Comment obtenir les cellules d'une grille sudoku avec OpenCV?

J'essaie depuis quelques jours d'obtenir une grille de sudoku à partir d'une image, et j'ai du mal à obtenir les petits carrés de la grille. Je travaille sur l'image ci-dessous. Je pensais que le traitement de l'image avec un filtre intelligent fonctionnerait bien, mais cela n'a pas fonctionné et je n'ai pas pu obtenir tous les contours de chaque carré. J'ai ensuite mis le seuil adaptatif, otsu et un seuillage classique à l'épreuve, mais à chaque fois, il ne semblait pas pouvoir capturer chaque petit carré.

Le but final est d'obtenir les cellules contenant un nombre et de reconnaître les nombres avec pytorch, donc je voudrais vraiment avoir des images nettes des nombres, donc la reconnaissance ne se gâche pas :)

Quelqu'un aurait-il une idée sur la façon d'y parvenir? Merci beaucoup d'avance! :RÉ

Voici une solution potentielle:

1. Obtenez une image binaire. Convertir l'image en niveaux de gris et seuil adaptatif

2. Filtrez tous les nombres et le bruit pour isoler uniquement les boîtes. Nous filtrons en utilisant la zone de contour pour supprimer les nombres, car nous ne voulons que chaque cellule individuelle

3. Correction des lignes de la grille. Effectuer la fermeture morphologique avec un noyau horizontal et vertical pour réparer les lignes de la grille.
4. Triez chaque cellule de haut en bas et de gauche à droite. Nous organisons chaque cellule dans un ordre séquentiel en utilisant imutils.contours.sort_contours()avec le paramètre top-to-bottometleft-to-right

Voici l'image binaire initiale (à gauche) et les nombres filtrés + lignes de grille réparées + image inversée (à droite)

Voici une visualisation de l'itération de chaque cellule

Les nombres détectés dans chaque cellule

Code

import cv2
from imutils import contours
import numpy as np

# Load image, grayscale, and adaptive threshold
image = cv2.imread('1.png')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV,57,5)

# Filter out all numbers and noise to isolate only boxes
cnts = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    area = cv2.contourArea(c)
    if area < 1000:
        cv2.drawContours(thresh, [c], -1, (0,0,0), -1)

# Fix horizontal and vertical lines
vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1,5))
thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, vertical_kernel, iterations=9)
horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5,1))
thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, horizontal_kernel, iterations=4)

# Sort by top to bottom and each row by left to right
invert = 255 - thresh
cnts = cv2.findContours(invert, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
(cnts, _) = contours.sort_contours(cnts, method="top-to-bottom")

sudoku_rows = []
row = []
for (i, c) in enumerate(cnts, 1):
    area = cv2.contourArea(c)
    if area < 50000:
        row.append(c)
        if i % 9 == 0:  
            (cnts, _) = contours.sort_contours(row, method="left-to-right")
            sudoku_rows.append(cnts)
            row = []

# Iterate through each box
for row in sudoku_rows:
    for c in row:
        mask = np.zeros(image.shape, dtype=np.uint8)
        cv2.drawContours(mask, [c], -1, (255,255,255), -1)
        result = cv2.bitwise_and(image, mask)
        result[mask==0] = 255
        cv2.imshow('result', result)
        cv2.waitKey(175)

cv2.imshow('thresh', thresh)
cv2.imshow('invert', invert)
cv2.waitKey()

Remarque: L'idée de tri a été adaptée d'une ancienne réponse précédente dans l' extraction des couleurs du solveur de cube Rubrik .

Si l'image ne contient que la grille sudoku bien ajustée, un moyen grossier pour y parvenir serait de diviser l'image en une grille 9X9 égale, puis d'essayer d'extraire le nombre dans chacune de cette grille.

