RREM 3 - Test del modello.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "2e405ef6",
   "metadata": {
    "papermill": {
     "duration": 0.008404,
     "end_time": "2022-06-14T16:09:02.336781",
     "exception": false,
     "start_time": "2022-06-14T16:09:02.328377",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Riconoscitore e risolutore di espressioni matematiche scritte a mano 3/3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9ec2cb6",
   "metadata": {},
   "source": [
    "![Logo Sapienza](logo.png \"Logo Sapienza\")\n",
    "\n",
    "Frasca Emanuele - 1836098"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9796156c",
   "metadata": {
    "papermill": {
     "duration": 0.008939,
     "end_time": "2022-06-14T16:09:02.355225",
     "exception": false,
     "start_time": "2022-06-14T16:09:02.346286",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Caricamento delle librerie"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f0243fd",
   "metadata": {},
   "source": [
    "Carichiamo le librerie che utilizzeremo nel progetto tra cui Numpy, Pandas, Matplotlib, OpenCV2, Sympy e Tensorflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "8298a04c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-14T16:09:02.375875Z",
     "iopub.status.busy": "2022-06-14T16:09:02.375359Z",
     "iopub.status.idle": "2022-06-14T16:09:07.920733Z",
     "shell.execute_reply": "2022-06-14T16:09:07.919085Z"
    },
    "papermill": {
     "duration": 5.559084,
     "end_time": "2022-06-14T16:09:07.923704",
     "exception": false,
     "start_time": "2022-06-14T16:09:02.364620",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sto per caricare le librerie necessarie...\n",
      "Sto per caricare Numpy, Pandas e Matplotlib...\n",
      "Sto per caricare os...\n",
      "Sto per caricare re, math e sympy...\n",
      "Sto per caricare PIL e OpenCV...\n",
      "Sto per caricare Tensorflow e Keras...\n",
      "Controllo se sono disponibili delle GPU per il training del modello:\n",
      "Non sono disponibili GPU\n",
      "Fatto\n"
     ]
    }
   ],
   "source": [
    "print(\"Sto per caricare le librerie necessarie...\")\n",
    "\n",
    "print(\"Sto per caricare Numpy, Pandas e Matplotlib...\")\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "%matplotlib inline\n",
    "\n",
    "print(\"Sto per caricare os...\")\n",
    "import os\n",
    "from os import listdir\n",
    "from os.path import isfile, join\n",
    "\n",
    "print(\"Sto per caricare re, math e sympy...\")\n",
    "import re\n",
    "import math\n",
    "from sympy import symbols, Eq, solve\n",
    "from sympy.parsing.sympy_parser import parse_expr\n",
    "from sympy import *\n",
    "\n",
    "print(\"Sto per caricare PIL e OpenCV...\")\n",
    "import cv2\n",
    "from PIL import Image\n",
    "\n",
    "print(\"Sto per caricare Tensorflow e Keras...\")\n",
    "import keras\n",
    "from keras import optimizers\n",
    "from keras import backend as K\n",
    "from keras.models import Model, Sequential, load_model\n",
    "from keras.layers import *\n",
    "from keras.layers import Input, Dense, Dropout, Flatten\n",
    "from keras.utils import np_utils\n",
    "from keras.layers.convolutional import Conv2D, MaxPooling2D\n",
    "from keras.utils.np_utils import to_categorical\n",
    "from keras.utils.vis_utils import plot_model\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "K.image_data_format()\n",
    "\n",
    "import tkinter as tk\n",
    "from PIL import Image, ImageGrab, ImageTk\n",
    "\n",
    "\n",
    "print(\"Controllo se sono disponibili delle GPU per il training del modello:\")\n",
    "gpu = tf.config.list_physical_devices('GPU')\n",
    "if len(gpu) == 0: print(\"Non sono disponibili GPU\") \n",
    "else: print(gpu)\n",
    "\n",
    "\n",
