花椒 GPUImage 处理流程

本文记录了使用花椒SDK进行视频捕获及处理的过程。详细展示了从视频源到过滤器,再到目标输出(如视频录制和显示)的GPUImage组件连接情况。通过对各个阶段输出的跟踪,帮助理解视频处理流水线的构建。

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花椒SDK 打印流程

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2016-11-02 23:32:45.540 VideoCaptureTest[1489:240945] self:0x12e577b70<GPUImageVideoCamera: 0x12e577b70> newTarget is <GPUImageFilter: 0x12e59ddb0>
2016-11-02 23:33:12.215 VideoCaptureTest[1489:240945] GPUImageVideoCamera self:0x12e577b70<GPUImageVideoCamera: 0x12e577b70> newTarget is <GPUImageFilter: 0x12e59ddb0>
2016-11-02 23:33:53.298 VideoCaptureTest[1489:240945] self:0x12e577b70<GPUImageVideoCamera: 0x12e577b70> textureLocation newTarget is <GPUImageFilter: 0x12e59ddb0>
2016-11-02 23:35:23.554 VideoCaptureTest[1489:240945] 0x12e59ddb0
2016-11-02 23:35:23.554 VideoCaptureTest[1489:240945] self:0x12e59ddb0<GPUImageFilter: 0x12e59ddb0> newTarget is <GPUImageFilter: 0x12e59e8a0>
2016-11-02 23:35:55.609 VideoCaptureTest[1489:240945] self:0x12e59ddb0<GPUImageFilter: 0x12e59ddb0> textureLocation newTarget is <GPUImageFilter: 0x12e59e8a0>
2016-11-02 23:36:13.564 VideoCaptureTest[1489:240945] self:0x12e59e8a0<GPUImageFilter: 0x12e59e8a0> newTarget is <HJGPUImageMovieWriter: 0x12e599a10>
2016-11-02 23:36:39.798 VideoCaptureTest[1489:240945] self:0x12e59e8a0<GPUImageFilter: 0x12e59e8a0> textureLocation newTarget is <HJGPUImageMovieWriter: 0x12e599a10>
2016-11-02 23:36:46.562 VideoCaptureTest[1489:240945] self:0x12e59e8a0<GPUImageFilter: 0x12e59e8a0> newTarget is <HJGPUImageView: 0x12e784d30; frame = (0 0; 320 568); layer = <CAEAGLLayer: 0x12e7795a0>>
2016-11-02 23:37:27.949 VideoCaptureTest[1489:240945] self:0x12e59e8a0<GPUImageFilter: 0x12e59e8a0> textureLocation newTarget is <HJGPUImageView: 0x12e784d30; frame = (0 0; 320 568); layer = <CAEAGLLayer: 0x12e7795a0>>
2016-11-02 23:37:32.295 VideoCaptureTest[1489:240945] fileer is <GPUImageFilter: 0x12e798a70>
2016-11-02 23:37:32.297 VideoCaptureTest[1489:240945] 0x12e59ddb0
2016-11-02 23:37:32.298 VideoCaptureTest[1489:240945] 0x12e59e8a0
2016-11-02 23:37:32.298 VideoCaptureTest[1489:240945] self:0x12e577b70<GPUImageVideoCamera: 0x12e577b70> newTarget is <GPUImageFilter: 0x12e5a2cc0>
2016-11-02 23:39:44.921 VideoCaptureTest[1489:240945] GPUImageVideoCamera self:0x12e577b70<GPUImageVideoCamera: 0x12e577b70> newTarget is <GPUImageFilter: 0x12e5a2cc0>
2016-11-02 23:41:39.165 VideoCaptureTest[1489:240945] self:0x12e577b70<GPUImageVideoCamera: 0x12e577b70> textureLocation newTarget is <GPUImageFilter: 0x12e5a2cc0>
2016-11-02 23:42:09.885 VideoCaptureTest[1489:240945] 0x12e5a2cc0
2016-11-02 23:42:09.886 VideoCaptureTest[1489:240945] self:0x12e5a2cc0<GPUImageFilter: 0x12e5a2cc0> newTarget is <GPUImageFilter: 0x12e5a1670>
2016-11-02 23:42:56.071 VideoCaptureTest[1489:240945] self:0x12e5a2cc0<GPUImageFilter: 0x12e5a2cc0> textureLocation newTarget is <GPUImageFilter: 0x12e5a1670>
2016-11-02 23:43:10.322 VideoCaptureTest[1489:240945] self:0x12e5a1670<GPUImageFilter: 0x12e5a1670> newTarget is <GPUImageFilter: 0x12e798a70>
2016-11-02 23:44:41.231 VideoCaptureTest[1489:240945] self:0x12e5a1670<GPUImageFilter: 0x12e5a1670> textureLocation newTarget is <GPUImageFilter: 0x12e798a70>
2016-11-02 23:44:47.528 VideoCaptureTest[1489:240945] self:0x12e798a70<GPUImageFilter: 0x12e798a70> newTarget is <GPUImageFilter: 0x12e798220>
2016-11-02 23:45:40.579 VideoCaptureTest[1489:240945] self:0x12e798a70<GPUImageFilter: 0x12e798a70> textureLocation newTarget is <GPUImageFilter: 0x12e798220>
2016-11-02 23:45:44.955 VideoCaptureTest[1489:240945] self:0x12e798220<GPUImageFilter: 0x12e798220> newTarget is <HJGPUImageMovieWriter: 0x12e599a10>
2016-11-02 23:46:32.568 VideoCaptureTest[1489:240945] self:0x12e798220<GPUImageFilter: 0x12e798220> textureLocation newTarget is <HJGPUImageMovieWriter: 0x12e599a10>
2016-11-02 23:46:36.104 VideoCaptureTest[1489:240945] self:0x12e798220<GPUImageFilter: 0x12e798220> newTarget is <HJGPUImageView: 0x12e784d30; frame = (0 0; 320 568); layer = <CAEAGLLayer: 0x12e7795a0>>
2016-11-02 23:48:19.950 VideoCaptureTest[1489:240945] self:0x12e798220<GPUImageFilter: 0x12e798220> textureLocation newTarget is <HJGPUImageView: 0x12e784d30; frame = (0 0; 320 568); layer = <CAEAGLLayer: 0x12e7795a0>>
2016-11-02 23:48:43.508 VideoCaptureTest[1489:240945] 0x12e798a70
2016-11-02 23:48:43.544 VideoCaptureTest[1489:242999] 0x12e5a2cc0
2016-11-02 23:48:43.579 VideoCaptureTest[1489:242999] 0x12e5a2cc0
2016-11-02 23:48:43.628 VideoCaptureTest[1489:242992] 0x12e5a2cc0
2016-11-02 23:48:43.680 VideoCaptureTest[1489:240969] 0x12e5a2cc0
2016-11-02 23:48:43.747 VideoCaptureTest[1489:242992] 0x12e5a2cc0
2016-11-02 23:48:43.815 VideoCaptureTest[1489:240969] 0x12e5a2cc0
2016-11-02 23:48:43.882 VideoCaptureTest[1489:242992] 0x12e5a2cc0
2016-11-02 23:48:43.948 VideoCaptureTest[1489:240969] 0x12e5a2cc0
2016-11-02 23:48:44.012 VideoCaptureTest[1489:242992] 0x12e5a2cc0
2016-11-02 23:48:44.079 VideoCaptureTest[1489:240969] 0x12e5a2cc0
2016-11-02 23:48:44.146 VideoCaptureTest[1489:242992] 0x12e5a2cc0
2016-11-02 23:48:44.213 VideoCaptureTest[1489:240969] 0x12e5a2cc0