Pas:

1. Prétraitement d'image (opération de fermeture)
2. Trouver un carré Sudoku et créer une image de masque
3. Recherche de lignes verticales
4. Recherche de lignes horizontales
5. Recherche de points de grille
6. Corriger les défauts
7. Extraire les chiffres de chaque cellule

Code:

# ==========import the necessary packages============
import imutils
import numpy as np
import cv2
from transform import four_point_transform
from PIL import Image
import pytesseract
import math
from skimage.filters import threshold_local

# =============== For Transformation ==============
def order_points(pts):
    """initialzie a list of coordinates that will be ordered
    such that the first entry in the list is the top-left,
    the second entry is the top-right, the third is the
    bottom-right, and the fourth is the bottom-left"""

    rect = np.zeros((4, 2), dtype = "float32")

    # the top-left point will have the smallest sum, whereas
    # the bottom-right point will have the largest sum
    s = pts.sum(axis = 1)
    rect[0] = pts[np.argmin(s)]
    rect[2] = pts[np.argmax(s)]

    # now, compute the difference between the points, the
    # top-right point will have the smallest difference,
    # whereas the bottom-left will have the largest difference
    diff = np.diff(pts, axis = 1)
    rect[1] = pts[np.argmin(diff)]
    rect[3] = pts[np.argmax(diff)]

    # return the ordered coordinates
    return rect


def four_point_transform(image, pts):
    # obtain a consistent order of the points and unpack them
    # individually
    rect = order_points(pts)
    (tl, tr, br, bl) = rect

    # compute the width of the new image, which will be the
    # maximum distance between bottom-right and bottom-left
    # x-coordiates or the top-right and top-left x-coordinates
    widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
    widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
    maxWidth = max(int(widthA), int(widthB))

    # compute the height of the new image, which will be the
    # maximum distance between the top-right and bottom-right
    # y-coordinates or the top-left and bottom-left y-coordinates
    heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
    heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
    maxHeight = max(int(heightA), int(heightB))

    # now that we have the dimensions of the new image, construct
    # the set of destination points to obtain a "birds eye view",
    # (i.e. top-down view) of the image, again specifying points
    # in the top-left, top-right, bottom-right, and bottom-left
    # order
    dst = np.array([
        [0, 0],
        [maxWidth - 1, 0],
        [maxWidth - 1, maxHeight - 1],
        [0, maxHeight - 1]], dtype = "float32")

    # compute the perspective transform matrix and then apply it
    M = cv2.getPerspectiveTransform(rect, dst)
    warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))

    # return the warped image
    return warped

############## To show image ##############
def show_image(img,title):
    cv2.imshow(title, img) 
    cv2.waitKey(0) 
    cv2.destroyAllWindows()  


def find_largest_feature(inp_img, scan_tl=None, scan_br=None):
    """
    Uses the fact the `floodFill` function returns a bounding box of the area it filled to find the biggest
    connected pixel structure in the image. Fills this structure in white, reducing the rest to black.
    """
    img = inp_img.copy()  # Copy the image, leaving the original untouched
    height, width = img.shape[:2]

    max_area = 0
    seed_point = (None, None)

    if scan_tl is None:
        scan_tl = [0, 0]

    if scan_br is None:
        scan_br = [width, height]

    # Loop through the image
    for x in range(scan_tl[0], scan_br[0]):
        for y in range(scan_tl[1], scan_br[1]):
            # Only operate on light or white squares
            if img.item(y, x) == 255 and x < width and y < height:  # Note that .item() appears to take input as y, x
                area = cv2.floodFill(img, None, (x, y), 64)
                if area[0] > max_area:  # Gets the maximum bound area which should be the grid
                    max_area = area[0]
                    seed_point = (x, y)

    # Colour everything grey (compensates for features outside of our middle scanning range
    for x in range(width):
        for y in range(height):
            if img.item(y, x) == 255 and x < width and y < height:
                cv2.floodFill(img, None, (x, y), 64)

    mask = np.zeros((height + 2, width + 2), np.uint8)  # Mask that is 2 pixels bigger than the image