    "print(\"Fatto\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inserimento espressione"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Utilizziamo Tkinker per permettere l'inserimento manuale di un'espressione matematica utilizzando un mouse/tavoletta grafica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class App:\n",
    "    def __init__(self, master, width, height):\n",
    "        self.master = master\n",
    "        self.width = width\n",
    "        self.height = height\n",
    "\n",
    "        # Creazione del canvas\n",
    "        self.canvas = tk.Canvas(self.master, width=self.width, height=self.height, bg=\"white\")\n",
    "        self.canvas.grid(row=0, column=0, columnspan=2)\n",
    "\n",
    "        # Associazione degli eventi del mouse\n",
    "        self.canvas.bind(\"<Button-1>\", self.on_button_press)\n",
    "        self.canvas.bind(\"<B1-Motion>\", self.on_move_press)\n",
    "\n",
    "        # Creazione dei pulsanti\n",
    "        self.clear_button = tk.Button(self.master, text=\"Clear\", command=self.clear_canvas)\n",
    "        self.clear_button.grid(row=1, column=0, sticky=\"ew\")\n",
    "        self.ok_button = tk.Button(self.master, text=\"OK\", command=self.save_canvas)\n",
    "        self.ok_button.grid(row=1, column=1, sticky=\"ew\")\n",
    "\n",
    "        # Variabili per tenere traccia del disegno\n",
    "        self.start_x = None\n",
    "        self.start_y = None\n",
    "        self.pen_color = \"black\"\n",
    "        self.pen_size = 5\n",
    "\n",
    "    def on_button_press(self, event):\n",
    "        self.start_x = event.x\n",
    "        self.start_y = event.y\n",
    "\n",
    "    def on_move_press(self, event):\n",
    "        end_x = event.x\n",
    "        end_y = event.y\n",
    "        self.canvas.create_line(self.start_x, self.start_y, end_x, end_y, fill=self.pen_color, width=self.pen_size, capstyle=tk.ROUND, smooth=True)\n",
    "        self.start_x = end_x\n",
    "        self.start_y = end_y\n",
    "\n",
    "    def clear_canvas(self):\n",
    "        self.canvas.delete(\"all\")\n",
    "\n",
    "    def save_canvas(self):\n",
    "        # Prende una screenshot del canvas e la salva nella variabile \"out_image\"\n",
    "        x = self.master.winfo_rootx() + self.canvas.winfo_x()\n",
    "        y = self.master.winfo_rooty() + self.canvas.winfo_y()\n",
    "        x1 = x + self.canvas.winfo_width()\n",
    "        y1 = y + self.canvas.winfo_height()\n",
    "        image = ImageGrab.grab((x, y, x1, y1))\n",
    "        image.save(\"espressioni/espressione_canvas.png\")\n",
    "        root.destroy()\n",
    "\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    root = tk.Tk()\n",
    "    root.title(\"Inserimento espressione\")\n",
    "    app = App(root, 1400, 500)\n",
    "    root.mainloop()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1487e0bf290>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = cv2.imread(\"espressioni/bordi.png\")\n",
    "gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "\n",
    "ret, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)\n",
    "\n",
    "contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
    "\n",
    "# Disegna i contorni in rosso sull'immagine\n",
    "cv2.drawContours(img, contours, -1, (256,231,36), 7)\n",
    "\n",
    "plt.imshow(img)\n",
    "\n",
    "# Mostra l'immagine\n",
    "#cv2.imshow(\"Bordi\", img)\n",
    "#cv2.waitKey(0)\n",
    "#cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1487f5ea310>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = cv2.imread(\"espressioni/bordi.png\")\n",
    "\n",
    "for contour in contours:\n",
    "    x, y, w, h = cv2.boundingRect(contour)\n",
    "    cv2.rectangle(img, (x, y), (x + w, y + h), (256,231,36), 7)\n",
    "\n",
    "plt.imshow(img)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d90e86fc",
   "metadata": {
    "papermill": {
     "duration": 0.33404,
     "end_time": "2022-06-14T16:13:37.577676",
     "exception": false,
