        "outputId": "54299b3c-b766-4333-a140-f7c487e05db0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "#6\n",
        "print('Total greseli = ' + str(acc))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total greseli = 0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cTRj3mzLUBM7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#7\n",
        "def predict_grade_scenario_1(path, subject, number):\n",
        "    image = cv.imread(path)\n",
        "    #print_img(image)\n",
        "\n",
        "    # blur the image using a 7 by 7 kernel\n",
        "    image = cv.GaussianBlur(image, (7, 7), 0)\n",
        "\n",
        "    # take the lower part of the image\n",
        "    orig_h, orig_w, _ = image.shape\n",
        "    image = image[int(orig_h * 0.41):int(orig_h * 0.88)]\n",
        "\n",
        "    # transform the image to grayscale\n",
        "    grayscale_image = cv.cvtColor(image, cv.COLOR_BGR2GRAY)\n",
        "\n",
        "    left_grayscale_image = grayscale_image[:, :orig_w//2]\n",
        "    right_grayscale_image = grayscale_image[:, orig_w//2:]\n",
        "\n",
        "    _, _, cols_l, rows_l = find_table(left_grayscale_image)\n",
        "    _, _, cols_r, rows_r = find_table(right_grayscale_image)\n",
        "\n",
        "    number_ok = 0\n",
        "    # load ground truth correct answers / barem\n",
        "    if subject == 'F':\n",
        "        if number == 1:\n",
        "            ground_truth_content = np.loadtxt(folder_barem + 'Fizica_varianta1.txt', dtype=str)\n",
        "        elif number == 2:\n",
        "            ground_truth_content = np.loadtxt(folder_barem + 'Fizica_varianta2.txt', dtype=str)\n",
        "        elif number == 3:\n",
        "            ground_truth_content = np.loadtxt(folder_barem + 'Fizica_varianta3.txt', dtype=str)\n",
        "        else:\n",
        "            ground_truth_content = np.loadtxt(folder_barem + 'Fizica_varianta4.txt', dtype=str)\n",
        "    else:\n",
        "        if number == 1:\n",
        "            ground_truth_content = np.loadtxt(folder_barem + 'Informatica_varianta1.txt', dtype=str)\n",
        "        elif number == 2:\n",
        "            ground_truth_content = np.loadtxt(folder_barem + 'Informatica_varianta2.txt', dtype=str)\n",
        "        elif number == 3:\n",
        "            ground_truth_content = np.loadtxt(folder_barem + 'Informatica_varianta3.txt', dtype=str)\n",
        "        else:\n",
        "            ground_truth_content = np.loadtxt(folder_barem + 'Informatica_varianta4.txt', dtype=str)\n",
        "\n",
        "    ground_truth_left = ground_truth_content[1:16]\n",
        "    ground_truth_right = ground_truth_content[16:-1]\n",
        "\n",
        "    result_l, image_l = find_x(left_grayscale_image, cols_l, rows_l)\n",
        "    #print(result_l)\n",
        "    result_r, image_r = find_x(right_grayscale_image, cols_r, rows_r)\n",
        "    #print(result_r)\n",
        "\n",
        "    left_mis = 0;\n",
        "    for r in range(15):\n",
        "        tr_ans = ground_truth_left[r][1]\n",
        "        if r + 1 not in result_l.keys():\n",
        "            left_mis += 1\n",
        "        else:\n",
        "            if len(result_l[r+1]) > 1:\n",
        "                left_mis += 1\n",
        "            else:\n",
        "                if result_l[r+1][0] != tr_ans:\n",
        "                    left_mis += 1\n",
        "                else:\n",
        "                    number_ok += 1\n",
        "\n",
        "    right_mis = 0;\n",
        "    for r in range(15):\n",
        "        tr_ans = ground_truth_right[r][1]\n",
        "        if r + 1 not in result_r.keys():\n",
        "            right_mis += 1\n",
        "        else:\n",
        "            if len(result_r[r+1]) > 1:\n",
        "                right_mis += 1\n",
        "            else:\n",
        "                if result_r[r+1][0] != tr_ans:\n",
        "                    right_mis += 1\n",
        "                else:\n",
        "                    number_ok += 1\n",
        "\n",
        "    return (number_ok * 3) / 10 + 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V9Kr20d2USHx",
        "colab_type": "code",
        "outputId": "8e0ebfe3-c34c-44eb-88b7-56586c64e665",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#8\n",
        "acc = 0\n",
        "for i in range(1, 151):\n",
        "    image_name = 'image_' + str(i) + '.jpg'\n",
        "   \n",
        "    print(image_name)\n",
        "    file = open(folder_in + 'image_' + str(i) +'.txt', 'r')\n",
        "    first_line = file.readline()\n",
        "    file.close() \n",
        "    first_line = first_line.replace('\\n', '')\n",
        "    info = first_line.split(' ')\n",
        "\n",
        "    grade = predict_grade_scenario_1(folder_in + image_name, info[0], int(info[1]))\n",
        "\n",
        "    if ground_truth_hw_dict[i] == str(grade):\n",
        "        acc += 1\n",
        "    else:\n",
        "        print(\"GRESEALA\")\n",
        "\n",
        "print(\"Acc = \" + str(acc * 100 / 150) + \" %\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "image_1.jpg\n",
            "image_2.jpg\n",
            "image_3.jpg\n",
            "image_4.jpg\n",
            "image_5.jpg\n",
            "image_6.jpg\n",
            "image_7.jpg\n",
            "image_8.jpg\n",
            "image_9.jpg\n",
            "image_10.jpg\n",
            "image_11.jpg\n",
            "image_12.jpg\n",
            "image_13.jpg\n",
            "image_14.jpg\n",
            "image_15.jpg\n",
            "image_16.jpg\n",
            "image_17.jpg\n",
            "image_18.jpg\n",
            "image_19.jpg\n",
            "image_20.jpg\n",
            "image_21.jpg\n",
            "image_22.jpg\n",
            "image_23.jpg\n",
            "image_24.jpg\n",
            "image_25.jpg\n",
            "image_26.jpg\n",
            "image_27.jpg\n",
            "image_28.jpg\n",
            "image_29.jpg\n",
            "image_30.jpg\n",
            "image_31.jpg\n",
            "image_32.jpg\n",
            "image_33.jpg\n",
            "image_34.jpg\n",
            "image_35.jpg\n",
            "image_36.jpg\n",
            "image_37.jpg\n",
            "image_38.jpg\n",
            "image_39.jpg\n",
            "image_40.jpg\n",
            "image_41.jpg\n",
            "image_42.jpg\n",
            "image_43.jpg\n",
            "image_44.jpg\n",
            "image_45.jpg\n",
            "image_46.jpg\n",
            "image_47.jpg\n",
            "image_48.jpg\n",
            "image_49.jpg\n",
            "image_50.jpg\n",
            "image_51.jpg\n",
            "image_52.jpg\n",
            "image_53.jpg\n",
            "image_54.jpg\n",
            "image_55.jpg\n",
            "image_56.jpg\n",
            "image_57.jpg\n",
            "image_58.jpg\n",
            "image_59.jpg\n",
            "image_60.jpg\n",
            "image_61.jpg\n",
            "image_62.jpg\n",
            "image_63.jpg\n",
            "image_64.jpg\n",
            "image_65.jpg\n",
            "image_66.jpg\n",
            "image_67.jpg\n",
            "image_68.jpg\n",
            "image_69.jpg\n",
            "image_70.jpg\n",
            "image_71.jpg\n",
            "image_72.jpg\n",
            "image_73.jpg\n",
            "image_74.jpg\n",
            "image_75.jpg\n",
            "image_76.jpg\n",
            "image_77.jpg\n",
            "image_78.jpg\n",
            "image_79.jpg\n",
            "image_80.jpg\n",
            "image_81.jpg\n",
            "image_82.jpg\n",
            "image_83.jpg\n",
            "image_84.jpg\n",
            "image_85.jpg\n",
            "image_86.jpg\n",
            "image_87.jpg\n",
            "image_88.jpg\n",
            "image_89.jpg\n",
            "image_90.jpg\n",
            "image_91.jpg\n",
            "image_92.jpg\n",
            "image_93.jpg\n",
            "image_94.jpg\n",
            "image_95.jpg\n",
            "image_96.jpg\n",
            "image_97.jpg\n",
            "image_98.jpg\n",
            "image_99.jpg\n",
            "image_100.jpg\n",
            "image_101.jpg\n",
            "image_102.jpg\n",
            "image_103.jpg\n",
            "image_104.jpg\n",
            "image_105.jpg\n",
            "image_106.jpg\n",
            "image_107.jpg\n",
            "image_108.jpg\n",
            "image_109.jpg\n",
            "image_110.jpg\n",
            "image_111.jpg\n",
            "image_112.jpg\n",
            "image_113.jpg\n",
            "image_114.jpg\n",
            "image_115.jpg\n",
            "image_116.jpg\n",
            "image_117.jpg\n",
            "image_118.jpg\n",
            "image_119.jpg\n",
            "image_120.jpg\n",
            "image_121.jpg\n",
            "image_122.jpg\n",
            "image_123.jpg\n",
            "image_124.jpg\n",
            "image_125.jpg\n",
            "image_126.jpg\n",
            "image_127.jpg\n",
            "image_128.jpg\n",
            "image_129.jpg\n",
            "image_130.jpg\n",
            "image_131.jpg\n",
            "image_132.jpg\n",
            "image_133.jpg\n",
            "image_134.jpg\n",
            "image_135.jpg\n",
            "image_136.jpg\n",
            "image_137.jpg\n",
            "image_138.jpg\n",
            "image_139.jpg\n",
            "image_140.jpg\n",
            "image_141.jpg\n",
            "image_142.jpg\n",
            "image_143.jpg\n",
            "image_144.jpg\n",
            "image_145.jpg\n",
            "image_146.jpg\n",
            "image_147.jpg\n",
            "image_148.jpg\n",
            "image_149.jpg\n",
            "image_150.jpg\n",
            "Acc = 100.0 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JHc6nIHQe275",
        "colab_type": "text"
      },
      "source": [
        "# **Scenario 2 (intermediate)**\n",
        "You receive a test set containing 50 rotated and 50 perspective images annotated with the view (rotated or perspective), the option (F or I) and the digit (1, 2, 3 or 4). For each image you have to output the corresponding grade"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s2xfTEhLECsR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#1\n",
        "def transform_rotaded_and_perspective_in_scanned(img_path, template_path = data_dir_drive + '/data/template.jpg'):\n",
        "    img_template = cv.imread(template_path)\n",
        "\n",
        "    (h, w) = img_template.shape[:2]\n",
        "    width = 3500\n",
        "    r = width / float(w)\n",
        "    dim = (width, int(h * r))\n",
        "    img_template = cv.resize(img_template, dim, interpolation = cv.INTER_AREA)\n",
        "\n",
        "    img_query = cv.imread(img_path)\n",
        "    img_template = cv.cvtColor(img_template, cv.COLOR_BGR2RGB)\n",
        "    img_query = cv.cvtColor(img_query, cv.COLOR_BGR2RGB)\n",
        "\n",
        "    # create ORB object\n",
        "    orb = cv.ORB_create(nfeatures=15000)\n",
        "    # get the keypoints and the corresponding descriptors\n",
        "    kp_template, des_template = orb.detectAndCompute(img_template, None)\n",
        "\n",
        "    kp_query, des_query = orb.detectAndCompute(img_query, None) \n",
        "\n",
        "    # drawing keypoints\n",
        "    drawing_img1 = np.copy(img_template)\n",
        "    cv.drawKeypoints(img_template, kp_template, drawing_img1, flags=cv.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)\n",
        "\n",
        "    # drawing keypoints\n",
        "    drawing_img2 = np.copy(img_query)\n",
        "    cv.drawKeypoints(img_query, kp_query, drawing_img2, flags=cv.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)\n",
        "\n",
        "    #plt.figure(figsize=(14, 14))\n",
        "    #plt.subplot(1, 2, 1)\n",
        "    #plt.imshow(drawing_img1)\n",
        "    #plt.subplot(1, 2, 2)\n",
        "    #plt.imshow(drawing_img2)\n",
        "    #plt.show()\n",
        "\n",
        "    # help here: https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_feature2d/py_matcher/py_matcher.html\n",
        "    # https://docs.opencv.org/master/dc/dc3/tutorial_py_matcher.html\n",
        "\n",
        "    # create BFMatcher object\n",
        "    # matcher takes normType, which is set to cv2.NORM_L2 for SIFT and SURF, cv2.NORM_HAMMING for ORB, FAST and BRIEF\n",
        "    bf = cv.BFMatcher(cv.NORM_HAMMING, crossCheck=True)\n",
        "\n",
        "    # Match descriptors.\n",
        "    matches = bf.match(des_query, des_template) # query_image, train_image\n",
        "    # Sort them in the order of their distance.\n",
        "    matches = sorted(matches, key = lambda x:x.distance)\n",
        "\n",
        "    # Draw first 50 matches.\n",
        "    #plt.figure(figsize=(14, 14))\n",
        "    #img3 = cv.drawMatches(img_template, kp_template, img_query, kp_query, matches[:50], None, flags=cv.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)\n",
        "    #plt.imshow(img3)\n",
        "    #plt.show()\n",
        "\n",
        "    matches = matches[:int(0.5 * len(matches))]\n",
        "    points_template = np.zeros((len(matches), 2))\n",
        "    points_query = np.zeros((len(matches), 2))\n",
        "\n",
        "    for i, match in enumerate(matches):\n",
        "        points_template[i, :] = kp_template[match.trainIdx].pt\n",
        "        points_query[i, :] = kp_query[match.queryIdx].pt\n",
        "\n",
        "    M, _ = cv.findHomography(points_query, points_template, cv.RANSAC, ransacReprojThreshold = 8)\n",
        "\n",
        "    # use homography to get the aligned image \n",
        "    height, width, _ = img_template.shape # the shape with respect to the template image\n",
        "    aligned_image2 = cv.warpPerspective(img_query, M, (width, height), flags=cv.INTER_NEAREST)\n",
        "\n",
        "    return aligned_image2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eqwMEvCmhjHD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#2\n",
        "def set_global_cols_and_rows_for_scenario_2():\n",
        "    img_template = cv.imread(data_dir_drive + '/data/template.jpg')\n",
        "    (h, w) = img_template.shape[:2]\n",
        "    width = 3500\n",
        "    r = width / float(w)\n",
        "    dim = (width, int(h * r))\n",
        "    img_template = cv.resize(img_template, dim, interpolation = cv.INTER_AREA)\n",
        "    orig_h, orig_w, _ = img_template.shape\n",
        "    img_template = img_template[int(orig_h * 0.17):int(orig_h * 0.83)]\n",
        "    img_template = img_template[:, int(orig_w * 0.05):int(orig_w * 0.95)]\n",
        "\n",
        "    #print_img(image)\n",
        "    img_template = cv.GaussianBlur(img_template,(7,7),0)\n",
        "    img_template = cv.cvtColor(img_template, cv.COLOR_BGR2GRAY)\n",
        "\n",
        "    left_img_template = img_template[:, :orig_w//2]\n",
        "    right_img_template = img_template[:, orig_w//2:]\n",
        "\n",
        "    _, _, cols_l, rows_l = find_table(left_img_template)\n",
        "    _, _, cols_r, rows_r = find_table(right_img_template)\n",
        "    return cols_l, rows_l, cols_r, rows_r\n",
        "\n",
        "cols_l, rows_l, cols_r, rows_r = set_global_cols_and_rows_for_scenario_2()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "d735e31b-4530-44d2-a22b-f23bd8294283",
        "id": "cKDl4b1-HHpI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#3\n",
        "# test\n",
        "acc_perspective = 0\n",
        "\n",
        "for i in range(1, 151):\n",
        "    image_name = 'perspective_' + str(i) + '.jpg'\n",
        "   \n",
        "    print(image_name)\n",
        "    # load image\n",
        "\n",
        "    try:\n",
        "        aligned_image2 = transform_rotaded_and_perspective_in_scanned(folder_in + image_name)\n",
        "        #print_img(aligned_image2)\n",
        "\n",
        "        # take the lower part of the image\n",
        "        orig_h, orig_w, _ = aligned_image2.shape\n",
        "        aligned_image2 = aligned_image2[int(orig_h * 0.17):int(orig_h * 0.83)]\n",
        "        image = aligned_image2[:, int(orig_w * 0.05):int(orig_w * 0.95)]\n",
        "\n",
        "        #print_img(image)\n",
        "        img_blur = cv.GaussianBlur(image,(7,7),0)\n",
        "        grayscale_image = cv.cvtColor(img_blur, cv.COLOR_BGR2GRAY)\n",
        "        th = cv.adaptiveThreshold(grayscale_image,255,cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY,11,2)\n",
        "\n",
        "        left_grayscale_image = th[:, :orig_w//2]\n",
        "        right_grayscale_image = th[:, orig_w//2:]\n",
        "\n",
        "        # load ground truth\n",
        "        ground_truth_content = np.loadtxt(folder_in + 'image_' + str(i) + '.txt', dtype=str)\n",
        "        ground_truth_left = ground_truth_content[1:16]\n",
        "        ground_truth_right = ground_truth_content[16:-1]\n",
        "\n",
        "        result_l, image_l = find_x(left_grayscale_image, cols_l, rows_l)\n",
        "        #print(result_l)\n",
        "        result_r, image_r = find_x(right_grayscale_image, cols_r, rows_r)\n",
        "        #print(result_r)\n",
        "\n",
        "        left_err = 0;\n",
        "        for r in range(15):\n",
        "            tr_ans = ground_truth_left[r][1]\n",
        "            if r + 1 not in result_l.keys():\n",
        "                print(\"LINIE NECOMPLETATA\")\n",
        "                left_err += 1\n",
        "            else:\n",
        "                if len(result_l[r+1]) > 1:\n",
        "                    print(\"PREA MULTE RANDURI COMPLETATE\")\n",
        "                    left_err += 1\n",
        "                else:\n",
        "                    if result_l[r+1][0] != tr_ans:\n",
        "                        left_err = left_err + 1\n",
        "\n",
        "        if left_err:\n",
        "            print(\"GRESEALA PE STANGA\")\n",
        "            print_img(image_l)\n",
        "\n",
        "        right_err = 0;\n",
        "        for r in range(15):\n",
        "            tr_ans = ground_truth_right[r][1]\n",
        "            if r + 1 not in result_r.keys():\n",
        "                print(\"LINIE NECOMPLETATA\")\n",
        "                right_err += 1\n",
        "            else:\n",
        "                if len(result_r[r+1]) > 1:\n",
        "                    print(\"PREA MULTE RANDURI COMPLETATE\")\n",
        "                    right_err += 1\n",
        "                else:\n",
        "                    if result_r[r+1][0] != tr_ans:\n",
        "                        right_err = right_err + 1\n",
        "\n",
        "        if right_err:\n",
        "            print(\"GRESEALA PE DREAPTA\")\n",
        "            print_img(image_r)\n",
        "\n",
        "        if left_err == 0 and right_err == 0:\n",
        "            print(\"OK\")\n",
        "            acc_perspective = acc_perspective + 1\n",
        "        else:\n",
        "            print_img(th)\n",
        "\n",
        "    except Exception as e:\n",
        "        print(e)\n",
        " \n",
        "print('ACC = ' + str(acc_perspective))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "perspective_1.jpg\n",
            "OK\n",
            "perspective_2.jpg\n",
            "OK\n",
            "perspective_3.jpg\n",
            "OK\n",
            "perspective_4.jpg\n",
            "OK\n",
            "perspective_5.jpg\n",
            "OK\n",
            "perspective_6.jpg\n",
            "OK\n",
            "perspective_7.jpg\n",
            "OK\n",
            "perspective_8.jpg\n",
            "OK\n",
            "perspective_9.jpg\n",
            "OK\n",
            "perspective_10.jpg\n",
            "OK\n",
            "perspective_11.jpg\n",
            "OK\n",
            "perspective_12.jpg\n",
            "OK\n",
            "perspective_13.jpg\n",
            "OK\n",
            "perspective_14.jpg\n",
            "OK\n",
            "perspective_15.jpg\n",
            "OK\n",
            "perspective_16.jpg\n",
            "OK\n",
            "perspective_17.jpg\n",
            "OK\n",
            "perspective_18.jpg\n",
            "OK\n",
            "perspective_19.jpg\n",
            "OK\n",
            "perspective_20.jpg\n",
            "OK\n",
            "perspective_21.jpg\n",
            "OK\n",
            "perspective_22.jpg\n",
            "OK\n",
            "perspective_23.jpg\n",
            "OK\n",
            "perspective_24.jpg\n",
            "OK\n",
            "perspective_25.jpg\n",
            "OK\n",
            "perspective_26.jpg\n",
            "OK\n",
            "perspective_27.jpg\n",
            "OK\n",
            "perspective_28.jpg\n",
            "OK\n",
            "perspective_29.jpg\n",
            "OK\n",
            "perspective_30.jpg\n",
            "OK\n",
            "perspective_31.jpg\n",
            "OK\n",
            "perspective_32.jpg\n",
            "OK\n",
            "perspective_33.jpg\n",
            "OK\n",
            "perspective_34.jpg\n",
            "OK\n",
            "perspective_35.jpg\n",
            "OK\n",
            "perspective_36.jpg\n",
            "OK\n",
            "perspective_37.jpg\n",
            "OK\n",
            "perspective_38.jpg\n",
            "OK\n",
            "perspective_39.jpg\n",
            "OK\n",
            "perspective_40.jpg\n",
            "OK\n",
            "perspective_41.jpg\n",
            "OK\n",
            "perspective_42.jpg\n",
            "OK\n",
            "perspective_43.jpg\n",
            "OK\n",
            "perspective_44.jpg\n",
            "OK\n",
            "perspective_45.jpg\n",
            "OK\n",
            "perspective_46.jpg\n",
            "OK\n",
            "perspective_47.jpg\n",
            "OK\n",
            "perspective_48.jpg\n",
            "OK\n",
            "perspective_49.jpg\n",
            "OK\n",
            "perspective_50.jpg\n",
            "OK\n",
            "perspective_51.jpg\n",
            "OK\n",
            "perspective_52.jpg\n",
            "OK\n",
