-
Notifications
You must be signed in to change notification settings - Fork 9
/
detect.py
105 lines (92 loc) · 3.59 KB
/
detect.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
# 方法一：knn检测方法
# import cv2
# import numpy as np
# knn = cv2.createBackgroundSubtractorKNN(detectShadows = True)
# es = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (20,12))
# # camera = cv2.VideoCapture("movie.mpg")
# camera = cv2.VideoCapture(0)
# def drawCnt(fn, cnt):
# if cv2.contourArea(cnt) > 1400:
# (x, y, w, h) = cv2.boundingRect(cnt)
# cv2.rectangle(fn, (x, y), (x + w, y + h), (255, 255, 0), 2)
# while True:
# ret, frame = camera.read()
# cv2.imshow("Capture_Test",frame)
# if not ret:
# continue
# fg = knn.apply(frame.copy())
# fg_bgr = cv2.cvtColor(fg, cv2.COLOR_GRAY2BGR)
# bw_and = cv2.bitwise_and(fg_bgr, frame)
# draw = cv2.cvtColor(bw_and, cv2.COLOR_BGR2GRAY)
# draw = cv2.GaussianBlur(draw, (21, 21), 0)
# draw = cv2.threshold(draw, 10, 255, cv2.THRESH_BINARY)[1]
# draw = cv2.dilate(draw, es, iterations = 2)
# print(draw)
# image, contours, hierarchy = cv2.findContours(draw.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# for c in contours:
# drawCnt(frame, c)
# cv2.imshow("motion detection", frame)
# if cv2.waitKey(int(1000 / 12)) & 0xff == ord("q"):
# break
# camera.release()
# cv2.destroyAllWindows()
#方法二：mog2方法
# import cv2
# import numpy as np
# camera = cv2.VideoCapture(0) # 参数0表示第一个摄像头
# bs = cv2.createBackgroundSubtractorKNN(detectShadows=True)
# es = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
# while True:
# grabbed, frame_lwpCV = camera.read()
# if not grabbed:
# continue
# fgmask = bs.apply(frame_lwpCV) # 背景分割器，该函数计算了前景掩码
# # 二值化阈值处理，前景掩码含有前景的白色值以及阴影的灰色值，在阈值化图像中，将非纯白色（244~255）的所有像素都设为0，而不是255
# th = cv2.threshold(fgmask.copy(), 244, 255, cv2.THRESH_BINARY)[1]
# # 下面就跟基本运动检测中方法相同，识别目标，检测轮廓，在原始帧上绘制检测结果
# dilated = cv2.dilate(th, es, iterations=2) # 形态学膨胀
# image, contours, hierarchy = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # 该函数计算一幅图像中目标的轮廓
# for c in contours:
# if cv2.contourArea(c) > 1600:
# (x, y, w, h) = cv2.boundingRect(c)
# cv2.rectangle(frame_lwpCV, (x, y), (x + w, y + h), (255, 255, 0), 2)
# cv2.imshow('mog', fgmask)
# cv2.imshow('thresh', th)
# cv2.imshow('detection', frame_lwpCV)
# key = cv2.waitKey(1) & 0xFF
# # 按'q'健退出循环
# if key == ord('q'):
# break
# # When everything done, release the capture
# camera.release()
# cv2.destroyAllWindows()
#方法三：grabcut方法
import numpy as np
import cv2
import time # 引入time模块
cap = cv2.VideoCapture(0)
# ticks = time.time()
# # 读入图片
# img = cv2.imread("./car.jpg")
while True:
ret,frame = cap.read()
if not ret:
continue
cv2.imshow("Capture_Test",frame)
# 创建一个和加载图像一样形状的 填充为0的掩膜
img=frame
mask = np.zeros(img.shape[:2], np.uint8)
# 创建以0填充的前景和背景模型
bgdModel = np.zeros((1, 65), np.float64)
fgdModel = np.zeros((1, 65), np.float64)
# 定义一个矩形
rect = (100, 50, 421, 378)
cv2.grabCut(img, mask, rect, bgdModel, fgdModel, 5, cv2.GC_INIT_WITH_RECT)
mask2 = np.where((mask == 2) | (mask == 0), 0, 1).astype("uint8")
img = img*mask2[:, :, np.newaxis]
# print(time.time()-ticks)
cv2.imshow("cut", img)
# cv2.imshow('origin', cv2.imread("./car.jpg"))
cv2.waitKey(1)
cap.release()
cv2.destroyAllWindows()