     "start_time": "2022-06-14T16:13:37.243636",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Riconoscimento"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bcf0682",
   "metadata": {},
   "source": [
    "Carichiamo il modello allenato in precedenza dal disco."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3e9167d7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-14T16:13:38.283668Z",
     "iopub.status.busy": "2022-06-14T16:13:38.283247Z",
     "iopub.status.idle": "2022-06-14T16:13:38.389624Z",
     "shell.execute_reply": "2022-06-14T16:13:38.388799Z"
    },
    "papermill": {
     "duration": 0.4407,
     "end_time": "2022-06-14T16:13:38.391700",
     "exception": false,
     "start_time": "2022-06-14T16:13:37.951000",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = load_model(\"modello.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4959d02",
   "metadata": {},
   "source": [
    "Carico l'espressione da riconoscere dal disco e la stampo a schermo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "3d240116",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-14T16:13:39.055793Z",
     "iopub.status.busy": "2022-06-14T16:13:39.055380Z",
     "iopub.status.idle": "2022-06-14T16:13:39.276011Z",
     "shell.execute_reply": "2022-06-14T16:13:39.275310Z"
    },
    "papermill": {
     "duration": 0.553918,
     "end_time": "2022-06-14T16:13:39.277695",
     "exception": false,
     "start_time": "2022-06-14T16:13:38.723777",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1f5124b59d0>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "espressione = 'espressioni/nuove/22.png'\n",
    "\n",
    "img = cv2.imread(espressione, cv2.IMREAD_GRAYSCALE)\n",
    "plt.figure(figsize = (12,12))\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43aa1b23",
   "metadata": {},
   "source": [
    "Per mezzo di OpenCV cerco i singoli elementi che compongono l'espressione, poi li stampo a video."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0. 0. 0. 1. 0.]]\n"
     ]
    }
   ],
   "source": [
    "labels = [1,2,7]\n",
    "Y = to_categorical(labels, num_classes = 9)\n",
    "print(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "# Carica l'immagine binaria\n",
    "img = cv2.imread('espressioni/nuove/33.png', 0)\n",
    "\n",
    "# Trova le componenti connesse e le statistiche\n",
    "n_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(img)\n",
    "\n",
    "# Per ogni componente connessa (esclusa quella dello sfondo)\n",
    "for i in range(1, n_labels):\n",
    "\n",
    "    # Crea un'immagine vuota dello stesso formato dell'immagine originale\n",
    "    component_img = np.zeros_like(img)\n",
    "\n",
    "    # Imposta i pixel corrispondenti alla componente connessa su 255\n",
    "    component_img[labels == i] = 255\n",
    "\n",
    "    cv2.imshow('Largest component', component_img)\n",
    "    cv2.waitKey(0)\n",
    "    cv2.destroyAllWindows()\n",
    "\n",
    "    # Salva l'immagine della componente connessa\n",
    "    cv2.imwrite('component_' + str(i) + '.png', component_img)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 ... 1 1 1]\n",
      "[1 1 1 ... 1 1 1]\n",
      "[1 1 1 ... 1 1 1]\n",
      "[1 1 1 ... 1 1 1]\n",
      "[1 1 1 ... 1 1 1]\n",
      "[1 1 1 ... 1 1 1]\n",
      "[1 1 1 ... 1 1 1]\n",
      "[1 1 1 ... 1 1 1]\n",
      "[1 1 1 ... 1 1 1]\n",
      "[1 1 1 ... 1 1 1]\n",
      "[1 1 1 ... 1 1 1]\n",
      "[1 1 1 ... 1 1 1]\n",
      "[1 1 1 ... 1 1 1]\n",
      "[1 1 1 ... 1 1 1]\n",
      "[1 1 1 ... 1 1 1]\n",
      "[1 1 1 ... 1 1 1]\n",
      "[1 1 1 ... 1 1 1]\n",
      "[1 1 1 ... 1 1 1]\n",
      "[1 1 1 ... 1 1 1]\n",
      "[1 1 1 ... 1 1 1]\n",
      "[1 1 1 ... 1 1 1]\n",
      "[1 1 1 ... 1 1 1]\n",
      "[1 1 1 ... 1 1 1]\n",
      "[1 1 1 ... 1 1 1]\n",
      "[1 1 1 ... 1 1 1]\n",
      "[1 1 1 ... 1 1 1]\n",
      "[1 1 1 ... 1 1 1]\n",
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      "[[1 1 1 ... 1 1 1]\n",
      " [1 1 1 ... 1 1 1]\n",
