303 lines
14 KiB
Swift
303 lines
14 KiB
Swift
//
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// VideoConvertor2.swift
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// tdvideo
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//
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// Created by aaa on 2024/1/24.
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// 视频转码
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//红蓝立体
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//com.nsk.tdvideo
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/*
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let rr:AVAsynchronousCIImageFilteringRequest?
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// 创建视频合成器，用于交叉眼视频 AVAsynchronousCIImageFilteringRequest
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let videoComposition = AVMutableVideoComposition(asset: asset) { request in
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// 获取当前帧的时间戳
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let time = request.compositionTime
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// callback with progress
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progress?(Float(time.value) / Float(duration.value))
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}
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videoComposition.renderSize = CGSize(width: Int(videoSize.width), height: Int(videoSize.height/2))
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videoComposition.frameDuration = CMTime(value: 1, timescale: 30)
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playerItem?.videoComposition = videoComposition
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//开始播放
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let nc:NotificationCenter = NotificationCenter.default
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let noti:NSNotification = NSNotification.init(name: NSNotification.Name(rawValue: "upvideo"), object: nil)
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nc.post(noti as Notification)
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// Based on code from https://www.finnvoorhees.com/words/reading-and-writing-spatial-video-with-avfoundation
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// CIScreenBlendMode: 通过将颜色通道值反转并相乘，然后将结果反转回来，将两个图像合成为屏幕混合效果。
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// CIHardLightBlendMode: 使用源图像的亮度来决定如何混合两个图像。较亮的像素将更多地影响结果。
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// CILightenBlendMode: 比较两个图像的像素，并选择较亮的像素作为最终结果。
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// CIColorDodgeBlendMode: 使用源图像的颜色信息来增加目标图像的颜色亮度。
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// CIColorBurnBlendMode: 使用源图像的颜色信息来降低目标图像的颜色亮度。
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// CIDarkenBlendMode: 比较两个图像的像素，并选择较暗的像素作为最终结果。
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// CILinearDodgeBlendMode: 使用线性增加的方式将两个图像相加，产生一种亮度叠加的效果。
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// CIMultiplyBlendMode: 将两个图像的像素值相乘，产生一种乘法混合效果。
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// CISourceOverCompositing: 将源图像放在目标图像上方，产生一种覆盖混合效果。
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*/
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import Foundation
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import AVKit
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import VideoToolbox
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import CoreImage
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import ImageIO
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class VideoConvertor2 {
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///在立体视频中，正在处理的当前帧的左眼视图。
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var leftEyeImage: CVPixelBuffer?
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///在立体视频中，正在处理的当前帧的右眼视图。
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var rightEyeImage: CVPixelBuffer?
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//空间视频 交叉眼 红蓝立体 高斯模糊
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var type = 0
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func convertVideo( asset : AVAsset, outputFile: URL, progress: ((Float)->())? = nil ) async throws {
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do {
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try FileManager.default.removeItem(atPath: outputFile.path)
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print("视频文件删除成功")
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} catch {
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print("删除视频文件出错：\(error)")
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}
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let assetReader = try AVAssetReader(asset: asset)
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//检查是否为空间视频 print("该视频不是空间视频或图片")
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let userDataItems = try await asset.loadMetadata(for:.quickTimeMetadata)
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let spacialCharacteristics = userDataItems.filter { $0.identifier?.rawValue == "mdta/com.apple.quicktime.spatial.format-version" }
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if spacialCharacteristics.count == 0 {
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print("不是空间视频")
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}
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//获取输入视频的方向和大小(用于设置输出方向)
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let (orientation, videoSize) = try await getOrientationAndResolutionSizeForVideo(asset: asset)
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//输出宽度为宽度的一半
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//我们有两个并排的视频，我们保持长宽比
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let vw:VideoWriter?
