-
Notifications
You must be signed in to change notification settings - Fork 2
/
util.py
108 lines (96 loc) · 3.63 KB
/
util.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
import cv2
import numpy as np
## TO STACK ALL THE IMAGES IN ONE WINDOW
def stackImages(imgArray,scale,lables=[]):
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):
imgArray[x][y] = cv2.resize(imgArray[x][y], (0, 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])
hor_con[x] = np.concatenate(imgArray[x])
ver = np.vstack(hor)
ver_con = np.concatenate(hor)
else:
for x in range(0, rows):
imgArray[x] = cv2.resize(imgArray[x], (0, 0), None, scale, scale)
if len(imgArray[x].shape) == 2: imgArray[x] = cv2.cvtColor(imgArray[x], cv2.COLOR_GRAY2BGR)
hor= np.hstack(imgArray)
hor_con= np.concatenate(imgArray)
ver = hor
if len(lables) != 0:
eachImgWidth= int(ver.shape[1] / cols)
eachImgHeight = int(ver.shape[0] / rows)
#print(eachImgHeight)
for d in range(0, rows):
for c in range (0,cols):
cv2.rectangle(ver,(c*eachImgWidth,eachImgHeight*d),(c*eachImgWidth+len(lables[d][c])*13+27,30+eachImgHeight*d),(255,255,255),cv2.FILLED)
cv2.putText(ver,lables[d][c],(eachImgWidth*c+10,eachImgHeight*d+20),cv2.FONT_HERSHEY_COMPLEX,0.7,(255,0,255),2)
return ver
def rectContour(contours):
rectCon = []
max_area = 0
for i in contours:
area = cv2.contourArea(i)
if area > 3000:
peri = cv2.arcLength(i, True)
approx = cv2.approxPolyDP(i, 0.02 * peri, True)
if len(approx) == 4:
rectCon.append(i)
rectCon = sorted(rectCon, key=cv2.contourArea,reverse=True)
return rectCon
def circularContours(contours):
c = []
for cnt in contours:
area = cv2.contourArea(cnt)
if(area>10):
peri = cv2.arcLength(cnt, True)
approx = cv2.approxPolyDP(cnt, 0.02 * peri, True)
if(len(approx)==8):
c.append(cnt)
return c
def getCornerPoints(cont):
peri = cv2.arcLength(cont, True) # LENGTH OF CONTOUR
approx = cv2.approxPolyDP(cont, 0.02 * peri, True) # APPROXIMATE THE POLY TO GET CORNER POINTS
return approx
def reorder(myPoints):
myPoints = myPoints.reshape((4, 2)) # REMOVE EXTRA BRACKET
#print(myPoints)
myPointsNew = np.zeros((4, 1, 2), np.int32) # NEW MATRIX WITH ARRANGED POINTS
add = myPoints.sum(1)
#print(add)
#print(np.argmax(add))
myPointsNew[0] = myPoints[np.argmin(add)] #[0,0]
myPointsNew[3] =myPoints[np.argmax(add)] #[w,h]
diff = np.diff(myPoints, axis=1)
myPointsNew[1] =myPoints[np.argmin(diff)] #[w,0]
myPointsNew[2] = myPoints[np.argmax(diff)] #[h,0]
return myPointsNew
def splitBoxes(img,r,c):
rows = np.vsplit(img,r)
boxes=[]
for r in rows:
cols= np.hsplit(r,c)
for box in cols:
boxes.append(box)
return boxes
def getArray(rows,columns,boxes):
countR=0
countC=0
choices = columns
myPixelVal = np.zeros((rows,columns))
for image in boxes:
totalPixels = cv2.countNonZero(image)
myPixelVal[countR][countC]= totalPixels
countC += 1
if (countC==choices):countC=0;countR +=1
return myPixelVal