solver.py

import random
from PIL import ImageFont, ImageDraw, Image
import cv2
import numpy as np
import os
import src.solve as solve
from typing import Tuple
import pytesseract
import re
import shutil
import tensorflow as tf
from tensorflow import keras
import random


# Setup class names
with open("class_names.txt", "r") as f:  # reading them in from class_names.txt
    class_names = [names.strip() for names in f.readlines()]

model1 = tf.keras.models.load_model('model/model30.h5')
model2 = tf.keras.models.load_model('model/model15.h5')
model3 = tf.keras.models.load_model('model/model2.h5')

palabras_1 = []
# Borrar el directorio de imagenes
folder = 'output'
for filename in os.listdir(folder):
    file_path = os.path.join(folder, filename)
    try:
        if os.path.isfile(file_path) or os.path.islink(file_path):
            os.unlink(file_path)
        elif os.path.isdir(file_path):
            shutil.rmtree(file_path)
    except Exception as e:
        print('Failed to delete %s. Reason: %s' % (file_path, e))
folder = 'wordsPuzzle'
for filename in os.listdir(folder):
    file_path = os.path.join(folder, filename)
    try:
        if os.path.isfile(file_path) or os.path.islink(file_path):
            os.unlink(file_path)
        elif os.path.isdir(file_path):
            shutil.rmtree(file_path)
    except Exception as e:
        print('Failed to delete %s. Reason: %s' % (file_path, e))


def get_words(img):
    #print(type(img))
	# str to filepath
    img = Image.open(img)
	# Display image
    # img.show()
    text = pytesseract.image_to_string(img, lang="spa+eng", config="--psm 11")
    text = text.upper()
    text = re.split('\W+', text)
    text.pop()
    #palabras_1 = text
    #print(palabras_1)
    # array to string text
    text = ' '.join(text)
    # add comma to text
    text = text.replace(" ", ",")
    return text


def getmat(listaCuadrados, filas, columnas):
    matrix = [[0 for i in range(columnas)] for j in range(filas)]
    matrixT = [[0 for i in range(columnas)] for j in range(filas)]
    listaCuadrados.sort(key=lambda y: y["altura"])
    e = 0
    for i in range(filas):
        lista2 = listaCuadrados[e:e+columnas]
        e = e+columnas
        lista2.sort(key=lambda y: y["anchura"])
        j = 0
        for lista in lista2:
            matrix[i][j] = lista["letra"]
            matrixT[i][j] = lista
            j = j + 1
    return matrix, matrixT


# Obtener las filas y las columnas de la sopa
def get_colums_and_rows(listaCuadrados):
    columnas = 0
    filas = 1
    alturaAnt = listaCuadrados[0]["altura"]
    for lista in listaCuadrados:
        if (lista["altura"] > alturaAnt + 6):
            filas = filas + 1
        alturaAnt = lista["altura"]
        if (filas == 1):
            columnas = columnas + 1
    return filas, columnas


def read_board(img, words):
    #(type(img))
	# str to filepath
    img = Image.open(img)
	# Display image
    img.show()
	# Print words
    #print("Palabras a buscar: ", palabras_1)


def solve_puzzle(img, words):
        # Borrar el directorio de imagenes
    folder = 'output'
    for filename in os.listdir(folder):
        file_path = os.path.join(folder, filename)
        try:
            if os.path.isfile(file_path) or os.path.islink(file_path):
                os.unlink(file_path)
            elif os.path.isdir(file_path):
                shutil.rmtree(file_path)
        except Exception as e:
            print('Failed to delete %s. Reason: %s' % (file_path, e))
    folder = 'wordsPuzzle'
    for filename in os.listdir(folder):
        file_path = os.path.join(folder, filename)
        try:
            if os.path.isfile(file_path) or os.path.islink(file_path):
                os.unlink(file_path)
            elif os.path.isdir(file_path):
                shutil.rmtree(file_path)
        except Exception as e:
            print('Failed to delete %s. Reason: %s' % (file_path, e))
    # print(type(img))
	# str to filepath
    #print(type(words))
    #print(words)
    # img = Image.open(img)
	# Pil to opencv compatible
    pil_image = Image.open(img).convert('RGB')
    open_cv_image = np.array(pil_image)
    # Convert RGB to BGR
    open_cv_image = open_cv_image[:, :, ::-1].copy()
	# Display image
	# Print words
    img = open_cv_image
    # string to array
    words = words.split(",")
    # remove last ,
    #print(words)

    imgc = img.copy()
    imgsol = img.copy()
    imgc = cv2.cvtColor(imgc, cv2.COLOR_RGB2GRAY)
    imgc = np.invert(imgc)
    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    blur = cv2.GaussianBlur(gray, (5, 5), 0)
	# save the blurred image
    #cv2.imwrite("output/blur.png", blur)
	# display blurred image
    threshten = cv2.threshold(
        blur, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
    thresh = cv2.adaptiveThreshold(threshten, 255, 1, 1, 11, 2)
    contours, hierarchy = cv2.findContours(
        thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
	# Draw contours and save the image

    characters = np.array([
        'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J',
        'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T',
        'U', 'V', 'W', 'X', 'Y', 'Z', 'Á', 'É', 'Í', 'Ñ', 'Ó', 'Ú', '?'])