      " [1 1 1 ... 1 1 1]\n",
      " ...\n",
      " [1 1 1 ... 1 1 1]\n",
      " [1 1 1 ... 1 1 1]\n",
      " [1 1 1 ... 1 1 1]]\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "\n",
    "# carica l'immagine\n",
    "img = cv2.imread(\"espressioni/nuove/33.png\")\n",
    "\n",
    "# converte l'immagine in scala di grigi\n",
    "gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "# applica una soglia all'immagine\n",
    "ret, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)\n",
    "\n",
    "# trova le componenti connesse\n",
    "connectivity = 8\n",
    "output = cv2.connectedComponentsWithStats(thresh, connectivity, cv2.CV_32S)\n",
    "labels = output[1]\n",
    "\n",
    "for etichetta in labels:\n",
    "    print(etichetta)\n",
    "\n",
    "\n",
    "print(labels)\n",
    "stats = output[2]\n",
    "\n",
    "# identifica l'etichetta della componente connessa più grande\n",
    "max_label = 1\n",
    "max_area = stats[1, cv2.CC_STAT_AREA]\n",
    "for label in range(2, output[0]):\n",
    "    area = stats[label, cv2.CC_STAT_AREA]\n",
    "    if area > max_area:\n",
    "        max_label = label\n",
    "        max_area = area\n",
    "\n",
    "# crea un'immagine vuota e copia solo la componente connessa più grande\n",
    "mask = cv2.inRange(labels, max_label, max_label)\n",
    "largest_component = cv2.bitwise_and(img, img, mask=mask)\n",
    "\n",
    "# visualizza l'immagine con la componente connessa più grande\n",
    "cv2.imshow('Largest component', largest_component)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1758.0\n",
      "forse è un esponente 1\n",
      "[1]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2, math\n",
    "\n",
    "# Leggi l'immagine e convertila in scala di grigi\n",
    "img = cv2.imread(\"espressioni/nuove/22.png\", cv2.IMREAD_GRAYSCALE)\n",
    "\n",
    "img=~img\n",
    "# Applica la segmentazione a soglia per ottenere una maschera binaria\n",
    "ret, thresh = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)\n",
    "\n",
    "\n",
    "\n",
    "# Rimuovi i pixel isolati tramite l'operazione di opening\n",
    "#kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3,3))\n",
    "#opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)\n",
    "\n",
    "# Trova i contorni degli elementi nell'immagine\n",
    "contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
    "\n",
    "contours=sorted(contours, key=lambda ctr: cv2.boundingRect(ctr)[0])\n",
    "\n",
    "area_base = cv2.contourArea(contours[0])\n",
    "print(area_base)\n",
    "\n",
    "# Disegna i bordi individuati nell'immagine originale e salva l'elenco dei bordi in un file\n",
    "train_data = []\n",
    "esponenti = []\n",
    "for i, cnt in enumerate(contours):\n",
    "    # Estrai la porzione di immagine delimitata dal rettangolo del contorno\n",
    "    \n",
    "    \n",
    "    x, y, w, h = cv2.boundingRect(cnt)\n",
    "\n",
    "    area = cv2.contourArea(cnt)\n",
    "    # Se non mi trovo sul primo elemento\n",
    "    if i !=0:\n",
    "        xo, yo, wo, ho = cv2.boundingRect(contours[i-1])\n",
    "        #if y+h > yo+ho and y+h:\n",
    "        if y<yo and h<ho and w<wo:\n",
    "            area_esponente = cv2.contourArea(contours[i])\n",
    "            area_numero = cv2.contourArea(contours[i-1])\n",
    "            if (area_esponente<1/2*area_numero):\n",
    "                esponenti.append(i)\n",
    "                print(\"forse è un esponente\",i)\n",
    "                print(esponenti)\n",
    "\n",
    "\n",
    "    \n",
    "    square_size = max(w, h)\n",
    "    x1 = x + w//2 - square_size//2\n",
    "    y1 = y + h//2 - square_size//2\n",
    "    cropped = img[y1:y1+square_size, x1:x1+square_size]\n",
    "\n",
    "    #cropped = img[y:y+h, x:x+w]\n",
    "\n",
    "    # Aggiungi l'immagine alla lista\n",
    "    cropped = cv2.resize(cropped,(32,32))\n",
    "    _,cropped=cv2.threshold(cropped,0,255,cv2.THRESH_BINARY)\n",
