def detect(img):
canny = thresh(img)
sobel = cv2.Sobel(canny, cv2.CV_64F, 1, 0, ksize=5)
row, col = sobel.shape
sobel = cv2.erode(sobel, np.ones([int(row / 15), 1]), iterations=1)
sobel = cv2.dilate(sobel, np.ones([int(row / 1.5), 1]), iterations=1)
sobel = np.array(sobel, dtype=np.uint8)
_, sobel = cv2.threshold(sobel, 200, 255, cv2.THRESH_BINARY)
# print(canny.shape)
lines = cv2.HoughLinesP(sobel, 1, np.pi / 180, 150, None, int(0.5 * row),
30)
# print("Initial lines : " , len(lines))
# val = 0
if lines is not None:
lines = lines.tolist()
lines.sort(key=lambda x: x[0][0])
print("Initial Lines : ", len(lines))
lines = deletelines(lines)
print("After deletion : ", len(lines))
for line in lines:
# print(line)
cv2.line(img, (line[0][0], line[0][1]), (line[0][2], line[0][3]),
(0, 255, 0), 2)
if line[0][0] == line[0][2]:
theta = 90
else:
theta = slope(line)
# print(theta)
if theta > 80:
cv2.line(img, (line[0][0], line[0][1]),
(line[0][2], line[0][3]), (0, 255, 0), 2)
# val+=1
# print(val)
cv2.imshow("Output", img)
# cv2.imshow("Sobel",sobel)
# cv2.imshow("Canny",canny)
cv2.waitKey(0)
vertical_lines = [x[0][0] for x in lines]
# print(vertical_lines)
return vertical_lines
def detect(img): # Detect Vertical Lines
canny = thresh(img) # Thresh
sobel = cv2.Sobel(canny, cv2.CV_64F, 1, 0,
ksize=5) # Apply Sobel filter -- Y
row, col = sobel.shape
# Morphological Opn
sobel = cv2.erode(sobel, np.ones([int(row / 15), 1]), iterations=1)
sobel = cv2.dilate(sobel, np.ones([int(row / 1.5), 1]), iterations=1)
sobel = np.array(sobel, dtype=np.uint8)
_, sobel = cv2.threshold(sobel, 200, 255, cv2.THRESH_BINARY)
# print(canny.shape)
# Detect Vertical Lines
lines = cv2.HoughLinesP(sobel, 1, np.pi / 180, 150, None, int(0.4 * row),
30)
# print("Initial lines : " , len(lines))
if lines is not None:
lines = lines.tolist()
lines.sort(key=lambda x: x[0][0])
# print("Initial Lines : ",len(lines))
lines = deletelines(lines) # Delete Clustered Lines
# print("After deletion : ",len(lines))
for line in lines:
# cv2.line(img,(line[0][0],line[0][1]),(line[0][2],line[0][3]),(0,255,0),2)
if line[0][0] == line[0][2]:
theta = 90
else:
theta = slope(line)
# cv2.imshow("Output",img)
# cv2.imshow("Sobel",sobel)
# cv2.imshow("Canny",canny)
# cv2.waitKey(0)
vertical_lines = [x[0][0] for x in lines]
return vertical_lines
def detect(img): #Horizontal Lines Detection
canny = rectangle.thresh(img) # Thresh image
sobel = cv2.Sobel(canny,cv2.CV_64F,0,1,ksize=5) # Sobel Filtering
row,col = sobel.shape
# Morphological Operations
sobel = cv2.erode(sobel,np.ones([1,int(col/10)]),iterations=1)
sobel = cv2.dilate(sobel,np.ones([1,int(col/2)]),iterations=1)
sobel = np.array(sobel,dtype=np.uint8)
_,sobel = cv2.threshold(sobel,200,255,cv2.THRESH_BINARY)
# Detect Lines
lines = cv2.HoughLinesP(sobel,1,np.pi/180,150,None,col/2,30)
val = 0
if lines is not None: # Condition Check
lines = lines.tolist()
lines.sort(key= lambda x : x[0][1])
# print(len(lines))
lines = deletelines(lines) # Delete clustered lines
for line in lines:
if line[0][0] == line[0][2]:
theta = 90
else :
theta = slope(line)
if theta < 10: # Condition Check
# cv2.line(img,(line[0][0],line[0][1]),(line[0][2],line[0][3]),(0,255,0),2)
val+=1
# cv2.imshow("Sobel",sobel)
# cv2.imshow("Canny",canny)
# cv2.waitKey(0)
return lines
def detect(img):
canny = thresh(img)
sobel = cv2.Sobel(canny, cv2.CV_64F, 0, 1, ksize=5)
row, col = sobel.shape
sobel = cv2.erode(sobel, np.ones([1, int(col / 10)]), iterations=1)
sobel = cv2.dilate(sobel, np.ones([1, int(col / 2)]), iterations=1)
_, sobel = cv2.threshold(sobel, 200, 255, cv2.THRESH_BINARY)
sobel = np.array(sobel, dtype=np.uint8)
# print(sobel.shape,sobel.dtype)
lines = cv2.HoughLinesP(sobel, 1, np.pi / 180, 150, None, col / 2, 30)
# print("Initial lines : " , len(lines))
val = 0
if lines is not None:
lines = lines.tolist()
lines.sort(key=lambda x: x[0][1])
print(len(lines))
lines = deletelines(lines)
for line in lines:
if line[0][0] == line[0][2]:
theta = 90
else:
theta = slope(line)
# print(theta)
if theta < 10:
cv2.line(img, (line[0][0], line[0][1]),
(line[0][2], line[0][3]), (0, 255, 0), 2)
val += 1
print(val)
# cv2.imshow("Sobel",sobel)
# cv2.imshow("Canny",canny)
# cv2.waitKey(0)
return lines