    offset = 7
    minh = 7
    minw = 2
    i = 0

    listaCuadrados = list()
    fnt = ImageFont.truetype("fonts/Roboto-Black.ttf", 20)
    for cnt in reversed(contours):
        contCuadrados = {}
        if cv2.contourArea(cnt) > 18 and cv2.contourArea(cnt) < 1000:
            [x, y, w, h] = cv2.boundingRect(cnt)
            if h > minh and w > minw:
                i = i + 1
                height, width = imgc.shape
                y0 = 0
                y1 = height
                x0 = 0
                x1 = width
                if height > y+h+offset:
                    y1 = y+h+offset
                if width > x+w+offset:
                    x1 = x+w+offset
                if y-offset > 0:
                    y0 = y-offset
                if x-offset > 0:
                    x0 = x-offset
                cv2.rectangle(img, (x0, y0), (x1, y1), (0, 255, 0), 2)
                img2 = imgc[y0:y1, x0:x1]
                img2 = cv2.resize(img2, (28, 28))
                img_array = img2.reshape(1, 28, 28, 1)
                prediction1 = np.argmax(model1.predict(img_array))
                prediction2 = np.argmax(model2.predict(img_array))
                prediction3 = np.argmax(model3.predict(img_array))
                pred = 0
                if prediction1 == prediction2 and prediction2 == prediction3:
                   pred = prediction1
                elif  prediction1 == prediction2:
                   pred = prediction1
                elif  prediction2 == prediction3:
                   pred = prediction2
                elif  prediction1 == prediction3:
                   pred = prediction3
                else:
                   pred = 32
                #print(characters[pred])
                contCuadrados["anchura"] = x
                contCuadrados["altura"] = y
                contCuadrados["centrox"] = (x + x + w)/2
                contCuadrados["centroy"] = (y + y + h)/2
                contCuadrados["letra"] = characters[pred]
                listaCuadrados.append(contCuadrados)
                img_pil = Image.fromarray(img)
                draw = ImageDraw.Draw(img_pil)
                draw.text(((x0+x1)/2, y0-10),
                          characters[pred], font=fnt, fill=(255, 0, 0, 0))
                img = np.array(img_pil)
    #cv2.imwrite("output/Tablero_Labels.png", img)
    filas, columnas = get_colums_and_rows(listaCuadrados)
    # print listaCuadrados
    # print(listaCuadrados)
    matrix, matrixT = getmat(listaCuadrados, filas, columnas)
    palabrasxy = []

    # print()
    # print("Palabras a buscar:")
    # for i in words:
    #    print(i)
    # print()
    # for i in range(filas):
    #     for j in range(columnas):
    #        print(matrix[i][j], end = " ")
    #     print()
    # print()
    # print()
    image_new = imgsol.copy()
    overlay = imgsol.copy()
    index = 0
    index2 = 0
    for i in words:
        #(i)
        xy_positionsvec, find = solve.find_word(matrix, i)
        if find:
            palabrasxy.append(xy_positionsvec)
            xy_positionsvec = palabrasxy[index]
            # print(len(xy_positionsvec))
            xy = xy_positionsvec[0]
            xy2 = xy_positionsvec[len(xy_positionsvec)-1]
            # print(xy["x"], " ",xy["y"])
            # print(xy2["x"], " ",xy2["y"])
            coordreal = matrixT[xy["x"]][xy["y"]]
            coordreal2 = matrixT[xy2["x"]][xy2["y"]]
            centrox = coordreal["centrox"]
            centroy = coordreal["centroy"]
            centrox2 = coordreal2["centrox"]
            centroy2 = coordreal2["centroy"]
            centrox = int(centrox)
            centroy = int(centroy)
            centrox2 = int(centrox2)
            centroy2 = int(centroy2)
            color = (random.randint(0, 255), random.randint(
                0, 255), random.randint(0, 255))
            cv2.line(overlay, (centrox, centroy), (centrox2, centroy2), color,
                     thickness=int(abs(coordreal["altura"] - coordreal["centroy"])*2))
            overlay2 = imgsol.copy()
            cv2.line(overlay2, (centrox, centroy), (centrox2, centroy2), color,
                     thickness=int(abs(coordreal["altura"] - coordreal["centroy"])*2))
            image_word = cv2.addWeighted(overlay2, 0.4, image_new, 1 - 0.4, 0)
            cv2.imwrite("wordsPuzzle/" + words[index2] + ".jpg", image_word)
            # append the image into a numpy array
            
            #print(words[index2])
    
            index += 1
        index2 += 1
    alpha = 0.4  # Transparency factor
    image_new = cv2.addWeighted(overlay, alpha, image_new, 1 - alpha, 0)
    cv2.imwrite("output/Tablero_solucion.png", image_new)
    # return the images in wordsPuzzle folder as numpy arrays
    return image_new