    "\n",
    "\n",
    "    cropped=np.reshape(cropped,(32,32,1))\n",
    "\n",
    "\n",
    "\n",
    "    train_data.append(cropped)\n",
    "    # Disegna il rettangolo sulle immagini originali\n",
    "    #cv2.rectangle(img, (x1, y1), (x1+square_size, y1+square_size), (0, 0, 255), 2)\n",
    "\n",
    "# Salva le immagini originali con i bordi disegnati\n",
    "cv2.imwrite(\"borders.jpg\", img)\n",
    "\n",
    "# Utilizza le immagini estratte per le elaborazioni successive\n",
    "for img in train_data:\n",
    "\n",
    "    break\n",
    "\n",
    "\n",
    "rows = int(math.sqrt(len(train_data))) + 1\n",
    "cols = rows\n",
    "img_count = 0\n",
    "\n",
    "fig, axes = plt.subplots(nrows=rows, ncols=cols, figsize=(12,12))\n",
    "\n",
    "plt.subplots_adjust(top=0.95)\n",
    "\n",
    "\n",
    "for i in range(rows):\n",
    "    for j in range(cols):  \n",
    "        if(i*4+j in esponenti):\n",
    "            axes[i, j].imshow(train_data[img_count])\n",
    "            axes[i, j].set_title('Esponente', fontstyle='italic')\n",
    "            img_count+=1\n",
    "        elif img_count < len(train_data):\n",
    "            axes[i, j].imshow(train_data[img_count])\n",
    "            axes[i, j].set_title('Simbolo', fontstyle='italic')\n",
    "            \n",
    "            #axes.set_title(label, fontfamily='serif', loc='left', fontsize='medium')\n",
    "            img_count+=1\n",
    "        else:\n",
    "            fig.delaxes(axes[i,j])\n",
    "\n",
    "\n",
    "#fig.delaxes(axes[3,3])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "23dd8b7c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-14T16:13:40.227682Z",
     "iopub.status.busy": "2022-06-14T16:13:40.227277Z",
     "iopub.status.idle": "2022-06-14T16:13:40.261190Z",
     "shell.execute_reply": "2022-06-14T16:13:40.260290Z"
    },
    "papermill": {
     "duration": 0.408954,
     "end_time": "2022-06-14T16:13:40.263323",
     "exception": false,
     "start_time": "2022-06-14T16:13:39.854369",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ho trovato: 13 contorni\n",
      "397\n",
      "273\n",
      "476\n",
      "418\n",
      "334\n",
      "232\n",
      "354\n",
      "326\n",
      "222\n",
      "316\n",
      "249\n",
      "308\n",
      "478\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "\n",
    "img = cv2.imread(espressione,cv2.IMREAD_GRAYSCALE)\n",
    "\n",
    "if img is not None:\n",
    "    img=~img\n",
    "    _,thresh=cv2.threshold(img,15,255,cv2.THRESH_BINARY)\n",
    "    ctrs,_=cv2.findContours(thresh,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)\n",
    "    cnt=sorted(ctrs, key=lambda ctr: cv2.boundingRect(ctr)[0])\n",
    "\n",
    "    plt.figure(figsize = (12,12))\n",
    "    plt.imshow(thresh)\n",
    "\n",
    "    w=int(28)\n",
    "    h=int(28)\n",
    "    train_data=[]\n",
    "    print(\"Ho trovato: \" + str(len(cnt)) + \" contorni\")\n",
    "\n",
    "    rects=[]\n",
    "    for c in cnt :\n",
    "        x,y,w,h= cv2.boundingRect(c)\n",
    "        ss = max(w, h)\n",
    "        print(ss)\n",
    "        im_resize = img[y:y+ss,x:x+ss]\n",
    "        im_resize = cv2.resize(im_resize,(28,28))\n",
    "      \n",
    "        train_data.append(im_resize)\n",
    "    \n",
    "\n",
    "rows = int(math.sqrt(len(train_data))) + 1\n",
    "cols = rows\n",
    "img_count = 0\n",
    "\n",
    "fig, axes = plt.subplots(nrows=rows, ncols=cols, figsize=(12,12))\n",
    "\n",
    "for i in range(rows):\n",
    "    for j in range(cols):        \n",
    "        if img_count < len(train_data):\n",
    "            axes[i, j].imshow(train_data[img_count])\n",
    "            img_count+=1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa600795",
   "metadata": {},
   "source": [