            "perspective_53.jpg\n",
            "OK\n",
            "perspective_54.jpg\n",
            "OK\n",
            "perspective_55.jpg\n",
            "OK\n",
            "perspective_56.jpg\n",
            "OK\n",
            "perspective_57.jpg\n",
            "OK\n",
            "perspective_58.jpg\n",
            "OK\n",
            "perspective_59.jpg\n",
            "OK\n",
            "perspective_60.jpg\n",
            "OK\n",
            "perspective_61.jpg\n",
            "OK\n",
            "perspective_62.jpg\n",
            "OK\n",
            "perspective_63.jpg\n",
            "OK\n",
            "perspective_64.jpg\n",
            "OK\n",
            "perspective_65.jpg\n",
            "OK\n",
            "perspective_66.jpg\n",
            "OK\n",
            "perspective_67.jpg\n",
            "OK\n",
            "perspective_68.jpg\n",
            "OK\n",
            "perspective_69.jpg\n",
            "OK\n",
            "perspective_70.jpg\n",
            "OK\n",
            "perspective_71.jpg\n",
            "OK\n",
            "perspective_72.jpg\n",
            "OK\n",
            "perspective_73.jpg\n",
            "OK\n",
            "perspective_74.jpg\n",
            "OK\n",
            "perspective_75.jpg\n",
            "OK\n",
            "perspective_76.jpg\n",
            "OK\n",
            "perspective_77.jpg\n",
            "OK\n",
            "perspective_78.jpg\n",
            "OK\n",
            "perspective_79.jpg\n",
            "OK\n",
            "perspective_80.jpg\n",
            "OK\n",
            "perspective_81.jpg\n",
            "OK\n",
            "perspective_82.jpg\n",
            "OK\n",
            "perspective_83.jpg\n",
            "OK\n",
            "perspective_84.jpg\n",
            "OK\n",
            "perspective_85.jpg\n",
            "OK\n",
            "perspective_86.jpg\n",
            "OK\n",
            "perspective_87.jpg\n",
            "OK\n",
            "perspective_88.jpg\n",
            "OK\n",
            "perspective_89.jpg\n",
            "OK\n",
            "perspective_90.jpg\n",
            "OK\n",
            "perspective_91.jpg\n",
            "OK\n",
            "perspective_92.jpg\n",
            "OK\n",
            "perspective_93.jpg\n",
            "OK\n",
            "perspective_94.jpg\n",
            "OK\n",
            "perspective_95.jpg\n",
            "OK\n",
            "perspective_96.jpg\n",
            "OK\n",
            "perspective_97.jpg\n",
            "OK\n",
            "perspective_98.jpg\n",
            "OK\n",
            "perspective_99.jpg\n",
            "OK\n",
            "perspective_100.jpg\n",
            "OK\n",
            "perspective_101.jpg\n",
            "OK\n",
            "perspective_102.jpg\n",
            "OK\n",
            "perspective_103.jpg\n",
            "OK\n",
            "perspective_104.jpg\n",
            "OK\n",
            "perspective_105.jpg\n",
            "OK\n",
            "perspective_106.jpg\n",
            "OK\n",
            "perspective_107.jpg\n",
            "OK\n",
            "perspective_108.jpg\n",
            "OK\n",
            "perspective_109.jpg\n",
            "OK\n",
            "perspective_110.jpg\n",
            "OK\n",
            "perspective_111.jpg\n",
            "OK\n",
            "perspective_112.jpg\n",
            "OK\n",
            "perspective_113.jpg\n",
            "OK\n",
            "perspective_114.jpg\n",
            "OK\n",
            "perspective_115.jpg\n",
            "OK\n",
            "perspective_116.jpg\n",
            "OK\n",
            "perspective_117.jpg\n",
            "OK\n",
            "perspective_118.jpg\n",
            "OK\n",
            "perspective_119.jpg\n",
            "OK\n",
            "perspective_120.jpg\n",
            "OK\n",
            "perspective_121.jpg\n",
            "OK\n",
            "perspective_122.jpg\n",
            "OK\n",
            "perspective_123.jpg\n",
            "OK\n",
            "perspective_124.jpg\n",
            "OK\n",
            "perspective_125.jpg\n",
            "OK\n",
            "perspective_126.jpg\n",
            "OK\n",
            "perspective_127.jpg\n",
            "OK\n",
            "perspective_128.jpg\n",
            "OK\n",
            "perspective_129.jpg\n",
            "OK\n",
            "perspective_130.jpg\n",
            "OK\n",
            "perspective_131.jpg\n",
            "OK\n",
            "perspective_132.jpg\n",
            "OK\n",
            "perspective_133.jpg\n",
            "OK\n",
            "perspective_134.jpg\n",
            "OK\n",
            "perspective_135.jpg\n",
            "OK\n",
            "perspective_136.jpg\n",
            "OK\n",
            "perspective_137.jpg\n",
            "OK\n",
            "perspective_138.jpg\n",
            "OK\n",
            "perspective_139.jpg\n",
            "OK\n",
            "perspective_140.jpg\n",
            "OK\n",
            "perspective_141.jpg\n",
            "OK\n",
            "perspective_142.jpg\n",
            "OK\n",
            "perspective_143.jpg\n",
            "OK\n",
            "perspective_144.jpg\n",
            "OK\n",
            "perspective_145.jpg\n",
            "OK\n",
            "perspective_146.jpg\n",
            "OK\n",
            "perspective_147.jpg\n",
            "OK\n",
            "perspective_148.jpg\n",
            "OK\n",
            "perspective_149.jpg\n",
            "OK\n",
            "perspective_150.jpg\n",
            "OK\n",
            "ACC = 150\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "a8717fff-19bd-4610-dd9a-ad00eaf85d83",
        "id": "sxxkbPFMM2YR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#4\n",
        "# test  classifier \n",
        "acc_rotation = 0\n",
        "\n",
        "for i in range(1, 151):\n",
        "    image_name = 'rotation_' + str(i) + '.jpg'\n",
        "   \n",
        "    print(image_name)\n",
        "    # load image\n",
        "\n",
        "    try:\n",
        "        aligned_image2 = transform_rotaded_and_perspective_in_scanned(folder_in + image_name)\n",
        "        #print_img(aligned_image2)\n",
        "\n",
        "        # take the lower part of the image\n",
        "        orig_h, orig_w, _ = aligned_image2.shape\n",
        "        aligned_image2 = aligned_image2[int(orig_h * 0.17):int(orig_h * 0.83)]\n",
        "        image = aligned_image2[:, int(orig_w * 0.05):int(orig_w * 0.95)]\n",
        "\n",
        "        #print_img(image)\n",
        "        img_blur = cv.GaussianBlur(image,(7,7),0)\n",
        "        grayscale_image = cv.cvtColor(img_blur, cv.COLOR_BGR2GRAY)\n",
        "        th = cv.adaptiveThreshold(grayscale_image,255,cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY,11,2)\n",
        "\n",
        "        left_grayscale_image = th[:, :orig_w//2]\n",
        "        right_grayscale_image = th[:, orig_w//2:]\n",
        "\n",
        "        # load ground truth\n",
        "        ground_truth_content = np.loadtxt(folder_in + 'image_' + str(i) + '.txt', dtype=str)\n",
        "        ground_truth_left = ground_truth_content[1:16]\n",
        "        ground_truth_right = ground_truth_content[16:-1]\n",
        "\n",
        "        result_l, image_l = find_x(left_grayscale_image, cols_l, rows_l)\n",
        "        #print(result_l)\n",
        "        result_r, image_r = find_x(right_grayscale_image, cols_r, rows_r)\n",
        "        #print(result_r)\n",
        "\n",
        "        left_err = 0;\n",
        "        for r in range(15):\n",
        "            tr_ans = ground_truth_left[r][1]\n",
        "            if r + 1 not in result_l.keys():\n",
        "                print(\"LINIE NECOMPLETATA\")\n",
        "                left_err += 1\n",
        "            else:\n",
        "                if len(result_l[r+1]) > 1:\n",
        "                    print(\"PREA MULTE RANDURI COMPLETATE\")\n",
        "                    left_err += 1\n",
        "                else:\n",
        "                    if result_l[r+1][0] != tr_ans:\n",
        "                        left_err = left_err + 1\n",
        "\n",
        "        if left_err:\n",
        "            print(\"GRESEALA PE STANGA\")\n",
        "            print_img(image_l)\n",
        "\n",
        "        right_err = 0;\n",
        "        for r in range(15):\n",
        "            tr_ans = ground_truth_right[r][1]\n",
        "            if r + 1 not in result_r.keys():\n",
        "                print(\"LINIE NECOMPLETATA\")\n",
        "                right_err += 1\n",
        "            else:\n",
        "                if len(result_r[r+1]) > 1:\n",
        "                    print(\"PREA MULTE RANDURI COMPLETATE\")\n",
        "                    right_err += 1\n",
        "                else:\n",
        "                    if result_r[r+1][0] != tr_ans:\n",
        "                        right_err = right_err + 1\n",
        "\n",
        "        if right_err:\n",
        "            print(\"GRESEALA PE DREAPTA\")\n",
        "            print_img(image_r)\n",
        "\n",
        "        if left_err == 0 and right_err == 0:\n",
        "            print(\"OK\")\n",
        "            acc_rotation += 1\n",
        "        else:\n",
        "            print_img(th)\n",
        "    except Exception as e:\n",
        "        print(e)\n",
        " \n",
        "print('ACC = ' + str(acc_rotation))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "rotation_1.jpg\n",
            "OK\n",
            "rotation_2.jpg\n",
            "OK\n",
            "rotation_3.jpg\n",
            "OK\n",
            "rotation_4.jpg\n",
            "OK\n",
            "rotation_5.jpg\n",
            "OK\n",
            "rotation_6.jpg\n",
            "OK\n",
            "rotation_7.jpg\n",
            "OK\n",
            "rotation_8.jpg\n",
            "OK\n",
            "rotation_9.jpg\n",
            "OK\n",
            "rotation_10.jpg\n",
            "OK\n",
            "rotation_11.jpg\n",
            "OK\n",
            "rotation_12.jpg\n",
            "OK\n",
            "rotation_13.jpg\n",
            "OK\n",
            "rotation_14.jpg\n",
            "OK\n",
            "rotation_15.jpg\n",
            "OK\n",
            "rotation_16.jpg\n",
            "OK\n",
            "rotation_17.jpg\n",
            "OK\n",
            "rotation_18.jpg\n",
            "OK\n",
            "rotation_19.jpg\n",
            "OK\n",
            "rotation_20.jpg\n",
            "OK\n",
            "rotation_21.jpg\n",
            "OK\n",
            "rotation_22.jpg\n",
            "OK\n",
            "rotation_23.jpg\n",
            "OK\n",
            "rotation_24.jpg\n",
            "OK\n",
            "rotation_25.jpg\n",
            "OK\n",
            "rotation_26.jpg\n",
            "OK\n",
            "rotation_27.jpg\n",
            "OK\n",
            "rotation_28.jpg\n",
            "OK\n",
            "rotation_29.jpg\n",
            "OK\n",
            "rotation_30.jpg\n",
            "OK\n",
            "rotation_31.jpg\n",
            "OK\n",
            "rotation_32.jpg\n",
            "OK\n",
            "rotation_33.jpg\n",
            "OK\n",
            "rotation_34.jpg\n",
            "OK\n",
            "rotation_35.jpg\n",
            "OK\n",
            "rotation_36.jpg\n",
            "OK\n",
            "rotation_37.jpg\n",
            "OK\n",
            "rotation_38.jpg\n",
            "OK\n",
            "rotation_39.jpg\n",
            "OK\n",
            "rotation_40.jpg\n",
            "OK\n",
            "rotation_41.jpg\n",
            "OK\n",
            "rotation_42.jpg\n",
            "OK\n",
            "rotation_43.jpg\n",
            "OK\n",
            "rotation_44.jpg\n",
            "OK\n",
            "rotation_45.jpg\n",
            "OK\n",
            "rotation_46.jpg\n",
            "OK\n",
            "rotation_47.jpg\n",
            "OK\n",
            "rotation_48.jpg\n",
            "OK\n",
            "rotation_49.jpg\n",
            "OK\n",
            "rotation_50.jpg\n",
            "OK\n",
            "rotation_51.jpg\n",
            "OK\n",
            "rotation_52.jpg\n",
            "OK\n",
            "rotation_53.jpg\n",
            "OK\n",
            "rotation_54.jpg\n",
            "OK\n",
            "rotation_55.jpg\n",
            "OK\n",
            "rotation_56.jpg\n",
            "OK\n",
            "rotation_57.jpg\n",
            "OK\n",
            "rotation_58.jpg\n",
            "OK\n",
            "rotation_59.jpg\n",
            "OK\n",
            "rotation_60.jpg\n",
            "OK\n",
            "rotation_61.jpg\n",
            "OK\n",
            "rotation_62.jpg\n",
            "OK\n",
            "rotation_63.jpg\n",
            "OK\n",
            "rotation_64.jpg\n",
            "OK\n",
            "rotation_65.jpg\n",
            "OK\n",
            "rotation_66.jpg\n",
            "OK\n",
            "rotation_67.jpg\n",
            "OK\n",
            "rotation_68.jpg\n",
            "OK\n",
            "rotation_69.jpg\n",
            "OK\n",
            "rotation_70.jpg\n",
            "OK\n",
            "rotation_71.jpg\n",
            "OK\n",
            "rotation_72.jpg\n",
            "OK\n",
            "rotation_73.jpg\n",
            "OK\n",
            "rotation_74.jpg\n",
            "OK\n",
            "rotation_75.jpg\n",
            "OK\n",
            "rotation_76.jpg\n",
            "OK\n",
            "rotation_77.jpg\n",
            "OK\n",
            "rotation_78.jpg\n",
            "OK\n",
            "rotation_79.jpg\n",
            "OK\n",
            "rotation_80.jpg\n",
            "OK\n",
            "rotation_81.jpg\n",
            "OK\n",
            "rotation_82.jpg\n",
            "OK\n",
            "rotation_83.jpg\n",
            "OK\n",
            "rotation_84.jpg\n",
            "OK\n",
            "rotation_85.jpg\n",
            "OK\n",
            "rotation_86.jpg\n",
            "OK\n",
            "rotation_87.jpg\n",
            "OK\n",
            "rotation_88.jpg\n",
            "OK\n",
            "rotation_89.jpg\n",
            "OK\n",
            "rotation_90.jpg\n",
            "OK\n",
            "rotation_91.jpg\n",
            "OK\n",
            "rotation_92.jpg\n",
            "OK\n",
            "rotation_93.jpg\n",
            "OK\n",
            "rotation_94.jpg\n",
            "OK\n",
            "rotation_95.jpg\n",
            "OK\n",
            "rotation_96.jpg\n",
            "OK\n",
            "rotation_97.jpg\n",
            "OK\n",
            "rotation_98.jpg\n",
            "OK\n",
            "rotation_99.jpg\n",
            "OK\n",
            "rotation_100.jpg\n",
            "OK\n",
            "rotation_101.jpg\n",
            "OK\n",
            "rotation_102.jpg\n",
            "OK\n",
            "rotation_103.jpg\n",
            "OK\n",
            "rotation_104.jpg\n",
            "OK\n",
            "rotation_105.jpg\n",
            "OK\n",
            "rotation_106.jpg\n",
            "OK\n",
            "rotation_107.jpg\n",
            "OK\n",
            "rotation_108.jpg\n",
            "OK\n",
            "rotation_109.jpg\n",
            "OK\n",
            "rotation_110.jpg\n",
            "OK\n",
            "rotation_111.jpg\n",
            "OK\n",
            "rotation_112.jpg\n",
            "OK\n",
            "rotation_113.jpg\n",
            "OK\n",
            "rotation_114.jpg\n",
            "OK\n",
            "rotation_115.jpg\n",
            "OK\n",
            "rotation_116.jpg\n",
            "OK\n",
            "rotation_117.jpg\n",
            "OK\n",
            "rotation_118.jpg\n",
            "OK\n",
            "rotation_119.jpg\n",
            "OK\n",
            "rotation_120.jpg\n",
            "OK\n",
            "rotation_121.jpg\n",
            "OK\n",
            "rotation_122.jpg\n",
            "OK\n",
            "rotation_123.jpg\n",
            "OK\n",
            "rotation_124.jpg\n",
            "OK\n",
            "rotation_125.jpg\n",
            "OK\n",
            "rotation_126.jpg\n",
            "OK\n",
            "rotation_127.jpg\n",
            "OK\n",
            "rotation_128.jpg\n",
            "OK\n",
            "rotation_129.jpg\n",
            "OK\n",
            "rotation_130.jpg\n",
            "OK\n",
            "rotation_131.jpg\n",
            "OK\n",
            "rotation_132.jpg\n",
            "OK\n",
            "rotation_133.jpg\n",
            "OK\n",
            "rotation_134.jpg\n",
            "OK\n",
            "rotation_135.jpg\n",
            "OK\n",
            "rotation_136.jpg\n",
            "OK\n",
            "rotation_137.jpg\n",
            "OK\n",
            "rotation_138.jpg\n",
            "OK\n",
            "rotation_139.jpg\n",
            "OK\n",
            "rotation_140.jpg\n",
            "OK\n",
            "rotation_141.jpg\n",
            "OK\n",
            "rotation_142.jpg\n",
            "OK\n",
            "rotation_143.jpg\n",
            "OK\n",
            "rotation_144.jpg\n",
            "OK\n",
            "rotation_145.jpg\n",
            "OK\n",
            "rotation_146.jpg\n",
            "OK\n",
            "rotation_147.jpg\n",
            "OK\n",
            "rotation_148.jpg\n",
            "OK\n",
            "rotation_149.jpg\n",
            "OK\n",
            "rotation_150.jpg\n",
            "OK\n",
            "ACC = 150\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "2XvKjsz_oYGP",
        "colab": {}
      },
      "source": [
        "#5\n",
        "def predict_grade_scenario_2(path, subject, number):\n",
        "    cols_l, rows_l, cols_r, rows_r = set_global_cols_and_rows_for_scenario_2()\n",
        "\n",
        "    aligned_image2 = transform_rotaded_and_perspective_in_scanned(path)\n",
        "    #print_img(aligned_image2)\n",
        "\n",
        "    # take the lower part of the image\n",
        "    orig_h, orig_w, _ = aligned_image2.shape\n",
        "    aligned_image2 = aligned_image2[int(orig_h * 0.17):int(orig_h * 0.83)]\n",
        "    image = aligned_image2[:, int(orig_w * 0.05):int(orig_w * 0.95)]\n",
        "\n",
        "    #print_img(image)\n",
        "    img_blur = cv.GaussianBlur(image,(7,7),0)\n",
        "    grayscale_image = cv.cvtColor(img_blur, cv.COLOR_BGR2GRAY)\n",
        "    th = cv.adaptiveThreshold(grayscale_image,255,cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY,11,2)\n",
        "\n",
        "    left_grayscale_image = th[:, :orig_w//2]\n",
        "    right_grayscale_image = th[:, orig_w//2:]\n",
        "\n",
        "    number_ok = 0\n",
        "    # load ground truth correct answers / barem\n",
        "    if subject == 'F':\n",
        "        if number == 1:\n",
        "            ground_truth_content = np.loadtxt(folder_barem + 'Fizica_varianta1.txt', dtype=str)\n",
        "        elif number == 2:\n",
        "            ground_truth_content = np.loadtxt(folder_barem + 'Fizica_varianta2.txt', dtype=str)\n",
        "        elif number == 3:\n",
        "            ground_truth_content = np.loadtxt(folder_barem + 'Fizica_varianta3.txt', dtype=str)\n",
        "        else:\n",
        "            ground_truth_content = np.loadtxt(folder_barem + 'Fizica_varianta4.txt', dtype=str)\n",
        "    else:\n",
        "        if number == 1:\n",
        "            ground_truth_content = np.loadtxt(folder_barem + 'Informatica_varianta1.txt', dtype=str)\n",
        "        elif number == 2:\n",
        "            ground_truth_content = np.loadtxt(folder_barem + 'Informatica_varianta2.txt', dtype=str)\n",
        "        elif number == 3:\n",
        "            ground_truth_content = np.loadtxt(folder_barem + 'Informatica_varianta3.txt', dtype=str)\n",
        "        else:\n",
        "            ground_truth_content = np.loadtxt(folder_barem + 'Informatica_varianta4.txt', dtype=str)\n",
        "\n",
        "    ground_truth_left = ground_truth_content[1:16]\n",
        "    ground_truth_right = ground_truth_content[16:-1]\n",
        "\n",
        "    result_l, image_l = find_x(left_grayscale_image, cols_l, rows_l)\n",
        "    #print(result_l)\n",
        "    result_r, image_r = find_x(right_grayscale_image, cols_r, rows_r)\n",
        "    #print(result_r)\n",
        "\n",
        "    left_mis = 0;\n",
        "    for r in range(15):\n",
        "        tr_ans = ground_truth_left[r][1]\n",
        "        if r + 1 not in result_l.keys():\n",
        "            left_mis += 1\n",
        "        else:\n",
        "            if len(result_l[r+1]) > 1:\n",
        "                left_mis += 1\n",
        "            else:\n",
        "                if result_l[r+1][0] != tr_ans:\n",
        "                    left_mis += 1\n",
        "                else:\n",
        "                    number_ok += 1\n",
        "\n",
        "    right_mis = 0;\n",
        "    for r in range(15):\n",
        "        tr_ans = ground_truth_right[r][1]\n",
        "        if r + 1 not in result_r.keys():\n",
        "            right_mis += 1\n",
        "        else:\n",
        "            if len(result_r[r+1]) > 1:\n",
        "                right_mis += 1\n",
        "            else:\n",
        "                if result_r[r+1][0] != tr_ans:\n",
        "                    right_mis += 1\n",
        "                else:\n",
        "                    number_ok += 1\n",
        "\n",
        "    return (number_ok * 3) / 10 + 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "dd05e890-7d3c-4354-9841-e8a87358dc67",
        "id": "JPOxyek_oYGW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#6\n",
        "acc = 0\n",
        "for i in range(1, 151):\n",
        "    image_name = 'perspective_' + str(i) + '.jpg'\n",
        "   \n",
        "    print(image_name)\n",
        "    file = open(folder_in + 'image_' + str(i) +'.txt', 'r')\n",
        "    first_line = file.readline()\n",
        "    file.close() \n",
        "    first_line = first_line.replace('\\n', '')\n",
        "    info = first_line.split(' ')\n",
        "\n",
        "    grade = predict_grade_scenario_2(folder_in + image_name, info[0], int(info[1]))\n",
