Extract a fixed number of squares from an image with Python/OpenCV

Question

I have several scanned images I would like to compute with Python/Opencv. Each of these images (see an example below) contains n rows of coloured squares. Each of these squares have the same size. The goal is to crop each of these squares and to extract the data from it.

I have found there a code which is able to extract squares from an image.

Here is my code where I have used it :

import numpy as np
import cv2
from matplotlib import pyplot as plt

def angle_cos(p0, p1, p2):
    import numpy as np

    d1, d2 = (p0-p1).astype('float'), (p2-p1).astype('float')
    return abs( np.dot(d1, d2) / np.sqrt( np.dot(d1, d1)*np.dot(d2, d2) ) )

def find_squares(img):
    import cv2 as cv
    import numpy as np

    img = cv.GaussianBlur(img, (5, 5), 0)
    squares = []
    for gray in cv.split(img):
        for thrs in range(0, 255, 26):
            if thrs == 0:
                bin = cv.Canny(gray, 0, 50, apertureSize=5)
                bin = cv.dilate(bin, None)
            else:
                _retval, bin = cv.threshold(gray, thrs, 255, cv.THRESH_BINARY)
            contours, _hierarchy = cv.findContours(bin, cv.RETR_LIST, cv.CHAIN_APPROX_SIMPLE)
            for cnt in contours:
                cnt_len = cv.arcLength(cnt, True)
                cnt = cv.approxPolyDP(cnt, 0.02*cnt_len, True)
                if len(cnt) == 4 and cv.contourArea(cnt) > 1000 and cv.isContourConvex(cnt):
                    cnt = cnt.reshape(-1, 2)
                    max_cos = np.max([angle_cos( cnt[i], cnt[(i+1) % 4], cnt[(i+2) % 4] ) for i in range(4)])
                    if max_cos < 0.1:
                        squares.append(cnt)
    print(len(squares))
    return squares

img = cv2.imread("test_squares.jpg",1)

plt.axis("off")
plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
plt.show()

squares = find_squares(img)
cv2.drawContours( img, squares, -1, (0, 255, 0), 1 )
plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
plt.show()

However, it finds two many squares (100 instead of 15 !!). Looking at the image, it seems that Opencv find a lot of contours for each square.

I'm pretty sure that it can be optimized since the squares have more or less the same size and far from each other. As a very beginner in Opencv, I haven't found yet a way to give more criteria in the function "find squares" in order to get only 15 squares at the end of the routine. Maybe the contour area can be maximized ?

I have also found there a more detailed code (very close to the previous one) but it seems to be developed in a old version of Opencv. I haven't managed to make it work (and so to modify it).

Answer 1

This is another more robust method.

I used this code to find the contours in the image (the full code can be found in this gist):

import cv2
import numpy as np
import matplotlib.pyplot as plt

# Define square size
min_square_size = 987
# Read Image
img = cv2.imread('/home/stephen/Desktop/3eY0k.jpg')
# Threshold and find edges
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Threshold the image - segment white background from post it notes
_, thresh = cv2.threshold(gray, 250, 255, cv2.THRESH_BINARY_INV);
# Find the contours
_, contours, _ = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)

I iterated through the contours. I only looked at the contours that were a reasonable size. I found the four corners of each contour.

# Create a list for post-it images
images = []
# Iterate through the contours in the image
for contour in contours:
    area = cv2.contourArea(contour)
    # If the contour is not really small, or really big
    h,w = img.shape[0], img.shape[1]
    if area > min_square_size and area < h*w-(2*(h+w)):
        # Get the four corners of the contour
        epsilon = .1 * cv2.arcLength(contour, True)
        approx = cv2.approxPolyDP(contour, epsilon, True)
        # Draw the point
        for point in approx: cv2.circle(img, tuple(point[0]), 2, (255,0,0), 2)
        # Warp it to a square
        pts1 = np.float32(approx)
        pts2 = np.float32([[0,0],[300,0],[300,300],[0,300]])
        M = cv2.getPerspectiveTransform(pts1,pts2)
        dst = cv2.warpPerspective(img,M,(300,300))
        # Add the square to the list of images
        images.append(dst.copy())

The post-it notes are squares, but because the camera warps the objects in the image they do not appear as squares. I used warpPerspective to make the post-it notes square shapes. Only a few of them are shown in this plot (there are more that didn't fit):

Answer 2

If your problem is that too many contours (edges) are found in the image, my suggestion is to modify the edge-finding part first. It'll be by far the easiest modification to make.

In particular, you'll need to change this call:

bin = cv.Canny(gray, 0, 50, apertureSize=5)

The cv.Canny() function takes as arguments two threshold values, the aperture size, and a boolean to indicate whether a precise form of gradient is used. Play with those parameters, and my guess is, you'll get much better results.