仅检查 OpenCV 中视频源的特定部分

Question

如何获取特定宽度和高度的网络摄像头视频源？

我对 OpenCV 库的经验为零，所以我需要这方面的帮助。此代码来自 geeksforgeeks.com。这是我现在唯一的东西。

我想要实现的是，我只想检测视频源指定区域中的运动。

import cv2, time, pandas



from datetime import datetime 



static_back = None
motion_list = [ None, None ] 
time = [] 
df = pandas.DataFrame(columns = ["Start", "End"]) 
video = cv2.VideoCapture(0) 



while True: 
    check, frame = video.read() 
    motion = 0
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) 
    gray = cv2.GaussianBlur(gray, (21, 21), 0)



if static_back is None: 
    static_back = gray 
    continue

diff_frame = cv2.absdiff(static_back, gray) 

thresh_frame = cv2.threshold(diff_frame, 30, 255, cv2.THRESH_BINARY)[1] 
thresh_frame = cv2.dilate(thresh_frame, None, iterations = 2) 

(cnts, _) = cv2.findContours(thresh_frame.copy(),  
                   cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) 

for contour in cnts: 
    if cv2.contourArea(contour) < 50000: 
        continue
    motion = 1

    (x, y, w, h) = cv2.boundingRect(contour) 
    # making green rectangle arround the moving object 
    cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 3) 

motion_list.append(motion) 

motion_list = motion_list[-2:] 

if motion_list[-1] == 1 and motion_list[-2] == 0: 
    time.append(datetime.now()) 

if motion_list[-1] == 0 and motion_list[-2] == 1: 
    time.append(datetime.now()) 

cv2.imshow("Gray Frame", gray) 

cv2.imshow("Difference Frame", diff_frame) 

cv2.imshow("Threshold Frame", thresh_frame) 

cv2.imshow("Color Frame", frame) 

key = cv2.waitKey(1) 
if key == ord('q'): 
    # if something is movingthen it append the end time of movement 
    if motion == 1: 
        time.append(datetime.now()) 
    break


for i in range(0, len(time), 2): 
    df = df.append({"Start":time[i], "End":time[i + 1]}, ignore_index = True)

df.to_csv("Time_of_movements.csv") 
video.release() 
cv2.destroyAllWindows()

Answer 1

似乎您想获得每帧特定区域的感兴趣区域 (ROI)。为了在 OpenCV 中做到这一点，我们可以使用边界框坐标裁剪图像。考虑(0,0)作为图像与左上角左到右为x方向和顶部至底部作为y方向。如果我们有一个 ROI(x1, y1)的左上角顶点和(x2,y2)右下角顶点，我们可以通过以下方式裁剪图像：

ROI = frame[y1:y2, x1:x2]

举例说明：

-------------------------------------------
|                                         | 
|    (x1, y1)                             |
|      ------------------------           |
|      |                      |           |
|      |                      |           | 
|      |         ROI          |           |  
|      |                      |           |   
|      |                      |           |   
|      |                      |           |       
|      ------------------------           |   
|                           (x2, y2)      |    
|                                         |             
|                                         |             
|                                         |             
-------------------------------------------

我们能够做到这一点，因为图像在 OpenCV 中存储为 Numpy 数组。这是 Numpy 数组索引和切片的绝佳资源。获得所需的 ROI 后，您可以在该区域进行运动检测。