内容概要:该研究通过在黑龙江省某示范村进行24小时实地测试,比较了燃煤炉具与自动/手动进料生物质炉具的污染物排放特征。结果显示,生物质炉具相比燃煤炉具显著降低了PM2.5、CO和SO2的排放(自动进料分别降低41.2%、54.3%、40.0%;手动进料降低35.3%、22.1%、20.0%),但NOx排放未降低甚至有所增加。研究还发现,经济性和便利性是影响生物质炉具推广的重要因素。该研究不仅提供了实际排放数据支持,还通过Python代码详细复现了排放特征比较、减排效果计算和结果可视化,进一步探讨了燃料性质、动态排放特征、碳平衡计算以及政策建议。 适合人群:从事环境科学研究的学者、政府环保部门工作人员、能源政策制定者、关注农村能源转型的社会人士。 使用场景及目标:①评估生物质炉具在农村地区的推广潜力;②为政策制定者提供科学依据,优化补贴政策;③帮助研究人员深入了解生物质炉具的排放特征和技术改进方向;④为企业研发更高效的生物质炉具提供参考。 其他说明:该研究通过大量数据分析和模拟,揭示了生物质炉具在实际应用中的优点和挑战,特别是NOx排放增加的问题。研究还提出了多项具体的技术改进方向和政策建议,如优化进料方式、提高热效率、建设本地颗粒厂等,为生物质炉具的广泛推广提供了可行路径。此外,研究还开发了一个智能政策建议生成系统,可以根据不同地区的特征定制化生成政策建议,为农村能源转型提供了有力支持。
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