仅检查OpenCV中视频供稿的特定部分
如何获取特定宽度和高度的网络摄像头视频提要?
我对OpenCV库零经验,因此在这方面我需要帮助。这段代码来自geeksforgeeks.com。这是我现在唯一的东西。
我想要实现的是,我只想检测视频供稿中指定区域的运动。
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()
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似乎您想获取每一帧特定区域的关注区域(ROI)。要在OpenCV中执行此操作,我们可以使用边界框坐标裁剪图像。将其
(0,0)
视为图像的左上角,从左到右作为x方向,从上到下作为y方向。如果我们具有ROI(x1, y1)
的左上角和(x2,y2)
右下角,则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) | | | | | | | -------------------------------------------
由于图像在OpenCV中存储为Numpy数组,因此我们能够做到这一点。这是Numpy数组索引和切片的重要资源。获得所需的ROI后,您就可以在该区域进行运动检测了。