diff --git a/compressed_output.mp4 b/compressed_output.mp4 new file mode 100644 index 0000000..050d7bc Binary files /dev/null and b/compressed_output.mp4 differ diff --git a/compression_report.html b/compression_report.html new file mode 100644 index 0000000..35f78cb --- /dev/null +++ b/compression_report.html @@ -0,0 +1,409 @@ + + + + + + Compression Report + + + +
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+

Smart Behavioral Video Compression Report

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Source: video_sample_1.mov | Output: compressed_output.mp4

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+
Original Size
33.19 MB
+
Compressed Size
2.42 MB
+
Reduction
92.7%
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Motion Discard Threshold
0.020
+
Processing Time
73.44 s
+
Original Duration
122.42 s
+
Compressed Duration
14.0 s
+
Frames Kept
168 (2.3%)
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Frames Discarded
7177 (97.7%)
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+
+
+

Storyboard

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One thumbnail per kept segment.

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+ +
+ Segment 1 +
+ Segment 1
+ 0.0s - 0.23s
+ Frames: 2
+ Reason: context_frame
+ Faces: 0
+ Motion avg: 0.117 +
+
+ +
+ Segment 2 +
+ Segment 2
+ 3.23s - 3.23s
+ Frames: 1
+ Reason: context_frame
+ Faces: 0
+ Motion avg: 0.003 +
+
+ +
+ Segment 3 +
+ Segment 3
+ 6.23s - 6.23s
+ Frames: 1
+ Reason: context_frame
+ Faces: 0
+ Motion avg: 0.005 +
+
+ +
+ Segment 4 +
+ Segment 4
+ 9.25s - 9.25s
+ Frames: 1
+ Reason: context_frame
+ Faces: 0
+ Motion avg: 0.038 +
+
+ +
+ Segment 5 +
+ Segment 5
+ 12.25s - 12.25s
+ Frames: 1
+ Reason: context_frame
+ Faces: 0
+ Motion avg: 0.023 +
+
+ +
+ Segment 6 +
+ Segment 6
+ 15.0s - 18.55s
+ Frames: 24
+ Reason: motion_above_threshold
+ Faces: 0
+ Motion avg: 0.173 +
+
+ +
+ Segment 7 +
+ Segment 7
+ 21.55s - 21.55s
+ Frames: 1
+ Reason: context_frame
+ Faces: 0
+ Motion avg: 0.069 +
+
+ +
+ Segment 8 +
+ Segment 8
+ 24.55s - 24.55s
+ Frames: 1
+ Reason: context_frame
+ Faces: 0
+ Motion avg: 0.003 +
+
+ +
+ Segment 9 +
+ Segment 9
+ 27.55s - 27.55s
+ Frames: 1
+ Reason: context_frame
+ Faces: 0
+ Motion avg: 0.026 +
+
+ +
+ Segment 10 +
+ Segment 10
+ 30.55s - 30.55s
+ Frames: 1
+ Reason: context_frame
+ Faces: 0
+ Motion avg: 0.041 +
+
+ +
+ Segment 11 +
+ Segment 11
+ 33.57s - 38.25s
+ Frames: 7
+ Reason: context_frame
+ Faces: 0
+ Motion avg: 0.184 +
+
+ +
+ Segment 12 +
+ Segment 12
+ 41.25s - 41.25s
+ Frames: 1
+ Reason: context_frame
+ Faces: 0
+ Motion avg: 0.017 +
+
+ +
+ Segment 13 +
+ Segment 13
+ 44.15s - 50.15s
+ Frames: 18
+ Reason: motion_above_threshold
+ Faces: 0
+ Motion avg: 0.194 +
+
+ +
+ Segment 14 +
+ Segment 14
+ 53.15s - 53.15s
+ Frames: 1
+ Reason: context_frame
+ Faces: 0
+ Motion avg: 0.032 +
+
+ +
+ Segment 15 +
+ Segment 15
+ 56.15s - 56.15s
+ Frames: 1
+ Reason: context_frame
+ Faces: 0
+ Motion avg: 0.003 +
+
+ +
+ Segment 16 +
+ Segment 16
+ 59.15s - 62.1s
+ Frames: 10
+ Reason: context_frame
+ Faces: 0
+ Motion avg: 0.185 +
+
+ +
+ Segment 17 +
+ Segment 17
+ 65.12s - 80.75s
+ Frames: 50
+ Reason: context_frame
+ Faces: 0
+ Motion avg: 0.209 +
+
+ +
+ Segment 18 +
+ Segment 18
+ 83.75s - 85.2s
+ Frames: 3
+ Reason: context_frame
+ Faces: 0
+ Motion avg: 0.129 +
+
+ +
+ Segment 19 +
+ Segment 19
+ 88.2s - 88.2s
+ Frames: 1
+ Reason: context_frame
+ Faces: 0
+ Motion avg: 0.065 +
+
+ +
+ Segment 20 +
+ Segment 20
+ 91.2s - 91.2s
+ Frames: 1
+ Reason: context_frame
+ Faces: 0
+ Motion avg: 0.076 +
+
+ +
+ Segment 21 +
+ Segment 21
+ 94.2s - 95.45s
+ Frames: 2
+ Reason: context_frame
+ Faces: 0
+ Motion avg: 0.122 +
+
+ +
+ Segment 22 +
+ Segment 22
+ 98.45s - 98.45s
+ Frames: 1
+ Reason: context_frame
+ Faces: 0
