├── README.md
├── input
    └── document.jpg
├── output
    └── document.jpg
├── warp_perspective_basic.py
└── warp_perspective.py


/README.md:
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1 | # Warp_perspective
2 | Warp perspective using OpenCV
3 | 


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/input/document.jpg:
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https://raw.githubusercontent.com/codegiovanni/Warp_perspective/HEAD/input/document.jpg


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/output/document.jpg:
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https://raw.githubusercontent.com/codegiovanni/Warp_perspective/HEAD/output/document.jpg


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/warp_perspective_basic.py:
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 1 | import cv2
 2 | import numpy as np
 3 | 
 4 | img = cv2.imread('input/document.jpg')
 5 | 
 6 | # Pixel values in the original image
 7 | input_points = np.float32([[83, 18], [342, 53], [14, 389], [295, 436]])
 8 | 
 9 | # Output image size
10 | width = 400
11 | height = int(width * 1.414)  # for A4
12 | 
13 | # Desired points values in the output image
14 | converted_points = np.float32([[0, 0], [width, 0], [0, height], [width, height]])
15 | 
16 | # Perspective transformation
17 | matrix = cv2.getPerspectiveTransform(input_points, converted_points)
18 | img_output = cv2.warpPerspective(img, matrix, (width, height))
19 | 
20 | cv2.imshow('Original', img)
21 | cv2.imshow('Warped perspective', img_output)
22 | 
23 | # cv2.imwrite('output/document.jpg', img_output)
24 | 
25 | cv2.waitKey(0)
26 | 


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/warp_perspective.py:
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 1 | import cv2
 2 | import numpy as np
 3 | 
 4 | 
 5 | def biggest_contour(contours):
 6 |     biggest = np.array([])
 7 |     max_area = 0
 8 |     for i in contours:
 9 |         area = cv2.contourArea(i)
10 |         if area > 1000:
11 |             peri = cv2.arcLength(i, True)
12 |             approx = cv2.approxPolyDP(i, 0.015 * peri, True)
13 |             if area > max_area and len(approx) == 4:
14 |                 biggest = approx
15 |                 max_area = area
16 |     return biggest
17 | 
18 | 
19 | img = cv2.imread('input/document.jpg')
20 | img_original = img.copy()
21 | 
22 | # Image modification
23 | gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
24 | gray = cv2.bilateralFilter(gray, 20, 30, 30)
25 | edged = cv2.Canny(gray, 10, 20)
26 | 
27 | # Contour detection
28 | contours, hierarchy = cv2.findContours(edged.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
29 | contours = sorted(contours, key=cv2.contourArea, reverse=True)[:10]
30 | 
31 | biggest = biggest_contour(contours)
32 | 
33 | cv2.drawContours(img, [biggest], -1, (0, 255, 0), 3)
34 | 
35 | # Pixel values in the original image
36 | points = biggest.reshape(4, 2)
37 | input_points = np.zeros((4, 2), dtype="float32")
38 | 
39 | points_sum = points.sum(axis=1)
40 | input_points[0] = points[np.argmin(points_sum)]
41 | input_points[3] = points[np.argmax(points_sum)]
42 | 
43 | points_diff = np.diff(points, axis=1)
44 | input_points[1] = points[np.argmin(points_diff)]
45 | input_points[2] = points[np.argmax(points_diff)]
46 | 
47 | (top_left, top_right, bottom_right, bottom_left) = input_points
48 | bottom_width = np.sqrt(((bottom_right[0] - bottom_left[0]) ** 2) + ((bottom_right[1] - bottom_left[1]) ** 2))
49 | top_width = np.sqrt(((top_right[0] - top_left[0]) ** 2) + ((top_right[1] - top_left[1]) ** 2))
50 | right_height = np.sqrt(((top_right[0] - bottom_right[0]) ** 2) + ((top_right[1] - bottom_right[1]) ** 2))
51 | left_height = np.sqrt(((top_left[0] - bottom_left[0]) ** 2) + ((top_left[1] - bottom_left[1]) ** 2))
52 | 
53 | # Output image size
54 | max_width = max(int(bottom_width), int(top_width))
55 | # max_height = max(int(right_height), int(left_height))
56 | max_height = int(max_width * 1.414)  # for A4
57 | 
58 | # Desired points values in the output image
59 | converted_points = np.float32([[0, 0], [max_width, 0], [0, max_height], [max_width, max_height]])
60 | 
61 | # Perspective transformation
62 | matrix = cv2.getPerspectiveTransform(input_points, converted_points)
63 | img_output = cv2.warpPerspective(img_original, matrix, (max_width, max_height))
64 | 
65 | # Image shape modification for hstack
66 | gray = np.stack((gray,) * 3, axis=-1)
67 | edged = np.stack((edged,) * 3, axis=-1)
68 | 
69 | img_hor = np.hstack((img_original, gray, edged, img))
70 | cv2.imshow("Contour detection", img_hor)
71 | cv2.imshow("Warped perspective", img_output)
72 | 
73 | # cv2.imwrite('output/document.jpg', img_output)
74 | 
75 | cv2.waitKey(0)


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