Python 代码实现高性能异构视频处理系统
视频解码模块
import cv2
class VideoDecoder:
def open_video(self, file_path):
cap = cv2.VideoCapture(file_path)
if not cap.isOpened():
raise Exception(f"Error opening video file: {file_path}")
return cap
帧处理模块
import cv2
class FrameProcessor:
def process_frame(self, frame):
# 示例处理：转为灰度图像
processed_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
return processed_frame
视频编码模块
import cv2
class VideoEncoder:
def create_video_writer(self, output_path, codec, fps, frame_size, is_color):
writer = cv2.VideoWriter(output_path, codec, fps, frame_size, is_color)
if not writer.isOpened():
raise Exception(f"Error opening video writer: {output_path}")
return writer
后处理模块
import cv2
class PostProcessor:
def post_process(self, frame):
# 示例后处理：应用高斯滤波
processed_frame = cv2.GaussianBlur(frame, (5, 5), 0)
return processed_frame
结果输出模块
class ResultOutput:
def output_result(self, writer, frame):
writer.write(frame)
主函数
import cv2
from video_decoder import VideoDecoder
from frame_processor import FrameProcessor
from video_encoder import VideoEncoder
from post_processor import PostProcessor
from result_output import ResultOutput
def main(input_video, output_video):
decoder = VideoDecoder()
frame_processor = FrameProcessor()
encoder = VideoEncoder()
post_processor = PostProcessor()
result_output = ResultOutput()
cap = decoder.open_video(input_video)
codec = cv2.VideoWriter_fourcc(*'MJPG')
fps = cap.get(cv2.CAP_PROP_FPS)
frame_size = (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))
writer = encoder.create_video_writer(output_video, codec, fps, frame_size, is_color=False)
while True:
ret, frame = cap.read()
if not ret:
break
processed_frame = frame_processor.process_frame(frame)
post_processed_frame = post_processor.post_process(processed_frame)
result_output.output_result(writer, post_processed_frame)
cap.release()
writer.release()
if __name__ == "__main__":
import sys
if len(sys.argv) < 3:
print(f"Usage: {sys.argv[0]} <input_video> <output_video>")
sys.exit(1)
input_video = sys.argv[1]
output_video = sys.argv[2]
main(input_video, output_video)
运行程序
确保所有模块文件（video_decoder.py、frame_processor.py、video_encoder.py、post_processor.py、result_output.py和main.py）在同一目录下，然后运行以下命令：
python main.py input.mp4 output.avi
依赖安装
需要确保安装了OpenCV库，可以使用以下命令安装：
pip install opencv-python
这样，我们就实现了一个高性能异构视频处理系统，包含视频解码、帧处理、视频编码、后处理和结果输出的模块化实现。这些模块可以单独开发和测试，最终集成到主程序中实现完整的视频处理功能。
C++ 代码实现高性能异构视频处理系统
视频解码模块
#include <iostream>
#include <opencv2/opencv.hpp>
class VideoDecoder {
public:
cv::VideoCapture openVideo(const std::string& filePath) {
cv::VideoCapture cap(filePath);
if (!cap.isOpened()) {
std::cerr << "Error opening video file: " << filePath << std::endl;
exit(1);
}
return cap;
}
cv::Mat getNextFrame(cv::VideoCapture& cap) {
cv::Mat frame;
if (!cap.read(frame)) {
return cv::Mat(); // Return an empty matrix if no frame is read
}
return frame;
}
};
帧处理模块
#include <opencv2/opencv.hpp>
class FrameProcessor {
public:
cv::Mat processFrame(const cv::Mat& frame) {
cv::Mat processedFrame;
// Example processing: convert to grayscale
cv::cvtColor(frame, processedFrame, cv::COLOR_BGR2GRAY);
return processedFrame;
}
cv::cuda::GpuMat processFrameOnGPU(const cv::cuda::GpuMat& frame) {
cv::cuda::GpuMat processedFrame;
// Example processing: apply Gaussian blur on GPU
cv::cuda::GaussianBlur(frame, processedFrame, cv::Size(5, 5), 1);
return processedFrame;
}
};
视频编码模块
#include <opencv2/opencv.hpp>
class VideoEncoder {
public:
cv::VideoWriter createVideoWriter(const std::string& outputPath, int codec, double fps, cv::Size frameSize, bool isColor) {
cv::VideoWriter writer(outputPath, codec, fps, frameSize, isColor);
if (!writer.isOpened()) {
std::cerr << "Error opening video writer: " << outputPath << std::endl;
exit(1);
}
return writer;
}
};
后处理模块
#include <opencv2/opencv.hpp>
class PostProcessor {
public:
void postProcess(cv::Mat& frame) {
// Example post-processing: apply a filter
cv::GaussianBlur(frame, frame, cv::Size(5, 5), 0);
}
void postProcessOnGPU(cv::cuda::GpuMat& frame) {
// Example post-processing: apply a filter on GPU
cv::cuda::GaussianBlur(frame, frame, cv::Size(5, 5), 0);
}
};
结果输出模块
#include <opencv2/opencv.hpp>
class ResultOutput {
public:
void outputResult(cv::VideoWriter& writer, const cv::Mat& frame) {
writer.write(frame);
}
void outputResultOnGPU(cv::VideoWriter&
主函数
#include <iostream>
#include <opencv2/opencv.hpp>
#include "VideoDecoder.h"
#include "FrameProcessor.h"
#include "VideoEncoder.h"
#include "PostProcessor.h"
#include "ResultOutput.h"
int main(int argc, char* argv[]) {
if (argc < 3) {
std::cerr << "Usage: " << argv[0] << " <input_video> <output_video>" << std::endl;
return 1;
}
std::string inputVideo = argv[1];
std::string outputVideo = argv[2];
VideoDecoder decoder;
FrameProcessor frameProcessor;
VideoEncoder encoder;
PostProcessor postProcessor;
ResultOutput resultOutput;
cv::VideoCapture cap = decoder.openVideo(inputVideo);
int codec = cv::VideoWriter::fourcc('M', 'J', 'P', 'G');
double fps = cap.get(cv::CAP_PROP_FPS);
cv::Size frameSize(cap.get(cv::CAP_PROP_FRAME_WIDTH), cap.get(cv::CAP_PROP_FRAME_HEIGHT));
cv::VideoWriter writer = encoder.createVideoWriter(outputVideo, codec, fps, frameSize);
cv::Mat frame;
while (cap.read(frame)) {
cv::Mat processedFrame = frameProcessor.processFrame(frame);
cv::Mat postProcessedFrame = postProcessor.postProcess(processedFrame);
resultOutput.outputResult(writer, postProcessedFrame);
}
cap.release();
writer.release();
return 0;
}
编译和运行
g++ -o video_processor main.cpp VideoDecoder.cpp FrameProcessor.cpp VideoEncoder.cpp PostProcessor.cpp ResultOutput.cpp `pkg-config --cflags --libs opencv4`
./video_processor input.mp4 output.avi
确保已经安装了OpenCV库，并在系统路径中正确配置了pkg-config。
这样，整个系统就完整了，包括视频解码、帧处理、视频编码、后处理和结果输出的模块化实现。这些模块可以单独开发和测试，最终集成到主程序中实现完整的视频处理功能。
