covid19_hackathon.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "covid19 hackathon.ipynb",
      "provenance": [],
      "mount_file_id": "1l8c9EGhpPkcqhdYGRuvCO28LXGIQpSI4",
      "authorship_tag": "ABX9TyMruiKGyDlrB+bO8zBQ71ec",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/GmBeHappy/AIAT-AI-HACKATHON-COVID-19-recognize-x-ray/blob/main/covid19_hackathon.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SIEEHazelp-P"
      },
      "source": [
        "import cv2\n",
        "#อิมพอร์ต cv2 ที่มาจาก OpenCV\n",
        "import matplotlib.pyplot as plt\n",
        "#อิมพอร์ต pyplot เข้ามาในชื่อ plt\n",
        "from imutils import paths"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-4h9NDcFmGWV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "outputId": "52ddac58-dec6-444e-a131-b2c6c31c5888"
      },
      "source": [
        "image_paths = list(paths.list_images(\"drive/My Drive/all_data_added\"))\n",
        "#อ่านรูปาภาพสัตว์ทั้งหมดจากโฟลเดอร์\n",
        "image = cv2.imread(image_paths[10])\n",
        "#ลองใช้ cv2 อ่านรูปขึ้นมาสักรูป\n",
        "new_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
        "#หลังจากอ่านมาเสร็จแล้ว เราต้องแปลงสีก่อนเพราะ cv2 นั้นเมื่ออ่านรูปเข้ามา จะมีค่าพื้นฐานของสีเป็น BGR ซึ่งสีที่เราใช้ๆกันอยู่อย่างคุ้นตาคือ RGB เพราะฉะนั้นเราต้องแปลงก่อน\n",
        "plt.figure()\n",
        "#เราใช้ pyplot ในการโชว์รูปขึ้นมา แต่ก่อนที่เราจะโชว์ขึ้นมาได้เราต้องสร้างตัว figure ขึ้นมาเสียก่อน เหมือนกับการสร้างหน้าต่างโปรแกรมขึ้นมาเพื่อที่จะให้เอารูปที่จะโชว์ไปแปะ\n",
        "plt.imshow(new_image)\n",
        "#ใช้ฟังก์ชั่น imshow ในการโชว์รูปออกมา"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fdbd27e1940>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0-MtcRx-mbel"
      },
      "source": [
        "import random\n",
        "#อิมพอร์ต random เข้ามาเพื่อที่เราจะใช้ฟังก์ชั่น shuffle\n",
        "import numpy as np\n",
        "#อิมพอร์ต numpy เข้ามาในชื่อ np\n",
        "random.shuffle(image_paths)\n",
        "#ใช้ฟังก์ชั่น shuffle ในการสลับรูปภาพเพื่อให้ภาพมันไม่เรียงติดกันเหมือนเดิมในทุกครั้ง\n",
        "data = []\n",
        "labels = []\n",
        "#สร้างตัวแปรอาเรย์ไว้รอเก็บค่าจากการแปลงรูปเป็นข้อมูล โดย data จะใช้เก็บข้อมูลเทรน ส่วน labels จะใช้เก็บเป็นข้อมูลสอน/คำตอบ\n",
        "for image_path in image_paths:\n",
        "#วนลูปเพื่อทำการแปลงค่าจากรูปภาพเป็นข้อมูล\n",
        "    image = cv2.imread(image_path)\n",
        "    #อ่านรูปจาก image_path\n",
        "    image = cv2.resize(image, (32, 32))\n",
        "    #ปรับไซส์ให้รูปภาพมีคุณภาพต่ำลง(ตอนเทรนนิ่งข้อมูล คอมเราจะได้ไม่ทำงานหนักเกินไป)\n",
        "    labels.append(image_path.split(\"/\")[-2])\n",
        "    #เอาชื่อ image_path ของแต่ละรูปมาเก็บค่าลงตัวแปรอาเรย์ labels\n",
        "    data.append(image)\n",
        "    #เอาข้อมูลรูปภาพใส่ตัวแปรอาเรย์ data เก็บไว้\n",
        "data = np.array(data)\n",
        "labels = np.array(labels)\n",
        "from sklearn.preprocessing import LabelBinarizer\n",
        "lb = LabelBinarizer()\n",
        "#อิมพอร์ต LabelBinarizer ของ sklearn มาใช้\n",
        "labels = lb.fit_transform(labels)\n",
        "#เรียกใช้ฟังก์ชั่น fit_transform เพื่อแปลง labels เป็นเลขฐานสอง\n",
        "\n",
        "from keras import backend\n",
        "from keras.models import Sequential\n",
        "from keras.layers.convolutional import Conv2D\n",
        "from keras.layers.core import Dense, Flatten\n",
        "from sklearn.model_selection import train_test_split\n",
        "#อิมพอร์ตพวกของต่างๆที่จะต้องใช้จาก keras\n",
        "X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size=0.1, random_state=5)\n",
        "#เรียกใช้ฟังก์ชั่น train_test_split เพื่อทำการสร้างข้อมูลเทรนกับเทส\n",
        "model = Sequential()\n",
        "#ใช้โมเดล Sequential ของ keras\n",
        "model.add(Conv2D(32, (3, 3), input_shape=(32, 32 ,3), activation='relu', padding='same'))\n",