+ Motion avg: 0.032 +
+
+ +
+ Segment 23 +
+ Segment 23
+ 101.45s - 101.45s
+ Frames: 1
+ Reason: context_frame
+ Faces: 0
+ Motion avg: 0.025 +
+
+ +
+ Segment 24 +
+ Segment 24
+ 104.45s - 110.3s
+ Frames: 20
+ Reason: context_frame
+ Faces: 0
+ Motion avg: 0.269 +
+
+ +
+ Segment 25 +
+ Segment 25
+ 113.3s - 113.3s
+ Frames: 1
+ Reason: context_frame
+ Faces: 0
+ Motion avg: 0.034 +
+
+ +
+ Segment 26 +
+ Segment 26
+ 116.3s - 121.15s
+ Frames: 16
+ Reason: context_frame
+ Faces: 0
+ Motion avg: 0.214 +
+
+ +
+
+
+ + diff --git a/demo.mp4 b/demo.mp4 new file mode 100644 index 0000000..5656033 Binary files /dev/null and b/demo.mp4 differ diff --git a/segments_kept.json b/segments_kept.json new file mode 100644 index 0000000..688ce32 --- /dev/null +++ b/segments_kept.json @@ -0,0 +1,281 @@ +{ + "source_video": "video_sample_1.mov", + "compressed_video": "compressed_output.mp4", + "original_size_mb": 33.19, + "compressed_size_mb": 2.42, + "reduction_pct": 92.7, + "original_duration_sec": 122.42, + "compressed_duration_sec": 14.0, + "original_fps": 60.0, + "output_fps": 12, + "frames_original": 7345, + "frames_kept": 168, + "processing_time_sec": 73.44, + "segments": [ + { + "segment_id": 1, + "start_sec": 0.0, + "end_sec": 0.23, + "frames_in_segment": 2, + "reason_kept": "context_frame", + "face_count_in_segment": 0, + "motion_score_avg": 0.117, + "thumbnail_b64": 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" 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" 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" 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" 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" 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" 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" 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" 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" 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" 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" 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" 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" 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" 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" 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" 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" 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" 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" 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" 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" 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" 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xQuyEgscEDgAf7Q4qC40e40iyikvIik8xJKk/dUfT6/pVuXN7pnazuT6nCNJlltI51lM0aBn4Y8HJHsM/jwKpFdoJyQRzwetRNiR43z0HNQ3MhLCNTk1SVlZkyfM9DZ/sqZ9NN6IioLZ+YcbOmR7ZqjOsSy+XFIhO3JOCBn9a6/wRf3Gow/2MtvG4CgKWJG/JHB/PP4ViXcltYeJ55I4YLmCGUxugQCNgODgfyPfr7VnBzUnobTUOVNPUwJiQqoOp6mq5OOO2a7uTQxrc63O50jJBVFUD5MZwPTr1wa5m60G8+1TrBZXWxHI+6WAx/tYHFaKtGb0IlTlDco2qI13ErvtjZwCx7DPWuj1fQbXa14NRQRvyCw3Z9hiuXYZyuD71bKSttiRHYKvGMn5sY4HrWdVPmVmEbWdysLd5HYQBpAnOQO30p9pPPb3BZAvTBLLnFWbJJBOlsqMwkGWRM7myOM+3NdLa+HjJIrTB4kESjIIJLHOfpj+tS9dyb8pl6ZZS300TjeshO55TzjH3Tj6g/nXUWlhBptuCzKpUfPI3H/6quWltFbQrHEoARQuT1IHrWP4mltriIWZlUuvzyKRjZxwc+vXinohJObsjD1fXRd3EtvGqm2AwDj7x/vfSsZ8LEynGCQePbP8AjVnR2hWUR3P3ZepIzj6/lWnqemWtq6CBnPmncrE9Fxj+efyq0/e5bD5Pc5rmTp8reaULEMVCIqr945orV0TSpP7XtWkKvEN0m4HoVx+XJX86KmSdyVKx9V6laxX2nXFrPGskcsZUqwyDx6V4nr2gwra6dNYILea3kkDtDGFbOQQxYc57D0xXubsAhPtXm2tRrHeXEQwEdtwFVG61KctbC6Fd3mr6VJHqE8s15E25JJAPu4Axx19efWvJvGrTWHia8VUVPOCPgoDzjBIyOOc16Vpc7WlxhehNcd8XbYG5sdRTq6NE+O2OR/M/lVJLcV9Rvw+uXntruySbDqGlQMeDkAfzHP1rY1WJoJZyqkxSKNgbkLjmuK8B3Jh8R2wDfLKrofptJH6gV6TfhZonjPB6rWc1c0U7WM+7vDBpTNFucRx/KqjJIA4Fcjava6YLjULhNlyF8y3RyQQWBBUj1/DiujtY3liVBGMdCzHAOPb1rF1PRZxdpkl4AwJB53Y7c89T