-
Notifications
You must be signed in to change notification settings - Fork 0
/
main.py
126 lines (98 loc) · 4.03 KB
/
main.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
import cv2
import numpy as np
frameheight = 640
framewidth = 480
# Add your custom path here
# where the video you want is stored.
url = '/home/aftaab/8DayPlan/UniProjects/DocumentScanner/test_video.mp4'
cap = cv2.VideoCapture(url)
cap.set(3, 640)
cap.set(4, 480)
cap.set(10, 150)
def preProcessing(img):
imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
imgBlur = cv2.GaussianBlur(imgGray, (5,5), 1)
imgCanny = cv2.Canny(imgBlur, 200, 200)
kernel = np.ones((5,5))
imgDial = cv2.dilate((imgCanny), kernel, iterations = 2)
imgThres = cv2.erode(imgDial, kernel, iterations = 1)
return imgThres
def getContours(img):
biggest = np.array([])
maxArea = 0
contours, hierarchy = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
for cnt in contours:
area = cv2.contourArea(cnt)
if area > 5000:
cv2.drawContours(imgContour, cnt, -1, (255, 0, 0), 20)
peri = cv2.arcLength(cnt, True)
approx = cv2.approxPolyDP(cnt, 0.02*peri, True)
if area > maxArea and len(approx) == 4:
biggest = approx
maxArea = area
return biggest
def reorder(myPoints):
myPoints = myPoints.reshape((4,2))
myPointsNew = np.zeros((4, 1, 2), np.int32)
add = myPoints.sum(1)
myPointsNew[0] = myPoints[np.argmin(add)]
myPointsNew[3] = myPoints[np.argmax(add)]
diff = np.diff(myPoints, axis=1)
myPointsNew[1] = myPoints[np.argmin(diff)]
myPointsNew[2] = myPoints[np.argmax(diff)]
return myPointsNew
def getWarp(img, biggest):
biggest = reorder(biggest)
pts1 = np.float32(biggest)
pts2 = np.float32([[0, 0], [framewidth, 0], [0, frameheight], [framewidth, frameheight]])
matrix = cv2.getPerspectiveTransform(pts1, pts2)
imgOutput = cv2.warpPerspective(img, matrix, (framewidth, frameheight))
imgCropped = imgOutput[20:imgOutput.shape[0]-20, 20:imgOutput.shape[1]-20]
imgCropped = cv2.resize(imgCropped, (framewidth, frameheight))
return imgCropped
def stackImages(scale,imgArray):
rows = len(imgArray)
cols = len(imgArray[0])
rowsAvailable = isinstance(imgArray[0], list)
width = imgArray[0][0].shape[1]
height = imgArray[0][0].shape[0]
if rowsAvailable:
for x in range ( 0, rows):
for y in range(0, cols):
if imgArray[x][y].shape[:2] == imgArray[0][0].shape [:2]:
imgArray[x][y] = cv2.resize(imgArray[x][y], (0, 0), None, scale, scale)
else:
imgArray[x][y] = cv2.resize(imgArray[x][y], (imgArray[0][0].shape[1], imgArray[0][0].shape[0]), None, scale, scale)
if len(imgArray[x][y].shape) == 2: imgArray[x][y]= cv2.cvtColor( imgArray[x][y], cv2.COLOR_GRAY2BGR)
imageBlank = np.zeros((height, width, 3), np.uint8)
hor = [imageBlank]*rows
hor_con = [imageBlank]*rows
for x in range(0, rows):
hor[x] = np.hstack(imgArray[x])
ver = np.vstack(hor)
else:
for x in range(0, rows):
if imgArray[x].shape[:2] == imgArray[0].shape[:2]:
imgArray[x] = cv2.resize(imgArray[x], (0, 0), None, scale, scale)
else:
imgArray[x] = cv2.resize(imgArray[x], (imgArray[0].shape[1], imgArray[0].shape[0]), None,scale, scale)
if len(imgArray[x].shape) == 2: imgArray[x] = cv2.cvtColor(imgArray[x], cv2.COLOR_GRAY2BGR)
hor= np.hstack(imgArray)
ver = hor
return ver
while True:
sucess, img = cap.read()
cv2.resize(img, (framewidth, frameheight))
imgContour = img.copy()
imgThres = preProcessing(img)
biggest = getContours(imgThres)
if biggest.size != 0:
imgWarped = getWarp(img, biggest)
imageArray = ([img, imgThres], [imgContour, imgWarped])
else:
imageArray = ([img, imgThres], [img, img])
stackedImages = stackImages(0.6, imageArray)
cv2.imshow("Work Flow", stackedImages)
cv2.imshow("Result", imgWarped)
if cv2.waitKey(1) & 0xFF == ord('q'):
break