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if(type == 3){
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//空间视频+红蓝立体
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vw = VideoWriter(url: outputFile, width: Int(videoSize.width), height: Int(videoSize.height), orientation: orientation, sessionStartTime: CMTime(value: 1, timescale: 30 ), isRealTime: false, queue: .main)
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}
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else{
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//交叉眼 + 平行眼
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vw = VideoWriter(url: outputFile, width: Int(videoSize.width), height: Int(videoSize.height/2), orientation: orientation, sessionStartTime: CMTime(value: 1, timescale: 30 ), isRealTime: false, queue: .main)
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}
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//加载视频轨道
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let output = try await AVAssetReaderTrackOutput(
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track: asset.loadTracks(withMediaType: .video).first!,
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outputSettings: [
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AVVideoDecompressionPropertiesKey: [
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kVTDecompressionPropertyKey_RequestedMVHEVCVideoLayerIDs: [0, 1] as CFArray,
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],
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]
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)
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assetReader.add(output)
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assetReader.startReading()
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let duration = try await asset.load(.duration)
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while let nextSampleBuffer = output.copyNextSampleBuffer() {
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guard let taggedBuffers = nextSampleBuffer.taggedBuffers else { return }
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let leftEyeBuffer = taggedBuffers.first(where: {
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$0.tags.first(matchingCategory: .stereoView) == .stereoView(.leftEye)
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})?.buffer
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let rightEyeBuffer = taggedBuffers.first(where: {
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$0.tags.first(matchingCategory: .stereoView) == .stereoView(.rightEye)
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})?.buffer
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if let leftEyeBuffer,
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let rightEyeBuffer,
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case let .pixelBuffer(leftEyePixelBuffer) = leftEyeBuffer,
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case let .pixelBuffer(rightEyePixelBuffer) = rightEyeBuffer {
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leftEyeImage = leftEyePixelBuffer
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rightEyeImage = rightEyePixelBuffer
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let lciImage = CIImage(cvPixelBuffer: leftEyePixelBuffer)
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let rciImage = CIImage(cvPixelBuffer: rightEyePixelBuffer)
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//交叉眼
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if(type == 2){
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let newpb = joinImages( leftImage: lciImage, rightImage:rciImage )
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let time = CMSampleBufferGetOutputPresentationTimeStamp(nextSampleBuffer)
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_ = vw!.add(image: newpb, presentationTime: time)
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print( "Added frame at \(time)")
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// callback with progress
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progress?( Float(time.value)/Float(duration.value))
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// This sleep is needed to stop memory blooming - keeps around 280Mb rather than spiraling up to 8+Gig!
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try await Task.sleep(nanoseconds: 3_000_000)
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}
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//红蓝立体
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if(type == 3){
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// 创建红色和蓝色滤镜
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let redColorMatrix: [CGFloat] = [
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0.0, 0.0, 0.0, 0.0, 0.0, // 红色通道
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0.0, 0.0, 0.0, 0.0, 0.0, // 绿色通道
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0.0, 0.0, 1.0, 0.0, 0.0, // 蓝色通道
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0.0, 0.0, 0.0, 1.0, 0.0 // 透明通道
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]
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let blueColorMatrix: [CGFloat] = [
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1.0, 0.0, 0.0, 0.0, 0.0, // 红色通道
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0.0, 0.0, 0.0, 0.0, 0.0, // 绿色通道
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0.0, 0.0, 0.0, 0.0, 0.0, // 蓝色通道
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0.0, 0.0, 0.0, 1.0, 0.0 // 透明通道
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]
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let redFilter = CIFilter(name: "CIColorMatrix")!
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redFilter.setValue(lciImage, forKey: kCIInputImageKey)
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redFilter.setValue(CIVector(values: redColorMatrix, count: redColorMatrix.count), forKey: "inputRVector")
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let blueFilter = CIFilter(name: "CIColorMatrix")!
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blueFilter.setValue(rciImage, forKey: kCIInputImageKey)
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blueFilter.setValue(CIVector(values: blueColorMatrix, count: blueColorMatrix.count), forKey: "inputBVector")
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// 获取处理后的图像
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if let redOutputImage = redFilter.outputImage,
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let blueOutputImage = blueFilter.outputImage {
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let compositeFilter = CIFilter(name: "CIScreenBlendMode")!
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compositeFilter.setValue(redOutputImage, forKey: kCIInputImageKey)
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compositeFilter.setValue(blueOutputImage, forKey: kCIInputBackgroundImageKey)
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let sharpenedFilter = CIFilter(name: "CISharpenLuminance")!
