def detect_line_hough(img):
img_edge = edge_detect.full_detect(img, is_binary=False)
lines = cv2.HoughLinesP(img_edge, 1, np.pi/2, 2, minLineLength=200, maxLineGap=10)
if lines is not None:
for x1, y1, x2, y2 in lines[0]:
# print (x1, y1), (x2, y2)
cv2.line(img, (x1, y1), (x2, y2), (255, 255, 255), 1)
return img
def compute_skew(img, is_binary=True, canny=True):
img = edge_detect.full_detect(img, is_binary, canny)
angle = line_detect.process(img, binary_image=True)[1]
# angle = line_detect.houghlines(img, binary_image=True)
angle = -angle if angle < 90 else (180 - angle)
angle = -(90 - angle) if angle > 45 else angle
angle = -(90 + angle) if angle < -45 else angle
return angle
def compute_skew(img, is_binary=True, canny=True):
img = edge_detect.full_detect(img, is_binary, canny)
angle = line_detect.process(img, binary_image=True)[1]
# angle = line_detect.houghlines(img, binary_image=True)
angle = -angle if angle < 90 else (180 - angle)
angle = -(90 - angle) if angle > 45 else angle
angle = -(90 + angle) if angle < -45 else angle
return angle
def detect_line_hough(img):
img_edge = edge_detect.full_detect(img, is_binary=False)
lines = cv2.HoughLinesP(img_edge,
1,
np.pi / 2,
2,
minLineLength=200,
maxLineGap=10)
if lines is not None:
for x1, y1, x2, y2 in lines[0]:
# print (x1, y1), (x2, y2)
cv2.line(img, (x1, y1), (x2, y2), (255, 255, 255), 1)
return img
def detect_line(img):
img_edge = edge_detect.full_detect(img, is_binary=False)
sum_x = np.sum(img_edge, axis=0)
# max_xs = bn.argpartsort(-sum_x, 10)[:10]
max_xs = sum_x.argsort()[-10:]
sum_y = np.sum(img_edge, axis=1)
max_ys = bn.argpartsort(-sum_y, 10)[:10]
height, width = img_edge.shape[:2]
for x in max_xs:
# print (x, 0), (x, height)
cv2.line(img, (x, 0), (x, height), (255, 255, 255), 1)
for y in max_ys:
# print (0, y), (width, y)
cv2.line(img, (0, y), (width, y), (255, 255, 255), 1)
return img
def detect_line(img):
img_edge = edge_detect.full_detect(img, is_binary=False)
sum_x = np.sum(img_edge, axis=0)
# max_xs = bn.argpartsort(-sum_x, 10)[:10]
max_xs = sum_x.argsort()[-10:]
sum_y = np.sum(img_edge, axis=1)
max_ys = bn.argpartsort(-sum_y, 10)[:10]
height, width = img_edge.shape[:2]
for x in max_xs:
# print (x, 0), (x, height)
cv2.line(img, (x, 0), (x, height), (255, 255, 255), 1)
for y in max_ys:
# print (0, y), (width, y)
cv2.line(img, (0, y), (width, y), (255, 255, 255), 1)
return img