    # Highlight the main feature
    if all([p is not None for p in seed_point]):
        cv2.floodFill(img, mask, seed_point, 255)



    for x in range(width):
        for y in range(height):
            if img.item(y, x) == 64:  # Hide anything that isn't the main feature
                cv2.floodFill(img, mask, (x, y), 0)

    return img


################# Preprocessing of sudoku image ###############
def preprocess(image,case):
    ratio = image.shape[0] / 500.0
    orig = image.copy()
    image = imutils.resize(image, height = 500)

    if case == True:

        gray = cv2.GaussianBlur(image,(5,5),0)
        gray = cv2.cvtColor(gray,cv2.COLOR_BGR2GRAY)
        mask = np.zeros((gray.shape),np.uint8)
        kernel1 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(11,11))

        close = cv2.morphologyEx(gray,cv2.MORPH_CLOSE,kernel1)
        div = np.float32(gray)/(close)
        res = np.uint8(cv2.normalize(div,div,0,255,cv2.NORM_MINMAX))
        res2 = cv2.cvtColor(res,cv2.COLOR_GRAY2BGR)
        edged = cv2.Canny(res, 75, 200)

        cnts = cv2.findContours(edged.copy(), cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
        cnts = cnts[0] if imutils.is_cv2() else cnts[1]
        cnts = sorted(cnts, key = cv2.contourArea, reverse = True)[:5]

        # loop over the contours
        for c in cnts:
            # approximate the contour
            rect = cv2.boundingRect(c)
            area = cv2.contourArea(c)

            cv2.rectangle(edged.copy(), (rect[0],rect[1]), (rect[2]+rect[0],rect[3]+rect[1]), (0,0,0), 2)
            peri = cv2.arcLength(c, True)
            approx = cv2.approxPolyDP(c, 0.02 * peri, True)

            # if our approximated contour has four points, then we
            # can assume that we have found our screen
            if len(approx) == 4:
                screenCnt = approx
                #print(screenCnt)
                break

        # show the contour (outline) of the piece of paper
        #print(screenCnt)
        cv2.drawContours(image, [screenCnt], -1, (0, 255, 0), 2)

        # apply the four point transform to obtain a top-down
        # view of the original image    
        warped = four_point_transform(orig, screenCnt.reshape(4, 2) * ratio)
        warped1 = cv2.resize(warped,(610,610))
        warp = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY) 
        T = threshold_local(warp, 11, offset = 10, method = "gaussian")
        warp = (warp > T).astype("uint8") * 255
        th3 = cv2.adaptiveThreshold(warp,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,\
            cv2.THRESH_BINARY_INV,11,2) 
        kernel = np.ones((5,5),np.uint8)
        dilation =cv2.GaussianBlur(th3,(5,5),0)

    else :

        warped = image
        warped1 = cv2.resize(warped,(610,610))
        warp = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY) 
        T = threshold_local(warp, 11, offset = 10, method = "gaussian")
        warp = (warp > T).astype("uint8") * 255
        th3 = cv2.adaptiveThreshold(warp,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,\
            cv2.THRESH_BINARY_INV,11,2)

    #show_image(warped1,"preprocessed")

    return th3,warped1,warped

def grids(img,warped2):
    #print("im:",img.shape)
    img2 = img.copy()
    img = np.zeros((500,500,3), np.uint8)

    ratio2 = 3
    kernel_size = 3
    lowThreshold = 30

    frame = img

    img = cv2.resize(frame,(610,610))

    for i in range(10):
        cv2.line(img, (0,(img.shape[0]//9)*i),(img.shape[1],(img.shape[0]//9)*i), (255, 255, 255), 3, 1)
        cv2.line(warped2, (0,(img.shape[0]//9)*i),(img.shape[1],(img.shape[0]//9)*i), (125, 0, 55), 3, 1)

    for j in range(10):
        cv2.line(img, ((img.shape[1]//9)*j, 0), ((img.shape[1]//9)*j, img.shape[0]), (255, 255, 255), 3, 1)
        cv2.line(warped2, ((img.shape[1]//9)*j, 0), ((img.shape[1]//9)*j, img.shape[0]), (125, 0, 55), 3, 1)

    #show_image(warped2,"grids")
    return img

############### Finding out the intersection pts to get the grids #########
def grid_points(img,warped2):
    img1 = img.copy()
    kernelx = cv2.getStructuringElement(cv2.MORPH_RECT,(2,10))

    dx = cv2.Sobel(img,cv2.CV_16S,1,0)
    dx = cv2.convertScaleAbs(dx)
    c=cv2.normalize(dx,dx,0,255,cv2.NORM_MINMAX)
    c = cv2.morphologyEx(c,cv2.MORPH_DILATE,kernelx,iterations = 1)
    cy = cv2.cvtColor(c,cv2.COLOR_BGR2GRAY)
    closex = cv2.morphologyEx(cy,cv2.MORPH_DILATE,kernelx,iterations = 1)