        "\n",
        "    if ground_truth_hw_dict[i] == str(grade):\n",
        "        acc += 1\n",
        "    else:\n",
        "        print(\"GRESEALA\")\n",
        "\n",
        "print(\"Acc = \" + str(acc * 100 / 150) + \" %\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "perspective_1.jpg\n",
            "perspective_2.jpg\n",
            "perspective_3.jpg\n",
            "perspective_4.jpg\n",
            "perspective_5.jpg\n",
            "perspective_6.jpg\n",
            "perspective_7.jpg\n",
            "perspective_8.jpg\n",
            "perspective_9.jpg\n",
            "perspective_10.jpg\n",
            "perspective_11.jpg\n",
            "perspective_12.jpg\n",
            "perspective_13.jpg\n",
            "perspective_14.jpg\n",
            "perspective_15.jpg\n",
            "perspective_16.jpg\n",
            "perspective_17.jpg\n",
            "perspective_18.jpg\n",
            "perspective_19.jpg\n",
            "perspective_20.jpg\n",
            "perspective_21.jpg\n",
            "perspective_22.jpg\n",
            "perspective_23.jpg\n",
            "perspective_24.jpg\n",
            "perspective_25.jpg\n",
            "perspective_26.jpg\n",
            "perspective_27.jpg\n",
            "perspective_28.jpg\n",
            "perspective_29.jpg\n",
            "perspective_30.jpg\n",
            "perspective_31.jpg\n",
            "perspective_32.jpg\n",
            "perspective_33.jpg\n",
            "perspective_34.jpg\n",
            "perspective_35.jpg\n",
            "perspective_36.jpg\n",
            "perspective_37.jpg\n",
            "perspective_38.jpg\n",
            "perspective_39.jpg\n",
            "perspective_40.jpg\n",
            "perspective_41.jpg\n",
            "perspective_42.jpg\n",
            "perspective_43.jpg\n",
            "perspective_44.jpg\n",
            "perspective_45.jpg\n",
            "perspective_46.jpg\n",
            "perspective_47.jpg\n",
            "perspective_48.jpg\n",
            "perspective_49.jpg\n",
            "perspective_50.jpg\n",
            "perspective_51.jpg\n",
            "perspective_52.jpg\n",
            "perspective_53.jpg\n",
            "perspective_54.jpg\n",
            "perspective_55.jpg\n",
            "perspective_56.jpg\n",
            "perspective_57.jpg\n",
            "perspective_58.jpg\n",
            "perspective_59.jpg\n",
            "perspective_60.jpg\n",
            "perspective_61.jpg\n",
            "perspective_62.jpg\n",
            "perspective_63.jpg\n",
            "perspective_64.jpg\n",
            "perspective_65.jpg\n",
            "perspective_66.jpg\n",
            "perspective_67.jpg\n",
            "perspective_68.jpg\n",
            "perspective_69.jpg\n",
            "perspective_70.jpg\n",
            "perspective_71.jpg\n",
            "perspective_72.jpg\n",
            "perspective_73.jpg\n",
            "perspective_74.jpg\n",
            "perspective_75.jpg\n",
            "perspective_76.jpg\n",
            "perspective_77.jpg\n",
            "perspective_78.jpg\n",
            "perspective_79.jpg\n",
            "perspective_80.jpg\n",
            "perspective_81.jpg\n",
            "perspective_82.jpg\n",
            "perspective_83.jpg\n",
            "perspective_84.jpg\n",
            "perspective_85.jpg\n",
            "perspective_86.jpg\n",
            "perspective_87.jpg\n",
            "perspective_88.jpg\n",
            "perspective_89.jpg\n",
            "perspective_90.jpg\n",
            "perspective_91.jpg\n",
            "perspective_92.jpg\n",
            "perspective_93.jpg\n",
            "perspective_94.jpg\n",
            "perspective_95.jpg\n",
            "perspective_96.jpg\n",
            "perspective_97.jpg\n",
            "perspective_98.jpg\n",
            "perspective_99.jpg\n",
            "perspective_100.jpg\n",
            "perspective_101.jpg\n",
            "perspective_102.jpg\n",
            "perspective_103.jpg\n",
            "perspective_104.jpg\n",
            "perspective_105.jpg\n",
            "perspective_106.jpg\n",
            "perspective_107.jpg\n",
            "perspective_108.jpg\n",
            "perspective_109.jpg\n",
            "perspective_110.jpg\n",
            "perspective_111.jpg\n",
            "perspective_112.jpg\n",
            "perspective_113.jpg\n",
            "perspective_114.jpg\n",
            "perspective_115.jpg\n",
            "perspective_116.jpg\n",
            "perspective_117.jpg\n",
            "perspective_118.jpg\n",
            "perspective_119.jpg\n",
            "perspective_120.jpg\n",
            "perspective_121.jpg\n",
            "perspective_122.jpg\n",
            "perspective_123.jpg\n",
            "perspective_124.jpg\n",
            "perspective_125.jpg\n",
            "perspective_126.jpg\n",
            "perspective_127.jpg\n",
            "perspective_128.jpg\n",
            "perspective_129.jpg\n",
            "perspective_130.jpg\n",
            "perspective_131.jpg\n",
            "perspective_132.jpg\n",
            "perspective_133.jpg\n",
            "perspective_134.jpg\n",
            "perspective_135.jpg\n",
            "perspective_136.jpg\n",
            "perspective_137.jpg\n",
            "perspective_138.jpg\n",
            "perspective_139.jpg\n",
            "perspective_140.jpg\n",
            "perspective_141.jpg\n",
            "perspective_142.jpg\n",
            "perspective_143.jpg\n",
            "perspective_144.jpg\n",
            "perspective_145.jpg\n",
            "perspective_146.jpg\n",
            "perspective_147.jpg\n",
            "perspective_148.jpg\n",
            "perspective_149.jpg\n",
            "perspective_150.jpg\n",
            "Acc = 100.0 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xNEUvn7-q4Zm",
        "colab_type": "code",
        "outputId": "27da0977-31b6-4456-87fe-7453d2e42ea4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#7\n",
        "acc = 0\n",
        "for i in range(1, 151):\n",
        "    image_name = 'rotation_' + str(i) + '.jpg'\n",
        "   \n",
        "    print(image_name)\n",
        "    file = open(folder_in + 'image_' + str(i) +'.txt', 'r')\n",
        "    first_line = file.readline()\n",
        "    file.close() \n",
        "    first_line = first_line.replace('\\n', '')\n",
        "    info = first_line.split(' ')\n",
        "\n",
        "    grade = predict_grade_scenario_2(folder_in + image_name, info[0], int(info[1]))\n",
        "\n",
        "    if ground_truth_hw_dict[i] == str(grade):\n",
        "        acc += 1\n",
        "    else:\n",
        "        print(\"GRESEALA\")\n",
        "\n",
        "print(\"Acc = \" + str(acc * 100 / 150) + \" %\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "rotation_1.jpg\n",
            "rotation_2.jpg\n",
            "rotation_3.jpg\n",
            "rotation_4.jpg\n",
            "rotation_5.jpg\n",
            "rotation_6.jpg\n",
            "rotation_7.jpg\n",
            "rotation_8.jpg\n",
            "rotation_9.jpg\n",
            "rotation_10.jpg\n",
            "rotation_11.jpg\n",
            "rotation_12.jpg\n",
            "rotation_13.jpg\n",
            "rotation_14.jpg\n",
            "rotation_15.jpg\n",
            "rotation_16.jpg\n",
            "rotation_17.jpg\n",
            "rotation_18.jpg\n",
            "rotation_19.jpg\n",
            "rotation_20.jpg\n",
            "rotation_21.jpg\n",
            "rotation_22.jpg\n",
            "rotation_23.jpg\n",
            "rotation_24.jpg\n",
            "rotation_25.jpg\n",
            "rotation_26.jpg\n",
            "rotation_27.jpg\n",
            "rotation_28.jpg\n",
            "rotation_29.jpg\n",
            "rotation_30.jpg\n",
            "rotation_31.jpg\n",
            "rotation_32.jpg\n",
            "rotation_33.jpg\n",
            "rotation_34.jpg\n",
            "rotation_35.jpg\n",
            "rotation_36.jpg\n",
            "rotation_37.jpg\n",
            "rotation_38.jpg\n",
            "rotation_39.jpg\n",
            "rotation_40.jpg\n",
            "rotation_41.jpg\n",
            "rotation_42.jpg\n",
            "rotation_43.jpg\n",
            "rotation_44.jpg\n",
            "rotation_45.jpg\n",
            "rotation_46.jpg\n",
            "rotation_47.jpg\n",
            "rotation_48.jpg\n",
            "rotation_49.jpg\n",
            "rotation_50.jpg\n",
            "rotation_51.jpg\n",
            "rotation_52.jpg\n",
            "rotation_53.jpg\n",
            "rotation_54.jpg\n",
            "rotation_55.jpg\n",
            "rotation_56.jpg\n",
            "rotation_57.jpg\n",
            "rotation_58.jpg\n",
            "rotation_59.jpg\n",
            "rotation_60.jpg\n",
            "rotation_61.jpg\n",
            "rotation_62.jpg\n",
            "rotation_63.jpg\n",
            "rotation_64.jpg\n",
            "rotation_65.jpg\n",
            "rotation_66.jpg\n",
            "rotation_67.jpg\n",
            "rotation_68.jpg\n",
            "rotation_69.jpg\n",
            "rotation_70.jpg\n",
            "rotation_71.jpg\n",
            "rotation_72.jpg\n",
            "rotation_73.jpg\n",
            "rotation_74.jpg\n",
            "rotation_75.jpg\n",
            "rotation_76.jpg\n",
            "rotation_77.jpg\n",
            "rotation_78.jpg\n",
            "rotation_79.jpg\n",
            "rotation_80.jpg\n",
            "rotation_81.jpg\n",
            "rotation_82.jpg\n",
            "rotation_83.jpg\n",
            "rotation_84.jpg\n",
            "rotation_85.jpg\n",
            "rotation_86.jpg\n",
            "rotation_87.jpg\n",
            "rotation_88.jpg\n",
            "rotation_89.jpg\n",
            "rotation_90.jpg\n",
            "rotation_91.jpg\n",
            "rotation_92.jpg\n",
            "rotation_93.jpg\n",
            "rotation_94.jpg\n",
            "rotation_95.jpg\n",
            "rotation_96.jpg\n",
            "rotation_97.jpg\n",
            "rotation_98.jpg\n",
            "rotation_99.jpg\n",
            "rotation_100.jpg\n",
            "rotation_101.jpg\n",
            "rotation_102.jpg\n",
            "rotation_103.jpg\n",
            "rotation_104.jpg\n",
            "rotation_105.jpg\n",
            "rotation_106.jpg\n",
            "rotation_107.jpg\n",
            "rotation_108.jpg\n",
            "rotation_109.jpg\n",
            "rotation_110.jpg\n",
            "rotation_111.jpg\n",
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            "rotation_144.jpg\n",
            "rotation_145.jpg\n",
            "rotation_146.jpg\n",
            "rotation_147.jpg\n",
            "rotation_148.jpg\n",
            "rotation_149.jpg\n",
            "rotation_150.jpg\n",
            "Acc = 100.0 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f3F0--jDzF2_",
        "colab_type": "code",
        "outputId": "70e087d2-9220-47dd-e2fa-7c258f0a190e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#8\n",
        "acc = 0\n",
        "for i in range(1, 151):\n",
        "    image_name = 'image_' + str(i) + '.jpg'\n",
        "   \n",
        "    print(image_name)\n",
        "    file = open(folder_in + 'image_' + str(i) +'.txt', 'r')\n",
        "    first_line = file.readline()\n",
        "    file.close() \n",
        "    first_line = first_line.replace('\\n', '')\n",
        "    info = first_line.split(' ')\n",
        "\n",
        "    grade = predict_grade_scenario_2(folder_in + image_name, info[0], int(info[1]))\n",
        "\n",
        "    if ground_truth_hw_dict[i] == str(grade):\n",
        "        acc += 1\n",
        "    else:\n",
        "        print(\"GRESEALA\")\n",
        "\n",
        "print(\"Acc = \" + str(acc * 100 / 150) + \" %\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "image_1.jpg\n",
            "image_2.jpg\n",
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            "image_146.jpg\n",
            "image_147.jpg\n",
            "image_148.jpg\n",
            "image_149.jpg\n",
            "image_150.jpg\n",
            "Acc = 100.0 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sAXuZYfUSNOC",
        "colab_type": "text"
      },
      "source": [
        "# **Scenario 3 (no annotations)**\n",
        "You receive a test set containing 75 images (scanned, rotated or perspective view). There is no annotation available. For each image you have to output the corresponding grade"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "wyLg__f7xIpp",
        "colab": {}
      },
      "source": [
        "#1\n",
        "def detect_subject_choice(choice_patch_grayscale, rows, cols):\n",
        "\n",
        "    # apply findCountour function in order to extract all the countour in the image\n",
        "    # take only the black (ish) pixels\n",
        "    mask = np.uint8(choice_patch_grayscale < 50)\n",
        "    contours, hierarchy = cv.findContours(mask, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)\n",
        "    \n",
        "    areas = []\n",
        "    coords = []\n",
        "    if len(contours) != 0:\n",
        "        for idx_det in range(len(contours)):\n",
        "            contour = contours[idx_det] \n",
        "            contour = np.squeeze(contour)  \n",
        "            \n",
        "            if contour.ndim == 1:\n",
        "                continue\n",
        "                \n",
        "            x_min = np.min(contour[:, 0])\n",
        "            x_max = np.max(contour[:, 0])\n",
        "\n",
        "            y_min = np.min(contour[:, 1])\n",
        "            y_max = np.max(contour[:, 1])\n",
        "            areas.append(((x_max - x_min) * (y_max - y_min)))\n",
        "            coords.append([(x_min, y_min), (x_max, y_max)])\n",
        "    if len(areas) < 2:\n",
        "        return None\n",
        "    \n",
        "    areas = np.array(areas)\n",
        "    indices = areas.argsort()\n",
        "    bbox_1 = coords[indices[-1]]\n",
        "    bbox_2 = coords[indices[-2]]\n",
        "\n",
        "    choice_patch_color = np.dstack((choice_patch_grayscale, choice_patch_grayscale, choice_patch_grayscale))\n",
        "    cv.rectangle(choice_patch_color, bbox_1[0], bbox_1[1], color=(255, 0, 255), thickness=5)\n",
        "    cv.rectangle(choice_patch_color, bbox_2[0], bbox_2[1], color=(0, 255, 255), thickness=5)\n",
        "    #cv2_imshow(choice_patch_color)\n",
        "\n",
        "    return bbox_1, bbox_2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UndKM7y7Su3c",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#2\n",
        "def make_data_set():\n",
        "\n",
        "    if not os.path.exists(folder_digits + '1'):\n",
        "        os.makedirs(folder_digits + '1')\n",
        "    if not os.path.exists(folder_digits + '2'):\n",
        "        os.makedirs(folder_digits + '2')\n",
        "    if not os.path.exists(folder_digits + '3'):\n",
        "        os.makedirs(folder_digits + '3')\n",
        "    if not os.path.exists(folder_digits + '4'):\n",
        "        os.makedirs(folder_digits + '4')\n",
        "\n",
        "    for f in glob.iglob(folder_digits + '1/*'):\n",
        "        os.remove(f)\n",
        "    for f in glob.iglob(folder_digits + '2/*'):\n",
        "        os.remove(f)\n",
        "    for f in glob.iglob(folder_digits + '3/*'):\n",
        "        os.remove(f)\n",
        "    for f in glob.iglob(folder_digits + '4/*'):\n",
        "        os.remove(f)\n",
        "\n",
        "    for i in range(1, 151): \n",
        "        file = open(folder_in + 'image_' + str(i) +'.txt', 'r')\n",
        "        first_line = file.readline()\n",
        "        file.close() \n",
        "        first_line = first_line.replace('\\n', '')\n",
        "        info = first_line.split(' ')\n",
        "\n",
        "        image_name = 'image_' + str(i) + '.jpg'\n",
        "    \n",
        "        print(image_name)\n",
        "        # load image\n",
        "        image = cv.imread(folder_in + image_name)\n",
        "\n",
        "        try:\n",
        "            #aligned_image2 = transform_rotaded_and_perspective_in_scanned(folder_in + image_name)\n",
        "            #print_img(aligned_image2)\n",
        "\n",
        "            # take the lower part of the image\n",
        "            #orig_h, orig_w, _ = aligned_image2.shape\n",
        "            #image = aligned_image2[int(orig_h * 0):int(orig_h * 0.85)]\n",
        "\n",
        "            orig_h, orig_w, _ = image.shape\n",
        "            image = image[int(orig_h * 0.41):int(orig_h * 0.88)]\n",
        "\n",
        "            img_blur = cv.GaussianBlur(image,(7,7),0)\n",
        "            grayscale_image = cv.cvtColor(img_blur, cv.COLOR_BGR2GRAY)\n",
        "            th = cv.adaptiveThreshold(grayscale_image,255,cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY,11,2)\n",
        "\n",
        "            left_grayscale_image = th[:, :orig_w//2]\n",
        "            right_grayscale_image = th[:, orig_w//2:]\n",
        "\n",
        "            _, bbox_l, cols_l, rows_l = find_table(left_grayscale_image)\n",
        "            _, bbox_r, cols_r, rows_r = find_table(right_grayscale_image)\n",
        "\n",
        "            x_min = cols_r[-2][0][0]\n",
        "            x_max = cols_r[-1][0][0]\n",
        "            y_min = 0 \n",
        "            y_max = rows_r[0][1][1] - 2*(rows_r[1][1][1]-rows_r[0][1][1])\n",
        "\n",
        "            patch = grayscale_image[:, orig_w//2:]\n",
        "            patch = patch[y_min:y_max, x_min:x_max]\n",
        "\n",
        "            bbox_1, bbox_2 = detect_subject_choice(patch, rows_r, cols_r)\n",
        "\n",
        "            (x_min_1, y_min_1), (x_max_1, y_max_1) = bbox_1\n",
        "            (x_min_2, y_min_2), (x_max_2, y_max_2) = bbox_2\n",
        "            \n",
        "            patch1 = patch[y_min_1 + 10:y_max_1 - 10, x_min_1 + 10:x_max_1 - 10]\n",
        "            patch2 = patch[y_min_2 + 10:y_max_2 - 10, x_min_2 + 10:x_max_2 - 10]\n",
        "\n",
        "            mean_patch_box_1 = np.round(patch1.mean())\n",
        "            mean_patch_box_2 = np.round(patch2.mean())\n",
        "\n",
        "            choice_patch_color = np.dstack((patch, patch, patch))\n",
        "            if mean_patch_box_1 < mean_patch_box_2:\n",
        "                cv.imwrite(folder_digits + info[1] + '/' + str(i) + '.jpg', patch1)\n",
        "                cv.rectangle(choice_patch_color, (x_min_1 + 10, y_min_1 + 10), (x_max_1 - 10, y_max_1 - 10), color=(255, 0, 255), thickness=5)\n",
        "            else:\n",
        "                cv.imwrite(folder_digits + info[1] + '/' + str(i) + '.jpg', patch2)\n",
        "                cv.rectangle(choice_patch_color, (x_min_2 + 10, y_min_2 + 10), (x_max_2 - 10, y_max_2 - 10), color=(0, 255, 255), thickness=5)\n",
        "            #cv2_imshow(choice_patch_color)\n",
        "            #print(info[0])\n",
        "        except Exception as e:\n",
        "            print(e)\n",
        "            continue"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i2_8c5cK0lmk",
        "colab_type": "code",
        "outputId": "6723b0bc-0468-4b85-b3e1-cdbbba1b16fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#3\n",
        "make_data_set()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "image_1.jpg\n",
            "image_2.jpg\n",
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            "image_150.jpg\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3a_N9IXO5kcX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#4\n",
        "# Training settings    \n",
        "kwargs={}\n",
        "class Args():\n",
        "  def __init__(self):\n",
        "      self.batch_size = 64\n",
        "      self.test_batch_size = 64\n",
        "      self.epochs = 10\n",
        "      self.lr = 0.01\n",
        "      self.momentum = 0.9\n",
        "      self.seed = 1\n",
        "      self.log_interval = int(10000 / self.batch_size)\n",
        "      self.cuda = False\n",
        "\n",
        "args = Args()\n",
        "\n",
        "use_cuda = torch.cuda.is_available()\n",
        "torch.manual_seed(args.seed)\n",
        "device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n",
        "\n",
        "kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vidlQ4nf9jjb",
        "colab_type": "code",
        "outputId": "0b6023ab-e864-4f26-ae97-4763e18b1b06",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "referenced_widgets": [
            "831094f2778a49d0803b308005267d81",
            "b41562885a6c4aa590b2e10a6f5cb41b",
            "ee3fa3feb80947848f2b433daf43410d",
            "3d4372dd62334634a298d84f48f79673",
            "e12abc622e224002b5a2551d93fb9e6b",
            "bbe9ce6908c947bd87d4b7ce5cc32f4e",
            "7c9218d15e994a03882078787fee5cd7",
            "a7554683ea9a4d97b62b878b6017d413",
            "ece36463e5a74af78bd0938881d6b2af",
            "61599f04be7648a18deeb9f3ab237f49",
            "a5a8c7405f414272a01e102acdf8db08",
            "fe76099f21174af19f3e13bc8149a6c1",
            "5a9843055c60486fb7e8c90faba84736",
            "54fa21f4ccd840539c01cc899ad24597",
            "30bbae6debec400fb7f2480677e413e2",
            "d3b5139ab40e42eeb03e5c74992d0435",
            "607fcadec515431fb989da14c02590a4",
            "8d90d2d4520947b9a9a314e704fcd5dd",
            "e266fe4e920b42f289b63fc993403069",
            "0f3f6d664beb4db7a5a3bf9bd93b1281",
            "34a86fbafe5143a993cb1bd68b45b0ad",
            "65c3af21d636498f9c60b1302b19fda8",
            "136bbf613940452ca5172a7d842626fa",
            "60f4761adc284a7da408f44b2b312f27",
            "7e51c954b5bc49a7a5601f30ce0af161",
            "ac0eea67ae264b06851e431fc09aafa6",
            "9996301ae68741718ddc337570add292",
            "38386919c1ea4861af6d099effae6ccf",
            "f2417bf4b0644fa3a127dc0574fc942f",
            "2e77f2a843e24a35989a53dfb338436f",
            "eb92e27b30e34299bf12b24c31dec8c5",
            "9caee32db8ea415989a5b4c4c61829be"
          ]
        }
      },
      "source": [
        "#5\n",
        "train_loader = torch.utils.data.DataLoader(\n",
