51 lines
2.2 KiB
Markdown
51 lines
2.2 KiB
Markdown
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# Video Summary and Classification
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## Example:
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What you see above is a 15 second excerpt of a 2 minute overlayed synopsis of a 2.5h video from an on campus web cam.
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The synopsis took 40 minutes from start to finish on a 8 core machine and used a maximum of 6Gb of RAM.
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However since the contour extraction could be performed on a video stream, the benchmark results show that a single core would be enough to process a video faster than real time.
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## Heatmap
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## Benchmark
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Below you can find the benchmark results for a 10 minutes clip, with the stacked time per component on the x-axis.
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The tests were done on a machine with a Ryzen 3700X with 8 cores 16 threads and 32 Gb of RAM.
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On my configuration 1 minutes of of the original Video can be processed in about 20 seconds, the expected processing time is about 1/3 of the orignial video length.
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- CE = Contour Extractor
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- LE = LayerFactory
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- LM = LayerManager
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- EX = Exporter
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### Configuration
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./Application/Config.py
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"min_area": 100, min area in pixels
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"max_area": 9000000, max area in pixels
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"threashold": 6, luminance difference threashold
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"resizeWidth": 1000, video is scaled down internally
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"inputPath": None, overwritten in main.py
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"outputPath": None, overwritten in main.py
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"maxLayerLength": 5000, max langth of Layer
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"minLayerLength": 10, min langth of Layer
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"tolerance": 100, max distance between contours to be aggragated into layer
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"maxLength": None,
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"ttolerance": 60, number of frames movement can be apart until a new layer is created
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"videoBufferLength": 100, Buffer Length of Video Reader Componenent
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"LayersPerContour": 220, number of layers a single contour can belong to
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"avgNum": 10, number of images that should be averaged before calculating the difference
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### notes:
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optional:
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install tensorflow==1.15.0 and tensorflow-gpu==1.15.0, cuda 10.2 and 10.0, copy missing files from 10.0 to 10.2, restart computer, set maximum vram
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