+lKL2ZWi5kbGiavFceVFdqsdxIvrkN7Z9av6vpNtqAikeESNFnAJPAIwe9YPkRyCbfhGUfLgcAMuDx+FQ6H4lkgP2bVGJ2kqJs8rj19axlTa96JfMr2MbxBoNvp0JmjuBEx4SLG7d+Ncy37kZOSzdCe9ep31ql1cfvRmKRSqyRgfdPUZ64rC8SotrHDp8Vik0BQlQUYFD7VVOs/h3YqlLTm6HI22oT29sscTmPDby6nDZ4A57dKdCxWPcc5Y5pjaa8ADyxyoobGXUgZ9KtWsZluYUwCrMAR6jvXWnZXZz21sbXh/xfdabHLbvCJbeKPcAT8y/Q/U1UuNW1TW528zIiYErbxDj6n14zWlNp1tdM0VsIYZCpLvjrggjP5H86o2Jk06W8DMRKy+SrR5/i4yD6YP5e9cceSS50dDUk7FG0mis7iMxJuupQQCVwsY7/U8Vekgle1CqEllPztsAPzMRjpxxnP4Vpf2C8klvKbfYwBVpM8geuDWwltDAcQRBeAuR6Cmld3Jk0lZGZpemLaT+czKZSm3hen49TxxWwvA55pNgDZxXKa74nkt9RW0tGCxo4E0gGSeeQK1MUrszfEGuX737xxzSQQDG0IcZ47nvWdFqCmWR7zfKZCN7liWIHHXPpXQarpx1CWKHy5UnPzCMIdz59B71ysttPFcvBMhVoXKyDrtIOCOKmMlM2qU3Te5YEiC4UlAEI+72HtVy+v2ur3zM9TgD0A6CsyfLONvanW4IYyN0FdCepz9DsfDmIknun/iwoJ9B1/z7UVm+H7mW9kSzUbgDkBe/PT8zRQkpas462IdOXKlc+o7m5zFhD9Sa4bXwTcbwSa6a8O0MQx249a5zUUDDI4rSULR0NoS1MlBk5PWoPEun2+saJJBdOyCH98rqMlSAf6E1HeahbaeAbqdI1zxvYCpLDVLK8JFrdQynuFcE/lXOdFjy67tjod7BNZXHnqjBllA4DA5xnoa9PspGvLG3ujj97Gr/L05Gap+JdMhu9MlVlIQYb5RyuO4rJ03XItNSOxjzJawkIrMSXbPJwAOeoGAP5VN+hdubVHRNGqZKKFDEk/Wo54fMQhjkGrBBkQNtYbhnDDBH1pqjHB6UJknLXUE7SSxAv5owBtHUeprCsbdNSuXhyzSmYq0US7nKgcvk8eg69/z76W0UzmdSQSu01zmq+HVnlR7JhbbQ27ylwWz7inHQtu5U0fV5LNQI4ZZNPM/kw+ZgMw9fTqDUOveLJJZ3tdLhBKNtMzjOCPSpdP/AHCpYzqB5ZGzjg4rnbxBbXc8e9chyWI9+ay9nBzvYttqJJfefPDaeeXnmKszHGSSW/8ArVd0LS3c/aZwSAp8uNepOO/oK1bKGCLSbea4kjLTps8n+MEAsGx1x1H5etbOgacZLSGUnYHjG4KTl/rVKbUeUU1rf0M2wtWuJ5Hs1yQQV3AbcEnhvbHpzWzDpW77St4FZXKFSox05xjsM1o6dp8VirhGZ3kILMe+On6VaZVx/SoitCZzu9DJui2cD8qrnpyMVpMgLHIrH1edrc4iwW9+1aIzKmt3/wDZ+nTzjG8LhP8AePAry5mLOWYksecmup8UXbz2Sqzn/WjIP0NcmetVshxOs0nxlNpGmpFaRb7wrIr3EpztB+7t9weeag8G2sep6w1vcqZI5ImMnJz65z9cVzWc8VueG7i/s5JpbBgm6MozlQccZGPxH6VL0TsVZyZ0mueBgqPLpsjAjny2Oc/Q9c1xN0ksChHRkyAfmGKuRavfw3S3K3c7TA5JaQnPsfUV3Wt2thJYL9nihaW6QBeQ33sc++OufakpSjpvcluKV2VvhbpeWa/mXCr82T6Dp/U/gKK62wtk0vRYbWMbS4BI9B/nFFaOxnSo86531PQb+eNU+8ORkY5zXJ61f/Y7G5u3T5YY2k255bAziiiuqpsYw3PDdS1O51S7e7unLuTwOyj0AqtZ3M1rOJYHZHU5yKKK5jsR7J4a1P8AtfSYrmTAc/LIPcd6tNaxebuKJu7NgZ/OiioYnuTKMHHVf5USIB6UUUhEZ9B0pjIByB+FFFDAydSs4xIJlxyc/jWBqHhuG/8A+PVNs4fLzgkq4/E9aKKl6M1WsTf0nTIo8XM+6Sdl2qZByg7j8Tk/jWzEqqoVAFA4AHaiihGc3dlhRnk4zQduOcUUU7EFRgPMIzXN6yskt+scIySMk9lHrRRUVZOEHJGlOKlJJi3WkWVzaCCeFXGM7sYYH1B6iuM1fwlcWxMlgxuI/wC4fvj/ABooryaOIqRludkoRaMCK3keUR7SHJ27Twc+lavh6eSK6ntsIm4cmXjaR6+nXvRRXtNXVmcsG4vmRRulh8+WSLJjUdT3Of5V1Pw8s3vb4zTH9zFwCfbqfywPxooq7WMKr5lr1Z6BPIbm5CrxuIUewooorKV7m05OFkj/2Q==" 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" 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" 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cUPIXtgFPAGAp6VXmfYoijzuPX2ojDlvcdSpz2sbWm6O+prK6qUA+7kYVj0AB7c1mSK0UxizhuQwPbFdb8O71lM2nrAZC5EgJbgtgADGOvGaXWbuzsfFzwXVpbTxQsVuGVM5c43HJ64Ix+BqYymp2toW40/Zp31OKnJCKh6k5NMhuJIXHkyNGxyCyEg4Pau21bRY9U1E+ViGNGC/KAAE56DufyrkdWsobfUJY7QzKitgrJjcD3GRWiqqo7Ih05Q1ZHp20ahB5kmxPNXc/oM8n8q3tW0CJd94b+LynOQxXOfpg81zLDtVt47hmSNRIyBeFGSC2Mce9Z1U1JWYRs1ZlcQPI7iANIqc5A7fSn2c81vc5GBngkpnH096sWYk86O2WMsHXJSMEM+RxmujttAMkgaYPEoiUZBGSxzu+n/16l66MTtHYydNs7i/vEkbKtwTJ1xjOD+YNdXY6fHZQqq4Lgcvjr/hVq0tYraJY4gAEULk9SB61T12ZI7Y2wlKTTjpggqv19xmnoiUnJ2RzWp6mmpXz2wb/AEZc7W/vMB1+lYtxF5UpUtx1/DtTJ91ndkRk/I2VJH86ck0txdGe4mbdITvkIyeetUu42rKxZsZj5hUltxURxqvGSaK0vD+ls+rWrl0lgG6QOvquPxHJXrRSe+hCZ9DaJqML+JryV4VUyoqiQDntn+VdiORkV5bpE5SYPuOR39a7/Tb8SRKGIAHvWrheN0Vz2epW8T6eLu2DrgSL0Nef3SN5c0Eq7WZSvPuMV6bcyCRSu1tp+6SODXH63YrIzHJ3dsDpUculxc2p53a/aXhjt7e5ePMBKjdgFgP05pbi2ZNO1mKBGaOWIMik5PTn69KtPD9k1CFCu0bmA9OQT/WtOxmjjuwsuAj/AC80k9TSTOT8GvHP4Z1u0lcJtQyAt0+71/8AHBW1F4ku9XsBpGj2Ma6faooWe5J3fKPbgE4/LJ6Zxfi8JXGkaq+o6JEbm1nBWW0BAZc/3ckAj2PatrSNBbSIZdQ1GCOy0+ImUW24MzMTwOCVUZxwCcnHTHO7asZ3aOasdQsZHhjEqmSRM4zn6jPrU+q6ZBfLG5hWRo84DHsRg1zupgfarq6VFUhiwVfugMOlM0HxQ6H7PqZPyttWX0+v5158qbXvROtzvIyvEehWthbCWOZYX5CxAZD/AOHvXKt+5GWyWbue9ep30CXNwPNVDFICBKozlTjjPasPxKiWsUOnx2Mc0BQlRtOUPtV06z0juyalL7RyVtqEtvYvbxnYJWDSMOpwMAe3U/nSpI7F55WZnclmYnJYnuaY2mvAoeaOVF3Yy6kDNWbWPzbiJAAQzgY9q6k7K7Oezbsa2geKrmwM0dxBHcxxpuUsPnX0GfyqpPqOoavPKzoEjcE+RAmM+5xyT1ransoL1jDD5MThTuk28nBBGT+FZ+nSSabc3QZiJXQwo0YBwSDzyMY9fb3rkjyS99HQ+a6izMs/KguFkijDTDpkYEfvz1P8q054GltgsKeY2N33MHLEYOB06k+1aEHh2QXUNw8b4JwSzY2DOc49TzW2ltDAcQxBeMZHoP8A9dUtXciVoqyMjS9KW0n85iplKbcBen49enFbKIQOeadsUHOK5bxH4nls7k2djsDrgSSOM4PoK0SMdWyp4l1+4W8azsyYo8DMmOW+men/ANasf+1pJLhZb9nuAnbdgseOp+grb1awiv42MWwTkgDPODnn+tcrfQyWtxLbupBjcowIx8w4NRGSmbTpSpmnFImqXk6CJIopmzHH/cA9D64HJ796m1bTPISJreI7SNv1x3/p9ag0E20Oo28l45SKMMxI55Ckj6844pbq/NxdZQEIB8qls49/zrbkk5K2xClHkd9ze8I25tbe4uLhWRy2zawIK468fX+VFV9Evp9QlS1YFjnOQc7uf8aKqME1qedWxLpy5UrnqFq21wAe9dDpt4YZVJ3MMdjjFeE3fjXUprsyQEQx5+WJcHj3Ndx4N8Yx6vILa5AhugMrj7r/AP16uEuh2ziz1v7aZR8+AOo5qhf7ZFYD6jtVCC4CgZbPpmpWn3nB6U5JWMlc5vWLYOwJHzA5Bx0rJe385HilGQwIrqb0BlIxjvmufnJjfpx71ztGyOf0zx34m0WS4tY4YL5LTMTPNAxIGeCxBH61heIPFuta7cxtql0zRxMGSBBtRfw7n3OTVPXn2eIbxo2IYyZyDjqBVC6807ZZCzB8gMxzkjH+IqrlnX3cUsvmqCW+UHgdV5wSfpWFZQR6jNDDbpcNLKWMoSMHYo6EZIzx9K3/AAZKLywdXYmaAeWR1yhyV/qPwFP1fQDcyRvayfZhGrA+UuCc/TFStCm7mfo+sPY/LFHJLYecIYxJgM3Yn25z7fjTNe8Wyy3D2ulwjKNt85xnBHpRYqIFjsplAMZG30ODWBdqLe7njLrlZCWI9+ay9nBzvYttqJNe/aJ4LTzy88xVmY4ySS3/ANar2haY7t9pmBICny416sccZ9BWpZQQR6RbzXMsZM6bBD/ECAWDY646j24rZ0DTzJZwzE7A8Y3BScv9apTajYU1rf0M3T7Zri4ke0XJ4K7gCuCTw3tj05rZi0rcbgXgVg5QqVGOBzgegzWhp1hFYq+xmdpMFmY9cdP0q2yrtz69qhLTUmdS70Mm6Zs4H5VX3ccitFkDMcgZrH1Sf7Mx24LelWkZkWpXwsrOacjPloWx715VLI8srSOxZ2JYk9zXUeI9RklsGTdgO4BH6/0rkieavYcUdlpHjIafbxTzQfbdRXevmT4IQY+Ug9cg/oPeqXhM/b/EKRXK+ctzvEwcZ3Agkk/jzXNZrU0HUrrSLsXdrGrttKkN6f5FTbR2G9dzp/EPgqW3Uy6XvmQcmMn5h9PX+dcc5e3LK4YOOCGGCK7yz8erLfKt3AsFqUw3JZg2evHb2/8A1VpeJTb3Vj5cflSG4AEfQ9T94fTr+FKNSa0aJfKtyh8LtMyWv5lwi/Pk+g6f1P4CiursLdNM0SG2jG0uASPQdv6frRVuxFGjzrnfU8UUfmOa0dEk26pbhTt5Izn2ooolszpp/Ej2zSbzzbSJy3zFcHJySRxWism4Z3YPtRRWibcU2Y1IpTaQkpBwc5/Gs69tlkUtnBooqWI8o8UQ+R4kul7Ha35qK3n0Zb7wZA8MeLiMNIoHViCc/mP6UUVLL6GT4W/tOy1JWhtJijgJKGQgbT3yfp/Ounn1v7NeGOeJ0RQN4YAdc4IHU9DRRUOT5rGvIuTmJ9Ss4/MWZcYJB6d6wNQ8ORX/ABaptmD5ecElXH4nrRRSbsxLWB0Gk6bFGRdThpJyu1TKOUHp+JyfxrZiVVUBAFA4AHaiikjObu2WFGRzjNDbcdqKKZBTcAyEA1zWtxyzaisUIySMk9lHrRRUVZOEHJGlOKlJJjrnR7G4sxBPAjjGd2MNn1B7VxmseE7i2zJYt9oi/un74/xooryqOIqQlvudk4RaOeEbBtrKwIONuOc+lbOgJCJZzcLl4h8qHtzyfwoor2Zv3TmpJOauZd47m5mA6FyeB711vw8s3vb4zTHMMQwM+3X9MD8aKKqJjXS28z0CeQ3NyFX+I7R7Ciiiole5pOThZI//2Q==" + } + ], + "frames_discarded_reasons": { + "near_duplicate_phash": 4554, + "low_motion_no_face": 2623, + "total_discarded": 7177 + } +} \ No newline at end of file diff --git a/solution.py b/solution.py new file mode 100644 index 0000000..10d40ff --- /dev/null +++ b/solution.py @@ -0,0 +1,642 @@ +""" +video_compression.py +Sentio Mind · Project 2 · Smart Behavioral Video Compression + +Copy this file to solution.py and fill in every TODO block. +Do not rename any function. +Run: python solution.py +Requires ffmpeg installed on your system: sudo apt install ffmpeg +""" + +import cv2 +import json +import base64 +import subprocess +import time +import numpy as np +from pathlib import Path + +# --------------------------------------------------------------------------- +# CONFIG +# --------------------------------------------------------------------------- +VIDEO_IN = Path("video_sample_1.mov") +VIDEO_OUT = Path("compressed_output.mp4") +REPORT_HTML_OUT = Path("compression_report.html") +SEGMENTS_JSON_OUT = Path("segments_kept.json") + +PHASH_THRESHOLD = 0.95 # similarity above this = near-duplicate, discard +MOTION_KEEP_THRESH = 0.15 # keep frame if motion exceeds this (no face needed) +MOTION_DISCARD_THRESH = 0.05 # definitely discard below this +CONTEXT_EVERY_SEC = 3 # force-keep one frame every this many seconds +OUTPUT_FPS = 12 # frame rate of the output video +OUTPUT_CRF = 28 # ffmpeg quality: lower = better quality + larger file +CALIBRATION_WINDOW_SEC = 30 # bonus: estimate motion discard threshold from first N seconds +MOTION_DISCARD_MIN = 0.02 +MOTION_DISCARD_MAX = 0.12 +PROGRESS_EVERY_FRAMES = 300 +MOTION_ANALYSIS_WIDTH = 160 +FACE_ANALYSIS_WIDTH = 160 +PREPROCESS_GRAY_WIDTH = 160 + +ACTIVE_MOTION_DISCARD_THRESH = MOTION_DISCARD_THRESH + + +# --------------------------------------------------------------------------- +# PERCEPTUAL HASH +# --------------------------------------------------------------------------- + +def compute_phash(frame: np.ndarray) -> int: + """ + Compute a perceptual hash of the frame. + Steps: resize to 32×32 grayscale → DCT → threshold at mean → flatten to bit string. + Return a 64-bit integer hash. + + You can use the imagehash library (imagehash.phash) or implement manually. + TODO: implement + """ + if frame.ndim == 2: + gray = frame + else: + gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + resized = cv2.resize(gray, (32, 32), interpolation=cv2.INTER_AREA).astype(np.float32) + dct = cv2.dct(resized) + low_freq = dct[:8, :8] + thresh = float(np.mean(low_freq)) + bits = (low_freq > thresh).astype(np.uint8).flatten() + + # Fast 64-bit packed representation for cheap Hamming distance. + h = 0 + for b in bits: + h = (h << 1) | int(b) + return h + + +def phash_similarity(h1: str, h2: str) -> float: + """ + Compare two hash strings. Return 1.0 if identical, 0.0 if completely different. + Formula: 1.0 - (hamming_distance / length) + TODO: implement + """ + if h1 is None or h2 is None: + return 0.0 + + if isinstance(h1, int) and isinstance(h2, int): + hamming_distance = (h1 ^ h2).bit_count() + return 1.0 - (hamming_distance / 64.0) + + if isinstance(h1, str) and isinstance(h2, str) and len(h1) == len(h2): + hamming_distance = sum(ch1 != ch2 for ch1, ch2 in zip(h1, h2)) + return 1.0 - (hamming_distance / len(h1)) + + return 0.0 + + +# --------------------------------------------------------------------------- +# MOTION SCORE +# --------------------------------------------------------------------------- + +def compute_motion_score(prev_gray, curr_gray: np.ndarray) -> float: + """ + Dense optical flow between two grayscale frames. Return mean magnitude, ~0.0-1.0. + If prev_gray is None, return 0.0. + TODO: cv2.calcOpticalFlowFarneback + Params: pyr_scale=0.5, levels=3, winsize=15, iterations=3, poly_n=5, poly_sigma=1.2 + """ + if prev_gray is None: + return 0.0 + if curr_gray.ndim == 3: + curr_gray = cv2.cvtColor(curr_gray, cv2.COLOR_BGR2GRAY) + if prev_gray.ndim == 3: + prev_gray = cv2.cvtColor(prev_gray, cv2.COLOR_BGR2GRAY) + # Downsample for speed; motion thresholds remain stable on reduced grayscale. + if curr_gray.shape[1] > MOTION_ANALYSIS_WIDTH: + scale = MOTION_ANALYSIS_WIDTH / curr_gray.shape[1] + new_size = (MOTION_ANALYSIS_WIDTH, max(1, int(curr_gray.shape[0] * scale))) + prev_proc = cv2.resize(prev_gray, new_size, interpolation=cv2.INTER_AREA) + curr_proc = cv2.resize(curr_gray, new_size, interpolation=cv2.INTER_AREA) + else: + prev_proc = prev_gray + curr_proc = curr_gray + + flow = cv2.calcOpticalFlowFarneback( + prev_proc, + curr_proc, + None, + pyr_scale=0.5, + levels=3, + winsize=15, + iterations=3, + poly_n=5, + poly_sigma=1.2, + flags=0, + ) + mag, _ = cv2.cartToPolar(flow[..., 0], flow[..., 1]) + return float(np.mean(mag)) + + +# --------------------------------------------------------------------------- +# FACE PRESENCE CHECK +# --------------------------------------------------------------------------- + +def has_face(frame: np.ndarray, cascade) -> bool: + """ + True if at least one face detected. Use the Haar cascade passed in. + Equalise histogram on grayscale first for better CCTV detection. + TODO: cascade.detectMultiScale — scaleFactor=1.1, minNeighbors=3, minSize=(20,20) + """ + if