import cv2 import numpy as np

img = cv2.imread('Bluelinegap50blur.jpg') gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) edges = cv2.Canny(img, 50, 200)

find lines

rho = 1 theta = np.pi / 180 threshold = 10 min_line_length = 0 max_line_gap = 10 line_image = np.copy(edges) * 0 lines = cv2.HoughLinesP(edges, rho, theta, threshold, np.array([]), min_line_length, max_line_gap)

draw found lines

img_dst = cv2.cvtColor(gray,cv2.COLOR_GRAY2RGB) for line in lines[0]: cv2.line(img_dst, (line[0], line[1]), (line[2], line[3]), (0,255,0), 2)

find corners

gray = np.float32(gray) dst = cv2.cornerHarris(gray, 15, 5, 0.04)

non-maximum suppression via dilation

kernel = np.ones((10,10)) max_dst = cv2.dilate(dst, kernel)
dst = dst * (dst == max_dst)

sort points by strength, find their positions and take 5 highest ones

sortIdx = np.argsort(dst.flatten())[::-1][:5]

draw them

w = img.shape[1]

for idx in sortIdx: cv2.circle(img_dst,(idx % w, idx / w) , 3, (0,0,255), -1)

...

import cv2
import numpy as np
 
np

img = cv2.imread('Bluelinegap50blur.jpg')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(img, 50, 200)
 
200)

# find lines
 
lines
rho = 1
theta = np.pi / 180
threshold = 10
min_line_length = 0
max_line_gap = 10
line_image = np.copy(edges) * 0
lines = cv2.HoughLinesP(edges, rho, theta, threshold, np.array([]),
                    min_line_length, max_line_gap)
 
max_line_gap)
# draw found lines
 
lines        
img_dst = cv2.cvtColor(gray,cv2.COLOR_GRAY2RGB)
for line in lines[0]:
    cv2.line(img_dst, (line[0], line[1]), (line[2], line[3]), (0,255,0), 2)
 
2)

# find corners
 
corners
gray = np.float32(gray)
dst = cv2.cornerHarris(gray, 15, 5, 0.04)
 
0.04)
# non-maximum suppression via dilation
 
dilation
kernel = np.ones((10,10))
max_dst = cv2.dilate(dst, kernel) 
 
dst = dst * (dst == max_dst)
 
max_dst)
# sort points by strength, find their positions and take 5 highest ones
 
ones
sortIdx = np.argsort(dst.flatten())[::-1][:5]
 
np.argsort(dst.flatten())[::-1][:5]
# draw them
 
them
w = img.shape[1]
 
img.shape[1]

for idx in sortIdx:
   cv2.circle(img_dst,(idx % w, idx / w) , 3, (0,0,255), -1)-1)