        "    datasets.MNIST('../data', train=True, download=True,\n",
        "                   transform=transforms.Compose([\n",
        "                       transforms.ToTensor(),\n",
        "                       \n",
        "                   ])),\n",
        "    batch_size=args.batch_size, shuffle=True,drop_last=True, **kwargs)\n",
        "\n",
        "test_loader = torch.utils.data.DataLoader(\n",
        "    datasets.MNIST('../data', train=False, transform=transforms.Compose([\n",
        "                       transforms.ToTensor(),\n",
        "                   ])),\n",
        "    batch_size=args.test_batch_size, shuffle=True,drop_last=True, **kwargs)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ../data/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "831094f2778a49d0803b308005267d81",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting ../data/MNIST/raw/train-images-idx3-ubyte.gz to ../data/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ../data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ece36463e5a74af78bd0938881d6b2af",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting ../data/MNIST/raw/train-labels-idx1-ubyte.gz to ../data/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ../data/MNIST/raw/t10k-images-idx3-ubyte.gz\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "607fcadec515431fb989da14c02590a4",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting ../data/MNIST/raw/t10k-images-idx3-ubyte.gz to ../data/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ../data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7e51c954b5bc49a7a5601f30ce0af161",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting ../data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ../data/MNIST/raw\n",
            "Processing...\n",
            "Done!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tWBJuP6x6X5O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#6\n",
        "no_filters1 = 20\n",
        "no_filter2 = 50\n",
        "no_neurons1 = 500\n",
        "class CNN(nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.conv1 = nn.Conv2d(1, no_filters1, 5, 1)\n",
        "        self.conv2 = nn.Conv2d(no_filters1, no_filter2, 5, 1)\n",
        "        self.fc1 = nn.Linear(4*4*no_filter2, no_neurons1)\n",
        "        self.fc2 = nn.Linear(no_neurons1, 10)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = F.relu(self.conv1(x))\n",
        "        x = F.max_pool2d(x, 2, 2)\n",
        "        x = F.relu(self.conv2(x))\n",
        "        x = F.max_pool2d(x, 2, 2)\n",
        "        x = x.view(-1, 4*4*no_filter2)\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = self.fc2(x)\n",
        "        return F.log_softmax(x, dim=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1WJxm0PFVH5P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#7\n",
        "def train(args, model, device, train_loader, optimizer, epoch):\n",
        "    model.train()\n",
        "    for batch_idx, (data, target) in enumerate(train_loader):\n",
        "        data, target = data.to(device), target.to(device)\n",
        "        optimizer.zero_grad()\n",
        "        output = model(data)\n",
        "        loss = F.nll_loss(output, target)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        if batch_idx % args.log_interval == 0:\n",
        "            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n",
        "                epoch, batch_idx * len(data), len(train_loader.dataset),\n",
        "                100. * batch_idx / len(train_loader), loss.item()))\n",
        "\n",
        "def test(args, model, device, test_loader):\n",
        "    model.eval()\n",
        "    test_loss = 0\n",
        "    correct = 0\n",
        "    with torch.no_grad():\n",
        "        for data, target in test_loader:\n",
        "            data, target = data.to(device), target.to(device)\n",
        "            output = model(data)\n",
        "            test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss\n",
        "            pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability\n",
        "            correct += pred.eq(target.view_as(pred)).sum().item()\n",
        "\n",
        "    test_loss /= len(test_loader.dataset)\n",
        "\n",
        "    print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'.format(\n",
        "        test_loss, correct, len(test_loader.dataset),\n",
        "        100. * correct / len(test_loader.dataset)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C5TTKIlxVOAx",
        "colab_type": "code",
        "outputId": "e617b71c-085f-42fb-a303-75fb9ffbe21a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#8\n",
        "model = CNN().to(device)\n",
        "\n",
        "optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)\n",
        "\n",
        "for epoch in range(1, args.epochs + 1):\n",
        "    train(args, model, device, train_loader, optimizer, epoch)\n",
        "    test(args, model, device, test_loader)\n",
        "\n",
        "torch.save(model.state_dict(), folder_models + \"mnist_cnn.pt\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train Epoch: 1 [0/60000 (0%)]\tLoss: 2.301512\n",
            "Train Epoch: 1 [9984/60000 (17%)]\tLoss: 0.424392\n",
            "Train Epoch: 1 [19968/60000 (33%)]\tLoss: 0.215760\n",
            "Train Epoch: 1 [29952/60000 (50%)]\tLoss: 0.132523\n",
            "Train Epoch: 1 [39936/60000 (67%)]\tLoss: 0.005768\n",
            "Train Epoch: 1 [49920/60000 (83%)]\tLoss: 0.045205\n",
            "Train Epoch: 1 [59904/60000 (100%)]\tLoss: 0.064776\n",
            "\n",
            "Test set: Average loss: 0.0663, Accuracy: 9767/10000 (98%)\n",
            "\n",
            "Train Epoch: 2 [0/60000 (0%)]\tLoss: 0.068517\n",
            "Train Epoch: 2 [9984/60000 (17%)]\tLoss: 0.011490\n",
            "Train Epoch: 2 [19968/60000 (33%)]\tLoss: 0.017338\n",
            "Train Epoch: 2 [29952/60000 (50%)]\tLoss: 0.203606\n",
            "Train Epoch: 2 [39936/60000 (67%)]\tLoss: 0.013635\n",
            "Train Epoch: 2 [49920/60000 (83%)]\tLoss: 0.036105\n",
            "Train Epoch: 2 [59904/60000 (100%)]\tLoss: 0.046529\n",
            "\n",
            "Test set: Average loss: 0.0454, Accuracy: 9840/10000 (98%)\n",
            "\n",
            "Train Epoch: 3 [0/60000 (0%)]\tLoss: 0.014061\n",
            "Train Epoch: 3 [9984/60000 (17%)]\tLoss: 0.029951\n",
            "Train Epoch: 3 [19968/60000 (33%)]\tLoss: 0.027346\n",
            "Train Epoch: 3 [29952/60000 (50%)]\tLoss: 0.005577\n",
            "Train Epoch: 3 [39936/60000 (67%)]\tLoss: 0.137973\n",
            "Train Epoch: 3 [49920/60000 (83%)]\tLoss: 0.206117\n",
            "Train Epoch: 3 [59904/60000 (100%)]\tLoss: 0.003570\n",
            "\n",
            "Test set: Average loss: 0.0339, Accuracy: 9863/10000 (99%)\n",
            "\n",
            "Train Epoch: 4 [0/60000 (0%)]\tLoss: 0.004793\n",
            "Train Epoch: 4 [9984/60000 (17%)]\tLoss: 0.015553\n",
            "Train Epoch: 4 [19968/60000 (33%)]\tLoss: 0.054765\n",
            "Train Epoch: 4 [29952/60000 (50%)]\tLoss: 0.069190\n",
            "Train Epoch: 4 [39936/60000 (67%)]\tLoss: 0.040440\n",
            "Train Epoch: 4 [49920/60000 (83%)]\tLoss: 0.008372\n",
            "Train Epoch: 4 [59904/60000 (100%)]\tLoss: 0.009399\n",
            "\n",
            "Test set: Average loss: 0.0364, Accuracy: 9863/10000 (99%)\n",
            "\n",
            "Train Epoch: 5 [0/60000 (0%)]\tLoss: 0.003460\n",
            "Train Epoch: 5 [9984/60000 (17%)]\tLoss: 0.002586\n",
            "Train Epoch: 5 [19968/60000 (33%)]\tLoss: 0.078859\n",
            "Train Epoch: 5 [29952/60000 (50%)]\tLoss: 0.002207\n",
            "Train Epoch: 5 [39936/60000 (67%)]\tLoss: 0.092400\n",
            "Train Epoch: 5 [49920/60000 (83%)]\tLoss: 0.006767\n",
            "Train Epoch: 5 [59904/60000 (100%)]\tLoss: 0.038272\n",
            "\n",
            "Test set: Average loss: 0.0305, Accuracy: 9886/10000 (99%)\n",
            "\n",
            "Train Epoch: 6 [0/60000 (0%)]\tLoss: 0.088704\n",
            "Train Epoch: 6 [9984/60000 (17%)]\tLoss: 0.004123\n",
            "Train Epoch: 6 [19968/60000 (33%)]\tLoss: 0.017787\n",
            "Train Epoch: 6 [29952/60000 (50%)]\tLoss: 0.004457\n",
            "Train Epoch: 6 [39936/60000 (67%)]\tLoss: 0.010595\n",
            "Train Epoch: 6 [49920/60000 (83%)]\tLoss: 0.022794\n",
            "Train Epoch: 6 [59904/60000 (100%)]\tLoss: 0.004068\n",
            "\n",
            "Test set: Average loss: 0.0268, Accuracy: 9893/10000 (99%)\n",
            "\n",
            "Train Epoch: 7 [0/60000 (0%)]\tLoss: 0.007029\n",
            "Train Epoch: 7 [9984/60000 (17%)]\tLoss: 0.023427\n",
            "Train Epoch: 7 [19968/60000 (33%)]\tLoss: 0.003313\n",
            "Train Epoch: 7 [29952/60000 (50%)]\tLoss: 0.019744\n",
            "Train Epoch: 7 [39936/60000 (67%)]\tLoss: 0.020171\n",
            "Train Epoch: 7 [49920/60000 (83%)]\tLoss: 0.022106\n",
            "Train Epoch: 7 [59904/60000 (100%)]\tLoss: 0.001365\n",
            "\n",
            "Test set: Average loss: 0.0291, Accuracy: 9884/10000 (99%)\n",
            "\n",
            "Train Epoch: 8 [0/60000 (0%)]\tLoss: 0.001163\n",
            "Train Epoch: 8 [9984/60000 (17%)]\tLoss: 0.000633\n",
            "Train Epoch: 8 [19968/60000 (33%)]\tLoss: 0.000617\n",
            "Train Epoch: 8 [29952/60000 (50%)]\tLoss: 0.005645\n",
            "Train Epoch: 8 [39936/60000 (67%)]\tLoss: 0.000349\n",
            "Train Epoch: 8 [49920/60000 (83%)]\tLoss: 0.009568\n",
            "Train Epoch: 8 [59904/60000 (100%)]\tLoss: 0.003247\n",
            "\n",
            "Test set: Average loss: 0.0257, Accuracy: 9909/10000 (99%)\n",
            "\n",
            "Train Epoch: 9 [0/60000 (0%)]\tLoss: 0.071895\n",
            "Train Epoch: 9 [9984/60000 (17%)]\tLoss: 0.000607\n",
            "Train Epoch: 9 [19968/60000 (33%)]\tLoss: 0.028996\n",
            "Train Epoch: 9 [29952/60000 (50%)]\tLoss: 0.000318\n",
            "Train Epoch: 9 [39936/60000 (67%)]\tLoss: 0.032536\n",
            "Train Epoch: 9 [49920/60000 (83%)]\tLoss: 0.002672\n",
            "Train Epoch: 9 [59904/60000 (100%)]\tLoss: 0.002635\n",
            "\n",
            "Test set: Average loss: 0.0262, Accuracy: 9902/10000 (99%)\n",
            "\n",
            "Train Epoch: 10 [0/60000 (0%)]\tLoss: 0.029607\n",
            "Train Epoch: 10 [9984/60000 (17%)]\tLoss: 0.007647\n",
            "Train Epoch: 10 [19968/60000 (33%)]\tLoss: 0.000293\n",
            "Train Epoch: 10 [29952/60000 (50%)]\tLoss: 0.000643\n",
            "Train Epoch: 10 [39936/60000 (67%)]\tLoss: 0.002124\n",
            "Train Epoch: 10 [49920/60000 (83%)]\tLoss: 0.001106\n",
            "Train Epoch: 10 [59904/60000 (100%)]\tLoss: 0.005166\n",
            "\n",
            "Test set: Average loss: 0.0372, Accuracy: 9860/10000 (99%)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xUq8cJE6zBEJ",
        "colab_type": "code",
        "outputId": "274ed300-bee9-4b5b-ebd0-74eea709879e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#9\n",
        "model = CNN().to(device)\n",
        "model.load_state_dict(torch.load(folder_models + \"mnist_cnn.pt\", map_location=torch.device(device)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nmuD-K_3LHGK",
        "colab_type": "code",
        "outputId": "31ddff99-4e7b-4d70-f241-0230fd83fb7f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#10\n",
        "total = 0\n",
        "acc = 0\n",
        "with torch.no_grad():\n",
        "    for img_name in glob.iglob(folder_digits + '1/*'):\n",
        "        total += 1\n",
        "        #print(img_name)\n",
        "\n",
        "        img = cv.imread(img_name, cv.IMREAD_GRAYSCALE)\n",
        "        #img = img[5:-5, 5:-5]\n",
        "        img = cv.GaussianBlur(img, (7, 7), 0)\n",
        "        th = cv.adaptiveThreshold(img,255,cv.ADAPTIVE_THRESH_MEAN_C, cv.THRESH_BINARY,7,2)\n",
        "        th = cv.bitwise_not(th)\n",
        "        th = th [5:-5, 5:-5]\n",
        "        th = cv.copyMakeBorder(th, 10, 10, 10, 10, cv.BORDER_CONSTANT, None, 0)\n",
        "        #cv2_imshow(th)\n",
        "        resized = cv.resize(th, (28, 28), interpolation = cv.INTER_AREA)\n",
        "        resized = np.array(resized, dtype = float)\n",
        "        data = torch.from_numpy(np.expand_dims(np.expand_dims(resized, 0), 0))\n",
        "        data = data.type(torch.FloatTensor)\n",
        "\n",
        "        data = data.to(device)\n",
        "        output = model(data)\n",
        "        output = output[0].cpu().numpy()\n",
        "        output = output[1:5]\n",
        "        #print(output)\n",
        "\n",
        "        pred = output.argmax() + 1 # get the index of the max log-probability\n",
        "        #print(pred)\n",
        "        if pred == 1:\n",
        "            acc += 1\n",
        "        elif pred == 4 and output[3] - output[0] < 2000:\n",
        "            acc += 1\n",
        "        else:\n",
        "            cv2_imshow(th)\n",
        "            print(pred)\n",
        "            print(output)\n",
        "\n",
        "print(acc * 100 / total)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9boDjXBgLKaU",
        "colab_type": "code",
        "outputId": "f0aa8211-75f9-431f-efcd-b765bddd7eb3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "#11\n",
        "total = 0\n",
        "acc = 0\n",
        "diffs = []\n",
        "with torch.no_grad():\n",
        "    for img_name in glob.iglob(folder_digits + '2/*'):\n",
        "        total += 1\n",
        "        #print(img_name)\n",
        "\n",
        "        img = cv.imread(img_name, cv.IMREAD_GRAYSCALE)\n",
        "        #img = img[5:-5, 5:-5]\n",
        "        img = cv.GaussianBlur(img, (7, 7), 0)\n",
        "        th = cv.adaptiveThreshold(img,255,cv.ADAPTIVE_THRESH_MEAN_C, cv.THRESH_BINARY,7,2)\n",
        "        th = cv.bitwise_not(th)\n",
        "        th = th [5:-5, 5:-5]\n",
        "        th = cv.copyMakeBorder(th, 10, 10, 10, 10, cv.BORDER_CONSTANT, None, 0)\n",
        "        #cv2_imshow(th)\n",
        "        resized = cv.resize(th, (28, 28), interpolation = cv.INTER_AREA)\n",
        "        resized = np.array(resized, dtype = float)\n",
        "        data = torch.from_numpy(np.expand_dims(np.expand_dims(resized, 0), 0))\n",
        "        data = data.type(torch.FloatTensor)\n",
        "\n",
        "        data = data.to(device)\n",
        "        output = model(data)\n",
        "        output = output[0].cpu().numpy()\n",
        "        output = output[1:5]\n",
        "        #print(output)\n",
        "\n",
        "        pred = output.argmax() + 1 # get the index of the max log-probability\n",
        "        #print(pred)\n",
        "        if pred == 2:\n",
        "            acc += 1\n",
        "        else:\n",
        "            cv2_imshow(th)\n",
        "            print(pred)\n",
        "            print(output)\n",
        "\n",
        "print(acc * 100 / total)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=98x98 at 0x7FF982443400>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "4\n",
            "[-1687.4769 -1709.0989 -3848.8252     0.    ]\n",
            "97.43589743589743\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HKYj0nyO-Y00",
        "colab_type": "code",
        "outputId": "8cfbb4e4-d7a8-4fd2-9394-60739d387c94",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#12\n",
        "total = 0\n",
        "acc = 0\n",
        "with torch.no_grad():\n",
        "    for img_name in glob.iglob(folder_digits + '3/*'):\n",
        "        total += 1\n",
        "        #print(img_name)\n",
        "\n",
        "        img = cv.imread(img_name, cv.IMREAD_GRAYSCALE)\n",
        "        #img = img[5:-5, 5:-5]\n",
        "        img = cv.GaussianBlur(img, (7, 7), 0)\n",
        "        th = cv.adaptiveThreshold(img,255,cv.ADAPTIVE_THRESH_MEAN_C, cv.THRESH_BINARY,7,2)\n",
        "        th = cv.bitwise_not(th)\n",
        "        th = th [5:-5, 5:-5]\n",
        "        th = cv.copyMakeBorder(th, 10, 10, 10, 10, cv.BORDER_CONSTANT, None, 0)\n",
        "        #cv2_imshow(th)\n",
        "        resized = cv.resize(th, (28, 28), interpolation = cv.INTER_AREA)\n",
        "        resized = np.array(resized, dtype = float)\n",
        "        data = torch.from_numpy(np.expand_dims(np.expand_dims(resized, 0), 0))\n",
        "        data = data.type(torch.FloatTensor)\n",
        "\n",
        "        data = data.to(device)\n",
        "        output = model(data)\n",
        "        output = output[0].cpu().numpy()\n",
        "        output = output[1:5]\n",
        "        #print(output)\n",
        "\n",
        "        pred = output.argmax() + 1 # get the index of the max log-probability\n",
        "        #print(pred)\n",
        "        if pred == 3:\n",
        "            acc += 1\n",
        "        else:\n",
        "            cv2_imshow(th)\n",
        "            print(pred)\n",
        "            print(output)\n",
        "\n",
        "print(acc * 100 / total)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZsYwwajKK_zu",
        "colab_type": "code",
        "outputId": "cd8a30a9-a5f2-4d84-9e16-bd689fea8c14",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        }
      },
      "source": [
        "#13\n",
        "total = 0\n",
        "acc = 0\n",
        "with torch.no_grad():\n",
        "    for img_name in glob.iglob(folder_digits + '4/*'):\n",
        "        total += 1\n",
        "        #print(img_name)\n",
        "\n",
        "        img = cv.imread(img_name, cv.IMREAD_GRAYSCALE)\n",
        "        #img = img[5:-5, 5:-5]\n",
        "        img = cv.GaussianBlur(img, (7, 7), 0)\n",
        "        th = cv.adaptiveThreshold(img,255,cv.ADAPTIVE_THRESH_MEAN_C, cv.THRESH_BINARY,7,2)\n",
        "        th = cv.bitwise_not(th)\n",
        "        th = th [5:-5, 5:-5]\n",
        "        th = cv.copyMakeBorder(th, 10, 10, 10, 10, cv.BORDER_CONSTANT, None, 0)\n",
        "        #cv2_imshow(th)\n",
        "        resized = cv.resize(th, (28, 28), interpolation = cv.INTER_AREA)\n",
        "        resized = np.array(resized, dtype = float)\n",
        "        data = torch.from_numpy(np.expand_dims(np.expand_dims(resized, 0), 0))\n",
        "        data = data.type(torch.FloatTensor)\n",
        "\n",
        "        data = data.to(device)\n",
        "        output = model(data)\n",
        "        output = output[0].cpu().numpy()\n",
        "        output = output[1:5]\n",
        "        #print(output)\n",
        "\n",
        "        pred = output.argmax() + 1 # get the index of the max log-probability\n",
        "        #print(pred)\n",
        "        if pred == 4 and output[3] - output[0] > 2000:\n",
        "            acc += 1\n",
        "        else:\n",
        "            cv2_imshow(th)\n",
        "            print(pred)\n",
        "            print(output)\n",
        "\n",
        "print(acc * 100 / total)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=101x99 at 0x7FF98243F400>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "3\n",
            "[-3029.403  -2444.0122     0.     -1507.2048]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=100x101 at 0x7FF98243F438>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "4\n",
            "[-2578.469  -2929.4807 -4028.2766 -1815.5994]\n",
            "93.75\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Mpqe2h2-1vP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#14\n",
        "def eval_patch(patch):\n",
        "    with torch.no_grad():\n",
        "\n",
        "        (h, w) = patch.shape[:2]\n",
        "        if h == 0 or w == 0:\n",
        "            return None\n",
        "        width = 90\n",
        "        r = width / float(w)\n",
        "        dim = (width, int(h * r))\n",
        "        patch = cv.resize(patch, dim, interpolation = cv.INTER_AREA)\n",
        "\n",
        "        #img = img[5:-5, 5:-5]\n",
        "        img = cv.GaussianBlur(patch, (7, 7), 0)\n",
        "        th = cv.adaptiveThreshold(img,255,cv.ADAPTIVE_THRESH_MEAN_C, cv.THRESH_BINARY,7,2)\n",
        "        th = cv.bitwise_not(th)\n",
        "        th = th [5:-5, 5:-5]\n",
        "        th = cv.copyMakeBorder(th, 10, 10, 10, 10, cv.BORDER_CONSTANT, None, 0)\n",
        "        #cv2_imshow(th)\n",
        "        resized = cv.resize(th, (28, 28), interpolation = cv.INTER_AREA)\n",
        "        resized = np.array(resized, dtype = float)\n",
        "        data = torch.from_numpy(np.expand_dims(np.expand_dims(resized, 0), 0))\n",
        "        data = data.type(torch.FloatTensor)\n",
        "\n",
        "        data = data.to(device)\n",
        "        output = model(data)\n",
        "        output = output[0].cpu().numpy()\n",
        "        output = output[1:5]\n",
        "        #print(output)\n",
        "\n",
        "        pred = output.argmax() + 1 # get the index of the max log-probability\n",
        "        #print(pred)\n",
        "        if pred == 4 and output[3] - output[0] < 2000:\n",
        "            pred = 1\n",
        "        return pred"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-kWn_hN7i7ke",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#15\n",
        "def set_global_cols_and_rows_for_scenario_3():\n",
        "    img_template = cv.imread(data_dir_drive + '/data/template.jpg')\n",
        "    (h, w) = img_template.shape[:2]\n",
        "    width = 3500\n",
        "    r = width / float(w)\n",
        "    dim = (width, int(h * r))\n",
        "    img_template = cv.resize(img_template, dim, interpolation = cv.INTER_AREA)\n",
        "    orig_h, orig_w, _ = img_template.shape\n",
        "    img_template = img_template[int(orig_h * 0):int(orig_h * 0.83)]\n",
        "    img_template = img_template[:, int(orig_w * 0.05):int(orig_w * 0.95)]\n",
        "\n",
        "    #print_img(img_template)\n",
        "    img_template = cv.GaussianBlur(img_template,(7,7),0)\n",
