-
Notifications
You must be signed in to change notification settings - Fork 0
/
documentScanner.py
184 lines (139 loc) · 6.33 KB
/
documentScanner.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
import numpy as np
import cv2
import re
from matplotlib import pyplot as plt
# Use Gaussian Blurring combined with Adaptive Threshold.
def blur_and_threshold(gray):
gray = cv2.GaussianBlur(gray,(3,3),2)
threshold = cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY,11,2)
threshold = cv2.fastNlMeansDenoising(threshold, 11, 31, 9)
return threshold
# Find the Biggest Contour
def biggest_contour(contours,min_area):
biggest = None
max_area = 0
biggest_n=0
approx_contour=None
for n,i in enumerate(contours):
area = cv2.contourArea(i)
if area > min_area/10:
peri = cv2.arcLength(i,True)
approx = cv2.approxPolyDP(i,0.02*peri,True)
if area > max_area and len(approx)==4:
biggest = approx
max_area = area
biggest_n=n
approx_contour=approx
return biggest_n,approx_contour
def order_points(pts):
# initialzie a list of coordinates that will be ordered
# such that the first entry in the list is the top-left,
# the second entry is the top-right, the third is the
# bottom-right, and the fourth is the bottom-left
pts=pts.reshape(4,2)
rect = np.zeros((4, 2), dtype = "float32")
# the top-left point will have the smallest sum, whereas
# the bottom-right point will have the largest sum
s = pts.sum(axis = 1)
rect[0] = pts[np.argmin(s)]
rect[2] = pts[np.argmax(s)]
# now, compute the difference between the points, the
# top-right point will have the smallest difference,
# whereas the bottom-left will have the largest difference
diff = np.diff(pts, axis = 1)
rect[1] = pts[np.argmin(diff)]
rect[3] = pts[np.argmax(diff)]
# return the ordered coordinates
return rect
# ## Find the exact (x,y) coordinates of the biggest contour and crop it out
def four_point_transform(image, pts):
# obtain a consistent order of the points and unpack them
# individually
rect = order_points(pts)
(tl, tr, br, bl) = rect
# compute the width of the new image, which will be the
# maximum distance between bottom-right and bottom-left
# x-coordiates or the top-right and top-left x-coordinates
widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
maxWidth = max(int(widthA), int(widthB))
# compute the height of the new image, which will be the
# maximum distance between the top-right and bottom-right
# y-coordinates or the top-left and bottom-left y-coordinates
heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
maxHeight = max(int(heightA), int(heightB))
# now that we have the dimensions of the new image, construct
# the set of destination points to obtain a "birds eye view",
# (i.e. top-down view) of the image, again specifying points
# in the top-left, top-right, bottom-right, and bottom-left
# order
dst = np.array([
[0, 0],
[maxWidth - 1, 0],
[maxWidth - 1, maxHeight - 1],
[0, maxHeight - 1]], dtype = "float32")
# compute the perspective transform matrix and then apply it
M = cv2.getPerspectiveTransform(rect, dst)
warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))
# return the warped image
return warped
# Transformation of the image
# 1. Convert the image to grayscale.
# 2. Smoothen out the image by applying blurring and thresholding techniques.
# 3. Use Canny Edge Detection to find the edges.
# 4. Find the biggest contour and crop it out.
def transformation(image):
image=image.copy()
height, width, channels = image.shape
gray=cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
cv2.imwrite("1.jpg", gray)
image_size=gray.size
threshold=blur_and_threshold(gray)
cv2.imwrite("2.jpg", threshold)
# We need two threshold values, minVal and maxVal. Any edges with intensity gradient more than maxVal
# are sure to be edges and those below minVal are sure to be non-edges, so discarded.
# Those who lie between these two thresholds are classified edges or non-edges based on their connectivity.
# If they are connected to "sure-edge" pixels, they are considered to be part of edges.
# Otherwise, they are also discarded
edges = cv2.Canny(threshold,50,150,apertureSize = 7)
cv2.imwrite("3.jpg", edges)
contours, hierarchy = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
simplified_contours = []
for cnt in contours:
hull = cv2.convexHull(cnt)
simplified_contours.append(cv2.approxPolyDP(hull,
0.001*cv2.arcLength(hull,True),True))
simplified_contours = np.array(simplified_contours,dtype=object)
biggest_n,approx_contour = biggest_contour(simplified_contours,image_size)
threshold = cv2.drawContours(image, simplified_contours ,biggest_n, (0,255,0), 1)
dst = 0
if approx_contour is not None and len(approx_contour)==4:
approx_contour=np.float32(approx_contour)
dst=four_point_transform(threshold,approx_contour)
croppedImage = dst
return croppedImage
# **Increase the brightness of the image by playing with the "V" value (from HSV)**
def increase_brightness(img, value=30):
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
h, s, v = cv2.split(hsv)
lim = 255 - value
v[v > lim] = 255
v[v <= lim] += value
final_hsv = cv2.merge((h, s, v))
img = cv2.cvtColor(final_hsv, cv2.COLOR_HSV2BGR)
cv2.imwrite("4.jpg", img)
return img
# Sharpen the image using Kernel Sharpening Technique.
def final_image(rotated):
# Create our shapening kernel, it must equal to one eventually
kernel_sharpening = np.array([[0,-1,0],
[-1, 5,-1],
[0,-1,0]])
# Applying the sharpening kernel to the input image & displaying it.
sharpened = cv2.filter2D(rotated, -1, kernel_sharpening)
sharpened=increase_brightness(sharpened,30)
cv2.imwrite("5.jpg", sharpened)
return sharpened
# 1. Pass the image through the transformation function to crop out the biggest contour.
# 2. Brighten & Sharpen the image to get a final cleaned image.