我建议大家从不同的角度看问题
Morphological Transformations
可以将其应用于canny边缘检测,以改进hough线变换。
这并不完美,但可以让你开始:
import cv2
import numpy as np
from matplotlib import pyplot
def detect_lines(img):
temp = cv2.cvtColor(img,cv2.COLOR_BGR2HLS)
kernel = np.ones((5, 5), np.uint8) # < --- Added a kernel you can differ
lower = np.uint8([0, 160, 0])
upper = np.uint8([255, 255, 255])
white_mask = cv2.inRange(temp, lower, upper)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gray = cv2.blur(gray, (3, 3))
canny_low = 100
edges = cv2.Canny(white_mask, canny_low, canny_low * 3, apertureSize=3)
dilate = cv2.dilate(edges, kernel, iterations=2) # < --- Added a dilate, check link I provided
ero = cv2.erode(dilate, kernel, iterations=1) # < --- Added an erosion, check link I provided
lines = cv2.HoughLinesP(dilate, 1, np.pi/180, 10, 2, 80)
result = img.copy()
if lines is not None:
for x in range(0, len(lines)):
for x1, y1, x2, y2 in lines[x]:
print(x1, y1, x2, y2)
cv2.line(result, (x1, y1), (x2, y2), (255, 0, 0), 2)
pyplot.subplot(151), pyplot.imshow(img, cmap='gray')
pyplot.title('Original Image'), pyplot.xticks([]), pyplot.yticks([])
pyplot.subplot(152), pyplot.imshow(white_mask, cmap='gray')
pyplot.title('Mask Image'), pyplot.xticks([]), pyplot.yticks([])
pyplot.subplot(153), pyplot.imshow(edges, cmap='gray')
pyplot.title('Edge Image'), pyplot.xticks([]), pyplot.yticks([])
pyplot.subplot(154), pyplot.imshow(ero, cmap='gray')
pyplot.title('Dilate/Erosion Image'), pyplot.xticks([]), pyplot.yticks([]) # <--- Added a display
pyplot.subplot(155), pyplot.imshow(result, cmap='gray')
pyplot.title('Result Image'), pyplot.xticks([]), pyplot.yticks([])
pyplot.show()
return result # <--- You want to return the result right?
if __name__ == '__main__':
image = cv2.imread('receipt.jpg')
image = detect_lines(image)
cv2.imwrite('output.jpg', image)
另一种方法可能是调查
Corner Detection
然后在检测到的角点之间画一条线(我没有尝试过这种方法,但这只是为了灵感:)。