        "    img_template = cv.cvtColor(img_template, cv.COLOR_BGR2GRAY)\n",
        "\n",
        "    left_img_template = img_template[:, :orig_w//2]\n",
        "    right_img_template = img_template[:, orig_w//2:]\n",
        "\n",
        "    _, _, cols_l, rows_l = find_table(left_img_template)\n",
        "    _, _, cols_r, rows_r = find_table(right_img_template)\n",
        "\n",
        "    return cols_l, rows_l, cols_r, rows_r\n",
        "\n",
        "cols_l, rows_l, cols_r, rows_r = set_global_cols_and_rows_for_scenario_3()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "w9OuLJhM7OJ1",
        "colab": {}
      },
      "source": [
        "#16\n",
        "def predict_grade_scenario_3(path):\n",
        "    cols_l, rows_l, cols_r, rows_r = set_global_cols_and_rows_for_scenario_3()\n",
        "\n",
        "    aligned_image2 = transform_rotaded_and_perspective_in_scanned(path)\n",
        "    #print_img(aligned_image2)\n",
        "\n",
        "    # take the lower part of the image\n",
        "    orig_h, orig_w, _ = aligned_image2.shape\n",
        "    aligned_image2 = aligned_image2[int(orig_h * 0):int(orig_h * 0.83)]\n",
        "    image = aligned_image2[:, int(orig_w * 0.05):int(orig_w * 0.95)]\n",
        "\n",
        "    #print_img(image)\n",
        "    img_blur = cv.GaussianBlur(image,(7,7),0)\n",
        "    grayscale_image = cv.cvtColor(img_blur, cv.COLOR_BGR2GRAY)\n",
        "    th = cv.adaptiveThreshold(grayscale_image,255,cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY,11,2)\n",
        "\n",
        "    left_grayscale_image = th[:, :orig_w//2]\n",
        "    right_grayscale_image = th[:, orig_w//2:]\n",
        "\n",
        "    result_l, image_l = find_x(left_grayscale_image, cols_l, rows_l)\n",
        "    #print(result_l)\n",
        "    result_r, image_r = find_x(right_grayscale_image, cols_r, rows_r)\n",
        "    #print(result_r)\n",
        "\n",
        "    x_min = cols_r[-2][0][0]\n",
        "    x_max = cols_r[-1][0][0]\n",
        "    y_min = 0 \n",
        "    y_max = rows_r[0][1][1] - 2*(rows_r[1][1][1]-rows_r[0][1][1]) # 2 randuri in sus\n",
        "\n",
        "    patch = grayscale_image[:, orig_w//2:]\n",
        "    patch = patch[y_min:y_max, x_min:x_max]\n",
        "    #print_img(patch)\n",
        "\n",
        "    bbox_1, bbox_2 = detect_subject_choice(patch, rows_r, cols_r)\n",
        "\n",
        "    (x_min_1, y_min_1), (x_max_1, y_max_1) = bbox_1\n",
        "    (x_min_2, y_min_2), (x_max_2, y_max_2) = bbox_2\n",
        "            \n",
        "    patch1 = patch[y_min_1 + 10:y_max_1 - 10, x_min_1 + 10:x_max_1 - 10]\n",
        "    patch2 = patch[y_min_2 + 10:y_max_2 - 10, x_min_2 + 10:x_max_2 - 10]\n",
        "\n",
        "    if y_min_1 > y_min_2:\n",
        "        patch1, patch2 = patch2, patch1\n",
        "\n",
        "    mean_patch_box_1 = np.round(patch1.mean())\n",
        "    mean_patch_box_2 = np.round(patch2.mean())\n",
        "\n",
        "    subject, number = 0, 0\n",
        "    choice_patch_color = np.dstack((patch, patch, patch))\n",
        "    if mean_patch_box_1 < mean_patch_box_2: #Info\n",
        "        subject = 'I'\n",
        "        number = eval_patch(patch1)\n",
        "        cv.rectangle(choice_patch_color, (x_min_1 + 10, y_min_1 + 10), (x_max_1 - 10, y_max_1 - 10), color=(255, 0, 255), thickness=5)\n",
        "    else: #Fizica\n",
        "        subject = 'F'\n",
        "        number = eval_patch(patch2)\n",
        "        cv.rectangle(choice_patch_color, (x_min_2 + 10, y_min_2 + 10), (x_max_2 - 10, y_max_2 - 10), color=(0, 255, 255), thickness=5)\n",
        "\n",
        "    #print(subject)\n",
        "    #print(number)\n",
        "\n",
        "    number_ok = 0\n",
        "    # load ground truth correct answers / barem\n",
        "    if subject == 'F':\n",
        "        if number == 1:\n",
        "            ground_truth_content = np.loadtxt(folder_barem + 'Fizica_varianta1.txt', dtype=str)\n",
        "        elif number == 2:\n",
        "            ground_truth_content = np.loadtxt(folder_barem + 'Fizica_varianta2.txt', dtype=str)\n",
        "        elif number == 3:\n",
        "            ground_truth_content = np.loadtxt(folder_barem + 'Fizica_varianta3.txt', dtype=str)\n",
        "        else:\n",
        "            ground_truth_content = np.loadtxt(folder_barem + 'Fizica_varianta4.txt', dtype=str)\n",
        "    else:\n",
        "        if number == 1:\n",
        "            ground_truth_content = np.loadtxt(folder_barem + 'Informatica_varianta1.txt', dtype=str)\n",
        "        elif number == 2:\n",
        "            ground_truth_content = np.loadtxt(folder_barem + 'Informatica_varianta2.txt', dtype=str)\n",
        "        elif number == 3:\n",
        "            ground_truth_content = np.loadtxt(folder_barem + 'Informatica_varianta3.txt', dtype=str)\n",
        "        else:\n",
        "            ground_truth_content = np.loadtxt(folder_barem + 'Informatica_varianta4.txt', dtype=str)\n",
        "\n",
        "    ground_truth_left = ground_truth_content[1:16]\n",
        "    ground_truth_right = ground_truth_content[16:-1]\n",
        "\n",
        "    left_mis = 0;\n",
        "    for r in range(15):\n",
        "        tr_ans = ground_truth_left[r][1]\n",
        "        if r + 1 not in result_l.keys():\n",
        "            left_mis += 1\n",
        "        else:\n",
        "            if len(result_l[r+1]) > 1:\n",
        "                left_mis += 1\n",
        "            else:\n",
        "                if result_l[r+1][0] != tr_ans:\n",
        "                    left_mis += 1\n",
        "                else:\n",
        "                    number_ok += 1\n",
        "\n",
        "    right_mis = 0;\n",
        "    for r in range(15):\n",
        "        tr_ans = ground_truth_right[r][1]\n",
        "        if r + 1 not in result_r.keys():\n",
        "            right_mis += 1\n",
        "        else:\n",
        "            if len(result_r[r+1]) > 1:\n",
        "                right_mis += 1\n",
        "            else:\n",
        "                if result_r[r+1][0] != tr_ans:\n",
        "                    right_mis += 1\n",
        "                else:\n",
        "                    number_ok += 1\n",
        "\n",
        "    return (number_ok * 3) / 10 + 1, subject, number, th"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "71a92753-1688-4ed0-cc4d-4a3cb13e966a",
        "id": "RD-KBQj67OJ5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#17\n",
        "acc = 0\n",
        "for i in range(1, 151):\n",
        "    image_name = 'image_' + str(i) + '.jpg'\n",
        "   \n",
        "    print(image_name)\n",
        "    file = open(folder_in + 'image_' + str(i) +'.txt', 'r')\n",
        "    first_line = file.readline()\n",
        "    file.close() \n",
        "    first_line = first_line.replace('\\n', '')\n",
        "    info = first_line.split(' ')\n",
        "\n",
        "    try:\n",
        "        grade, subject, number, th = predict_grade_scenario_3(folder_in + image_name)\n",
        "\n",
        "        if ground_truth_hw_dict[i] == str(grade) and subject == info[0] and number == int(info[1]):\n",
        "            acc += 1\n",
        "        else:\n",
        "            print(\"GRESEALA\")\n",
        "            print( grade )\n",
        "            print( subject )\n",
        "            print( number )\n",
        "            print_img( th )\n",
        "\n",
        "    except Exception as e:\n",
        "        print(e)\n",
        "        continue\n",
        "\n",
        "print(\"Acc = \" + str(acc * 100 / 150) + \" %\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "image_1.jpg\n",
            "image_2.jpg\n",
            "image_3.jpg\n",
            "image_4.jpg\n",
            "image_5.jpg\n",
            "image_6.jpg\n",
            "image_7.jpg\n",
            "image_8.jpg\n",
            "image_9.jpg\n",
            "image_10.jpg\n",
            "image_11.jpg\n",
            "image_12.jpg\n",
            "image_13.jpg\n",
            "image_14.jpg\n",
            "image_15.jpg\n",
            "image_16.jpg\n",
            "image_17.jpg\n",
            "image_18.jpg\n",
            "image_19.jpg\n",
            "image_20.jpg\n",
            "image_21.jpg\n",
            "image_22.jpg\n",
            "image_23.jpg\n",
            "image_24.jpg\n",
            "image_25.jpg\n",
            "image_26.jpg\n",
            "image_27.jpg\n",
            "image_28.jpg\n",
            "image_29.jpg\n",
            "image_30.jpg\n",
            "image_31.jpg\n",
            "image_32.jpg\n",
            "image_33.jpg\n",
            "GRESEALA\n",
            "3.4\n",
            "F\n",
            "4\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=472x341 at 0x7F1D31A72EB8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "image_34.jpg\n",
            "image_35.jpg\n",
            "image_36.jpg\n",
            "image_37.jpg\n",
            "image_38.jpg\n",
            "image_39.jpg\n",
            "image_40.jpg\n",
            "image_41.jpg\n",
            "GRESEALA\n",
            "3.1\n",
            "F\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=472x341 at 0x7F1D31A72080>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "image_42.jpg\n",
            "image_43.jpg\n",
            "image_44.jpg\n",
            "image_45.jpg\n",
            "image_46.jpg\n",
            "GRESEALA\n",
            "2.2\n",
            "F\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=472x341 at 0x7F1D31A72EB8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "image_47.jpg\n",
            "image_48.jpg\n",
            "image_49.jpg\n",
            "image_50.jpg\n",
            "image_51.jpg\n",
            "image_52.jpg\n",
            "GRESEALA\n",
            "1.9\n",
            "I\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=472x341 at 0x7F1D31A72E10>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "image_53.jpg\n",
            "image_54.jpg\n",
            "image_55.jpg\n",
            "image_56.jpg\n",
            "image_57.jpg\n",
            "image_58.jpg\n",
            "image_59.jpg\n",
            "image_60.jpg\n",
            "image_61.jpg\n",
            "image_62.jpg\n",
            "image_63.jpg\n",
            "image_64.jpg\n",
            "image_65.jpg\n",
            "image_66.jpg\n",
            "image_67.jpg\n",
            "image_68.jpg\n",
            "image_69.jpg\n",
            "image_70.jpg\n",
            "image_71.jpg\n",
            "image_72.jpg\n",
            "image_73.jpg\n",
            "image_74.jpg\n",
            "image_75.jpg\n",
            "image_76.jpg\n",
            "image_77.jpg\n",
            "image_78.jpg\n",
            "image_79.jpg\n",
            "image_80.jpg\n",
            "image_81.jpg\n",
            "image_82.jpg\n",
            "image_83.jpg\n",
            "image_84.jpg\n",
            "image_85.jpg\n",
            "image_86.jpg\n",
            "image_87.jpg\n",
            "image_88.jpg\n",
            "image_89.jpg\n",
            "image_90.jpg\n",
            "image_91.jpg\n",
            "image_92.jpg\n",
            "GRESEALA\n",
            "3.1\n",
            "I\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=472x341 at 0x7F1D31A727B8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "image_93.jpg\n",
            "image_94.jpg\n",
            "image_95.jpg\n",
            "GRESEALA\n",
            "2.8\n",
            "I\n",
            "3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=472x341 at 0x7F1D31A72EB8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "image_96.jpg\n",
            "image_97.jpg\n",
            "image_98.jpg\n",
            "image_99.jpg\n",
            "image_100.jpg\n",
            "image_101.jpg\n",
            "image_102.jpg\n",
            "image_103.jpg\n",
            "image_104.jpg\n",
            "image_105.jpg\n",
            "image_106.jpg\n",
            "image_107.jpg\n",
            "image_108.jpg\n",
            "image_109.jpg\n",
            "image_110.jpg\n",
            "image_111.jpg\n",
            "image_112.jpg\n",
            "image_113.jpg\n",
            "image_114.jpg\n",
            "image_115.jpg\n",
            "GRESEALA\n",
            "3.7\n",
            "F\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=472x341 at 0x7F1D31A72908>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "image_116.jpg\n",
            "image_117.jpg\n",
            "image_118.jpg\n",
            "image_119.jpg\n",
            "image_120.jpg\n",
            "image_121.jpg\n",
            "image_122.jpg\n",
            "image_123.jpg\n",
            "image_124.jpg\n",
            "image_125.jpg\n",
            "image_126.jpg\n",
            "image_127.jpg\n",
            "image_128.jpg\n",
            "image_129.jpg\n",
            "image_130.jpg\n",
            "image_131.jpg\n",
            "image_132.jpg\n",
            "image_133.jpg\n",
            "image_134.jpg\n",
            "image_135.jpg\n",
            "image_136.jpg\n",
            "GRESEALA\n",
            "4.0\n",
            "F\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=472x341 at 0x7F1D31A72240>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "image_137.jpg\n",
            "image_138.jpg\n",
            "image_139.jpg\n",
            "image_140.jpg\n",
            "image_141.jpg\n",
            "image_142.jpg\n",
            "image_143.jpg\n",
            "image_144.jpg\n",
            "image_145.jpg\n",
            "image_146.jpg\n",
            "image_147.jpg\n",
            "image_148.jpg\n",
            "image_149.jpg\n",
            "image_150.jpg\n",
            "Acc = 94.66666666666667 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G-6HDDri5PUX",
        "colab_type": "code",
        "outputId": "8b10fa12-8ca8-47b7-da23-092bbb17f48a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#18\n",
        "acc = 0\n",
        "for i in range(1, 151):\n",
        "    image_name = 'perspective_' + str(i) + '.jpg'\n",
        "   \n",
        "    print(image_name)\n",
        "    file = open(folder_in + 'image_' + str(i) +'.txt', 'r')\n",
        "    first_line = file.readline()\n",
        "    file.close() \n",
        "    first_line = first_line.replace('\\n', '')\n",
        "    info = first_line.split(' ')\n",
        "\n",
        "    try:\n",
        "        grade, subject, number, th = predict_grade_scenario_3(folder_in + image_name)\n",
        "\n",
        "        if ground_truth_hw_dict[i] == str(grade) and subject == info[0] and number == int(info[1]):\n",
        "            acc += 1\n",
        "        else:\n",
        "            print(\"GRESEALA\")\n",
        "            print( grade )\n",
        "            print( subject )\n",
        "            print( number )\n",
        "            print_img( th )\n",
        "\n",
        "    except Exception as e:\n",
        "        print(e)\n",
        "        continue\n",
        "\n",
        "print(\"Acc = \" + str(acc * 100 / 150) + \" %\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "perspective_1.jpg\n",
            "perspective_2.jpg\n",
            "perspective_3.jpg\n",
            "perspective_4.jpg\n",
            "perspective_5.jpg\n",
            "perspective_6.jpg\n",
            "GRESEALA\n",
            "3.1\n",
            "I\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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sHFasiaV2qCSHEWRK2dJGm0dAGPZLDqZcs7SOTfWSnBhn2oTrtGjzsWBGF4Tuzt0FgR0vk7Q407UV55rt5kAtIC86jFeAimxW1ARme0wgETzdiCJgxOrSjM/FYtE1FemHIA4ZD7sRtmra6Q5BmDjPjbSWOTEsdjF1BO5ZXBhhG6MQQxjV4xCtpbWEVp/yudYdP1wzcMkjxjfHhs8knZvx+bBMUOcvW51Kp9Wr08oxElLr7KXWTKuvnKmhT7BzGHnea7njgFss7nK/+hlV/3lxhA3Kow2ovczevHJMVRj7GrulsRX/oTULqeHwODj2r+3FCROqz7gXgDCj7CgSROZ2a0F84+jVU5n1rbeDjzkjB0XEfwpfoHolTFfmz7tsQvQV6LF90iw7IAL7pteKgt4bTYRd+Yvm+DWcT0P1EjFsv0Tigokbahi7O7qZXqTQEBvfMHQ88rV+qkATYcf+ogQLBLJbAMov5gaQmN3g9u+xw9pHIcgE485fpL0dn8TEHtYmoGfW6bh+Dt6000RfnfSXoeJ296ud2SaZYF0z414OgvLoOtzctef8pLz/Vas3DrbinDkWwS8G/VjpSJjJ5AAQ8x43+AwEX+wxXHdRn79WALCEnqusrqpVc3TFLNg9wW+i4cM5iSM7Rb203Tp2S5KTHZEtlG97xG6zjlqjMIrUqnXdLqJ5xO5OPtD8e1Ob3Tz46arg7O7HCACWIxzFVSYgfwqLlE+nJ3Lh9onAzuhh64s8hzQTXHQ0E3ZUyAozrOy+WBsASv6GBGaOjbIM5r0UkMI+YYqBvFSx2PvKAFZ2vxo35nAIzfY1CWAYAVg4GG+tK54a9s4Kjuf8Kq213jWncpJMh4xI6qEm62zHPk3XztSv2dVPXQ7qrLhk5gKjzCkq4u5E7ZpyLe1B8V5J7XZtzoS3kOhiBDgc4Bn/Eu32XmsRb/X2t8oclFTcl2bdQTybIYqAjdwMBxvz+rGOMVri+XsxXp0BIFlOvd2zBhBvJEbff91lO2C8vgHA4Dd/Hh4OkBtAbiA3ACDfBhtE0QbDV1MQ7vAWhP32Ur3MF4k1B6DGq7qpogsUVi+LA5A8lpJwXhxhvdF2cwBqv0jmf/0nAOlBASqKx1itMF5NH5VCIpS1ZCZ5T9aTiNZtlw5rZjjmLzScG3lgwZzl1rh4TcMJgTo/QTLbNO6ScJLmxsL2xrE0Z5ne2JI77qnJCbkks72bdVZF0riQ5IGzSY3ZJ6XjMhv34TzChtHKbFcLfE/INAsV8RmxvhoNHhTBpcm6+ByMHGF7YFKeNJNTl5rMr7h1K85QUHwTDfru6ICwAAnUVduxAKbQMAL+RXgq1OHRlRpZVhz2obqLokfMqmGQKShmSxyscr62oNCOrp7VT5UQojoRyoGQcBKqvXUEADpG7KngPEmczXg/vEeaVumVSLds6tS1rwHlNHjJaWEtL0cTGkes3zaksETs1yqOEPxwM5xK2cuPNur0iUKvw5cZexSTDct3XwM4gt/oN+hqlz5vTwClQ6m/Ra6B6ZItX1ych1MdAbOVXi12PbcHWAvM7cvXyVtTWTTo0c8g7AGjKDi4+CsACECEZ4OTWv1bdBEYTwVD5syaSjH7DFQMKFrbbPNzGobwOdYd8ToNN2AHABTnEuujdsfoj6AOu2E3BMfi03wGCgx8Ep/KilV4OhwbmbbjVqaixuiC1vyvwFPgtWf4iX2lhKcXkUnWtTLXLT/IbNdYPZlYgQ55QGmLJySOnG6hp3wFQg1q6hWfod2ENNnSpM0j6+EjJZwxYkmSQdORkaweSGGOWq5K/7V/mz4JAVYs4AqJKcExxxosQNBb6rg+KH23IOyq/KOkb1jNxTxIq4J77M1h3KyvAETY++4iEBBaSSkZekuXgncBAFMCiL5p+4Am75GnhhkqNJwNt50aq13wQicSaTIIle5X5iGgyV1c5TJIXHtJXBicDdpmL5a3X/NYmlZsno01l1btc5pKaEKajJpHk7thctp1XbZ6bznH7rTWR9/oTKWUUtafm7R4mifEY1Ch7sURdkApIe1ryy279HkgWXdI+VvSP9tnhbfEaWOPDW2hVmCezF1OWF1gaidzpHvMSwL+xZntHk+UavPE/jMHsH8EQj2hVF5sTJxJxnUTzKcRMEeyihf2bITpHH89nq9erOFuHrVbJQTJHi6VVffT/KPVJqfVOSPWIt1SSinxLgred4TVFZtW7YF9h4BZh1Scpzzb0+ZQSkzBlhljl2Ce+sIiCYbRfqTZrm5UBMnd7vSMaXsR0f7OzfgABOm4pNzuSN/+LP7lTglJ7UwtGQDC653+y50DcrOdRwgUP7vODoc4jRyRQLEWulQHtjKCsv0EejQEGvRTdZRffFyTiRWAiq6G4eCd/oSNBCXiGPDaK/h7b+Wxt+Th0uZTB15DFiLRZKYOw3wzm5oGUdWuXAQ1tb0JuwM3OvzRBIKyFt4F8EdEZr43hn7rzhsY/jId723cr3eJACDMN9PG4hmWoXoTdgRIgaDZzrfEFo+Y+r/qj2DFq3e+n3/EDgBELwHlgkNVZzRIIwUNYSnaohPmYQ3GWUaASagBb6DbCv/K37zN/BPQR7Di8TvfL2isF4dvAVNMYEdnPuV3bmO2C19zFmGDG2wPfOVD7HaLK5hJAwgEeL6BDEWr1TOCFDcrtAqquQ2/Cxef50HRELLvLXmi3/QuztRyfAYCQUkDzBq++FPsCsW/Eu0DDH9RweIzclAIHB/4Y+K/wE/2YUCXxCsgbADeJBIWdVacp2t6e2DSQckW9sxvHrEb68CQs5DMQ4AccBSgqx9iN20Krb1s+A1mzz8ghCP6vISaQ0hG6gGBXXvZUYWLgwoQT6lSfOH0JZ2WtmNwKGPqGWzs/aIiS2CrvDcXp3kyOSz71a1rnhw3enE/0mJMvjTlYgr2WeqRXfWw2ZCXZh4UZYpnTyteDsb9eCszbfAx6djIr8Cg+ZIKyvvwtlIp1uFSqtc3gC/QK5fiRgLYAHL266tXfJc4HGj2vJqldv+rzfZZPy8XKZCx9voTHFPHxVl34qa0KRIbYPCGTF0am5C80Q5vv/z0Xq3g737xF3Yi7Kn9bjFdTFcbrwakHkTmCKPcLwBg/6hgY0JcuDTCnrO3XU+0yiZaRsfxX8PeY2IwKQIlfWHOkmauPVJzt8+2XOzdjM9FiAfmOJ067Zx0rIVstbL5ofRv++v7lUqnyFQgaXG/hiSZS9q9OC8LCCZqDs/Aa57GDwW9DYtHVnFsEN7OUVBox36TBTaYqz53rdxzAkequUtAwPgxPwaXLDFOVZK9UnUMGvwCg4RtcsEPvoBEohrqO3Fp86kLQVIkwW55q6sj/dpjgUAnn2HdmQbDjyCYBu/dIbpVFEtz1RQjcQEI5jkKJzHa1bymvbqaGBQQXvVkGsxJ2MSKA41cETMsArpN0T77KQV+skN1jv/XutoF4vAtOEEhrjHxpe96DQCRV7cngpNUb13x6/CbBvxrHQDskGl+MstY8LSrS8llIfrr8GTiWPc7r384AngL6RnCgb69hachEDftxNxBKlgussmkgb1fOuK52ISEj9il4z0l9qv1JBnE2q9UD3tZnBe7I4JU6OL7I/hov5JrcGYLQJNxaMzqOmFFTRixI2KOjfzhvdM42I7z7LHRr6HSDimZ9TqX3p2d0hzy85lokB26ZkSpv7F9QCII/4y9Ct60NWHr4rGKsQ9blGOgZdiyiBfZGzhN8O+TOOK9oMNSQIM7Wwh2QWCH6UICRQd2/F7C6g473pVSno3sfEiZcizL5qdsT+c0O4lsa+NGXKDmiWTJuPZCVfqZEvuXtdExnW5xp9ZUpNqvVXGnmHYbVsViH9K1IrkH91Rx6ZGtETQCLGbV0hj+he3w1XxSw1cM7BQxq83ALXIpopyCMPaZgL4YxrqjY/uPBiCBzWAIf0bCwN2axuLbwPTBBlGETawtLzxjkzNXaShE6+0VNqUj4JyKls2eE2maZZ20J/TlEdbdoA2AyQ8A6XeMmubhGDrvIT3JNB62f4av9kBjN4qhITfx8RWTHxKbfDV4iAIej+dbd2owzCchqZyxOz2ecIGseE+z6WoRU5Nz1zByO3uxfVJ4e6YSz87wQuIsDwpnaX4u8ddLPRegFh1af0LdaJFeIGFbvL4b63p8bN1E5jCade2Dc0YsgHqOyxKSmm3LM2JP3qNm7bxIwobgtWY646NdzkMtHpkEm9A0x9a9IAosDuH0Q21EJbEvyRqHCFexzWiwQatvQbHmZF48RJUw6rq82eaRTgTJ3jRiw/HI9U/KP8ciP6zXuq4R29BYhy+bMcxPSuk4WrHiJPicoIIirO8R4ONzSP/QZdcceQXGuhzh3Uk6R1Ga1eHfU0CwUy+EbKoOwhaXR0G9FYnxj5C5t0sSeS1+uh5dcdC4oQDhT+bkgnnjEaZzWs+MNn2wUGEzWHA8N/LAWrrhcO3AGeyyrOk9mvHZCDTouCdDvVovmsha4rp3YMVBe6wKf2hEWB/eKa9jLsY17CB26RhEu7dQvrq6YWQpbBCXkNj8tn3gtwoXB8keKp0CQNBTrsuIBQnv+u/SRmxoYR2jodscVcyfUkVHR7SKhQleXy1NB+XkL9tO+dlc3MOzxRKZdlrDfTFCDUqBUD6vda1yeZOSLlJxGOfNsZokj5Ikd+RpcjbX+GzWPCX+ZjiKvgbgoNb59tUH9Gw7YEehQyhprXnaFYVRk9N6kHQKVMon26qxMef5k2uNw2fSyvNfaJSvqsStlGU3VzVi3cg7yUHYsK7Y+8gwK25j3Rk1La5iDUCYSKRu0LFGrOP9AZBvfz545PfL0zxJYNPnfd8F0tgKw6vJHtF2VfNh5fbVojaRdPk15W3ccnNWJl1eHGHftUGN9tg+j+zh81RthvIX1RFYeMtUQq4gV1JGP1t2kk013dv41chfdJ67tsyNHk9XFHIrwK9Acir1ii6cB1V74Q8haMnFw3EWzA7WygjQhmqXx4pDDRIMXZB1aUnzX7/YMYYFnXtbehEesWHN2FGkPUdTSGXmVVD33D/600EhGhKMAA1ewc4xfELXVxW8f3/30xhi37C9mhfpN3/ZwseaegWlfQEEVae8gYs6WzRnwjaYv1QNrehdenjU8S7U27XaryPmRbbw9lhxQ2MdxQ+178Cw4tGudP27Ck+nHLMa3fUIuQ+y/Zq73rCUNPzzg/0/C6Og3c7x3rWgObu8fAWgvPRs8JY7Y8QKEPHPoZ1Kao7Vxf0y8cD1hI2/2oWgYcR2HND1UybHxeKQFGX1i1bjoKdrSHuRBEujBnX36aa/5cvzO9dv4FBkXZXmCSQCGRwcmqd6xgeZFaB0UTcEWbEKFWLf6PRQ5RalT06o9eVvrnMGAstE50LDs/7fP/RPP3hOUBb/eR7kkWMv4aeIvYuXywrT6Q6KRcDxZN5KtjCT9FP0xnH/dgSwr5YmBCWlpDaKenBKpn5LXt29IskOciNdG7/ii2fFmXmrR1X3KcuKpyFWPEi6PqcE+3kNMKCUrbcg2dFsLAKHX/Hp6yOrQKcNuk0jvwCVBmmS3HFHcrjnzkyZ6bF4+cqnOiwq708/7rWx/WVT62BKgoxknhKpw1ZZHaXiOZLVzwuoNLR4VgD2v/+TI0Vr3We4hcA775Lr9XNwup5RCmCxLVIoaZpj+g283mGRZFtVKXVqROm+oHkbnJTGGkD8+5//tlMWW0FTM7PVIN4Yb6DXt44Wr4sjbIsGxdCIRzttDixFX3/zm92i5BQ+m8YY/TqEjdIyPaUR6xg61mWl8cc4jO89fCBjMvVAjx1P5tiJmVdltWI3D4qLQd4gTW23C9uXZI293KaStsdOprA1afpzS6lNh2R3mxj/C2nm2JTbcrRLNovlXR0KrmFTn4VLmxL2tjH6o+mC5mZ8ARoaFPJtaCU8lXwq9t6LwjhjuaNmQohRp+wQjewkAeqbnF9WAoomzDab3/d1es/qlfqp7+ovoLJCg1OMGOpBo2HSf8YEUN+gEQDAjl79i094ejgW940dGoveRgBnPX8uGgDcdU5fqUrHldgQgdU1xHa0wBNJDP+Xr9jr9PBfBRZPY3Os20mWwbQtZxgBAKh/CaWN6eLzJEILr6sasepnHTYC1FT3uQUzr+f0oKideg51fniObdo/Zr4s8eorCpf7SKhgoBrW3llzX5gy2zmLLIMT5Vkjtmtt75nH/Rm268/HOcbOeuHj3pjpRD4fPq1ryekd9YIJ7M/KzNaUS7E99q+y+Epvfeg/n/w+GOu74qSQitvYCoLXnDViT4tPZo9abVFPTX17cyw0/hTgxX5DuyDExEx+dWcZV72wP3JwlWsyhqVsiifw1e5w5qVIrNOi2hcj7F/gK9iTTkuHqVDOv+YI+nDdNaQharbuLH17J9nbnhh9jKZrN6AseVBk9RPyxL6FfIf23XVF21lNon2bvIOG3q1ILJweFPm/tseqfbqTOlMpym292NvEFqV7rZ3DXsoBpNeg10YPDHKa73qhh4kGiYRlzfQvF0nYvljWT3ntsY6LJKUcRLKZ2TZlGF+NsRojitpZWOYo1qpq6lA5nKzPoxkE1JuwVfb/JZkrQOFxj9/OE7MlU8O+QV+DuUJmRpu2Su8aRL2fHMqaOQAkum0S1LOEJ/8KWdBZu+5Wd7UqxbcBHP6UzWhlj3UqKEa7jTxEbTsjvNxpELr9+gvPw3NL5AGF1G+++MMD8GqWOjEehHjLowxOVweXgQHg3gpD+HR9xn24VT4Zp4JiB4nIOr69Ibfe+kC6NrU0ePE6qxi2jzCfr3thBs126cnBi+uiy0DYUyy4gmhjtovbScXBgOkzHcbFOvDZuHTFpyFjricYB45Y55uAXCIr9uBtICbLsx3GW10UXsc2smK/2jLBKLgVmBNtrs+MRsPX6zP4DIAlELffcciJwNQdh2MVczSGUfqjrhT+LPg/uuTV62SZm63m1xJfV4UA3IvDTrfwFs3zCEcRSokVXu6EhScthaAKXnKCLix1enEMuB3SWUP0Tgs4vwsjT68zU0GsdeghZwQ+ixgJsyXoB2B6eXNrM96QEgKdvDlPcXCzYsPeR7R8TEOG1gu9t0ADAKh5N57Tac5MRXi/xYvE4Glz4LfzdoSOQr65u6IP/fsZ4rydsrCZqfDOi/UqbZB/LOfrdD4dMRFh1ZQetTcOQLIWlvDh3gythVwa6wJJsCpd69h6Fdfz16dFV7WOnUqElv+t3E8dWddKcadD269BW0DPOXYBzJUQoioTr4Bvh+rvKtqxYrtWYEfWfSFINVO/x+yq1T3+7Dw7BwDOZ7u/Mb/DvROiOkFKKV3sVmutdx57Xl67FpSVEMx8oFMSPIIkl9SkNh+lJHXh5e4LyftiVBpUGThDpnIiJyQn1kKVdcKUMqUkyAk1mUpSyhR5JID03bL2yJ2dq4Ij9pw8T4BJXBMDOtax72JpleWzKRbT8j5YwNUaAbJdo4avkBsMB69DlLY6AoDRMgvEMcE72r54HtCkgdhssyW4ewZ0vHw6mjpv/zb0bkj2AxhaDY7L/bho4lnObGF4as+R2P8xfYS1fxV/V2NAAUqNx+YvlD/DxhehQ79k26DMbRCeY5+OVjK0feRhUVgxw/WCLCcoPDWidm8kzFjTkSRN/CePIL+zCFOphhvpS2fFNVEHZd+SKDvWBIeUZpOtjItKe7dB7lmQkm3zFfdz0LGlSaB0i3pajOC9d+W77b3Pr31OF07Yc6vWsjhPhvVrHfXOkIod2xOX8BNfRCcFxTCT4wRC0UZnatC/GKvO27MUZg+L7w8AgIWqeJzUJO1gsNpnm+2ygywtyZUKT2G73NlmOxMMJ6Lf7EKZ2bo6preuCpac17vVzuvVrnl1aG2uibDx5h0IW/K2lv5MZw7v4wJNrDgAYgPuM2bdKkAs5yxK+pbqw2uLhz2BAFr6v1i48oXmCbgFAyQI27bd87J/xq7M3uENQAPup5k0vnbdoBaEf1XCE3ZEIL1LQFtYP7MsdvjoIzz1ZnR61soU7nDCII2E4ZyhO4fdXhT4hpCbX6esy3/Q04BpLsgkLSvu5AeRQ/hzr5VRUL8sFa96PfLyMZfi5Z1uJXYiDcSeB7MYWHusxyk7GB8r2HmoZxV4iPgYzLF9vfi92J6dJ9LqJgmCfhEpKIuEhacyH7BZdX/kRlJCx4G7V7PwLqq3ixpyp18vNvDnn+gFN13jXYO7tZuwyv7VIsN8/nfz1XwBTEpuDbE+4QeLnKkv8ApgI+b2zBQAvhtWLJCb+1oJwP3miRDO3xjxCcDB5Qnws8Qcnjav7N/Fah5sw0JgBmCOR0AcAKxqt0tHv50HWXEP604M4LWWFbtSDUBkghGqy9LAvjshXNM6tgmtQjyKMxtp7WclSLsODbciJDKXZOyGPGAOaOKotdamFR3clxxCceeHfzDav0wfTAGtMdUaOFLbnu9jBPB7cjT3aPTiK5HEnlkC28Sca8zM1rcZnwz/jqgtquZ4qZ56KUri/Hx+sgdha34OLauS1MEo35bxsWxM3neBhPXCag2SDlXbRP67T0X+dvxF9ygNg5EQiOVoN3vwXdCF+QSn7CvCo5n9dl1dEX9X/Vl3RnJ3UGBlUZOK24qgOwAar5PURxOH7mKeKShqwrBjXr1C31NgJgQA8c/zwI5RLvxS/VlfLNQ7qKl/yvzDMdi9m8NyCTLR5MQRfO/jHonjGs9MABZpca+IFWfK98Z8GqifCt6/6xzb32wnQDweQpa7Lin3XLXDCTy/EoFWg2GTbFYwSwOm1nYp94Ld0hQJsPKWTGFYhvQblmpa6g6+BfOGb+6C8ZT/E0AqMPYWvsOGw03xsf6HL6CRrIGNv/9rJR1IdYkpRVqCQGOWByz4brYCJ8JBWU8+SrwNAMTPS8GQi9JpSSeTVTBX2yXjVQcYUy7yTEOeXe9guOwZlDWYC0AssReC7X3Phu1Y8QEAZgCvMcID+E2osJB3D4HXG7lk3m6OJU3C02ayOzwGr/FvRFy5t47RXVdcZBm8LPhFI9iYo4BMmP19/fX70HO3dpJSF+GpZguSO0SFRjo/XVq8joA2i85ePqWXRtBGmBUPWmiTieF05OuUhvRRi+dmB4cTwp5a1ueijFgIIb7N5/PaJODR8c/nmK8M0RcAIHo4TswP+AjL3cdglTHMVeOlByD3VBNYYYFs1TAvVg8L56sflv84Fw3uy2e4ny5SHQPYIIo20ieiL59hI5B2f3kY1oWCf3dmu1Ys9XUIYCM3u+dTK99gqPNF7ruY7QrsK6m2AhaEau0jtdZTaq211sPGpPQdGvkF6NT4TtAANIbUWlOX1X55H6T1U84mhl8gWBrO2+aq/cJSw/ZU12u2e8+q9VMx1ZTkSxq66MMIawoD1G1vtmvA9RA2zd7Rq2M/22y3D11VoMc61qgZzNwtxFUaYT4MU0ABi3O3N5b+GIyHR9FqhdGDsGbxRQCLruuRjhqHx2sRhnPMhQAw7RFwV8bi4Kfdkft5PXa6jr5S8WIqwNdhMCrrDOuO2EfA20OXkLzPRMC6s47RSkERPBXe7CEQgFegb56nKRDKs2jQe5+VZz4CeKgr0jtpm78EXeKxvKh3bEkjJCha9Ow5u1E2XlFTNrblUEvwEYjq/Og602Z2Rr2fCg2AIIDRqvEWZxnag0ynFQcNGKSLomtixU290iHwuYzdT+WLTI0PYcX9oNpfeqWmnZrmpVfQQV1DV2V+LT703oQVVAIQ3STdLteet2D4MpyaUXqFKL3DR92ZsK8C2GzeQCQ6t2a0lZM6sFSx57l5ur8AovRfHa3X/KV+OunY1+wxTWggrLHqjEQcZ708pNlHCVXj0ggANpnUeig9WgEAFsDiuQuhaPJ0X74YXMKb/dDzz/0EreNcSgaVERTyDU0y6VECcTC/eGfhafWnBIdH5Z8sx2OlTDqyaCro2mmn9oQsdxmy7b0BVPdRumDhKW/maozVZgqcvPIc0wgzFZWq1naICr7ePJmjIpyo3B/sfbPGPC+bEyxWa8cANhIbyA1kPwe8iyNsQ/nwXEYj3zCo5nYxup3KR9PCbOczvjVr38ve4kf7b7YrIY6Msjunkvx3YAToVbV+yjjwf5fckibDrP50604nQ8btRNsVcXCKJFP79yU7MT6pkXeSj0CgKu5Z3yoJ9a77IMICCbcJgJ3/Ehs5UlyRlEr9uRS7NONr4G/QhMX7Ovmgd+SBzLco/egkmcE0TjARKgH3BpDcbss5j5JWz/fmefIHEn0yQh88NaOGTnGeAvPOnDgIW7J7Z6X97bEN4vmhhVAz+mexLBxi2y28ve7Sl6+zEAKQYoZ9dxUDRZ6H5787apfWwK0Sk/ZXKQ5t+EcDcacUUd7OS5Nu3x0EFLYJeygSdaEcjxz91DXkJUjYoO/5a6IBPIRUZoagx1EntZrqcvEFQmGk+oQeNSwiu/ocBAkbHvPzDbD519DuP6b+4DXKvpA2DEq1uObCoQDlVx+uPOe/FXYPRz+pjm0IBmWNQ4XrhQR++xbir0bnWDLA3TwrthDENNsutYaxp9ILmCV1c/RT1677i54jZCHEQkC0yx3RyQR3ed5xDjWSClYQxCP6xMy9Y1K3JsLOBTDHqnbVlD9beTv1U2IDGMVXNsm2IXD3bc8/Gg63DRW6/hFC+MSTVasnitrnLbLojwINs2627tmR9Sy70NBa63DWn9r6qqhNkqW6x6S2lHWtxS59s4f0jAbt2ygaZN6TuW5nXc38YzKjhR7UXvOktTbSkEekklJKueV266xtUPEuP5LH0zAQfi8d5xqJCyMsCfPx2f7fmuiGAcloyGSyTAe/5JemkhxuJTO1OZhyK0kel+btIiLTyeRDoKagkDIqczKjPz6LsKp3CjISeyqlAjY+IFeCWMdUZfdVUohMtSkwvcDYnTYXqeKPjXd6YdalSlFZjAMdFKmxUuZGua74JT/6sNgdbhOGmLStnWkI97dn3ZGSkqTMtYCp+dUt2Z3LuqNPNauOJGzvHeJRYPQYlHXsIj0TDpsVgm/A8wVKxX7okQ2Pw1Ln63kNjIAfUBjBKnlerWht04W/bTJB27pGFIuLrECeCtWdN0kOUT30TSiMaaK3vXsOtAk2ckfktW/G1wAvJF1WyNxO8eKt2uZUzWwXuZ4V7JbeW3kn//H/mExVXfxW2ijaxn1b9JkYA3C+TiSavK29EMydImq19+4b1kNmcvRnxc+LV3ATcs6v7UDhV2cIoHDBvVKPYsAuN59F2/w4FUwFwmlc6pgFykLDOTjYtSKD5tgurBj5IVTd7Hp5rNhfYhbh/m4JseK8Vt2DojMr7j9ije2Q7zO+tiIbzkyuwOwaSNr7DMR46xfIIDMJqT5wH7rrivviCTEQByeTGuv1inajHk3/Ungd5F8BjYdhQPLwG0M3i2efe6NrL49gHo/+hNVijG9hw15tD3m/hU+KYt101XvbrXF4BP8QuMLLkAT5Y7gC2prtakm5Kgjx6QAXTwDLMbrUDpwJulcFyr4EjXNsc75ixynkWuj6CnKYdGkFm+dYj7rgVeV3brhBBf5Vdnyt0XWneALEY988dNNnetjVLxXuH6NpvDayYg8pymucA4DVQlgOunjMDUqqxnm9rHi2yWX3q8g74Wnk0zxNv4Gzue4hMKwALA2jFkB5RloAo3FZXtMAlg3KqNBwtgZXKW0Ulg9DPYWeWgNfkXaqzj3qZ8zzbRYd6MzkIBNSak4npJRXxIqD5qjMJFMy5aRcksj2Ht0mNCfd1csCTSS3rh1LCzTF7jiSmDfgdYgNgHQ2fHDvtFVA4u3VyEwaMTSey3Kezaoa64sOynqPqvmpWBse/vZvtfWETqupYhZTzH4Nxa6ekUuxsYJIp5UAOuDbS/MTSsF3vZvx0ThHHlAKsJa6yMw+qh6AV8UhWkCOa4GLs2BaoNBwtqw4eElT7bINy7G/c+3MiRlw67zI4ezxqeg7N+iy27/1S4kIbk9ep93b1TxNyuivUrTfRNIYzQAmWRuS6gVp8Aktc8t9BZwN2lav8MQ0ZVWnzC2ubV7PReogYfsqKOYHKnPQrAA8qEz8P2FhYbHuGjSLZVS0jC/hzEVapAB8m4zN6ur9752bE/xO/KXZUnoYuENS3GUoPfern3Fw/usYsSSZeZg1GgEkyGTiu5s/grGMdWiL0A722Ery/6mJrhZt9AoUXLZfsV/1TtBWL9oi1QofjnOvTOgY7a6cD6E1aAdWXNB1BQCMjLjq940RlpqzcXakWjzmyjVQa6DNho0aR8EuWnEHXR9DS9Fec+wYAIQCIAQ32hdalqW8T1fZGdXiW760pU1n/Kh8m14XLkFxphgRhb6fM+yxM4CkkHET83yeZgx7ilGjr5p6jw3Avg5PmFSW3ScCosoOHtkniLaMgw6OktJ8WzfS+6WWMYAmFwoli7sUy7e2T+h60WcibN0BADicIMpVoUrHPVuxDq13yiO2S0znCzPTnZ91WlYsCIBPbZ+gOrTiErEAyca97Zjg7HyRQYG0Kyt+A3K7wxPw9pwfBp/8Zl60jc+w6tiii4LAk+pArW7yxALPJ8n8Av3ZoCv2FSgASA5RMPFaXQNqnrYaL+QY82mEg/HWxDyqTkWL6Wrz++ik2gXhI4wAh8jq1QUBKOVOajcvffn7kPD1fkYAO2h1vG8hxK/xZHjVU/ZS79WMT4Fgntz9qWC6iykWi/XCpU97yi9qyld8+L/GsnPq/RQ9nrL3QxQynvUi7NN6Nyq11keWujuzc08APRgVC7In7PdPazwB66e1+VaGrxe3BBIxgN0Iu5FGrIHogBjaWt3My8Q627d50C46PKNbB+eLH5NAYVDuaiOvORxeLcaN90uzGNpSkaodnCUVewNQyEAgRgPOaJDf50lFwTs7xOyQVHxG6vjkvwyzbTXdUP9y6iDZZkNNR9jC5bHi9D+NAvurBqs2sOIuCLHivrE7AssQHzD4++AdLo1aXTBFpjesZhB5aljlnIGOvix9NU/kXwICJOlXmv2V+/xcAHhUvlo9W/RFqCri1zMBszmr5/JV7wc56BpcPXadECxSm/tvELhBUpvfCg/a9dD3hKRLM74I3gbFWJNE3CJ2Z016+8CFdokzc/TfUGkIYL1+C42wAPN42vkWRapniy4ChhNH64A+DgCUeMIIqlO66o5ehf2NAN8BHOLQG3hJvqHg1TlI+LE4+fF4EOJbsIbiWvxdp8hip3IvOKEHh39T3MU6eE3ivd82xsmZUK0rYsUdwii3weiWkHNEi2acEUb5hAXwJPDUI+HW60/rrpzlmrCbNWtUdvgpZAJQdeeIjlEjfZc7qzWwmBLzADvwSoZjrvwL6CuTih34x983X/NXe4D+jAaqfioUve5A04j1iThj4DAF8J8DdenRpY2fMfabrK55eYsZnjT+9psQTVu87gUBDjp8xf9Q/dmkp2wibEDEiYADRgcAGgo4aAB4whPwBLziye9stQQAilPNjfmIVEOLLhopGeNIsil7j2FYx7YMdvdsOrOYwAYNLnNhwioRxqMQj0LM/yjEbL7443wOrLFeYQ0MsW7K/8IdAByKNXu0gADmmFkaK6C6oi8fXyNKMXpmBlsoS6nDHMjZoyPD5miJGeYz8WQ7fjYHMOq/oVJvPC9hbDQANKKgP10V68X0af20tuawJ6xfh9a2d0EYOfYsbomQrlhbtWFtZRPjf9cV0077YOg5Z0LHV60FboX3FfCMCfd1ONp5OXN3HfT77x8bzjcSxrVkn2izyGwLtY/T+I9/jCfx4PGAuVsB3J1B3P7ougScYfR5PjV9tsSdsDeKz93K+45Pw52wN4o7YW8Ud8LeKO6EvVHcCXujuBP2Y7D66gbc4Dq2n7/vreEGCXsHcGfFN4v/D2Y3L8KZ46mCAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=472x341 at 0x7F1D31A73240>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "perspective_7.jpg\n",
            "perspective_8.jpg\n",
            "perspective_9.jpg\n",
            "perspective_10.jpg\n",
            "perspective_11.jpg\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:44: RuntimeWarning: Mean of empty slice.\n",
            "/usr/local/lib/python3.6/dist-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
            "  ret = ret.dtype.type(ret / rcount)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "PASS\n",
            "perspective_12.jpg\n",
            "perspective_13.jpg\n",
            "perspective_14.jpg\n",
            "perspective_15.jpg\n",
            "perspective_16.jpg\n",
            "GRESEALA\n",
            "3.4\n",
            "F\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=472x341 at 0x7F1D31A73FD0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "perspective_17.jpg\n",
            "perspective_18.jpg\n",
            "perspective_19.jpg\n",
            "perspective_20.jpg\n",
            "perspective_21.jpg\n",
            "perspective_22.jpg\n",
            "perspective_23.jpg\n",
            "perspective_24.jpg\n",
            "perspective_25.jpg\n",
            "perspective_26.jpg\n",
            "perspective_27.jpg\n",
            "perspective_28.jpg\n",
            "perspective_29.jpg\n",
            "perspective_30.jpg\n",
            "perspective_31.jpg\n",
            "perspective_32.jpg\n",
            "perspective_33.jpg\n",
            "perspective_34.jpg\n",
            "perspective_35.jpg\n",
            "perspective_36.jpg\n",
            "perspective_37.jpg\n",
            "perspective_38.jpg\n",
            "perspective_39.jpg\n",
            "perspective_40.jpg\n",
            "perspective_41.jpg\n",
            "perspective_42.jpg\n",
            "perspective_43.jpg\n",
            "perspective_44.jpg\n",
            "perspective_45.jpg\n",
            "perspective_46.jpg\n",
            "perspective_47.jpg\n",
            "perspective_48.jpg\n",
            "perspective_49.jpg\n",
            "perspective_50.jpg\n",
            "perspective_51.jpg\n",
            "perspective_52.jpg\n",
            "perspective_53.jpg\n",
            "perspective_54.jpg\n",
            "perspective_55.jpg\n",
            "perspective_56.jpg\n",
            "perspective_57.jpg\n",
            "perspective_58.jpg\n",
            "perspective_59.jpg\n",
            "perspective_60.jpg\n",
            "perspective_61.jpg\n",
            "perspective_62.jpg\n",
            "perspective_63.jpg\n",
            "perspective_64.jpg\n",
            "perspective_65.jpg\n",
            "perspective_66.jpg\n",
            "perspective_67.jpg\n",
            "perspective_68.jpg\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:43: RuntimeWarning: Mean of empty slice.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "PASS\n",
            "perspective_69.jpg\n",
            "perspective_70.jpg\n",
            "perspective_71.jpg\n",
            "perspective_72.jpg\n",
            "perspective_73.jpg\n",
            "perspective_74.jpg\n",
            "perspective_75.jpg\n",
            "perspective_76.jpg\n",
            "perspective_77.jpg\n",
            "perspective_78.jpg\n",
            "perspective_79.jpg\n",
            "perspective_80.jpg\n",
            "perspective_81.jpg\n",
            "perspective_82.jpg\n",
            "perspective_83.jpg\n",
            "perspective_84.jpg\n",
            "perspective_85.jpg\n",
            "perspective_86.jpg\n",
            "perspective_87.jpg\n",
            "perspective_88.jpg\n",
            "perspective_89.jpg\n",
            "perspective_90.jpg\n",
            "perspective_91.jpg\n",
            "perspective_92.jpg\n",
            "GRESEALA\n",
            "3.1\n",
            "I\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=472x341 at 0x7F1D31A73240>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "perspective_93.jpg\n",
            "perspective_94.jpg\n",
            "perspective_95.jpg\n",
            "GRESEALA\n",
            "2.8\n",
            "I\n",
            "3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=472x341 at 0x7F1D31A73080>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "perspective_96.jpg\n",
            "perspective_97.jpg\n",
            "perspective_98.jpg\n",
            "perspective_99.jpg\n",
            "perspective_100.jpg\n",
            "perspective_101.jpg\n",
            "perspective_102.jpg\n",
            "perspective_103.jpg\n",
            "perspective_104.jpg\n",
            "perspective_105.jpg\n",
            "perspective_106.jpg\n",
            "perspective_107.jpg\n",
            "perspective_108.jpg\n",
            "perspective_109.jpg\n",
            "perspective_110.jpg\n",
            "perspective_111.jpg\n",
            "perspective_112.jpg\n",
            "perspective_113.jpg\n",
            "perspective_114.jpg\n",
            "perspective_115.jpg\n",
            "GRESEALA\n",
            "3.7\n",
            "F\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=472x341 at 0x7F1D31A73080>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "perspective_116.jpg\n",
            "perspective_117.jpg\n",
            "perspective_118.jpg\n",
            "perspective_119.jpg\n",
            "perspective_120.jpg\n",
            "perspective_121.jpg\n",
            "perspective_122.jpg\n",
            "perspective_123.jpg\n",
            "perspective_124.jpg\n",
            "perspective_125.jpg\n",
            "perspective_126.jpg\n",
            "perspective_127.jpg\n",
            "perspective_128.jpg\n",
            "perspective_129.jpg\n",
            "perspective_130.jpg\n",
            "perspective_131.jpg\n",
            "perspective_132.jpg\n",
            "perspective_133.jpg\n",
            "perspective_134.jpg\n",
            "perspective_135.jpg\n",
            "perspective_136.jpg\n",
            "perspective_137.jpg\n",
            "perspective_138.jpg\n",
            "perspective_139.jpg\n",
            "perspective_140.jpg\n",
            "perspective_141.jpg\n",
            "perspective_142.jpg\n",
            "perspective_143.jpg\n",
            "perspective_144.jpg\n",
            "perspective_145.jpg\n",
            "perspective_146.jpg\n",
            "perspective_147.jpg\n",
            "perspective_148.jpg\n",
            "perspective_149.jpg\n",
            "perspective_150.jpg\n",
            "Acc = 95.33333333333333 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5bS6MbQN5b_V",
        "colab_type": "code",
        "outputId": "a307bb2f-5114-453d-e9f7-32ff3fe6e555",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#19\n",
        "acc = 0\n",
        "for i in range(1, 151):\n",
        "    image_name = 'rotation_' + str(i) + '.jpg'\n",
        "   \n",
        "    print(image_name)\n",
        "    file = open(folder_in + 'image_' + str(i) +'.txt', 'r')\n",
        "    first_line = file.readline()\n",
        "    file.close() \n",
        "    first_line = first_line.replace('\\n', '')\n",
        "    info = first_line.split(' ')\n",
        "\n",
        "    grade, subject, number, th = predict_grade_scenario_3(folder_in + image_name)\n",
        "\n",
        "    try:\n",
        "        grade, subject, number, th = predict_grade_scenario_3(folder_in + image_name)\n",
        "\n",
        "        if ground_truth_hw_dict[i] == str(grade) and subject == info[0] and number == int(info[1]):\n",
        "            acc += 1\n",
        "        else:\n",
        "            print(\"GRESEALA\")\n",
        "            print( grade )\n",
        "            print( subject )\n",
        "            print( number )\n",
        "            print_img( th )\n",
        "\n",
        "    except Exception as e:\n",
        "        print(e)\n",
        "        continue\n",
        "\n",
        "print(\"Acc = \" + str(acc * 100 / 150) + \" %\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "rotation_1.jpg\n",
            "rotation_2.jpg\n",
            "rotation_3.jpg\n",
            "rotation_4.jpg\n",
            "rotation_5.jpg\n",
            "rotation_6.jpg\n",
            "rotation_7.jpg\n",
            "rotation_8.jpg\n",
            "rotation_9.jpg\n",
            "rotation_10.jpg\n",
            "rotation_11.jpg\n",
            "rotation_12.jpg\n",
            "rotation_13.jpg\n",
            "rotation_14.jpg\n",
            "rotation_15.jpg\n",
            "rotation_16.jpg\n",
            "GRESEALA\n",
            "3.4\n",
            "F\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=472x341 at 0x7F1D280D29E8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "rotation_17.jpg\n",
            "rotation_18.jpg\n",
            "rotation_19.jpg\n",
            "rotation_20.jpg\n",
            "rotation_21.jpg\n",
            "rotation_22.jpg\n",
            "rotation_23.jpg\n",
            "rotation_24.jpg\n",
            "rotation_25.jpg\n",
            "rotation_26.jpg\n",
            "rotation_27.jpg\n",
            "rotation_28.jpg\n",
            "rotation_29.jpg\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:43: RuntimeWarning: Mean of empty slice.\n",
            "/usr/local/lib/python3.6/dist-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
            "  ret = ret.dtype.type(ret / rcount)\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:44: RuntimeWarning: Mean of empty slice.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "GRESEALA\n",
            "3.4\n",
            "F\n",
            "None\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=472x341 at 0x7F1D280D2A58>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "rotation_30.jpg\n",
            "rotation_31.jpg\n",
            "rotation_32.jpg\n",
            "rotation_33.jpg\n",
            "rotation_34.jpg\n",
            "rotation_35.jpg\n",
            "rotation_36.jpg\n",
            "rotation_37.jpg\n",
            "rotation_38.jpg\n",
            "rotation_39.jpg\n",
            "rotation_40.jpg\n",
            "rotation_41.jpg\n",
            "GRESEALA\n",
            "3.7\n",
            "F\n",
            "3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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          "text": [
            "rotation_42.jpg\n",
            "rotation_43.jpg\n",
            "rotation_44.jpg\n",
            "rotation_45.jpg\n",
            "rotation_46.jpg\n",
            "rotation_47.jpg\n",
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            "rotation_51.jpg\n",
            "rotation_52.jpg\n",
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            "rotation_54.jpg\n",
            "rotation_55.jpg\n",
            "rotation_56.jpg\n",
            "rotation_57.jpg\n",
            "rotation_58.jpg\n",
            "rotation_59.jpg\n",
            "rotation_60.jpg\n",
            "rotation_61.jpg\n",
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            "rotation_64.jpg\n",
            "rotation_65.jpg\n",
            "rotation_66.jpg\n",
            "rotation_67.jpg\n",
            "rotation_68.jpg\n",
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            "rotation_70.jpg\n",
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            "rotation_74.jpg\n",
            "rotation_75.jpg\n",
            "rotation_76.jpg\n",
            "rotation_77.jpg\n",
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            "rotation_80.jpg\n",
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            "rotation_82.jpg\n",
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            "rotation_84.jpg\n",
            "rotation_85.jpg\n",
            "rotation_86.jpg\n",
            "rotation_87.jpg\n",
            "rotation_88.jpg\n",
            "rotation_89.jpg\n",
            "rotation_90.jpg\n",
            "rotation_91.jpg\n",
            "rotation_92.jpg\n",
            "GRESEALA\n",
            "3.1\n",
            "I\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=472x341 at 0x7F1D280D29B0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "rotation_93.jpg\n",
            "rotation_94.jpg\n",
            "rotation_95.jpg\n",
            "GRESEALA\n",
            "2.8\n",
            "I\n",
            "3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=472x341 at 0x7F1D280D2A20>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "rotation_96.jpg\n",
            "rotation_97.jpg\n",
            "rotation_98.jpg\n",
            "rotation_99.jpg\n",
            "rotation_100.jpg\n",
            "rotation_101.jpg\n",
            "rotation_102.jpg\n",
            "rotation_103.jpg\n",
            "rotation_104.jpg\n",
            "rotation_105.jpg\n",
            "rotation_106.jpg\n",
            "rotation_107.jpg\n",
            "rotation_108.jpg\n",
            "rotation_109.jpg\n",
            "rotation_110.jpg\n",
            "rotation_111.jpg\n",
            "rotation_112.jpg\n",
            "rotation_113.jpg\n",
            "rotation_114.jpg\n",
            "rotation_115.jpg\n",
            "GRESEALA\n",
            "3.7\n",
            "F\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=472x341 at 0x7F1D280D2A20>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "rotation_116.jpg\n",
            "rotation_117.jpg\n",
            "rotation_118.jpg\n",
            "rotation_119.jpg\n",
            "rotation_120.jpg\n",
            "rotation_121.jpg\n",
            "rotation_122.jpg\n",
            "rotation_123.jpg\n",
            "rotation_124.jpg\n",
            "rotation_125.jpg\n",
            "rotation_126.jpg\n",
            "rotation_127.jpg\n",
            "rotation_128.jpg\n",
            "rotation_129.jpg\n",
            "rotation_130.jpg\n",
            "rotation_131.jpg\n",
            "rotation_132.jpg\n",
            "rotation_133.jpg\n",
            "rotation_134.jpg\n",
            "rotation_135.jpg\n",
            "rotation_136.jpg\n",
            "GRESEALA\n",
            "4.0\n",
            "F\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=472x341 at 0x7F1D280D2A20>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "rotation_137.jpg\n",
            "rotation_138.jpg\n",
            "rotation_139.jpg\n",
            "rotation_140.jpg\n",
            "rotation_141.jpg\n",
            "rotation_142.jpg\n",
            "rotation_143.jpg\n",
            "rotation_144.jpg\n",
            "rotation_145.jpg\n",
            "rotation_146.jpg\n",
            "rotation_147.jpg\n",
            "rotation_148.jpg\n",
            "rotation_149.jpg\n",
            "rotation_150.jpg\n",
            "Acc = 95.33333333333333 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p1rcmBQTSl-I",
        "colab_type": "text"
      },
      "source": [
        "# **Scenario 4 (handwritten recognition)**\n",
        "Your receive a test set containing 25 scanned images. We will make sure to modify some content such that the grade written in red to be different that the grade obtained by a perfect grading system. For each image you have to output the corresponding handwritten grade."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "87QNhuzFwxV0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#1\n",