        "#ใข้คำสั่ง model.add() เพิ่มชั้น(Layer)ให้กับโมเดลโดยให้ชั้นนี้เป็น convolution layer ด้วยฟังก์ชั่น Conv2D โดยค่าพารามิเตอร์แรกคือ filters มีค่าเป็น 32 คือบอกว่า คือจำนวนฟิลเตอร์ทั้งหมดของเลเยอร์นี้มี 32 ชั้น, พารามิเตอร์ตัวที่สองคือ kernel_size มีค่าเป็น 3x3 และ input_shape=(32, 32 ,3) คือรูปขนาด 32x32 และมี 3 channels ในนี้ก็คือ RGB, activation function เป็น relu, และ padding เซ็ทไว้เป็น same  เพื่อให้ข้อมูลที่ส่งออกมีขนาดเท่าเดิม (ไม่ให้ output ที่ได้มีขนาดเล็กลง)\n",
        "model.add(Flatten())\n",
        "#เพิ่มชั้น(Layer) ที่เป็น flatten ให้กับโมเดลเพื่อแปลงข้อมูลจากภาพให้เป็นเวคเตอร์ ที่จะสามารถส่งต่อให้ชั้นต่อไปได้ (ง่ายๆคือเอาข้อมูลอาร์เรย์ขนาดหลายมิติจากชั้นที่แล้ว มาตีแผ่ออกเหลือแค่มิติเดียว)\n",
        "model.add(Dense(3, activation='softmax'))\n",
        "#เพิ่มชั้น(Layer) ที่เป็น dense ให้กับโมเดลซึ่งก็คือชั้นที่เป็น fully connected นั่นเอง และใช้ activation function เป็น softmax และมี output เป็นอาร์เรย์ขนาด (*, 3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h8nvl1MNnRLg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "1bcea2f3-0ac1-4b5d-8d13-cf33f68e1511"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_7\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_9 (Conv2D)            (None, 32, 32, 32)        896       \n",
            "_________________________________________________________________\n",
            "flatten_7 (Flatten)          (None, 32768)             0         \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 3)                 98307     \n",
            "=================================================================\n",
            "Total params: 99,203\n",
            "Trainable params: 99,203\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AGg6zY-mojIu"
      },
      "source": [
        "model.compile(loss='categorical_crossentropy', optimizer='adam')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3kAYVwZ-pcp4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "outputId": "5f4d95fc-f1ba-4a46-8d36-1a1cb5779348"
      },
      "source": [
        "model.fit(X_train, y_train, epochs=10, validation_split=0.2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 8618 samples, validate on 2155 samples\n",
            "Epoch 1/10\n",
            "8618/8618 [==============================] - 7s 768us/step - loss: 9.5384 - val_loss: 1.3855\n",
            "Epoch 2/10\n",
            "8618/8618 [==============================] - 7s 765us/step - loss: 1.0385 - val_loss: 1.3224\n",
            "Epoch 3/10\n",
            "8618/8618 [==============================] - 7s 756us/step - loss: 0.8804 - val_loss: 1.3974\n",
            "Epoch 4/10\n",
            "8618/8618 [==============================] - 7s 764us/step - loss: 0.7779 - val_loss: 1.4736\n",
            "Epoch 5/10\n",
            "8618/8618 [==============================] - 7s 764us/step - loss: 0.7065 - val_loss: 1.5874\n",
            "Epoch 6/10\n",
            "8618/8618 [==============================] - 7s 760us/step - loss: 0.6472 - val_loss: 1.7535\n",
            "Epoch 7/10\n",
            "8618/8618 [==============================] - 7s 763us/step - loss: 0.6273 - val_loss: 1.8749\n",
            "Epoch 8/10\n",
            "8618/8618 [==============================] - 7s 764us/step - loss: 0.5796 - val_loss: 2.1254\n",
            "Epoch 9/10\n",
            "8618/8618 [==============================] - 7s 767us/step - loss: 0.5272 - val_loss: 2.0953\n",
            "Epoch 10/10\n",
            "8618/8618 [==============================] - 7s 771us/step - loss: 0.4841 - val_loss: 2.3141\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.callbacks.History at 0x7fdbd26f1f98>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jSqKSVJZsZgo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "153b2f0f-69cd-4ad3-91ec-89e3ff88d091"
      },
      "source": [
        "y_pred = model.predict(X_test)\n",
        "#หลังจากที่เราเทรนตัวโมเดลเราเสร็จ คราวนี้เราจะใช้ข้อมูลเทสมาลองให้โมเดลมัน classified โดยใช้ฟังก์ชั่น predict\n",
