def find_lines_by_standard_hough(the_image):
cur_image = the_image.copy()
''' This blur operation is quite useful '''
# cur_image = cv2.GaussianBlur(cur_image,ksize=(5,5), sigmaX=0)
# cur_image = cv2.GaussianBlur(cur_image,ksize=(3,3), sigmaX=0)
''' open, it could remove the border '''
# kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
# cur_image = cv2.morphologyEx(cur_image, cv2.MORPH_OPEN, kernel)
# cv2_helper.Image.show(cur_image)
# low_threshold, high_threshold = 40, 80
low_threshold, high_threshold = 70, 140
cur_image = cv2.Canny(cur_image, low_threshold, high_threshold)
# cv2_helper.Image.show(cur_image)
lines = cv2.HoughLines(cur_image, rho=1, theta=numpy.pi/180, threshold= 200)
''' this is true line list'''
if numpy_helper.is_array_none(lines):
return []
lines = lines[0]
len(lines).pp()
''' you can decline the count line by canny low_threshold '''
if len(lines) > 200 or len(lines) < 1:
return []
show_lines(the_image, lines)
def find(low_threshold):
cur_image = the_image.copy()
high_threshold = low_threshold * 2
# low_threshold,high_threshold = 50, 100
cur_image.shape.pp()
threshold = int(cur_image.shape[0] * 0.4)
threshold.pp()
cur_image = cv2.Canny(cur_image, low_threshold, high_threshold)
lines = cv2.HoughLines(cur_image, rho=1, theta=numpy.pi/180, threshold= threshold)
''' this is true line list'''
if numpy_helper.is_array_none(lines):
return []
return lines[0]
