通过cap.get(cv2.CAP_PROP_FRAME_WIDTH),就能获取当前帧对象的宽度。 通过cap.get(cv2.CAP_PROP_FRAME_HEIGHT),就能获取当前帧对象的高度。 例如设置cv2.VideoCapture类对象的属性(这里是设置frame的宽度和高度): 语句ret = cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)将当前帧对象的宽度设置为640 像素。 语句ret...
importcv2importnumpyasnp# 1、读取视频cap=cv2.Videocapture("DOG.wmv")# 2、获取图像的属性(宽和高),并将其转换为整数frame_width=int(cap.get(3))frame_height=int(cap.get(4))# 3、创建保存视频的对象,设置编码格式,帧率,图像的宽高等out=cv2.Videowriter('outpy.avi',cv2.Videowriter_fourcc('m','...
cv2.CAP_PROP_FRAME_WIDTH:获取视频帧宽度 cv2.CAP_PROP_FRAME_HEIGHT:获取视频帧高度 基础用法 (仅供参考,可能出现代码不标准或无法运行情况) OpenCV是一个开源的计算机视觉库,主要用于图像和视频处理。以下是OpenCV库的一些常用函数: 读取和显示图像 import cv2 # 读取图像 img = cv2.imread('image.jpg') # ...
pip install opencv-python -i https://pypi.tuna.tsinghua.edu.cn/simple 2. 使用 importcv2#打开视频文件video = cv2.VideoCapture('视频地址')#检查视频是否成功打开ifnotvideo.isOpened():print('无法打开视频文件')#获取视频尺寸width =int(video.get(cv2.CAP_PROP_FRAME_WIDTH)) height=int(video.get(cv...
importcv2# 导入OpenCV库video_path='input_video.mp4'# 视频文件的路径cap=cv2.VideoCapture(video_path)# 打开视频文件# 获取原始视频的分辨率original_width=int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))# 获取视频宽度original_height=int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))# 获取视频高度print(f"原始视频分辨...
defgetFaceBox(net,frame,conf_threshold=0.7):frameOpencvDnn=frame.copy()frameHeight=frameOpencvDnn.shape[0]frameWidth=frameOpencvDnn.shape[1]blob=cv.dnn.blobFromImage(frameOpencvDnn,1.0,(300,300),[104,117,123],True,False)net.setInput(blob)detections=net.forward()bboxes=[]foriinrange(detectio...
martix=cv2.getPerspectiveTransform(waitdots, resultd) imgout=cv2.warpPerspective(img, martix, (frameWidth, frameHeight))returnimgout#图像显示:遍历帧#while True:#success,img=cap.read()img=cv2.imread("C:/Users/31132/Desktop/mtest.jpg")print(img.shape) ...
# 获取摄像头当前的分辨率width=int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))height=int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) 1. 2. 3. 这段代码使用get方法从摄像头对象cap中获取当前的分辨率,并将它们存储在width和height变量中。 步骤3:设置摄像头的分辨率 ...
def getFaceBox(net, frame, conf_threshold=0.7): frameOpencvDnn = frame.copy() frameHeight = frameOpencvDnn.shape[0] frameWidth = frameOpencvDnn.shape[1] blob = cv.dnn.blobFromImage(frameOpencvDnn, 1.0, (300, 300), [104, 117, 123], True, False) net.setInput(blob) detections = net...
x=0.5# start point/total widthy=0.8# start point/total widththreshold =60# BINARY thresholdblurValue =7# GaussianBlur parameterbgSubThreshold =50learningRate =0 # variablesisBgCaptured =0# whether the background captured defremoveBG(frame):#Subtra...