import cv2
import numpy as np
import math
!wget https://media.wired.com/photos/5c5354d391d0df22c1dee493/master/w_1024%2Cc_limit/Backchannel-Lena-Final.jpg
img = cv2.imread('Backchannel-Lena-Final.jpg')

h, w, c = img.shape
print(h, w, c)

mat = cv2.getRotationMatrix2D((w / 2, h / 2), 45, 0.5)
print(mat)

affine_img = cv2.warpAffine(img, mat, (w, h))
cv2.imwrite('data/dst/opencv_affine.jpg', affine_img)

import matplotlib.pyplot as plt
plt.subplot(211)
plt.imshow(affine_img)
plt.subplot(212)
plt.imshow(img)
plt.title('original')

plt.show()

affine_img_half = cv2.warpAffine(img, mat, (w, h // 2))
cv2.imwrite('data/dst/opencv_affine_half.jpg', affine_img_half)

plt.imshow(affine_img_half)
plt.show()

!wget https://i.stack.imgur.com/4wQjD.jpg

checker = '4wQjD.jpg'


gray = cv2.imread( checker )
edges = cv2.Canny(gray, 50, 150, apertureSize = 3)
cv2.imwrite('edges-50-150.jpg', edges)
minLineLength=100
lines = cv2.HoughLinesP(image=edges,rho=1,
                        theta=np.pi/180, threshold=100,
                        lines=np.array([]), minLineLength=minLineLength,
                        maxLineGap=80)

a,b,c = lines.shape
for i in range(a):
    x0, y0, x1, y1 = lines[i][0]
    cv2.line(gray, (lines[i][0][0], lines[i][0][1]), 
             (lines[i][0][2], lines[i][0][3]), 
             (0, 0, 255), 3, cv2.LINE_AA)
    cv2.imwrite('houghlines5.jpg',gray)
    break
tangent = (y1-y0)/(x0-x1)
print(tangent, np.arctan( tangent/np.pi*180 )+90 )
degree = np.arctan( tangent/np.pi*180 )+90
plt.imshow(gray)
plt.show()

def coords(cv2_image):
  edges = cv2.Canny(cv2_image, 50, 150, apertureSize = 3)
  minLineLength=100
  lines = cv2.HoughLinesP(image=edges,rho=1,
                          theta=np.pi/180, threshold=100,
                          lines=np.array([]), minLineLength=minLineLength,
                          maxLineGap=20)# 80
  return lines[0][0]

p0 = np.array((x0, y0))
p1 = np.array((x1, y1))

h, w, c = gray.shape
print(h, w, c)
degree = - np.arctan( tangent ) /np.pi*180

mean = (p0+p1)/2

mat = cv2.getRotationMatrix2D( tuple(mean.tolist()), degree, 0.5)
print( mat )

affine_img = cv2.warpAffine(gray, mat, (w, h))
c = coords( affine_img )
print( c )
plt.imshow(affine_img)
plt.show()