使用Python/OpenCV从图像中提取固定数量的正方形 [英] Extract a fixed number of squares from an image with Python/OpenCV

问题描述

我有几张要使用Python/Opencv计算的扫描图像.这些图像中的每一个(请参见下面的示例)都包含n行彩色正方形.这些正方形均具有相同的大小.目的是裁剪每个正方形并从中提取数据.

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.

我发现有的代码能够从图像中提取正方形.

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

这是我使用过的代码:

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()

但是，它会找到两个很多平方(100个而不是15个!!).查看图像，似乎Opencv可以为每个正方形找到很多轮廓.

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.

我非常确定可以对其进行优化，因为正方形的大小大致相同，并且彼此之间相距甚远.作为Opencv的初学者，我还没有找到一种在函数查找平方"中给出更多条件的方法，以便在例程结束时仅获得15个平方.也许轮廓区域可以最大化?

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 ?

我还发现有更详细的代码(非常接近上一个代码) )，但它似乎是在Opencv的旧版本中开发的.我没有设法使其正常工作(因此无法对其进行修改).

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).

推荐答案

这是我使用此代码在图像中找到轮廓(完整代码可在本要点中找到 ):

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)

我遍历了轮廓.我只看了一个合理大小的轮廓.我找到了每个轮廓的四个角.

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())

便利贴为正方形，但由于相机扭曲图像中的对象，因此它们不会显示为正方形.我使用warpPerspective使便签纸成为正方形.此图中仅显示了其中的一些(还有更多不适合的显示):

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):

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