frame.ndim == 2: + gray = frame + else: + gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + gray_eq = cv2.equalizeHist(gray) + if gray_eq.shape[1] > FACE_ANALYSIS_WIDTH: + scale = FACE_ANALYSIS_WIDTH / gray_eq.shape[1] + new_size = (FACE_ANALYSIS_WIDTH, max(1, int(gray_eq.shape[0] * scale))) + gray_eq = cv2.resize(gray_eq, new_size, interpolation=cv2.INTER_AREA) + + faces = cascade.detectMultiScale( + gray_eq, + scaleFactor=1.1, + minNeighbors=3, + minSize=(20, 20), + ) + return len(faces) > 0 + + +# --------------------------------------------------------------------------- +# MOTION THRESHOLD CALIBRATION +# --------------------------------------------------------------------------- + +def auto_calibrate_motion_threshold(video_path: Path, fps_hint: float, + window_sec: int = CALIBRATION_WINDOW_SEC) -> float: + """ + Estimate a robust low-motion discard threshold from the first window_sec of the video. + Uses quantiles of optical-flow magnitudes and clamps to a safe operational range. + """ + calib_cap = cv2.VideoCapture(str(video_path)) + if not calib_cap.isOpened(): + return MOTION_DISCARD_THRESH + + max_frames = max(2, int((fps_hint or 25.0) * window_sec)) + motion_scores = [] + prev_gray = None + seen = 0 + + while seen < max_frames: + ret, frame = calib_cap.read() + if not ret: + break + + curr_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + if curr_gray.shape[1] > PREPROCESS_GRAY_WIDTH: + scale = PREPROCESS_GRAY_WIDTH / curr_gray.shape[1] + new_size = (PREPROCESS_GRAY_WIDTH, max(1, int(curr_gray.shape[0] * scale))) + curr_gray = cv2.resize(curr_gray, new_size, interpolation=cv2.INTER_AREA) + score = compute_motion_score(prev_gray, curr_gray) + if prev_gray is not None: + motion_scores.append(score) + prev_gray = curr_gray + seen += 1 + + calib_cap.release() + + if len(motion_scores) < 20: + return MOTION_DISCARD_THRESH + + p20, p35, p50 = np.percentile(motion_scores, [20, 35, 50]) + adaptive = float((0.50 * p20) + (0.30 * p35) + (0.20 * p50)) + return float(np.clip(adaptive, MOTION_DISCARD_MIN, MOTION_DISCARD_MAX)) + + +# --------------------------------------------------------------------------- +# FRAME KEEP DECISION +# --------------------------------------------------------------------------- + +def should_keep_frame(frame: np.ndarray, + prev_frame, + prev_kept_hash, + last_kept_time_sec: float, + current_time_sec: float, + cascade) -> tuple: + """ + Apply the 5-step decision algorithm from README in order. + Return: (keep: bool, reason: str, motion_score: float, face_found: bool) + + Reason strings (use exactly these): + 'face_detected', 'motion_above_threshold', 'context_frame', + 'face_and_motion', 'discarded_duplicate', 'discarded_static' + + TODO: implement + """ + curr_hash = compute_phash(frame) + duplicate_candidate = False + if prev_kept_hash is not None: + sim = phash_similarity(prev_kept_hash, curr_hash) + duplicate_candidate = sim > PHASH_THRESHOLD + + curr_gray = frame if frame.ndim == 2 else cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + if prev_frame is None: + prev_gray = None + else: + prev_gray = prev_frame if prev_frame.ndim == 2 else cv2.cvtColor(prev_frame, cv2.COLOR_BGR2GRAY) + motion_score = compute_motion_score(prev_gray, curr_gray) + low_motion = motion_score < ACTIVE_MOTION_DISCARD_THRESH + + face_found = has_face(curr_gray, cascade) + if face_found: + if motion_score > MOTION_KEEP_THRESH: + return True, "face_and_motion", motion_score, True + return True, "face_detected", motion_score, True + + if motion_score > MOTION_KEEP_THRESH: + return True, "motion_above_threshold", motion_score, False + + if (current_time_sec - last_kept_time_sec) >= CONTEXT_EVERY_SEC: + return True, "context_frame", motion_score, False + + if duplicate_candidate: + return False, "discarded_duplicate", motion_score, False + + if low_motion: + return False, "discarded_static", motion_score, False + + return False, "discarded_static", motion_score, False + + +# --------------------------------------------------------------------------- +# THUMBNAIL HELPER +# --------------------------------------------------------------------------- + +def frame_to_b64_thumb(frame: np.ndarray, width: int = 200) -> str: + """Resize frame keeping aspect ratio, encode as base64 JPEG.""" + h, w = frame.shape[:2] + nh = int(h * width / w) + thumb = cv2.resize(frame, (width, nh), interpolation=cv2.INTER_AREA) + _, buf = cv2.imencode(".jpg", thumb, [cv2.IMWRITE_JPEG_QUALITY, 72]) + return base64.b64encode(buf).decode("utf-8") + + +# --------------------------------------------------------------------------- +# VIDEO WRITING +# --------------------------------------------------------------------------- + +def write_frames_to_video(kept_frames: list, output_path: Path, + fps: float, frame_size: tuple): + """ + Write kept_frames to a temporary file, then re-encode with ffmpeg to H.264 MP4. + + Steps: + 1. Write to temp_raw.avi using cv2.VideoWriter (mp4v codec) + 2. Call ffmpeg: ffmpeg -y -i temp_raw.avi -vcodec libx264 -crf CRF -preset fast out.mp4 + 3. Delete temp_raw.avi + + TODO: implement + """ + if not kept_frames: + raise ValueError("No frames were kept; cannot create compressed output video.") + + temp_raw = output_path.with_suffix(".temp_raw.avi") + writer = cv2.VideoWriter( + str(temp_raw), + cv2.VideoWriter_fourcc(*"mp4v"), + fps, + frame_size, + ) + if not writer.isOpened(): + raise RuntimeError("Failed to open temporary video writer.") + + try: + for frame in kept_frames: + if frame.shape[1] != frame_size[0] or frame.shape[0] != frame_size[1]: + frame = cv2.resize(frame, frame_size, interpolation=cv2.INTER_AREA) + writer.write(frame) + finally: + writer.release() + + ffmpeg_cmd = [ + "ffmpeg", + "-y", + "-loglevel", + "error", + "-i", + str(temp_raw), + "-vcodec", + "libx264", + "-pix_fmt", + "yuv420p", + "-r", + str(fps), + "-crf", + str(OUTPUT_CRF), + "-preset", + "fast", + str(output_path), + ] + + try: + subprocess.run(ffmpeg_cmd, check=True) + except FileNotFoundError as exc: + raise RuntimeError( + "ffmpeg is not installed or not in PATH. Install ffmpeg to produce H.264 output." + ) from exc + except subprocess.CalledProcessError as exc: + raise RuntimeError(f"ffmpeg failed while creating {output_path}.") from exc + finally: + temp_raw.unlink(missing_ok=True) + + +# --------------------------------------------------------------------------- +# HTML REPORT +# --------------------------------------------------------------------------- + +def generate_compression_report(segments: list, stats: dict, output_path: Path): + """ + Write a self-contained HTML file showing: + - Original vs compressed size (MB and % reduction) + - Original vs compressed duration (seconds) + - Processing time + - Storyboard grid: one thumbnail per segment + - Frames kept vs discarded count + + No CDN. Inline CSS only. Must work offline. + TODO: implement + """ + frames_original = stats.get("frames_original", 0) + frames_kept = stats.get("frames_kept", 0) + frames_discarded = stats.get("frames_discarded_reasons", {}).get("total_discarded", 0) + keep_pct = (100.0 * frames_kept / frames_original) if frames_original else 0.0 + discard_pct = (100.0 * frames_discarded / frames_original) if frames_original else 0.0 + + storyboard_cards = [] + for seg in segments: + storyboard_cards.append( + f""" +
+ Segment {seg[ +
+ Segment {seg["segment_id"]}
+ {seg["start_sec"]}s - {seg["end_sec"]}s
+ Frames: {seg["frames_in_segment"]}
+ Reason: {seg["reason_kept"]}
+ Faces: {seg["face_count_in_segment"]}
+ Motion avg: {seg["motion_score_avg"]} +
+
+ """ + ) + + if not storyboard_cards: + storyboard_cards.append('