        "from sklearn.metrics import accuracy_score, classification_report #อิมพอร์ต accuracy_score กับ classification_report จาก sklearn.metrics \n",
        "accuracy_score(y_test.argmax(axis=1), y_pred.argmax(axis=1))\n",
        "#จากนั้นมาเรียกใช้ฟังก์ชั่น accuracy_score เพื่อดูความแม่นของข้อมูลที่ predict กัน"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4682804674457429"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sPXOvoL2srAH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209
        },
        "outputId": "f445486e-ed4e-4060-cb77-bfa77105a8f3"
      },
      "source": [
        "print(classification_report(y_test.argmax(axis=1), y_pred.argmax(axis=1), target_names=lb.classes_))\n",
        "#เราลองมาดูผลการ classification อย่างละเอียดกัน"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "       covid       0.49      0.61      0.54       351\n",
            "      normal       0.45      0.45      0.45       412\n",
            "   pneumonia       0.47      0.37      0.41       435\n",
            "\n",
            "    accuracy                           0.47      1198\n",
            "   macro avg       0.47      0.48      0.47      1198\n",
            "weighted avg       0.47      0.47      0.46      1198\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mDWDLvFttG9R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 651
        },
        "outputId": "f2ac4521-05a1-48c7-99df-0bf781813d63"
      },
      "source": [
        "model_2 = Sequential()\n",
        "model_2.add(Conv2D(32, (3, 3), input_shape=(32, 32 ,3), activation='relu', padding='same'))\n",
        "model_2.add(Conv2D(64, (3, 3), activation='relu', padding='same'))\n",
        "model_2.add(Conv2D(64, (3, 3), activation='relu', padding='same'))\n",
        "model_2.add(Flatten())\n",
        "model_2.add(Dense(3, activation='softmax'))\n",
        "model_2.compile(loss='categorical_crossentropy', optimizer='adam')\n",
        "model_2.fit(X_train, y_train, epochs=10, validation_split=0.2)\n",
        "y_pred = model_2.predict(X_test)\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "print(accuracy_score(y_test.argmax(axis=1), y_pred.argmax(axis=1)))\n",
        "print(classification_report(y_test.argmax(axis=1), y_pred.argmax(axis=1), target_names=lb.classes_))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 435 samples, validate on 109 samples\n",
            "Epoch 1/10\n",
            "435/435 [==============================] - 4s 10ms/step - loss: 41.3961 - val_loss: 1.1039\n",
            "Epoch 2/10\n",
            "435/435 [==============================] - 4s 10ms/step - loss: 1.0180 - val_loss: 1.0658\n",
            "Epoch 3/10\n",
            "435/435 [==============================] - 4s 10ms/step - loss: 0.8891 - val_loss: 1.0927\n",
            "Epoch 4/10\n",
            "435/435 [==============================] - 4s 10ms/step - loss: 0.7538 - val_loss: 1.0801\n",
            "Epoch 5/10\n",
            "435/435 [==============================] - 4s 10ms/step - loss: 0.6475 - val_loss: 1.2755\n",
            "Epoch 6/10\n",
            "435/435 [==============================] - 4s 10ms/step - loss: 0.5568 - val_loss: 1.3629\n",
            "Epoch 7/10\n",
            "435/435 [==============================] - 4s 10ms/step - loss: 0.4289 - val_loss: 1.5025\n",
            "Epoch 8/10\n",
            "435/435 [==============================] - 4s 10ms/step - loss: 0.3434 - val_loss: 1.5063\n",
            "Epoch 9/10\n",
            "435/435 [==============================] - 4s 10ms/step - loss: 0.2640 - val_loss: 1.8196\n",
            "Epoch 10/10\n",
            "435/435 [==============================] - 4s 10ms/step - loss: 0.1882 - val_loss: 2.0233\n",
            "0.6397058823529411\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       covid       0.73      0.80      0.76        40\n",
