Opencv: определение линии на изображении (доп. g коды по изображению)

import cv2
import numpy as np
 
img = cv2.imread('cybforum.png', cv2.IMREAD_COLOR)
 
# Convert the image to gray-scale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
 
# Find the edges in the image using canny detector
edges = cv2.Canny(gray, 50, 200, None, 3)
 
# Resize image to 256px*256px
img_256px = cv2.resize(edges, dsize=(256, 256))
cv2.imshow("Result Image", img_256px)
cv2.waitKey(0)
 
rho = 1  # distance resolution in pixels of the Hough grid
theta = np.pi / 180  # angular resolution in radians of the Hough grid
threshold = 10  # minimum number of votes (intersections in Hough grid cell)
min_line_length = 5  # minimum number of pixels making up a line
max_line_gap = 10  # maximum gap in pixels between connectable line segments
line_image = np.copy(img) * 0  # creating a blank to draw lines on
 
# Run Hough on edge detected image
# Output "lines" is an array containing endpoints of detected line segments
lines = cv2.HoughLinesP(edges, rho, theta, threshold, np.array([]),
                    min_line_length, max_line_gap)
 
for line in lines:
    for x1,y1,x2,y2 in line:
        cv2.line(line_image,(x1,y1),(x2,y2),(255,0,0),5)
 
# Resize image to 256px*256px
img_256px = cv2.resize(line_image, dsize=(256, 256))
cv2.imshow("Result Image", img_256px)
cv2.waitKey(0)
kek = 1

from matplotlib import pyplot as plt
from skimage.morphology import medial_axis
import cv2
 
# Determine contour
img_file = 'very_hard_14px.png'
img = cv2.imread(img_file, cv2.IMREAD_COLOR)
 
# Convert the image to gray-scale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gray = cv2.bitwise_not(gray)
 
# Compute the medial axis (skeleton) and the distance transform
skel, distance = medial_axis(gray, return_distance=True)
 
# Distance to the background for pixels of the skeleton
dist_on_skel = distance * skel
 
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4))
ax1.imshow(gray, cmap=plt.cm.gray, interpolation='nearest')
ax1.axis('off')
ax2.imshow(dist_on_skel, interpolation='nearest')
ax2.contour(gray, [0.5], colors='w')
ax2.axis('off')
 
fig.subplots_adjust(hspace=0.01, wspace=0.01, top=1, bottom=0, left=0, right=1)
plt.show()

kernel = np.ones((5, 5), 'uint8')
check = cv2.erode(img, kernel, iterations=10)
cv2.imshow('image', check)

from skimage.morphology import skeletonize
import cv2
img = cv2.imread('cybforum.png', cv2.IMREAD_COLOR)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
_,bw=cv2.threshold(gray,  127,1, cv2.THRESH_BINARY_INV)
skel=~skeletonize(bw)
cv2.imwrite( 'skel.png', 255*skel)