    kernely = cv2.getStructuringElement(cv2.MORPH_RECT,(10,2))
    dy = cv2.Sobel(img,cv2.CV_16S,0,2)
    dy = cv2.convertScaleAbs(dy)
    c = cv2.normalize(dy,dy,0,255,cv2.NORM_MINMAX)
    c = cv2.morphologyEx(c,cv2.MORPH_DILATE,kernely,iterations = 1)
    cy = cv2.cvtColor(c,cv2.COLOR_BGR2GRAY)
    closey = cv2.morphologyEx(cy,cv2.MORPH_DILATE,kernelx,iterations = 1)

    res = cv2.bitwise_and(closex,closey)
    #gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
    ret, thresh = cv2.threshold(res,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)

    kernel = np.ones((6,6),np.uint8)


    # Perform morphology
    se = np.ones((8,8), dtype='uint8')
    image_close = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, se)
    image_close = cv2.morphologyEx(image_close, cv2.MORPH_OPEN, kernel)

    contour, hier = cv2.findContours        (image_close,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
    cnts = sorted(contour, key=cv2.contourArea, reverse=True)[:100]
    centroids = []
    for cnt in cnts:

        mom = cv2.moments(cnt)
        (x,y) = int(mom['m10']/mom['m00']), int(mom['m01']/mom['m00'])
        cv2.circle(img1,(x,y),4,(0,255,0),-1)
        cv2.circle(warped2,(x,y),4,(0,255,0),-1)
        centroids.append((x,y))

    #show_image(warped2,"grid_points")


    Points = np.array(centroids,dtype = np.float32)
    c = Points.reshape((100,2))
    c2 = c[np.argsort(c[:,1])]

    b = np.vstack([c2[i*10:(i+1)*10][np.argsort(c2[i*10:(i+1)*10,0])] for i in range(10)])
    bm = b.reshape((10,10,2))

    return c2,bm,cnts

############ Recognize digit images to number #############
def image_to_num(c2):     
    img = 255-c2
    text = pytesseract.image_to_string(img, lang="eng",config='--psm 6 --oem 3') #builder=builder)
    return list(text)[0]

###### To get the digit at the particular cell #############
def get_digit(c2,bm,warped1,cnts):
    num = []
    centroidx = np.empty((9, 9))
    centroidy = np.empty((9, 9))
    global list_images
    list_images = []
    for i in range(0,9):
        for j in range(0,9):

            x1,y1 = bm[i][j] # bm[0] row1 
            x2,y2 = bm[i+1][j+1]

            coordx = ((x1+x2)//2)
            coordy = ((y1+y2)//2)
            centroidx[i][j] = coordx
            centroidy[i][j] = coordy
            crop = warped1[int(x1):int(x2),int(y1):int(y2)]
            crop = imutils.resize(crop, height=69,width=67)
            c2 = cv2.cvtColor(crop, cv2.COLOR_BGR2GRAY)
            c2 = cv2.adaptiveThreshold(c2,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,\
                cv2.THRESH_BINARY_INV,11,2)
            kernel = np.ones((2,2),np.uint8)
            #c2 = cv2.morphologyEx(c2, cv2.MORPH_OPEN, kernel)
            c2= cv2.copyMakeBorder(c2,5,5,5,5,cv2.BORDER_CONSTANT,value=(0,0,0))
            no = 0
            shape=c2.shape
            w=shape[1]
            h=shape[0]
            mom = cv2.moments(c2)
            (x,y) = int(mom['m10']/mom['m00']), int(mom['m01']/mom['m00']) 
            c2 = c2[14:70,15:62]
            contour, hier = cv2.findContours (c2,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
            if cnts is not None:
                cnts = sorted(contour, key=cv2.contourArea,reverse=True)[:1]

