问题
提取图像中的矩形区域，可以算是LCD检测项目的原型验证里的一部分
算法梳理
- threshold.png：阈值分割算法，迅速分割出感兴趣的区域（显示屏亮色部分）
- dilate、erode：腐蚀和膨胀算法，用于消除‘麻点’噪声，图像效果好的话可以不使用
- canny.png：canny算法检测边缘轮廓
- contours.png：寻找轮廓中的闭包，找出面积较大的区块，拟合成四边形，确定四边形的定点
- transfer0.png：透视变换，消除畸变，就是将感兴趣的区域摆正、恢复像素尺寸的旋转拉伸过程
实现
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
183
184
185
186
187
188
189
190
191
192
#!/usr/bin/env python
# encoding: utf-8
import cv2
import numpy as np
import math
srcWidth = 0
srcHeight = 0
maxWidth = 1024
maxHeight = 600
class Config:
def __init__(self):
pass
src = "test18.png"
min_area = 100000
min_contours = 8
threshold_thresh = 50
epsilon_start = 50
epsilon_step = 10
class HoughPoints:
def __init__(self,rho=0,theta=0):
self.rhos = [rho]
self.thetas = [theta]
'''
@func 根据HoughLines转换出直线点
@param rho 距离
@param theta 角度
'''
def rt_to_point(img, rho, theta):
#垂直直线
if (theta < (np.pi/4. )) or (theta > (3.*np.pi/4.0)):
#该直线与第一行的交点
pt1 = (int(rho/np.cos(theta)),0)
#该直线与最后一行的焦点
pt2 = (int((rho-img.shape[0]*np.sin(theta))/np.cos(theta)),img.shape[0])
return pt1, pt2
else:
#水平直线, 该直线与第一列的交点
pt1 = (0,int(rho/np.sin(theta)))
#该直线与最后一列的交点
pt2 = (img.shape[1], int((rho-img.shape[1]*np.cos(theta))/np.sin(theta)))
return pt1, pt2
'''
@return [top-left, top-right, bottom-right, bottom-left]
'''
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
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
def point_distance(a,b):
return int(np.sqrt(np.sum(np.square(a - b))))
'''
@func 计算两条直线的交点
'''
def line_intersection(line1, line2):
xdiff = (line1[0][0] - line1[1][0], line2[0][0] - line2[1][0])
ydiff = (line1[0][1] - line1[1][1], line2[0][1] - line2[1][1])
def det(a, b):
return a[0] * b[1] - a[1] * b[0]
div = det(xdiff, ydiff)
if div == 0:
raise Exception('lines do not intersect')
d = (det(*line1), det(*line2))
x = det(d, xdiff) / div
y = det(d, ydiff) / div
return x, y
'''
@func 迪卡尔转极坐标
'''
def cart_to_polor(x1, y1, x2, y2):
diff = float(abs(x1-x2)) / abs(y1-y2)
theta = math.atan(diff)
#print("theta=%f, diff=%f, %f %f"%(theta, diff, abs(x1-x2), abs(y1-y2)))
rho = math.sin(theta)*(y1 - (x1/math.tan(theta)) )
return rho, theta
image = cv2.imread(Config.src)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
srcWidth, srcHeight, channels = image.shape
print(srcWidth, srcHeight)
# 中值平滑，消除噪声
binary = cv2.medianBlur(gray,7)
ret, binary = cv2.threshold(binary, Config.threshold_thresh, 255, cv2.THRESH_BINARY)
cv2.imwrite("1-threshold.png", binary, [int(cv2.IMWRITE_PNG_COMPRESSION), 9])
# 首先进行4次腐蚀（erosion），然后进行4次膨胀（dilation）
# 腐蚀操作将会腐蚀图像中白色像素，以此来消除小斑点
# 膨胀操作将使剩余的白色像素扩张并重新增长回去。
#binary = cv2.erode (binary, None, iterations = 2)
#binary = cv2.dilate(binary, None, iterations = 4)
#binary = cv2.erode (binary, None, iterations = 4)
#cv2.imwrite("2-erode-dilate.png", binary, [int(cv2.IMWRITE_PNG_COMPRESSION), 9])
# canny提取轮廓
binary = cv2.Canny(binary, 0, 60, apertureSize = 3)
cv2.imwrite("3-canny.png", binary, [int(cv2.IMWRITE_PNG_COMPRESSION), 9])
# 提取轮廓后，拟合外接多边形（矩形）
_,contours,_ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
print("len(contours)=%d"%(len(contours)))
for idx,c in enumerate(contours):
if len(c) < Config.min_contours:
continue
epsilon = Config.epsilon_start
while True:
approx = cv2.approxPolyDP(c,epsilon,True)
print("idx,epsilon,len(approx),len(c)=%d,%d,%d,%d"%(idx,epsilon,len(approx),len(c)))
if (len(approx) < 4):
break
if math.fabs(cv2.contourArea(approx)) > Config.min_area:
if (len(approx) > 4):
epsilon += Config.epsilon_step
print("epsilon=%d, count=%d"%(epsilon,len(approx)))
continue
else:
#for p in approx:
# cv2.circle(binary,(p[0][0],p[0][1]),8,(255,255,0),thickness=-1)
approx = approx.reshape((4, 2))
# 点重排序, [top-left, top-right, bottom-right, bottom-left]
src_rect = order_points(approx)
cv2.drawContours(image, c, -1, (0,255,255),1)
cv2.line(image, (src_rect[0][0],src_rect[0][1]),(src_rect[1][0],src_rect[1][1]),color=(100,255,100))
cv2.line(image, (src_rect[2][0],src_rect[2][1]),(src_rect[1][0],src_rect[1][1]),color=(100,255,100))
cv2.line(image, (src_rect[2][0],src_rect[2][1]),(src_rect[3][0],src_rect[3][1]),color=(100,255,100))
cv2.line(image, (src_rect[0][0],src_rect[0][1]),(src_rect[3][0],src_rect[3][1]),color=(100,255,100))
# 获取最小矩形包络
rect = cv2.minAreaRect(approx)
box = cv2.boxPoints(rect)
box = np.int0(box)
box = box.reshape(4,2)
box = order_points(box)
print("approx->box")
print(approx)
print(src_rect)
print(box)
w,h = point_distance(box[0],box[1]), \
point_distance(box[1],box[2])
print("w,h=%d,%d"%(w,h))
# 透视变换
dst_rect = np.array([
[0, 0],
[w - 1, 0],
[w - 1, h - 1],
[0, h - 1]],
dtype="float32")
M = cv2.getPerspectiveTransform(src_rect, dst_rect)
warped = cv2.warpPerspective(image, M, (w, h))
cv2.imwrite("transfer%d.png"%idx, warped, [int(cv2.IMWRITE_PNG_COMPRESSION), 9])
break
else:
print("failed %d area=%f"%(idx, math.fabs(cv2.contourArea(approx))))
break
cv2.imwrite("4-contours.png", binary, [int(cv2.IMWRITE_PNG_COMPRESSION), 9])
cv2.imwrite("5-cut.png", image, [int(cv2.IMWRITE_PNG_COMPRESSION), 9])
效果
问题
- 如何处理背景和矩形区域色差不大的图像？