    "Effettuiamo delle predizioni su ogni singolo elemento che abbiamo individuato nella fase precedente grazie ed OpenCV utilizzando il modello creato in precedenza."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['!', '(', ')', '+', '-', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'cos', 'div', 'eq', 'mul', 'sin', 'y', 'z', '[', ']']\n",
      "{'!': 0, '(': 1, ')': 2, '+': 3, '-': 4, '0': 5, '1': 6, '2': 7, '3': 8, '4': 9, '5': 10, '6': 11, '7': 12, '8': 13, '9': 14, 'cos': 15, 'div': 16, 'eq': 17, 'mul': 18, 'sin': 19, 'y': 20, 'z': 21, '[': 22, ']': 23}\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "diz = {}\n",
    "folders = [folder for folder in os.listdir(\"training\")]\n",
    "for folder,i in enumerate(folders):\n",
    "    diz[i] = folder\n",
    "labels = [key for key in diz]\n",
    "print(labels)\n",
    "print(diz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for elem, num in diz.items():\n",
    "        if num == prediction[0]:\n",
    "            expression = expression + elem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 36ms/step\n",
      "[[7.07965674e-12 8.76897860e-12 1.38433889e-10 1.00000000e+00\n",
      "  1.72526398e-14 3.12225523e-11 1.41907102e-11 1.28810809e-10\n",
      "  2.09434770e-12 2.22857910e-09 5.45558548e-15 9.97935086e-15\n",
      "  1.88332164e-16 3.19324130e-13 2.77461675e-13 6.66471218e-13\n",
      "  1.23468409e-08 1.19233765e-13 7.03576466e-14 7.61697156e-14\n",
      "  6.18257819e-15 1.35813805e-14 2.69026663e-13 2.17894813e-09]]\n",
      "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "Il valore cercato è [3] con una probabilità del: 100.0 %\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "\n",
    "train_data[0]=np.array(train_data[0])\n",
    "\n",
    "\n",
    "train_data[0]=train_data[0].reshape(1,32,32,1)\n",
    "\n",
    "\n",
    "\n",
    "probabilities=model.predict(train_data[0])\n",
    "prediction = np.argmax(probabilities, axis=-1)\n",
    "print(probabilities)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Normalizza il vettore a\n",
    "\n",
    "a_troncato = np.around(probabilities[0], decimals=-3)\n",
    "\n",
    "\n",
    "print(a_troncato)\n",
    "\n",
    "\n",
    "\n",
    "print(\"Il valore cercato è \" + str(prediction) + \" con una probabilità del: \" + str(max(probabilities[0])*100) + \" %\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "7c9a4764",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-14T16:13:40.932115Z",
     "iopub.status.busy": "2022-06-14T16:13:40.931764Z",
     "iopub.status.idle": "2022-06-14T16:13:41.129480Z",
     "shell.execute_reply": "2022-06-14T16:13:41.128711Z"
    },
    "papermill": {
     "duration": 0.535104,
     "end_time": "2022-06-14T16:13:41.131339",
     "exception": false,
     "start_time": "2022-06-14T16:13:40.596235",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 56ms/step\n",
      "Il valore cercato è [15] con una probabilità del: 99.97267127037048 %\n",
      "1/1 [==============================] - 0s 50ms/step\n",
      "Il valore cercato è [2] con una probabilità del: 99.94714856147766 %\n",
      "1/1 [==============================] - 0s 50ms/step\n",
      "Il valore cercato è [10] con una probabilità del: 99.99982118606567 %\n",
      "1/1 [==============================] - 0s 50ms/step\n",
      "Il valore cercato è [3] con una probabilità del: 100.0 %\n",
      "1/1 [==============================] - 0s 64ms/step\n",
      "Il valore cercato è [15] con una probabilità del: 99.99799728393555 %\n",
      "1/1 [==============================] - 0s 72ms/step\n",
      "Il valore cercato è [10] con una probabilità del: 99.99667406082153 %\n",
      "1/1 [==============================] - 0s 64ms/step\n",
      "Il valore cercato è [4] con una probabilità del: 99.28327798843384 %\n",
      "1/1 [==============================] - 0s 96ms/step\n",
      "Il valore cercato è [12] con una probabilità del: 100.0 %\n",
      "1/1 [==============================] - 0s 40ms/step\n",
      "Il valore cercato è [21] con una probabilità del: 99.9988317489624 %\n",