        "hack = {1:[0,3,6,9],\n",
        "        2:[2,5,8],\n",
        "        3:[1,4,7],\n",
        "        4:[0,3,6,9],\n",
        "        5:[2,5,8],\n",
        "        6:[1,4,7],\n",
        "        7:[0,3,6,9],\n",
        "        8:[2,5,8],\n",
        "        9:[1,4,7]}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FHSze-QmS7PE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#2\n",
        "def cut_background(image, proj):\n",
        "    w, h = image.shape[::-1]\n",
        "    left = 0\n",
        "    ref_sum_img = np.sum(image, axis = 0)\n",
        "    ref_sum_img = ref_sum_img //255\n",
        "    in_0_region = False\n",
        "    count_in_0_region = 0\n",
        "    line = 0\n",
        "    \n",
        "    for line in range(int(len(ref_sum_img) * proj)):\n",
        "        if ref_sum_img[line] < 5:\n",
        "            if not in_0_region:\n",
        "                count_in_0_region = 0\n",
        "                in_0_region = True\n",
        "            else:\n",
        "                count_in_0_region += 1\n",
        "        else:\n",
        "            in_0_region = False\n",
        "            if count_in_0_region >= 10:\n",
        "                left = line - 1\n",
        "                break\n",
        "\n",
        "    if count_in_0_region >= 10:\n",
        "        left = line - 1\n",
        "    return image[:, left:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H-bnlKEp19nf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#3\n",
        "def predict_first_digit(image):\n",
        "    w, h = image.shape[::-1]\n",
        "    img_model = image[:, :int(0.4 * w)]\n",
        "    img_model = cv.copyMakeBorder(img_model, 1, 1, 1, 1, cv.BORDER_CONSTANT, None, 0)\n",
        "\n",
        "    w, h = img_model.shape[::-1]\n",
        "\n",
        "    if w > h:\n",
        "        diff = w - h\n",
        "        img_model = cv.copyMakeBorder(img_model, diff // 2, diff - diff//2, 0, 0, cv.BORDER_CONSTANT, None, 0)\n",
        "\n",
        "    if w < h:\n",
        "        diff = h - w\n",
        "        img_model = cv.copyMakeBorder(img_model, 0, 0, diff // 2, diff - diff//2, cv.BORDER_CONSTANT, None, 0)\n",
        "\n",
        "    cv2_imshow(img_model)\n",
        "    resized = cv.resize(img_model, (28, 28), interpolation = cv.INTER_AREA)\n",
        "    resized = np.array(resized, dtype = float)\n",
        "    data = torch.from_numpy(np.expand_dims(np.expand_dims(resized, 0), 0))\n",
        "    data = data.type(torch.FloatTensor)\n",
        "\n",
        "    data = data.to(device)\n",
        "    output = model(data)\n",
        "    output = output[0].detach().cpu().numpy()\n",
        "    #print(output)\n",
        "    output = output[3:]\n",
        "    pred = output.argmax() + 3 # get the index of the max log-probability\n",
        "    return pred"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tv5KR-vM2m8g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#4\n",
        "def predict_second_digit(image, pred1):\n",
        "    w, h = image.shape[::-1]\n",
        "    img_model = image[:, int(0.5 * w):int(0.85 * w)]\n",
        "    img_model = cv.copyMakeBorder(img_model, 1, 1, 1, 1, cv.BORDER_CONSTANT, None, 0)\n",
        "\n",
        "    w, h = img_model.shape[::-1]\n",
        "\n",
        "    if w > h:\n",
        "        diff = w - h\n",
        "        img_model = cv.copyMakeBorder(img_model, diff // 2, diff - diff//2, 0, 0, cv.BORDER_CONSTANT, None, 0)\n",
        "\n",
        "    if w < h:\n",
        "        diff = h - w\n",
        "        img_model = cv.copyMakeBorder(img_model, 0, 0, diff // 2, diff - diff//2, cv.BORDER_CONSTANT, None, 0)\n",
        "\n",
        "    cv2_imshow(img_model)\n",
        "    resized = cv.resize(img_model, (28, 28), interpolation = cv.INTER_AREA)\n",
        "    resized = np.array(resized, dtype = float)\n",
        "    data = torch.from_numpy(np.expand_dims(np.expand_dims(resized, 0), 0))\n",
        "    data = data.type(torch.FloatTensor)\n",
        "\n",
        "    data = data.to(device)\n",
        "    output = model(data)\n",
        "    output = output[0].detach().cpu().numpy()\n",
        "\n",
        "    #print(output)\n",
        "    #print(hack[pred1])\n",
        "    output2 = output[hack[pred1]]\n",
        "    #print(output2)\n",
        "    pred = 3 * np.argmax(output2)\n",
        "\n",
        "    if pred1 == 3 or pred1 == 6 or pred1 == 9:\n",
        "        pred += 1\n",
        "    if pred1 == 2 or pred1 == 5 or pred1 == 8:\n",
        "        pred += 2\n",
        "\n",
        "    return pred"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0a4D4q7ojREp",
        "colab_type": "code",
        "outputId": "bb2aa884-18fd-4e1d-992d-a188047b8bbc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#5\n",
        "def make_data_set_handwritten():\n",
        "\n",
        "    if not os.path.exists(folder_digits + 'handwritten'):\n",
        "        os.makedirs(folder_digits + 'handwritten')\n",
        "\n",
        "    for f in glob.iglob(folder_digits + 'handwritten/*'):\n",
        "        os.remove(f)\n",
        "\n",
        "    perm = np.random.permutation(range(1, 151))\n",
        "    acc = 0\n",
        "    for i in range(1, 151): \n",
        "        file = open(folder_in + 'image_' + str(i) +'.txt', 'r')\n",
        "        first_line = file.readline()\n",
        "        file.close() \n",
        "        first_line = first_line.replace('\\n', '')\n",
        "        info = first_line.split(' ')\n",
        "\n",
        "        image_name = 'image_' + str(i) + '.jpg'\n",
        "    \n",
        "        print(image_name)\n",
        "        # load image\n",
        "        image = cv.imread(folder_in + image_name)\n",
        "\n",
        "        try:\n",
        "            orig_h, orig_w, _ = image.shape\n",
        "            image = image[int(orig_h * 0.29):int(orig_h * 0.33)]\n",
        "            image = image[:, int(orig_w * 0.08):int(orig_w * 0.29)]\n",
        "            orig_h, orig_w, _ = image.shape\n",
        "\n",
        "            img = cv.cvtColor(image, cv.COLOR_BGR2GRAY)\n",
        "            img = cv.GaussianBlur(img, (11, 11), 0)\n",
        "            th = cv.adaptiveThreshold(img,255,cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY,7,2)\n",
        "            th = cv.bitwise_not(th)\n",
        "            #cv2_imshow(th)\n",
        "\n",
        "            template = cv.imread(data_dir_drive + '/template_matching.JPG',0)\n",
        "            w, h = template.shape[::-1]\n",
        "            img = th.copy()\n",
        "            left_cut_size = int(orig_w * 0.25)\n",
        "            right_cut_size = int(orig_w * 0.55)\n",
        "            img = img[:, left_cut_size:right_cut_size]\n",
        "            method = eval('cv.TM_CCOEFF_NORMED')\n",
        "            # Apply template Matching\n",
        "            res = cv.matchTemplate(img,template, cv.TM_CCOEFF_NORMED)\n",
        "            min_val, max_val, min_loc, max_loc = cv.minMaxLoc(res)\n",
        "            top_left = (max_loc[0] + left_cut_size, max_loc[1])\n",
        "            bottom_right = (top_left[0] + w, top_left[1] + h)\n",
        "            #cv.rectangle(th,top_left, bottom_right, 255, 2)\n",
        "            #cv2_imshow(th)\n",
        "            new_th = th[:, :max_loc[0] + left_cut_size]\n",
        "            #cv2_imshow(new_th)\n",
        "        \n",
        "            template = cv.imread(data_dir_drive + '/template_matching2.JPG',0)\n",
        "            w, h = template.shape[::-1]\n",
        "            img = new_th.copy()\n",
        "            method = eval('cv.TM_CCOEFF_NORMED')\n",
        "            # Apply template Matching\n",
        "            res = cv.matchTemplate(img,template, cv.TM_CCOEFF_NORMED)\n",
        "            min_val, max_val, min_loc, max_loc = cv.minMaxLoc(res)\n",
        "            top_left = max_loc\n",
        "            bottom_right = (top_left[0] + w, top_left[1] + h)\n",
        "            #cv.rectangle(new_th,top_left, bottom_right, 255, 2)\n",
        "            #cv2_imshow(new_th)\n",
        "            new_th = new_th[:top_left[1], :]\n",
        "            #cv2_imshow(new_th)\n",
        "\n",
        "            new_th = cut_background(new_th, 0.5)\n",
        "            new_th = np.transpose(cut_background(np.transpose(new_th), 0.5))\n",
        "            new_th = np.fliplr(cut_background(np.fliplr(new_th), 0.1))\n",
        "            new_th = np.flipud(cut_background(np.flipud(new_th), 0.1))\n",
        "            #cv2_imshow(new_th)\n",
        "\n",
        "            pred1 = predict_first_digit(new_th)\n",
        "            #print(pred1)\n",
        "            pred2 = predict_second_digit(new_th, pred1)\n",
        "            #print(pred2)\n",
        "\n",
        "            print(\"RASPUNS = \" + str((10 * pred1 + pred2) / 10))\n",
        "            print(\"TRUE = \" + ground_truth_hw_dict[i])\n",
        "            if ground_truth_hw_dict[i] == str((10 * pred1 + pred2) / 10):\n",
        "                acc += 1\n",
        "\n",
        "        except Exception as e:\n",
        "            print(e)\n",
        "            \n",
        "    print(\"ACC = \" + str(acc / 150))\n",
        "\n",
        "make_data_set_handwritten()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "image_1.jpg\n",
            "RASPUNS = 4.3\n",
            "TRUE = 4.9\n",
            "image_2.jpg\n",
            "RASPUNS = 6.4\n",
            "TRUE = 6.7\n",
            "image_3.jpg\n",
            "RASPUNS = 3.4\n",
            "TRUE = 5.8\n",
            "image_4.jpg\n",
            "RASPUNS = 8.8\n",
            "TRUE = 8.5\n",
            "image_5.jpg\n",
            "RASPUNS = 8.2\n",
            "TRUE = 4.9\n",
            "image_6.jpg\n",
            "RASPUNS = 6.7\n",
            "TRUE = 6.7\n",
            "image_7.jpg\n",
            "RASPUNS = 3.4\n",
            "TRUE = 9.1\n",
            "image_8.jpg\n",
            "RASPUNS = 7.3\n",
            "TRUE = 3.7\n",
            "image_9.jpg\n",
            "RASPUNS = 3.7\n",
            "TRUE = 5.5\n",
            "image_10.jpg\n",
            "RASPUNS = 3.7\n",
            "TRUE = 5.5\n",
            "image_11.jpg\n",
            "RASPUNS = 3.4\n",
            "TRUE = 4.6\n",
            "image_12.jpg\n",
            "RASPUNS = 6.7\n",
            "TRUE = 6.7\n",
            "image_13.jpg\n",
            "RASPUNS = 7.3\n",
            "TRUE = 7.3\n",
            "image_14.jpg\n",
            "RASPUNS = 3.4\n",
            "TRUE = 6.4\n",
            "image_15.jpg\n",
            "RASPUNS = 4.9\n",
            "TRUE = 7.0\n",
            "image_16.jpg\n",
            "RASPUNS = 7.6\n",
            "TRUE = 4.6\n",
            "image_17.jpg\n",
            "RASPUNS = 4.6\n",
            "TRUE = 7.6\n",
            "image_18.jpg\n",
            "RASPUNS = 6.4\n",
            "TRUE = 6.1\n",
            "image_19.jpg\n",
            "RASPUNS = 4.3\n",
            "TRUE = 4.9\n",
            "image_20.jpg\n",
            "RASPUNS = 7.6\n",
            "TRUE = 7.6\n",
            "image_21.jpg\n",
            "RASPUNS = 6.4\n",
            "TRUE = 6.4\n",
            "image_22.jpg\n",
            "RASPUNS = 3.7\n",
            "TRUE = 5.5\n",
            "image_23.jpg\n",
            "RASPUNS = 6.4\n",
            "TRUE = 6.4\n",
            "image_24.jpg\n",
            "RASPUNS = 4.3\n",
            "TRUE = 7.3\n",
            "image_25.jpg\n",
            "RASPUNS = 4.3\n",
            "TRUE = 7.9\n",
            "image_26.jpg\n",
            "RASPUNS = 8.2\n",
            "TRUE = 8.5\n",
            "image_27.jpg\n",
            "RASPUNS = 5.5\n",
            "TRUE = 5.5\n",
            "image_28.jpg\n",
            "RASPUNS = 4.3\n",
            "TRUE = 8.8\n",
            "image_29.jpg\n",
            "RASPUNS = 6.4\n",
            "TRUE = 6.1\n",
            "image_30.jpg\n",
            "RASPUNS = 4.3\n",
            "TRUE = 7.9\n",
            "image_31.jpg\n",
            "RASPUNS = 7.3\n",
            "TRUE = 4.9\n",
            "image_32.jpg\n",
            "RASPUNS = 3.1\n",
            "TRUE = 5.2\n",
            "image_33.jpg\n",
            "RASPUNS = 7.6\n",
            "TRUE = 7.6\n",
            "image_34.jpg\n",
            "RASPUNS = 5.8\n",
            "TRUE = 7.0\n",
            "image_35.jpg\n",
            "RASPUNS = 4.6\n",
            "TRUE = 7.0\n",
            "image_36.jpg\n",
            "RASPUNS = 6.4\n",
            "TRUE = 6.4\n",
            "image_37.jpg\n",
            "RASPUNS = 3.4\n",
            "TRUE = 8.8\n",
            "image_38.jpg\n",
            "RASPUNS = 6.7\n",
            "TRUE = 6.7\n",
            "image_39.jpg\n",
            "RASPUNS = 4.9\n",
            "TRUE = 7.0\n",
            "image_40.jpg\n",
            "RASPUNS = 5.8\n",
            "TRUE = 5.8\n",
            "image_41.jpg\n",
            "RASPUNS = 6.4\n",
            "TRUE = 7.6\n",
            "image_42.jpg\n",
            "RASPUNS = 3.7\n",
            "TRUE = 9.7\n",
            "image_43.jpg\n",
            "RASPUNS = 6.7\n",
            "TRUE = 6.7\n",
            "image_44.jpg\n",
            "RASPUNS = 6.4\n",
            "TRUE = 6.4\n",
            "image_45.jpg\n",
            "RASPUNS = 5.8\n",
            "TRUE = 5.5\n",
            "image_46.jpg\n",
            "RASPUNS = 4.3\n",
            "TRUE = 7.3\n",
            "image_47.jpg\n",
            "RASPUNS = 4.6\n",
            "TRUE = 8.8\n",
            "image_48.jpg\n",
            "RASPUNS = 6.1\n",
            "TRUE = 6.4\n",
            "image_49.jpg\n",
            "RASPUNS = 3.4\n",
            "TRUE = 5.8\n",
            "image_50.jpg\n",
            "RASPUNS = 4.3\n",
            "TRUE = 7.0\n",
            "image_51.jpg\n",
            "RASPUNS = 6.7\n",
            "TRUE = 8.2\n",
            "image_52.jpg\n",
            "RASPUNS = 4.0\n",
            "TRUE = 4.9\n",
            "image_53.jpg\n",
            "RASPUNS = 4.6\n",
            "TRUE = 4.3\n",
            "image_54.jpg\n",
            "RASPUNS = 8.8\n",
            "TRUE = 8.8\n",
            "image_55.jpg\n",
            "RASPUNS = 6.7\n",
            "TRUE = 8.2\n",
            "image_56.jpg\n",
            "RASPUNS = 5.2\n",
            "TRUE = 5.2\n",
            "image_57.jpg\n",
            "RASPUNS = 5.2\n",
            "TRUE = 5.2\n",
            "image_58.jpg\n",
            "RASPUNS = 4.3\n",
            "TRUE = 5.5\n",
            "image_59.jpg\n",
            "RASPUNS = 3.1\n",
            "TRUE = 7.0\n",
            "image_60.jpg\n",
            "RASPUNS = 5.8\n",
            "TRUE = 7.9\n",
            "image_61.jpg\n",
            "RASPUNS = 7.3\n",
            "TRUE = 4.9\n",
            "image_62.jpg\n",
            "RASPUNS = 6.4\n",
            "TRUE = 4.6\n",
            "image_63.jpg\n",
            "RASPUNS = 5.2\n",
            "TRUE = 5.2\n",
            "image_64.jpg\n",
            "RASPUNS = 7.6\n",
            "TRUE = 7.6\n",
            "image_65.jpg\n",
            "RASPUNS = 3.4\n",
            "TRUE = 7.0\n",
            "image_66.jpg\n",
            "RASPUNS = 5.8\n",
            "TRUE = 9.4\n",
            "image_67.jpg\n",
            "RASPUNS = 4.6\n",
            "TRUE = 7.3\n",
            "image_68.jpg\n",
            "RASPUNS = 4.6\n",
            "TRUE = 4.6\n",
            "image_69.jpg\n",
            "RASPUNS = 4.3\n",
            "TRUE = 4.3\n",
            "image_70.jpg\n",
            "RASPUNS = 5.5\n",
            "TRUE = 4.0\n",
            "image_71.jpg\n",
            "RASPUNS = 6.4\n",
            "TRUE = 6.4\n",
            "image_72.jpg\n",
            "RASPUNS = 4.3\n",
            "TRUE = 4.9\n",
            "image_73.jpg\n",
            "RASPUNS = 5.5\n",
            "TRUE = 5.5\n",
            "image_74.jpg\n",
            "RASPUNS = 5.5\n",
            "TRUE = 9.4\n",
            "image_75.jpg\n",
            "RASPUNS = 4.6\n",
            "TRUE = 4.0\n",
            "image_76.jpg\n",
            "RASPUNS = 6.1\n",
            "TRUE = 6.1\n",
            "image_77.jpg\n",
            "RASPUNS = 7.3\n",
            "TRUE = 7.3\n",
            "image_78.jpg\n",
            "RASPUNS = 4.3\n",
            "TRUE = 4.0\n",
            "image_79.jpg\n",
            "RASPUNS = 5.5\n",
            "TRUE = 5.5\n",
            "image_80.jpg\n",
            "RASPUNS = 4.9\n",
            "TRUE = 5.5\n",
            "image_81.jpg\n",
            "RASPUNS = 5.8\n",
            "TRUE = 8.5\n",
            "image_82.jpg\n",
            "RASPUNS = 4.9\n",
            "TRUE = 4.9\n",
            "image_83.jpg\n",
            "RASPUNS = 6.7\n",
            "TRUE = 6.7\n",
            "image_84.jpg\n",
            "RASPUNS = 3.7\n",
            "TRUE = 5.2\n",
            "image_85.jpg\n",
            "RASPUNS = 4.6\n",
            "TRUE = 7.6\n",
            "image_86.jpg\n",
            "RASPUNS = 7.3\n",
            "TRUE = 4.9\n",
            "image_87.jpg\n",
            "RASPUNS = 5.5\n",
            "TRUE = 5.5\n",
            "image_88.jpg\n",
            "RASPUNS = 8.2\n",
            "TRUE = 9.1\n",
            "image_89.jpg\n",
            "RASPUNS = 6.4\n",
            "TRUE = 6.4\n",
            "image_90.jpg\n",
            "RASPUNS = 6.4\n",
            "TRUE = 6.1\n",
            "image_91.jpg\n",
            "RASPUNS = 4.6\n",
            "TRUE = 6.1\n",
            "image_92.jpg\n",
            "RASPUNS = 4.3\n",
            "TRUE = 5.5\n",
            "image_93.jpg\n",
            "RASPUNS = 8.8\n",
            "TRUE = 4.6\n",
            "image_94.jpg\n",
            "RASPUNS = 7.3\n",
            "TRUE = 7.9\n",
            "image_95.jpg\n",
            "RASPUNS = 4.6\n",
            "TRUE = 4.6\n",
            "image_96.jpg\n",
            "RASPUNS = 9.7\n",
            "TRUE = 6.7\n",
            "image_97.jpg\n",
            "RASPUNS = 5.5\n",
            "TRUE = 5.5\n",
            "image_98.jpg\n",
            "RASPUNS = 5.5\n",
            "TRUE = 5.5\n",
            "image_99.jpg\n",
            "RASPUNS = 3.7\n",
            "TRUE = 7.3\n",
            "image_100.jpg\n",
            "RASPUNS = 4.3\n",
            "TRUE = 4.9\n",
            "image_101.jpg\n",
            "RASPUNS = 4.9\n",
            "TRUE = 4.9\n",
            "image_102.jpg\n",
            "RASPUNS = 9.7\n",
            "TRUE = 5.2\n",
            "image_103.jpg\n",
            "RASPUNS = 5.5\n",
            "TRUE = 5.5\n",
            "image_104.jpg\n",
            "RASPUNS = 5.2\n",
            "TRUE = 5.2\n",
            "image_105.jpg\n",
            "RASPUNS = 3.1\n",
            "TRUE = 4.0\n",
            "image_106.jpg\n",
            "RASPUNS = 6.4\n",
            "TRUE = 6.4\n",
            "image_107.jpg\n",
            "RASPUNS = 3.4\n",
            "TRUE = 9.7\n",
            "image_108.jpg\n",
            "RASPUNS = 4.6\n",
            "TRUE = 8.8\n",
            "image_109.jpg\n",
            "RASPUNS = 3.4\n",
            "TRUE = 5.8\n",
            "image_110.jpg\n",
            "RASPUNS = 4.3\n",
            "TRUE = 7.3\n",
            "image_111.jpg\n",
            "RASPUNS = 9.4\n",
            "TRUE = 6.1\n",
            "image_112.jpg\n",
            "RASPUNS = 4.3\n",
            "TRUE = 4.3\n",
            "image_113.jpg\n",
            "RASPUNS = 6.4\n",
            "TRUE = 6.1\n",
            "image_114.jpg\n",
            "RASPUNS = 6.4\n",
            "TRUE = 6.4\n",
            "image_115.jpg\n",
            "RASPUNS = 5.2\n",
            "TRUE = 5.2\n",
            "image_116.jpg\n",
            "RASPUNS = 6.4\n",
            "TRUE = 6.4\n",
            "image_117.jpg\n",
            "RASPUNS = 4.9\n",
            "TRUE = 4.9\n",
            "image_118.jpg\n",
            "RASPUNS = 3.7\n",
            "TRUE = 3.7\n",
            "image_119.jpg\n",
            "RASPUNS = 5.8\n",
            "TRUE = 5.8\n",
            "image_120.jpg\n",
            "RASPUNS = 4.6\n",
            "TRUE = 4.6\n",
            "image_121.jpg\n",
            "RASPUNS = 6.7\n",
            "TRUE = 6.7\n",
            "image_122.jpg\n",
            "RASPUNS = 5.8\n",
            "TRUE = 5.5\n",
            "image_123.jpg\n",
            "RASPUNS = 8.2\n",
            "TRUE = 4.0\n",
            "image_124.jpg\n",
            "RASPUNS = 8.8\n",
            "TRUE = 8.8\n",
            "image_125.jpg\n",
            "RASPUNS = 6.4\n",
            "TRUE = 6.4\n",
            "image_126.jpg\n",
            "RASPUNS = 4.3\n",
            "TRUE = 4.3\n",
            "image_127.jpg\n",
            "RASPUNS = 3.1\n",
            "TRUE = 7.3\n",
            "image_128.jpg\n",
            "RASPUNS = 8.8\n",
            "TRUE = 8.8\n",
            "image_129.jpg\n",
            "RASPUNS = 6.4\n",
            "TRUE = 6.4\n",
            "image_130.jpg\n",
            "RASPUNS = 4.3\n",
            "TRUE = 4.3\n",
            "image_131.jpg\n",
            "RASPUNS = 6.4\n",
            "TRUE = 6.4\n",
            "image_132.jpg\n",
            "RASPUNS = 4.3\n",
            "TRUE = 4.3\n",
            "image_133.jpg\n",
            "RASPUNS = 3.7\n",
            "TRUE = 3.7\n",
            "image_134.jpg\n",
            "RASPUNS = 6.4\n",
            "TRUE = 6.4\n",
            "image_135.jpg\n",
            "RASPUNS = 3.4\n",
            "TRUE = 7.6\n",
            "image_136.jpg\n",
            "RASPUNS = 3.4\n",
            "TRUE = 5.8\n",
            "image_137.jpg\n",
            "RASPUNS = 5.8\n",
            "TRUE = 5.8\n",
            "image_138.jpg\n",
            "RASPUNS = 6.7\n",
            "TRUE = 6.7\n",
            "image_139.jpg\n",
            "RASPUNS = 4.0\n",
            "TRUE = 4.6\n",
            "image_140.jpg\n",
            "RASPUNS = 7.6\n",
            "TRUE = 7.3\n",
            "image_141.jpg\n",
            "RASPUNS = 3.7\n",
            "TRUE = 5.5\n",
            "image_142.jpg\n",