            for cnt in cnts:
                x,y,w,h = cv2.boundingRect(cnt)
                aspect_ratio = w/h
#               print(aspect_ratio)
                area = cv2.contourArea(cnt)
                #print(area)
                if area>120 and cnt.shape[0]>15 and aspect_ratio>0.2 and aspect_ratio<=0.9 : 
                    #print("area:",area)
                    c2 = find_largest_feature(c2)
                    #show_image(c2,"box2")
                    contour, hier = cv2.findContours (c2,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
                    cnts = sorted(contour, key=cv2.contourArea,reverse=True)[:1]
                    for cnt in cnts:
                        rect = cv2.boundingRect(cnt)
                        #cv2.rectangle(c2, (rect[0],rect[1]), (rect[2]+rect[0],rect[3]+rect[1]), (255,255,255), 2)
                        c2 = c2[rect[1]:rect[3]+rect[1],rect[0]:rect[2]+rect[0]]
                        c2= cv2.copyMakeBorder(c2,5,5,5,5,cv2.BORDER_CONSTANT,value=(0,0,0))
                        list_images.append(c2)
                    #show_image(c2,"box")
                    no = image_to_num(c2)
            num.append(no)
    centroidx = np.transpose(centroidx)
    centroidy = np.transpose(centroidy)
    return c2, num, centroidx, centroidy

######## creating matrix and filling numbers exist in the orig image #######
def sudoku_matrix(num):
    c = 0
    grid = np.empty((9, 9))
    for i in range(9):
        for j in range(9):
            grid[i][j] = int(num[c])

            c += 1
    grid = np.transpose(grid)
    return grid

######## Creating board to show the puzzle result in terminal##############
def board(arr):
    for i in range(9):

        if i%3==0 :
                print("+",end="")
                print("-------+"*3)

        for j in range(9):
            if j%3 ==0 :
                print("",end="| ")
            print(int(arr[i][j]),end=" ")

        print("",end="|")       
        print()

    print("+",end="")
    print("-------+"*3)
    return arr      

def check_col(arr,num,col):
    if  all([num != arr[i][col] for i in range(9)]):
        return True
    return False


def check_row(arr,num,row):
    if  all([num != arr[row][i] for i in range(9)]):
        return True
    return False


def check_cell(arr,num,row,col):
    sectopx = 3 * (row//3)
    sectopy = 3 * (col//3)

    for i in range(sectopx, sectopx+3):
        for j in range(sectopy, sectopy+3):
            if arr[i][j] == num:
                return True
    return False


def empty_loc(arr,l):
    for i in range(9):
        for j in range(9):
            if arr[i][j] == 0:
                l[0]=i
                l[1]=j
                return True              
    return False

#### Solving sudoku by back tracking############
def sudoku(arr):
    l=[0,0]

    if not empty_loc(arr,l):
        return True

    row = l[0]
    col = l[1]

    for num in range(1,10):
        if check_row(arr,num,row) and check_col(arr,num,col) and not check_cell(arr,num,row,col):
            arr[row][col] = int(num) 

            if(sudoku(arr)):
                return True

            # failure, unmake & try again
            arr[row][col] = 0

    return False

def overlay(arr,num,img,cx,cy):
    no = -1
    for i in range(9):
        for j in range(9):
            no += 1 
            #cv2.putText(img,str(no), (int(cx[i][j]),int(cy[i][j])),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2)
            if num[no] == 0:

                cv2.putText(img,str(int(arr[j][i])), (int(cx[i][j]-4),int(cy[i][j])+8),cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 4)

    cv2.imshow("Sudoku",img)
    cv2.waitKey(0)

case = "False" # If transformation is required set True 
image = cv2.imread("QupKb.png")

th3,warped1,warped = preprocess(image,case)
warped2 = warped1.copy()
img = grids(warped,warped2)
c2,bm,cnts = grid_points(img,warped2)
c2,num,cx,cy = get_digit(c2,bm,warped1,cnts)
grid = sudoku_matrix(num)
if(sudoku(grid)):
    arr = board(grid)
    overlay(arr,num,warped1,cx,cy)

else:
    print("There is no solution")

voilé:

th3:

warped2:

résultat sudoku:

Tous les chiffres extraits:

########## To view all the extracted digits ###############
_, axs = plt.subplots(1, len(list_images), figsize=(24, 24))
axs = axs.flatten()
for img, ax in zip(list_images, axs):
    ax.imshow(cv2.resize(img,(64,64)))
plt.show()

Références:

• points de grille

• obtenir des fonctionnalités (de chiffres)

• Exemples d'images