视频画质增强

复制import cv2
import numpy as np
from basicsr.archs.rrdbnet_arch import RRDBNet
from realesrgan import RealESRGANer

# 加载预训练 Real-ESRGAN 模型
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
upsampler = RealESRGANer(scale=4, model_path='RealESRGAN_x4plus.pth', model=model, tile=0, tile_pad=10, pre_pad=0, half=False)

# 读取低分辨率视频帧
cap = cv2.VideoCapture('input.mp4')
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))

# 输出视频（4x 分辨率）
out = cv2.VideoWriter('output.mp4', cv2.VideoWriter_fourcc(*'mp4v'), fps, (width*4, height*4))

while True:
    ret, frame = cap.read()
    if not ret:
        break
    # 逐帧超分辨率
    output, _ = upsampler.enhance(frame, outscale=4)
    out.write(output)

cap.release()
out.release()
print('视频增强完成，输出分辨率:', width*4, 'x', height*4)

复制package main

import (
	"fmt"
	"gocv.io/x/gocv"
	"image"
)

// 使用 OpenCV 的 DNN 超分辨率模块（类似 Real-ESRGAN 的轻量替代）
func main() {
	// 加载预训练模型（FSRCNN 示例）
	sr := gocv.NewDNNSuperres()
	defer sr.Close()
	sr.ReadModel("FSRCNN_x2.pb")
	sr.SetModel("fsrcnn", 2)

	// 读取视频
	cap, _ := gocv.VideoCaptureFile("input.mp4")
	defer cap.Close()

	writer, _ := gocv.VideoWriterFile("output.mp4", "mp4v", 30, 1280, 720, true)
	defer writer.Close()

	img := gocv.NewMat()
	for {
		if ok := cap.Read(&img); !ok {
			break
		}
		// 超分辨率处理
		upscaled := sr.Upsample(img)
		writer.Write(upscaled)
	}
	fmt.Println("视频增强完成")
}

复制// 使用 TensorFlow.js 的 ESRGAN 模型（浏览器端推理）
import * as tf from '@tensorflow/tfjs';
import {loadGraphModel} from '@tensorflow/tfjs-converter';

async function enhanceVideoFrame(frame) {
  // 加载预训练模型（假设已转换为 TFJS 格式）
  const model = await loadGraphModel('https://example.com/esrgan_model/model.json');
  
  // 将 HTMLVideoElement 或 Canvas 转为张量
  const tensor = tf.browser.fromPixels(frame)
    .expandDims(0)
    .toFloat()
    .div(255.0);
  
  // 推理
  const output = model.execute(tensor);
  
  // 后处理：张量 → Canvas
  const enhanced = tf.squeeze(output).mul(255).clipByValue(0, 255).cast('int32');
  const canvas = document.createElement('canvas');
  await tf.browser.toPixels(enhanced, canvas);
  
  tensor.dispose();
  output.dispose();
  enhanced.dispose();
  
  return canvas;
}

// 使用示例（在 requestAnimationFrame 循环中调用）
// const video = document.getElementById('inputVideo');
// const enhancedCanvas = await enhanceVideoFrame(video);