            "RASPUNS = 4.6\n",
            "TRUE = 4.3\n",
            "image_143.jpg\n",
            "RASPUNS = 9.1\n",
            "TRUE = 6.4\n",
            "image_144.jpg\n",
            "RASPUNS = 3.4\n",
            "TRUE = 8.8\n",
            "image_145.jpg\n",
            "RASPUNS = 6.7\n",
            "TRUE = 6.7\n",
            "image_146.jpg\n",
            "RASPUNS = 4.6\n",
            "TRUE = 4.3\n",
            "image_147.jpg\n",
            "RASPUNS = 3.1\n",
            "TRUE = 5.8\n",
            "image_148.jpg\n",
            "RASPUNS = 7.3\n",
            "TRUE = 7.3\n",
            "image_149.jpg\n",
            "RASPUNS = 7.6\n",
            "TRUE = 7.6\n",
            "image_150.jpg\n",
            "RASPUNS = 6.4\n",
            "TRUE = 6.4\n",
            "ACC = 0.4066666666666667\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "idWnNKMKGDhT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#6\n",
        "def predict_grade_scenario_4(path):\n",
        "    try:\n",
        "        image = cv.imread(path)\n",
        "        orig_h, orig_w, _ = image.shape\n",
        "        image = image[int(orig_h * 0.29):int(orig_h * 0.33)]\n",
        "        image = image[:, int(orig_w * 0.08):int(orig_w * 0.29)]\n",
        "        orig_h, orig_w, _ = image.shape\n",
        "\n",
        "        img = cv.cvtColor(image, cv.COLOR_BGR2GRAY)\n",
        "        img = cv.GaussianBlur(img, (11, 11), 0)\n",
        "        th = cv.adaptiveThreshold(img,255,cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY,7,2)\n",
        "        th = cv.bitwise_not(th)\n",
        "        #cv2_imshow(th)\n",
        "\n",
        "        template = cv.imread(data_dir_drive + '/template_matching.JPG',0)\n",
        "        w, h = template.shape[::-1]\n",
        "        img = th.copy()\n",
        "        left_cut_size = int(orig_w * 0.25)\n",
        "        right_cut_size = int(orig_w * 0.55)\n",
        "        img = img[:, left_cut_size:right_cut_size]\n",
        "        method = eval('cv.TM_CCOEFF_NORMED')\n",
        "        # Apply template Matching\n",
        "        res = cv.matchTemplate(img,template, cv.TM_CCOEFF_NORMED)\n",
        "        min_val, max_val, min_loc, max_loc = cv.minMaxLoc(res)\n",
        "        top_left = (max_loc[0] + left_cut_size, max_loc[1])\n",
        "        bottom_right = (top_left[0] + w, top_left[1] + h)\n",
        "        #cv.rectangle(th,top_left, bottom_right, 255, 2)\n",
        "        #cv2_imshow(th)\n",
        "        new_th = th[:, :max_loc[0] + left_cut_size]\n",
        "        #cv2_imshow(new_th)\n",
        "        \n",
        "        template = cv.imread(data_dir_drive + '/template_matching2.JPG',0)\n",
        "        w, h = template.shape[::-1]\n",
        "        img = new_th.copy()\n",
        "        method = eval('cv.TM_CCOEFF_NORMED')\n",
        "        # Apply template Matching\n",
        "        res = cv.matchTemplate(img,template, cv.TM_CCOEFF_NORMED)\n",
        "        min_val, max_val, min_loc, max_loc = cv.minMaxLoc(res)\n",
        "        top_left = max_loc\n",
        "        bottom_right = (top_left[0] + w, top_left[1] + h)\n",
        "        #cv.rectangle(new_th,top_left, bottom_right, 255, 2)\n",
        "        #cv2_imshow(new_th)\n",
        "        new_th = new_th[:top_left[1], :]\n",
        "        #cv2_imshow(new_th)\n",
        "\n",
        "        new_th = cut_background(new_th, 0.5)\n",
        "        new_th = np.transpose(cut_background(np.transpose(new_th), 0.5))\n",
        "        new_th = np.fliplr(cut_background(np.fliplr(new_th), 0.1))\n",
        "        new_th = np.flipud(cut_background(np.flipud(new_th), 0.1))\n",
        "        #cv2_imshow(new_th)\n",
        "\n",
        "        pred1 = predict_first_digit(new_th)\n",
        "        #print(pred1)\n",
        "        pred2 = predict_second_digit(new_th, pred1)\n",
        "        #print(pred2)\n",
        "\n",
        "        return (10 * pred1 + pred2) / 10\n",
        "\n",
        "    except Exception as e:\n",
        "        print(e)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vo5Mf3ftmSw-",
        "colab_type": "code",
        "outputId": "d7b4e750-47dd-4744-a3f5-c2f17139086c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#7\n",
        "acc = 0\n",
        "for i in range(1, 151): \n",
        "    image_name = 'image_' + str(i) + '.jpg'\n",
        "    print(image_name)\n",
        "    # load image\n",
        "    grade = predict_grade_scenario_4(folder_in + image_name)\n",
        "\n",
        "    print(\"RASPUNS = \" + str(grade))\n",
        "    print(\"TRUE = \" + ground_truth_hw_dict[i])\n",
        "    if ground_truth_hw_dict[i] == str(grade):\n",
        "        acc += 1\n",
        "\n",
        "print(\"ACC = \" + str(acc / 150))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "image_1.jpg\n",
            "RASPUNS = 4.3\n",
            "TRUE = 4.9\n",
            "image_2.jpg\n",
            "RASPUNS = 6.4\n",
            "TRUE = 6.7\n",
            "image_3.jpg\n",
            "RASPUNS = 3.4\n",
            "TRUE = 5.8\n",
            "image_4.jpg\n",
            "RASPUNS = 8.8\n",
            "TRUE = 8.5\n",
            "image_5.jpg\n",
            "RASPUNS = 8.2\n",
            "TRUE = 4.9\n",
            "image_6.jpg\n",
            "RASPUNS = 6.7\n",
            "TRUE = 6.7\n",
            "image_7.jpg\n",
            "RASPUNS = 3.4\n",
            "TRUE = 9.1\n",
            "image_8.jpg\n",
            "RASPUNS = 7.3\n",
            "TRUE = 3.7\n",
            "image_9.jpg\n",
            "RASPUNS = 3.7\n",
            "TRUE = 5.5\n",
            "image_10.jpg\n",
            "RASPUNS = 3.7\n",
            "TRUE = 5.5\n",
            "image_11.jpg\n",
            "RASPUNS = 3.4\n",
            "TRUE = 4.6\n",
            "image_12.jpg\n",
            "RASPUNS = 6.7\n",
            "TRUE = 6.7\n",
            "image_13.jpg\n",
            "RASPUNS = 7.3\n",
            "TRUE = 7.3\n",
            "image_14.jpg\n",
            "RASPUNS = 3.4\n",
            "TRUE = 6.4\n",
            "image_15.jpg\n",
            "RASPUNS = 4.9\n",
            "TRUE = 7.0\n",
            "image_16.jpg\n",
            "RASPUNS = 7.6\n",
            "TRUE = 4.6\n",
            "image_17.jpg\n",
            "RASPUNS = 4.6\n",
            "TRUE = 7.6\n",
            "image_18.jpg\n",
            "RASPUNS = 6.4\n",
            "TRUE = 6.1\n",
            "image_19.jpg\n",
            "RASPUNS = 4.3\n",
            "TRUE = 4.9\n",
            "image_20.jpg\n",
            "RASPUNS = 7.6\n",
            "TRUE = 7.6\n",
            "image_21.jpg\n",
            "RASPUNS = 6.4\n",
            "TRUE = 6.4\n",
            "image_22.jpg\n",
            "RASPUNS = 3.7\n",
            "TRUE = 5.5\n",
            "image_23.jpg\n",
            "RASPUNS = 6.4\n",
            "TRUE = 6.4\n",
            "image_24.jpg\n",
            "RASPUNS = 4.3\n",
            "TRUE = 7.3\n",
            "image_25.jpg\n",
            "RASPUNS = 4.3\n",
            "TRUE = 7.9\n",
            "image_26.jpg\n",
            "RASPUNS = 8.2\n",
            "TRUE = 8.5\n",
            "image_27.jpg\n",
            "RASPUNS = 5.5\n",
            "TRUE = 5.5\n",
            "image_28.jpg\n",
            "RASPUNS = 4.3\n",
            "TRUE = 8.8\n",
            "image_29.jpg\n",
            "RASPUNS = 6.4\n",
            "TRUE = 6.1\n",
            "image_30.jpg\n",
            "RASPUNS = 4.3\n",
            "TRUE = 7.9\n",
            "image_31.jpg\n",
            "RASPUNS = 7.3\n",
            "TRUE = 4.9\n",
            "image_32.jpg\n",
            "RASPUNS = 3.1\n",
            "TRUE = 5.2\n",
            "image_33.jpg\n",
            "RASPUNS = 7.6\n",
            "TRUE = 7.6\n",
            "image_34.jpg\n",
            "RASPUNS = 5.8\n",
            "TRUE = 7.0\n",
            "image_35.jpg\n",
            "RASPUNS = 4.6\n",
            "TRUE = 7.0\n",
            "image_36.jpg\n",
            "RASPUNS = 6.4\n",
            "TRUE = 6.4\n",
            "image_37.jpg\n",
            "RASPUNS = 3.4\n",
            "TRUE = 8.8\n",
            "image_38.jpg\n",
            "RASPUNS = 6.7\n",
            "TRUE = 6.7\n",
            "image_39.jpg\n",
            "RASPUNS = 4.9\n",
            "TRUE = 7.0\n",
            "image_40.jpg\n",
            "RASPUNS = 5.8\n",
            "TRUE = 5.8\n",
            "image_41.jpg\n",
            "RASPUNS = 6.4\n",
            "TRUE = 7.6\n",
            "image_42.jpg\n",
            "RASPUNS = 3.7\n",
            "TRUE = 9.7\n",
            "image_43.jpg\n",
            "RASPUNS = 6.7\n",
            "TRUE = 6.7\n",
            "image_44.jpg\n",
            "RASPUNS = 6.4\n",
            "TRUE = 6.4\n",
            "image_45.jpg\n",
            "RASPUNS = 5.8\n",
            "TRUE = 5.5\n",
            "image_46.jpg\n",
            "RASPUNS = 4.3\n",
            "TRUE = 7.3\n",
            "image_47.jpg\n",
            "RASPUNS = 4.6\n",
            "TRUE = 8.8\n",
            "image_48.jpg\n",
            "RASPUNS = 6.1\n",
            "TRUE = 6.4\n",
            "image_49.jpg\n",
            "RASPUNS = 3.4\n",
            "TRUE = 5.8\n",
            "image_50.jpg\n",
            "RASPUNS = 4.3\n",
            "TRUE = 7.0\n",
            "image_51.jpg\n",
            "RASPUNS = 6.7\n",
            "TRUE = 8.2\n",
            "image_52.jpg\n",
            "RASPUNS = 4.0\n",
            "TRUE = 4.9\n",
            "image_53.jpg\n",
            "RASPUNS = 4.6\n",
            "TRUE = 4.3\n",
            "image_54.jpg\n",
            "RASPUNS = 8.8\n",
            "TRUE = 8.8\n",
            "image_55.jpg\n",
            "RASPUNS = 6.7\n",
            "TRUE = 8.2\n",
            "image_56.jpg\n",
            "RASPUNS = 5.2\n",
            "TRUE = 5.2\n",
            "image_57.jpg\n",
            "RASPUNS = 5.2\n",
            "TRUE = 5.2\n",
            "image_58.jpg\n",
            "RASPUNS = 4.3\n",
            "TRUE = 5.5\n",
            "image_59.jpg\n",
            "RASPUNS = 3.1\n",
            "TRUE = 7.0\n",
            "image_60.jpg\n",
            "RASPUNS = 5.8\n",
            "TRUE = 7.9\n",
            "image_61.jpg\n",
            "RASPUNS = 7.3\n",
            "TRUE = 4.9\n",
            "image_62.jpg\n",
            "RASPUNS = 6.4\n",
            "TRUE = 4.6\n",
            "image_63.jpg\n",
            "RASPUNS = 5.2\n",
            "TRUE = 5.2\n",
            "image_64.jpg\n",
            "RASPUNS = 7.6\n",
            "TRUE = 7.6\n",
            "image_65.jpg\n",
            "RASPUNS = 3.4\n",
            "TRUE = 7.0\n",
            "image_66.jpg\n",
            "RASPUNS = 5.8\n",
            "TRUE = 9.4\n",
            "image_67.jpg\n",
            "RASPUNS = 4.6\n",
            "TRUE = 7.3\n",
            "image_68.jpg\n",
            "RASPUNS = 4.6\n",
            "TRUE = 4.6\n",
            "image_69.jpg\n",
            "RASPUNS = 4.3\n",
            "TRUE = 4.3\n",
            "image_70.jpg\n",
            "RASPUNS = 5.5\n",
            "TRUE = 4.0\n",
            "image_71.jpg\n",
            "RASPUNS = 6.4\n",
            "TRUE = 6.4\n",
            "image_72.jpg\n",
            "RASPUNS = 4.3\n",
            "TRUE = 4.9\n",
            "image_73.jpg\n",
            "RASPUNS = 5.5\n",
            "TRUE = 5.5\n",
            "image_74.jpg\n",
            "RASPUNS = 5.5\n",
            "TRUE = 9.4\n",
            "image_75.jpg\n",
            "RASPUNS = 4.6\n",
            "TRUE = 4.0\n",
            "image_76.jpg\n",
            "RASPUNS = 6.1\n",
            "TRUE = 6.1\n",
            "image_77.jpg\n",
            "RASPUNS = 7.3\n",
            "TRUE = 7.3\n",
            "image_78.jpg\n",
            "RASPUNS = 4.3\n",
            "TRUE = 4.0\n",
            "image_79.jpg\n",
            "RASPUNS = 5.5\n",
            "TRUE = 5.5\n",
            "image_80.jpg\n",
            "RASPUNS = 4.9\n",
            "TRUE = 5.5\n",
            "image_81.jpg\n",
            "RASPUNS = 5.8\n",
            "TRUE = 8.5\n",
            "image_82.jpg\n",
            "RASPUNS = 4.9\n",
            "TRUE = 4.9\n",
            "image_83.jpg\n",
            "RASPUNS = 6.7\n",
            "TRUE = 6.7\n",
            "image_84.jpg\n",
            "RASPUNS = 3.7\n",
            "TRUE = 5.2\n",
            "image_85.jpg\n",
            "RASPUNS = 4.6\n",
            "TRUE = 7.6\n",
            "image_86.jpg\n",
            "RASPUNS = 7.3\n",
            "TRUE = 4.9\n",
            "image_87.jpg\n",
            "RASPUNS = 5.5\n",
            "TRUE = 5.5\n",
            "image_88.jpg\n",
            "RASPUNS = 8.2\n",
            "TRUE = 9.1\n",
            "image_89.jpg\n",
            "RASPUNS = 6.4\n",
            "TRUE = 6.4\n",
            "image_90.jpg\n",
            "RASPUNS = 6.4\n",
            "TRUE = 6.1\n",
            "image_91.jpg\n",
            "RASPUNS = 4.6\n",
            "TRUE = 6.1\n",
            "image_92.jpg\n",
            "RASPUNS = 4.3\n",
            "TRUE = 5.5\n",
            "image_93.jpg\n",
            "RASPUNS = 8.8\n",
            "TRUE = 4.6\n",
            "image_94.jpg\n",
            "RASPUNS = 7.3\n",
            "TRUE = 7.9\n",
            "image_95.jpg\n",
            "RASPUNS = 4.6\n",
            "TRUE = 4.6\n",
            "image_96.jpg\n",
            "RASPUNS = 9.7\n",
            "TRUE = 6.7\n",
            "image_97.jpg\n",
            "RASPUNS = 5.5\n",
            "TRUE = 5.5\n",
            "image_98.jpg\n",
            "RASPUNS = 5.5\n",
            "TRUE = 5.5\n",
            "image_99.jpg\n",
            "RASPUNS = 3.7\n",
            "TRUE = 7.3\n",
            "image_100.jpg\n",
            "RASPUNS = 4.3\n",
            "TRUE = 4.9\n",
            "image_101.jpg\n",
            "RASPUNS = 4.9\n",
            "TRUE = 4.9\n",
            "image_102.jpg\n",
            "RASPUNS = 9.7\n",
            "TRUE = 5.2\n",
            "image_103.jpg\n",
            "RASPUNS = 5.5\n",
            "TRUE = 5.5\n",
            "image_104.jpg\n",
            "RASPUNS = 5.2\n",
            "TRUE = 5.2\n",
            "image_105.jpg\n",
            "RASPUNS = 3.1\n",
            "TRUE = 4.0\n",
            "image_106.jpg\n",
            "RASPUNS = 6.4\n",
            "TRUE = 6.4\n",
            "image_107.jpg\n",
            "RASPUNS = 3.4\n",
            "TRUE = 9.7\n",
            "image_108.jpg\n",
            "RASPUNS = 4.6\n",
            "TRUE = 8.8\n",
            "image_109.jpg\n",
            "RASPUNS = 3.4\n",
            "TRUE = 5.8\n",
            "image_110.jpg\n",
            "RASPUNS = 4.3\n",
            "TRUE = 7.3\n",
            "image_111.jpg\n",
            "RASPUNS = 9.4\n",
            "TRUE = 6.1\n",
            "image_112.jpg\n",
            "RASPUNS = 4.3\n",
            "TRUE = 4.3\n",
            "image_113.jpg\n",
            "RASPUNS = 6.4\n",
            "TRUE = 6.1\n",
            "image_114.jpg\n",
            "RASPUNS = 6.4\n",
            "TRUE = 6.4\n",
            "image_115.jpg\n",
            "RASPUNS = 5.2\n",
            "TRUE = 5.2\n",
            "image_116.jpg\n",
            "RASPUNS = 6.4\n",
            "TRUE = 6.4\n",
            "image_117.jpg\n",
            "RASPUNS = 4.9\n",
            "TRUE = 4.9\n",
            "image_118.jpg\n",
            "RASPUNS = 3.7\n",
            "TRUE = 3.7\n",
            "image_119.jpg\n",
            "RASPUNS = 5.8\n",
            "TRUE = 5.8\n",
            "image_120.jpg\n",
            "RASPUNS = 4.6\n",
            "TRUE = 4.6\n",
            "image_121.jpg\n",
            "RASPUNS = 6.7\n",
            "TRUE = 6.7\n",
            "image_122.jpg\n",
            "RASPUNS = 5.8\n",
            "TRUE = 5.5\n",
            "image_123.jpg\n",
            "RASPUNS = 8.2\n",
            "TRUE = 4.0\n",
            "image_124.jpg\n",
            "RASPUNS = 8.8\n",
            "TRUE = 8.8\n",
            "image_125.jpg\n",
            "RASPUNS = 6.4\n",
            "TRUE = 6.4\n",
            "image_126.jpg\n",
            "RASPUNS = 4.3\n",
            "TRUE = 4.3\n",
            "image_127.jpg\n",
            "RASPUNS = 3.1\n",
            "TRUE = 7.3\n",
            "image_128.jpg\n",
            "RASPUNS = 8.8\n",
            "TRUE = 8.8\n",
            "image_129.jpg\n",
            "RASPUNS = 6.4\n",
            "TRUE = 6.4\n",
            "image_130.jpg\n",
            "RASPUNS = 4.3\n",
            "TRUE = 4.3\n",
            "image_131.jpg\n",
            "RASPUNS = 6.4\n",
            "TRUE = 6.4\n",
            "image_132.jpg\n",
            "RASPUNS = 4.3\n",
            "TRUE = 4.3\n",
            "image_133.jpg\n",
            "RASPUNS = 3.7\n",
            "TRUE = 3.7\n",
            "image_134.jpg\n",
            "RASPUNS = 6.4\n",
            "TRUE = 6.4\n",
            "image_135.jpg\n",
            "RASPUNS = 3.4\n",
            "TRUE = 7.6\n",
            "image_136.jpg\n",
            "RASPUNS = 3.4\n",
            "TRUE = 5.8\n",
            "image_137.jpg\n",
            "RASPUNS = 5.8\n",
            "TRUE = 5.8\n",
            "image_138.jpg\n",
            "RASPUNS = 6.7\n",
            "TRUE = 6.7\n",
            "image_139.jpg\n",
            "RASPUNS = 4.0\n",
            "TRUE = 4.6\n",
            "image_140.jpg\n",
            "RASPUNS = 7.6\n",
            "TRUE = 7.3\n",
            "image_141.jpg\n",
            "RASPUNS = 3.7\n",
            "TRUE = 5.5\n",
            "image_142.jpg\n",
            "RASPUNS = 4.6\n",
            "TRUE = 4.3\n",
            "image_143.jpg\n",
            "RASPUNS = 9.1\n",
            "TRUE = 6.4\n",
            "image_144.jpg\n",
            "RASPUNS = 3.4\n",
            "TRUE = 8.8\n",
            "image_145.jpg\n",
            "RASPUNS = 6.7\n",
            "TRUE = 6.7\n",
            "image_146.jpg\n",
            "RASPUNS = 4.6\n",
            "TRUE = 4.3\n",
            "image_147.jpg\n",
            "RASPUNS = 3.1\n",
            "TRUE = 5.8\n",
            "image_148.jpg\n",
            "RASPUNS = 7.3\n",
            "TRUE = 7.3\n",
            "image_149.jpg\n",
            "RASPUNS = 7.6\n",
            "TRUE = 7.6\n",
            "image_150.jpg\n",
            "RASPUNS = 6.4\n",
            "TRUE = 6.4\n",
            "ACC = 0.4066666666666667\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GYDYGR45Hyio",
        "colab_type": "text"
      },
      "source": [
        "# **Running the entire code**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zc3YpOgDIz_z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#1\n",
        "def run_scenario_1():\n",
        "    ans = {}\n",
        "    for f in glob.iglob(input_folder_scanned + '*.jpg'):\n",
        "        aux = f.replace('.jpg', '')[-2:]\n",
        "        subject, number = aux[0], int(aux[1])\n",
        "        grade = predict_grade_scenario_1(f, subject, number)\n",
        "        ans[f.replace(input_folder_scanned, '')] = grade\n",
        "    \n",
        "    f = open(output_folder + 'lupascu_marian_407_task1.txt', 'w')\n",
        "    for k, v in ans.items():\n",
        "        f.write(\"%s\\t%2.2f\\n\" % (k, v))\n",
        "    f.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g_5f1AK1JXFw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#2\n",
        "def run_scenario_2():\n",
        "    ans = {}\n",
        "    for f in glob.iglob(input_folder_rotated_and_perspective + '*.jpg'):\n",
        "        aux = f.replace('.jpg', '')[-2:]\n",
        "        subject, number = aux[0], int(aux[1])\n",
        "        grade = predict_grade_scenario_2(f, subject, number)\n",
        "        ans[f.replace(input_folder_rotated_and_perspective, '')] = grade\n",
        "    \n",
        "    f = open(output_folder + 'lupascu_marian_407_task2.txt', 'w')\n",
        "    for k, v in ans.items():\n",
        "        f.write(\"%s\\t%2.2f\\n\" % (k, v))\n",
        "    f.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3uT1fTltJaZc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#3\n",
        "def run_scenario_3():\n",
        "    ans = {}\n",
        "    for f in glob.iglob(input_folder_no_annotation + '*.jpg'):\n",
        "        grade, _, _, _ = predict_grade_scenario_3(f)\n",
        "        ans[f.replace(input_folder_no_annotation, '')] = grade\n",
        "    \n",
        "    f = open(output_folder + 'lupascu_marian_407_task3.txt', 'w')\n",
        "    for k, v in ans.items():\n",
        "        f.write(\"%s\\t%2.2f\\n\" % (k, v))\n",
        "    f.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hBxWkYsQJavn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#4\n",
        "def run_scenario_4():\n",
        "    ans = {}\n",
        "    for f in glob.iglob(input_folder_handwritten + '*.jpg'):\n",
        "        grade = predict_grade_scenario_4(f)\n",
        "        ans[f.replace(input_folder_handwritten, '')] = grade\n",
        "    \n",
        "    f = open(output_folder + 'lupascu_marian_407_task4.txt', 'w')\n",
        "    for k, v in ans.items():\n",
        "        f.write(\"%s\\t%2.2f\\n\" % (k, v))\n",
        "    f.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OWIVgEqgIp99",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#5\n",
        "def run():\n",
        "    for f in glob.iglob(output_folder + '*'):\n",
        "        os.remove(f)\n",
        "\n",
        "    run_scenario_1()\n",
        "    run_scenario_2()\n",
        "    run_scenario_3()\n",
        "    run_scenario_4()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VFkcuuUJIoKq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#6\n",
        "run()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yhnAGE0m09EV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#7\n",
        "run_scenario_1()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pb4uE7VZ09gM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#8\n",
        "run_scenario_2()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qsb6Tsce095M",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#9\n",
        "run_scenario_3()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zrpQp2aM0-St",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#10\n",
        "run_scenario_4()"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}