            "      normal       0.70      0.51      0.59        51\n",
            "   pneumonia       0.53      0.64      0.58        45\n",
            "\n",
            "    accuracy                           0.64       136\n",
            "   macro avg       0.65      0.65      0.64       136\n",
            "weighted avg       0.65      0.64      0.64       136\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jTV0Lnxj3FaH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 56
        },
        "outputId": "e3241e1e-ee34-4f4f-e37f-eaf0f93e0880"
      },
      "source": [
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "import csv\n",
        "test_paths = list(paths.list_images(\"drive/My Drive/test_release\"))\n",
        "data2 = []\n",
        "#สร้างตัวแปรอาเรย์ไว้รอเก็บค่าจากการแปลงรูปเป็นข้อมูล โดย data จะใช้เก็บข้อมูลเทรน ส่วน labels จะใช้เก็บเป็นข้อมูลสอน/คำตอบ\n",
        "for test_path in test_paths:\n",
        "#วนลูปเพื่อทำการแปลงค่าจากรูปภาพเป็นข้อมูล\n",
        "    image = cv2.imread(test_path)\n",
        "    #อ่านรูปจาก image_path\n",
        "    image = cv2.resize(image, (32, 32))\n",
        "    #ปรับไซส์ให้รูปภาพมีคุณภาพต่ำลง(ตอนเทรนนิ่งข้อมูล คอมเราจะได้ไม่ทำงานหนักเกินไป)\n",
        "    data2.append(image)\n",
        "    #เอาข้อมูลรูปภาพใส่ตัวแปรอาเรย์ data เก็บไว้\n",
        "datatest = np.array(data2)\n",
        "\n",
        "y_pred = model_2.predict_classes(datatest)\n",
        "num=1\n",
        "\n",
        "def sendout():\n",
        "  row_list = [[\"Id\", \"Predicted\"]]\n",
        "  for i in range(len(datatest)):\n",
        "    #print(\"%s=%s\" % (i+1,y_pred[i]))\n",
        "    if(i+1<10):\n",
        "      dt=\"00\"+str(i+1)\n",
        "    elif(i+1<100):\n",
        "      dt=\"0\"+str(i+1)\n",
        "    else:\n",
        "      dt=str(i+1)\n",
        "    row_list.append([\"test\"+dt,str(y_pred[i])])\n",
        "  return row_list\n",
        "#sendout()\n",
        "print(sendout())\n",
        "\n",
        "csv.register_dialect('myDialect',\n",
        "                     delimiter=',',\n",
        "                     quoting=csv.QUOTE_ALL)\n",
        "with open('finallyyeah.csv', 'w', newline='') as file:\n",
        "    writer = csv.writer(file, dialect='myDialect')\n",
        "    writer.writerows(sendout())\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[['Id', 'Predicted'], ['test001', '1'], ['test002', '2'], ['test003', '1'], ['test004', '2'], ['test005', '2'], ['test006', '0'], ['test007', '1'], ['test008', '2'], ['test009', '1'], ['test010', '1'], ['test011', '2'], ['test012', '2'], ['test013', '2'], ['test014', '0'], ['test015', '0'], ['test016', '2'], ['test017', '2'], ['test018', '0'], ['test019', '2'], ['test020', '2'], ['test021', '1'], ['test022', '0'], ['test023', '1'], ['test024', '2'], ['test025', '0'], ['test026', '0'], ['test027', '2'], ['test028', '0'], ['test029', '1'], ['test030', '1'], ['test031', '2'], ['test032', '2'], ['test033', '1'], ['test034', '1'], ['test035', '0'], ['test036', '0'], ['test037', '0'], ['test038', '2'], ['test039', '1'], ['test040', '0'], ['test041', '2'], ['test042', '2'], ['test043', '2'], ['test044', '1'], ['test045', '2'], ['test046', '2'], ['test047', '2'], ['test048', '1'], ['test049', '1'], ['test050', '0'], ['test051', '1'], ['test052', '2'], ['test053', '2'], ['test054', '1'], ['test055', '2'], ['test056', '2'], ['test057', '2'], ['test058', '2'], ['test059', '0'], ['test060', '0'], ['test061', '0'], ['test062', '2'], ['test063', '0'], ['test064', '2'], ['test065', '0'], ['test066', '0'], ['test067', '1'], ['test068', '2'], ['test069', '1'], ['test070', '0'], ['test071', '2'], ['test072', '1'], ['test073', '0'], ['test074', '0'], ['test075', '0'], ['test076', '1'], ['test077', '2'], ['test078', '2'], ['test079', '0'], ['test080', '0'], ['test081', '1'], ['test082', '0'], ['test083', '2'], ['test084', '2'], ['test085', '0']]\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}