fourcc = cv2.VideoWriter_fourcc(*"XVID")
时间: 2024-09-18 18:06:12 浏览: 33
`cv2.VideoWriter_fourcc(*"XVID")` 是OpenCV(Computer Vision Library)中的一个函数,用于创建VideoWriter对象时指定视频文件的编码格式。`fourcc` 是Four Character Code(四位字符代码)的缩写,是一个表示视频编码标准的四个字母的组合。
在Python中,`*` 运算符用于解压字符串 `"XVID"`,将其每个字符作为一个参数传递给 `VideoWriter_fourcc()` 函数。`XVID` 代表一种常用的压缩标准,常用于Windows平台,对应的是DivX编码,它是一种MPEG-4的一部分,具有良好的质量和相对较小的文件大小,适合快速传输和存储。
当你用这个四字节代码初始化 `cv2.VideoWriter()` 创建一个新的视频文件时,OpenCV会在写入帧到文件时自动将视频数据转换成这种格式。例如:
```python
out = cv2.VideoWriter('output.avi', cv2.VideoWriter_fourcc(*"XVID"), fps, frame_size)
```
这里,`fps` 是帧率,`frame_size` 是每一帧的尺寸。通过这种方式,你可以创建一个名为 'output.avi' 的XVID编码视频文件。
相关问题
import cv2 import numpy as np # 创建混合高斯模型 fgbg = cv2.createBackgroundSubtractorMOG2(history=500, varThreshold=50, detectShadows=False) # 打开视频文件 cap = cv2.VideoCapture('t1.mp4') # 获取视频帧率、宽度和高度 fps = int(cap.get(cv2.CAP_PROP_FPS)) width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) # 创建前景视频对象 fg_out = cv2.VideoWriter('foreground_video.avi', cv2.VideoWriter_fourcc(*'XVID'), fps, (width, height)) # 初始化上一帧 prev_frame = None # 循环遍历视频帧 while True: ret, frame = cap.read() if not ret: break # 高斯模型背景减除法 fgmask = fgbg.apply(frame) # 缩放比例 scale_percent = 50 # 计算缩放后的新尺寸 width = int(frame.shape[1] * scale_percent / 100) height = int(frame.shape[0] * scale_percent / 100) dim = (width, height) # 缩放图像 frame = cv2.resize(frame, dim, interpolation=cv2.INTER_AREA) fgmask = cv2.resize(fgmask, dim, interpolation=cv2.INTER_AREA) # 形态学开运算去除噪点 kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)) opening = cv2.morphologyEx(fgmask, cv2.MORPH_OPEN, kernel) # 寻找轮廓并计算周长 contours, hierarchy = cv2.findContours(opening, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) for cnt in contours: perimeter = cv2.arcLength(cnt, True) if perimeter > 500: # 画出矩形框 x, y, w, h = cv2.boundingRect(cnt) cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2) # 视频稳定 if prev_frame is not None: # 计算帧间差分 diff = cv2.absdiff(frame, prev_frame) # 计算运动向量 _, motion = cv2.optflow.calcOpticalFlowFarneback(prev_frame, frame, None, 0.5, 3, 15, 3, 5, 1.2, 0) # 平移每一帧 M = np.float32([[1, 0, motion[:,:,0].mean()], [0, 1, motion[:,:,1].mean()]]) frame = cv2.warpAffine(frame, M, (frame.shape[1], frame.shape[0])) diff = cv2.warpAffine(diff, M, (diff.shape[1], diff.shape[0])) # 显示帧间差分 cv2.imshow('diff', diff) # 更新上一帧 prev_frame = frame.copy() cv2.imshow('frame', frame) cv2.imshow('fgmask', fgmask) if cv2.waitKey(1) & 0xFF == ord('q'): break # 释放对象 cap.release() fg_out.release() cv2.destroyAllWindows()改为4.5.3版本的opencv能用的程序
import cv2 import numpy as np # 创建混合高斯模型 fgbg = cv2.createBackgroundSubtractorMOG2(history=500, varThreshold=50, detectShadows=False) # 打开视频文件 cap = cv2.VideoCapture('t1.mp4') # 获取视频帧率、宽度和高度 fps = int(cap.get(cv2.CAP_PROP_FPS)) width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) # 创建前景视频对象 fourcc = cv2.VideoWriter_fourcc(*'XVID') fg_out = cv2.VideoWriter('foreground_video.avi', fourcc, fps, (width, height)) # 初始化上一帧 prev_frame = None # 循环遍历视频帧 while True: ret, frame = cap.read() if not ret: break # 高斯模型背景减除法 fgmask = fgbg.apply(frame) # 缩放比例 scale_percent = 50 # 计算缩放后的新尺寸 width = int(frame.shape[1] * scale_percent / 100) height = int(frame.shape[0] * scale_percent / 100) dim = (width, height) # 缩放图像 frame = cv2.resize(frame, dim, interpolation=cv2.INTER_AREA) fgmask = cv2.resize(fgmask, dim, interpolation=cv2.INTER_AREA) # 形态学开运算去除噪点 kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)) opening = cv2.morphologyEx(fgmask, cv2.MORPH_OPEN, kernel) # 寻找轮廓并计算周长 contours, hierarchy = cv2.findContours(opening, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) for cnt in contours: perimeter = cv2.arcLength(cnt, True) if perimeter > 500: # 画出矩形框 x, y, w, h = cv2.boundingRect(cnt) cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2) # 视频稳定 if prev_frame is not None: # 计算帧间差分 diff = cv2.absdiff(frame, prev_frame) # 计算运动向量 flow = cv2.calcOpticalFlowFarneback(prev_frame, frame, None, 0.5, 3, 15, 3, 5, 1.2, 0) # 平移每一帧 mean_flow = np.mean(flow, axis=(0, 1)) M = np.float32([[1, 0, mean_flow[0]], [0, 1, mean_flow[1]]]) frame = cv2.warpAffine(frame, M, (frame.shape[1], frame.shape[0])) diff = cv2.warpAffine(diff, M, (diff.shape[1], diff.shape[0])) # 显示帧间差分 cv2.imshow('diff', diff) # 更新上一帧 prev_frame = frame.copy() cv2.imshow('frame', frame) cv2.imshow('fgmask', fgmask) if cv2.waitKey(1) & 0xFF == ord('q'): break # 释放对象 cap.release() fg_out.release() cv2.destroyAllWindows()
import sys import cv2 from showPic import Ui_MainWindow from PyQt5 import QtGui from PyQt5.QtCore import * from PyQt5.QtGui import * from PyQt5.QtWidgets import * class videoShow(QMainWindow,Ui_MainWindow): def __init__(self): super().__init__() self.setupUi(self) @pyqtSlot() def on_pushButton_record_clicked(self): camera_path = 0 # 0:自带摄像头 1:外接摄像头 "xxx.mp4" "rtsp://admin:pwd@192.168.2.10/cam/..." capture = cv2.VideoCapture(camera_path) # 初始化播放器 流媒体 fourcc = cv2.VideoWriter_fourcc('M', 'P', '4', 'V') # XVID/DIVX MPEG MJPG X264 video_writer = cv2.VideoWriter("image/myself.mp4", fourcc, 25, (960, 540)) # 存放路径、、帧率fps、尺寸(且保证下面的frame也是这个尺寸) while True: flag, frame = capture.read() if flag is False: continue frame = cv2.resize(frame, (960, 540)) video_writer.write(frame) cv2.imshow("video", frame) key = cv2.waitKey(25) if key == 27: video_writer.release() break if __name__ == '__main__': app = QApplication(sys.argv) ui = videoShow() ui.show() sys.exit(app.exec_())在这样代码的基础上添加点击按钮播放已经录制好的视频
您可以在`videoShow`类中添加一个点击按钮播放已经录制好的视频的槽函数,然后在UI文件中添加一个按钮,并将其与该槽函数关联。以下是修改后的代码示例:
```python
import sys
import cv2
from showPic import Ui_MainWindow
from PyQt5 import QtGui
from PyQt5.QtCore import *
from PyQt5.QtGui import *
from PyQt5.QtWidgets import *
class videoShow(QMainWindow,Ui_MainWindow):
def __init__(self):
super().__init__()
self.setupUi(self)
@pyqtSlot()
def on_pushButton_record_clicked(self):
camera_path = 0 # 0:自带摄像头 1:外接摄像头 "xxx.mp4" "rtsp://admin:pwd@192.168.2.10/cam/..."
capture = cv2.VideoCapture(camera_path) # 初始化播放器 流媒体
fourcc = cv2.VideoWriter_fourcc('M', 'P', '4', 'V') # XVID/DIVX MPEG MJPG X264
video_writer = cv2.VideoWriter("image/myself.mp4", fourcc, 25, (960, 540)) # 存放路径、、帧率fps、尺寸(且保证下面的frame也是这个尺寸)
while True:
flag, frame = capture.read()
if flag is False:
continue
frame = cv2.resize(frame, (960, 540))
video_writer.write(frame)
cv2.imshow("video", frame)
key = cv2.waitKey(25)
if key == 27:
video_writer.release()
break
@pyqtSlot()
def on_pushButton_play_clicked(self):
video_path = "image/myself.mp4" # 已经录制好的视频路径
capture = cv2.VideoCapture(video_path) # 初始化播放器
while True:
flag, frame = capture.read()
if flag is False:
break
cv2.imshow("video", frame)
key = cv2.waitKey(25)
if key == 27:
break
capture.release()
if __name__ == '__main__':
app = QApplication(sys.argv)
ui = videoShow()
ui.show()
sys.exit(app.exec_())
```
在修改后的代码中,我们添加了一个名为`on_pushButton_play_clicked`的槽函数,并在该函数中使用`cv2.VideoCapture`打开已经录制好的视频,并通过`cv2.imshow`显示每一帧。您需要在UI文件中添加一个名为`pushButton_play`的按钮,并将其与该槽函数关联。当您点击该按钮时,录制好的视频将会开始播放。