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sharpenedFilter.setValue(compositeFilter.outputImage, forKey: kCIInputImageKey)
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sharpenedFilter.setValue(2, forKey: kCIInputSharpnessKey)
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// let colorControlsFilter = CIFilter(name: "CIColorControls")!
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// colorControlsFilter.setValue(sharpenedFilter.outputImage, forKey: kCIInputImageKey)
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// colorControlsFilter.setValue(0.7, forKey: kCIInputSaturationKey)
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let lastImg = sharpenedFilter.outputImage!
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let time = CMSampleBufferGetOutputPresentationTimeStamp(nextSampleBuffer)
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_ = vw!.add(image: lastImg, presentationTime: time)
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print( "Added frame at \(time)")
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// callback with progress
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progress?( Float(time.value)/Float(duration.value))
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// This sleep is needed to stop memory blooming - keeps around 280Mb rather than spiraling up to 8+Gig!
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try await Task.sleep(nanoseconds: 3_000_000)
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}
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}
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//高斯模糊
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if(type == 4){
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let filter1 = CIFilter(name: "CIGaussianBlur")!
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filter1.setValue(lciImage, forKey: kCIInputImageKey)
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let filter2 = CIFilter(name: "CIGaussianBlur")!
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filter2.setValue(rciImage, forKey: kCIInputImageKey)
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let newpb = joinImages( leftImage: filter1.outputImage!, rightImage:filter2.outputImage! )
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let time = CMSampleBufferGetOutputPresentationTimeStamp(nextSampleBuffer)
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_ = vw!.add(image: newpb, presentationTime: time)
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print( "Added frame at \(time)")
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// callback with progress
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progress?( Float(time.value)/Float(duration.value))
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// This sleep is needed to stop memory blooming - keeps around 280Mb rather than spiraling up to 8+Gig!
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try await Task.sleep(nanoseconds: 3_000_000)
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}
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}
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}
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print( "status - \(assetReader.status)")
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print( "status - \(assetReader.error?.localizedDescription ?? "None")")
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print( "Finished")
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_ = try await vw!.finish()
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}
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//获取ciimage的数据
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func isSpatialImage2(from ciImage: CIImage) {
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let context = CIContext()
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guard let cgImage = context.createCGImage(ciImage, from: ciImage.extent) else {
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return
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}
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let dataProvider = CGDataProvider(data: cgImage.dataProvider!.data! as CFData)
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let imageSource = CGImageSourceCreateWithDataProvider(dataProvider!, nil)
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let frameCount = CGImageSourceGetCount(imageSource!)
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print(frameCount)
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for index in 0..<frameCount {
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let properties = CGImageSourceCopyPropertiesAtIndex(imageSource!, index, nil) as? [CFString: Any]
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print(properties as Any)
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guard let frameImage = CGImageSourceCreateImageAtIndex(imageSource!, index, nil) else {
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continue
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}
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print(frameImage)
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}
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}
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func getOrientationAndResolutionSizeForVideo(asset:AVAsset) async throws -> (CGAffineTransform, CGSize) {
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guard let track = try await asset.loadTracks(withMediaType: AVMediaType.video).first
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else{throw VideoReaderError.invalidVideo}
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let naturalSize = try await track.load(.naturalSize)
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let naturalTransform = try await track.load(.preferredTransform)
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let size = naturalSize.applying(naturalTransform)
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return (naturalTransform, CGSize(width: abs(size.width), height: abs(size.height)) )
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}
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//将两张图片合成一张图片
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func joinImages( leftImage:CIImage, rightImage:CIImage) -> CIImage {
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let left = UIImage(ciImage: leftImage )
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let right = UIImage(ciImage: rightImage )
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let imageWidth = left.size.width/2 + right.size.width/2
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let imageHeight = left.size.height/2
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let newImageSize = CGSize(width:imageWidth, height: imageHeight);
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UIGraphicsBeginImageContextWithOptions(newImageSize, false, 1);
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left.draw(in: CGRect(x:0, y:0, width:imageWidth/2, height:imageHeight))
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right.draw(in: CGRect(x:imageWidth/2, y:0, width:imageWidth/2, height:imageHeight))
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let image = UIGraphicsGetImageFromCurrentImageContext()!
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UIGraphicsEndImageContext();
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let ci = CIImage(cgImage: image.cgImage!)
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return ci
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}
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}
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