      "1/1 [==============================] - 0s 40ms/step\n",
      "Il valore cercato è [17] con una probabilità del: 98.43035340309143 %\n",
      "1/1 [==============================] - 0s 56ms/step\n",
      "Il valore cercato è [4] con una probabilità del: 80.41917085647583 %\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "Il valore cercato è [18] con una probabilità del: 99.97040629386902 %\n",
      "\n",
      "\n",
      "L'espressione cercata è y**2+3y+4*cos(4) con una probabilità del  98.16802938779195 %\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "expression=''\n",
    "prob_tot = 0\n",
    "\n",
    "for i in range(len(train_data)):\n",
    "    \n",
    "    train_data[i]=np.array(train_data[i])\n",
    "    train_data[i]=train_data[i].reshape(1,32,32,1)\n",
    "    probabilities=model.predict(train_data[i])\n",
    "    \n",
    "    prediction = np.argmax(probabilities, axis=-1)\n",
    "\n",
    "    prob_tot = prob_tot + max(probabilities[0])*100\n",
    "\n",
    "    print(\"Il valore cercato è \" + str(prediction) + \" con una probabilità del: \" + str(max(probabilities[0])*100) + \" %\")\n",
    "    \n",
    "\n",
    "    if i in esponenti and i != 0:\n",
    "        expression = expression + \"**\"\n",
    "        \n",
    "    for j in range(10) :\n",
    "        if prediction[0] == j :\n",
    "            expression = expression + str(j)\n",
    "    \n",
    "    if prediction[0] == 10 :\n",
    "        expression = expression + \"+\"\n",
    "    if prediction[0] == 11 :\n",
    "        expression = expression + \"-\"\n",
    "    if prediction[0] == 12 :\n",
    "        expression = expression + \"*\"\n",
    "    if prediction[0] == 13 :\n",
    "        expression = expression + \"/\"\n",
    "    if prediction[0] == 14 :\n",
    "        expression = expression + \"=\"\n",
    "    if prediction[0] == 15 :\n",
    "        expression = expression + \"y\"\n",
    "    if prediction[0] == 16 :\n",
    "        expression = expression + \"z\"      \n",
    "    if prediction[0] == 17 :\n",
    "        expression = expression + \"(\"\n",
    "    if prediction[0] == 18 :\n",
    "        expression = expression + \")\"\n",
    "    if prediction[0] == 19 :\n",
    "        expression = expression + \"!\"\n",
    "    if prediction[0] == 20 :\n",
    "        expression = expression + \"sin\"\n",
    "    if prediction[0] == 21 :\n",
    "        expression = expression + \"cos\"\n",
    "    if prediction[0] == 22 :\n",
    "        expression = expression + \"[\"\n",
    "    if prediction[0] == 23 :\n",
    "        expression = expression + \"]\"\n",
    "\n",
    "\n",
    "img = cv2.imread(espressione, cv2.IMREAD_GRAYSCALE)\n",
    "\n",
    "plt.figure(figsize = (12,12))\n",
    "plt.imshow(img)\n",
    "print()\n",
    "print()\n",
    "print(\"L'espressione cercata è\", expression, \"con una probabilità del \", prob_tot/len(train_data), \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "967eef6c",
   "metadata": {},
   "source": [
    "# Parsing e calcolo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bb52892",
   "metadata": {},
   "source": [
    "Effettuiamo il parsing ed il calcolo dell'equazione associata all'espressione data grazie alla libreria Sympy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "177fbefa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imposto, a partire dall'espressione data una equazione = 0:\n",
      "y**2+3y+4*cos(4) = 0\n",
      "\n",
      "Stampo la soluzione dell'equazione:\n",
      "[-3/2 + sqrt(9 - 16*cos(4))/2, -sqrt(9 - 16*cos(4))/2 - 3/2]\n"
     ]
    }
   ],
   "source": [
    "from sympy.parsing.sympy_parser import standard_transformations, implicit_multiplication_application\n",
    "\n",
    "print(\"Imposto, a partire dall'espressione data una equazione = 0:\")\n",
    "\n",
    "print(str(expression) + \" = 0\")\n",
    "print()\n",
    "\n",