No segments were kept.

') + + html = f""" + + + + + Compression Report + + + +
+
+

Smart Behavioral Video Compression Report

+

Source: {stats.get("source_video", "")} | Output: {stats.get("compressed_video", "")}

+
+
Original Size
{stats.get("original_size_mb", 0)} MB
+
Compressed Size
{stats.get("compressed_size_mb", 0)} MB
+
Reduction
{stats.get("reduction_pct", 0)}%
+
Motion Discard Threshold
{ACTIVE_MOTION_DISCARD_THRESH:.3f}
+
Processing Time
{stats.get("processing_time_sec", 0)} s
+
Original Duration
{stats.get("original_duration_sec", 0)} s
+
Compressed Duration
{stats.get("compressed_duration_sec", 0)} s
+
Frames Kept
{frames_kept} ({keep_pct:.1f}%)
+
Frames Discarded
{frames_discarded} ({discard_pct:.1f}%)
+
+
+
+

Storyboard

+

One thumbnail per kept segment.

+
+ {"".join(storyboard_cards)} +
+
+
+ + +""" + + output_path.write_text(html, encoding="utf-8") + + +# --------------------------------------------------------------------------- +# MAIN +# --------------------------------------------------------------------------- + +if __name__ == "__main__": + t_start = time.time() + + if not VIDEO_IN.exists(): + raise FileNotFoundError( + f"Missing input video: {VIDEO_IN}. Place video_sample_1.mov in the project root." + ) + + cascade = cv2.CascadeClassifier( + cv2.data.haarcascades + "haarcascade_frontalface_default.xml" + ) + + cap = cv2.VideoCapture(str(VIDEO_IN)) + if not cap.isOpened(): + raise RuntimeError(f"Unable to open input video: {VIDEO_IN}") + + total = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) + fps_in = cap.get(cv2.CAP_PROP_FPS) or 25.0 + fw = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + fh = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + duration = total / fps_in + orig_mb = VIDEO_IN.stat().st_size / 1_000_000 + + print(f"Input: {VIDEO_IN} | {total} frames | {duration:.1f}s | {orig_mb:.1f} MB") + ACTIVE_MOTION_DISCARD_THRESH = auto_calibrate_motion_threshold(VIDEO_IN, fps_in) + print( + f"Calibrated motion discard threshold: {ACTIVE_MOTION_DISCARD_THRESH:.3f} " + f"(from first {CALIBRATION_WINDOW_SEC}s)" + ) + + kept_frames = [] + segments = [] + prev_gray = None + prev_hash = None + last_kept_t = -999.0 + cur_seg = None + disc_dup = 0 + disc_stat = 0 + + frame_idx = 0 + while True: + ret, frame = cap.read() + if not ret: + break + ts = frame_idx / fps_in + gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + if gray.shape[1] > PREPROCESS_GRAY_WIDTH: + scale = PREPROCESS_GRAY_WIDTH / gray.shape[1] + new_size = (PREPROCESS_GRAY_WIDTH, max(1, int(gray.shape[0] * scale))) + gray = cv2.resize(gray, new_size, interpolation=cv2.INTER_AREA) + + keep, reason, motion, face = should_keep_frame( + gray, prev_gray, prev_hash, last_kept_t, ts, cascade + ) + + if keep: + kept_frames.append(frame.copy()) + prev_hash = compute_phash(gray) + last_kept_t = ts + + if cur_seg is None or (ts - cur_seg["end_sec"]) > 2.5: + if cur_seg: + segments.append(cur_seg) + cur_seg = { + "segment_id": len(segments) + 1, + "start_sec": round(ts, 2), + "end_sec": round(ts, 2), + "frames_in_segment": 1, + "reason_kept": reason, + "face_count_in_segment": 1 if face else 0, + "motion_score_avg": round(motion, 3), + "thumbnail_b64": frame_to_b64_thumb(frame), + } + else: + prev_count = cur_seg["frames_in_segment"] + new_count = prev_count + 1 + cur_seg["end_sec"] = round(ts, 2) + cur_seg["frames_in_segment"] = new_count + cur_seg["face_count_in_segment"] += 1 if face else 0 + cur_seg["motion_score_avg"] = round( + ((cur_seg["motion_score_avg"] * prev_count) + motion) / new_count, + 3, + ) + else: + if "duplicate" in reason: + disc_dup += 1 + else: + disc_stat += 1 + + prev_gray = gray + frame_idx += 1 + + if frame_idx % PROGRESS_EVERY_FRAMES == 0 or frame_idx == total: + pct = (100.0 * frame_idx / total) if total else 0.0 + elapsed = time.time() - t_start + print( + f"Progress: {frame_idx}/{total} ({pct:.1f}%) | " + f"kept={len(kept_frames)} | elapsed={elapsed:.1f}s", + flush=True, + ) + + if cur_seg: + segments.append(cur_seg) + cap.release() + + print(f"Kept {len(kept_frames)} / {total} frames across {len(segments)} segments") + print("Writing compressed video ...") + write_frames_to_video(kept_frames, VIDEO_OUT, OUTPUT_FPS, (fw, fh)) + + comp_mb = VIDEO_OUT.stat().st_size / 1_000_000 if VIDEO_OUT.exists() else 0.0 + t_end = time.time() + + stats = { + "source_video": str(VIDEO_IN), + "compressed_video": str(VIDEO_OUT), + "original_size_mb": round(orig_mb, 2), + "compressed_size_mb": round(comp_mb, 2), + "reduction_pct": round((1 - comp_mb / (orig_mb + 1e-9)) * 100, 1), + "original_duration_sec": round(duration, 2), + "compressed_duration_sec": round(len(kept_frames) / OUTPUT_FPS, 2), + "original_fps": round(fps_in, 2), + "output_fps": OUTPUT_FPS, + "frames_original": total, + "frames_kept": len(kept_frames), + "processing_time_sec": round(t_end - t_start, 2), + "segments": segments, + "frames_discarded_reasons": { + "near_duplicate_phash": disc_dup, + "low_motion_no_face": disc_stat, + "total_discarded": total - len(kept_frames), + }, + } + + with open(SEGMENTS_JSON_OUT, "w") as f: + json.dump(stats, f, indent=2) + + generate_compression_report(segments, stats, REPORT_HTML_OUT) + + print() + print("=" * 55) + print(f" Done in {stats['processing_time_sec']}s") + print(f" Size: {orig_mb:.1f} MB → {comp_mb:.1f} MB ({stats['reduction_pct']}% smaller)") + print(f" Duration: {duration:.1f}s → {stats['compressed_duration_sec']:.1f}s") + print(f" Report → {REPORT_HTML_OUT}") + print(f" JSON → {SEGMENTS_JSON_OUT}") + print("=" * 55)