    "if \"[\" in expression or \"]\" in expression:\n",
    "    equation = expression.replace(\"[\", \"(\" )\n",
    "    equation = expression.replace(\"]\", \")\" )\n",
    "\n",
    "\n",
    "if \"y\" in expression:\n",
    "    y = symbols('y')\n",
    "\n",
    "    transformations = (standard_transformations + (implicit_multiplication_application,))\n",
    "\n",
    "    parsed =parse_expr(expression, transformations=transformations)\n",
    "\n",
    "    #eparsing = sympify(str(parsed))\n",
    "    #eparsing = eparsing.evalf()\n",
    "    #print(eparsing)\n",
    "    #print()\n",
    "\n",
    "    soluzione = solve(parsed, y)\n",
    "\n",
    "    print(\"Stampo la soluzione dell'equazione:\")\n",
    "    print(soluzione)\n",
    "else:\n",
    "    parsed = parse_expr(espressione)\n",
    "    eparsing = sympify(str(parsed))\n",
    "    eparsing = eparsing.evalf()\n",
    "    soluzione = eparsing.evalf()\n",
    "    print()\n",
    "    print(\"Stampo la soluzione dell'espressione:\")\n",
    "    print(soluzione)\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "from sklearn.cluster import DBSCAN\n",
    "\n",
    "\n",
    "espressione = 'espressioni/1.png'\n",
    "\n",
    "\n",
    "\n",
    "# Leggi l'immagine e convertila in scala di grigi\n",
    "img = cv2.imread(espressione)\n",
    "gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "# Applica il filtro di Canny per individuare i bordi forti\n",
    "edges = cv2.Canny(gray, 100, 200)\n",
    "\n",
    "# Applica la trasformata di Hough per individuare le linee dritte nell'immagine\n",
    "lines = cv2.HoughLinesP(edges, 1, np.pi/180, 100, minLineLength=50, maxLineGap=5)\n",
    "\n",
    "# Utilizza l'algoritmo di raggruppamento DBSCAN per identificare i gruppi di linee correlate\n",
    "X = np.array(lines)[:,0,:]\n",
    "clustering = DBSCAN(eps=20, min_samples=2).fit(X)\n",
    "\n",
    "# Disegna i bordi individuati nell'immagine originale e salva l'elenco dei bordi in un file\n",
    "with open(\"borders.txt\", \"w\") as f:\n",
    "    for i, line in enumerate(lines):\n",
    "        x1, y1, x2, y2 = line[0]\n",
    "        if clustering.labels_[i] != -1:\n",
    "            cv2.line(img, (x1, y1), (x2, y2), (0, 0, 255), 2)\n",
    "            f.write(f\"Border {i+1}: ({x1}, {y1}) - ({x2}, {y2})\\n\")\n",
    "cv2.imwrite(\"borders.jpg\", img)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import cv2\n",
    "\n",
    "# Leggi l'immagine e convertila in scala di grigi\n",
    "img = cv2.imread(\"espressioni/1.png\")\n",
    "gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "# Applica la segmentazione a soglia per ottenere una maschera binaria\n",
    "ret, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)\n",
    "\n",
    "# Rimuovi i pixel isolati tramite l'operazione di opening\n",
    "kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3,3))\n",
    "opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)\n",
    "\n",
    "# Trova i contorni degli elementi nell'immagine\n",
    "contours, hierarchy = cv2.findContours(opening, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
    "\n",
    "# Disegna i bordi individuati nell'immagine originale e salva l'elenco dei bordi in un file\n",
    "with open(\"borders.txt\", \"w\") as f:\n",
    "    for i, cnt in enumerate(contours):\n",
    "        # Calcola le dimensioni del rettangolo e la dimensione massima tra larghezza e altezza\n",
    "        x, y, w, h = cv2.boundingRect(cnt)\n",
    "        max_dim = max(w, h)\n",
    "        # Calcola le coordinate del vertice in alto a sinistra del rettangolo quadrato\n",
    "        x1 = x + w//2 - max_dim//2\n",
    "        y1 = y + h//2 - max_dim//2\n",
    "        # Disegna il rettangolo quadrato\n",
    "        cv2.rectangle(img, (x1, y1), (x1+max_dim, y1+max_dim), (0, 0, 255), 2)\n",
    "\n",
    "cv2.imwrite(\"borders.jpg\", img)\n"
   ]
  }
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