diff --git a/Vineet_Nagrale_231158.mp4 b/Vineet_Nagrale_231158.mp4 new file mode 100644 index 0000000..c0ab959 Binary files /dev/null and b/Vineet_Nagrale_231158.mp4 differ diff --git a/compressed_output_1.mp4 b/compressed_output_1.mp4 new file mode 100644 index 0000000..c694daf Binary files /dev/null and b/compressed_output_1.mp4 differ diff --git a/compression_report_1.html b/compression_report_1.html new file mode 100644 index 0000000..fcfb037 --- /dev/null +++ b/compression_report_1.html @@ -0,0 +1,212 @@ + + + + + + Sentio Mind Compression Report + + + +
+

Smart Behavioral Video Compression Report

+ +
+
+
File Size Reduction
+
97.6%
+
614.16 MB → 14.53 MB
+
+
+
Duration Comparison
+
10.8s
+
Originally 122.5s
+
+
+
Frames Kept
+
129 / 7169
+
+
+
Processing Time
+
46.0s
+
+
+ +

Storyboard Segments

+
+ +
+ Frame +

Seg 1 @ 0.0s

+ context_frame +
+ +
+ Frame +

Seg 2 @ 3.08s

+ context_frame +
+ +
+ Frame +

Seg 3 @ 6.15s

+ context_frame +
+ +
+ Frame +

Seg 4 @ 9.23s

+ context_frame +
+ +
+ Frame +

Seg 5 @ 12.3s

+ context_frame +
+ +
+ Frame +

Seg 6 @ 14.87s

+ motion_above_threshold +
+ +
+ Frame +

Seg 7 @ 21.62s

+ context_frame +
+ +
+ Frame +

Seg 8 @ 24.69s

+ context_frame +
+ +
+ Frame +

Seg 9 @ 27.77s

+ context_frame +
+ +
+ Frame +

Seg 10 @ 30.85s

+ context_frame +
+ +
+ Frame +

Seg 11 @ 33.92s

+ context_frame +
+ +
+ Frame +

Seg 12 @ 41.61s

+ context_frame +
+ +
+ Frame +

Seg 13 @ 44.69s

+ context_frame +
+ +
+ Frame +

Seg 14 @ 50.07s

+ context_frame +
+ +
+ Frame +

Seg 15 @ 53.32s

+ context_frame +
+ +
+ Frame +

Seg 16 @ 58.62s

+ context_frame +
+ +
+ Frame +

Seg 17 @ 61.18s

+ motion_above_threshold +
+ +
+ Frame +

Seg 18 @ 66.05s

+ context_frame +
+ +
+ Frame +

Seg 19 @ 69.13s

+ context_frame +
+ +
+ Frame +

Seg 20 @ 76.9s

+ context_frame +
+ +
+ Frame +

Seg 21 @ 87.93s

+ context_frame +
+ +
+ Frame +

Seg 22 @ 91.0s

+ context_frame +
+ +
+ Frame +

Seg 23 @ 95.36s

+ context_frame +
+ +
+ Frame +

Seg 24 @ 98.44s

+ context_frame +
+ +
+ Frame +

Seg 25 @ 101.51s

+ context_frame +
+ +
+ Frame +

Seg 26 @ 104.59s

+ context_frame +
+ +
+ Frame +

Seg 27 @ 116.55s

+ context_frame +
+ +
+
+ + + \ No newline at end of file diff --git a/segments_kept_1.json b/segments_kept_1.json new file mode 100644 index 0000000..293c5c3 --- /dev/null +++ b/segments_kept_1.json @@ -0,0 +1,291 @@ +{ + "source_video": "C:\\Users\\vinee\\Downloads\\Class_8_cctv_video_1.mov", + "compressed_video": "compressed_output_1.mp4", + "original_size_mb": 614.16, + "compressed_size_mb": 14.53, + "reduction_pct": 97.6, + "original_duration_sec": 122.52, + "compressed_duration_sec": 10.75, + "original_fps": 58.51, + "output_fps": 12, + "frames_original": 7169, + "frames_kept": 129, + "processing_time_sec": 45.99, + "segments": [ + { + "segment_id": 1, + "start_sec": 0.0, + "end_sec": 0.0, + "frames_in_segment": 1, + "reason_kept": "context_frame", + "face_count_in_segment": 0, + "motion_score_avg": 0.0, + "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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Ph7PKmoSwRoGEiKcknhvT05z39K0Nb1mKz8VSWtyiXFvA2yUheknQn328j86zXOp6bGv7v2S7nBSsAAvUjk1EXBfJzg9cV2f9gC91N57lJGyxZoz8u4noOOQo6Zrmjo91PNIbSzuPKUkjIyFH+9jFaKrGexDhKG4ugPax6lGb4qIcEHcMjJHFdFq2k6O9t9od0jQj5ZYiOfw71xbKzBgqkkDnA6VaZWJEal22rwg55xjp61jWg3JNMcXaOxCIS0rrbbpVXkHbgkeuKltJbmKYvEm44PVM4p9pG4uEgCMxkHKJkMwPT8K6W00J3fMvmQp5KjIYZYnkj9BVPXRkN2MbSbO5vdQVyz7upkIyF7g/mMYrrbazt9Mtdzui7R88jcf/AKqGudO0eJEmmSLChQCMsQOAcCqGq3lhqkCW8E6yc72ypAXqOp79TSbUVclJzdkZOoamNQvZbbfm25Kn+8R39hWPcL5LPHuyCQf8P51DJvtLrKkEqcg9cinLLJLcGeaVt0jHe+Mnnr/OrW9xtWVixYTYm2MrFmAVApwd2RiitLQNNZ9WtXLpLCMyB1Pdcceo5K9aKl36E3se8fCvwguixfbpL+4kldceQDiNc+o7mvRa5Dw9feXCEyAAK6VLkMAcgD61tOL3GqivZmZ4n09bqASLgSL0NcJdQs0MsUg2sVI5r0S7lDqV2ttP3WPQ1yOrwKzHn5u2O1ZuOlw5tTi9PWRrWOKKQhhCcZPBYADn8arapE8Mk+0ExSqNmeQpHP8AOtJIPst2EVcZY4GeOck1JeKssTRuMHqv1rPc1c7O6M9Ji1msEUqqdm2MsMgccCuO0oJp3iuyN7NiRZwilBwSWKnPHTrz6EV0MavJEsaxrnpuc8H04HesO4s5k1Vo7gEo4LK4JA3cY4z65qo9GN6XR7LZp5sE0K8vE29PpWD48jnufCl5HbjIDxzP/ujr/MH8DVvw7q0F2qXVtIGaM+XcR55Q9CD+WRXSz2MUmSVD20ykMCMjBHII9DXWnoc1tTxTw7NKunarBbb/ADXh3LsXJGFcDH/AmWuDJ8oZY/Oa+gn8D6ZolrqV5ayygS2zrhpQQi9cD15A6+leY69pTwItrZWBmics7s67juPcEdKwqVFGVu5rCF437HJwX0kVm0CHaHOXYHrxgD+f50qO7bpZCzO5yWJySfWln0qa0Aa4hmjXd1dCBmprSPzrmGPAKlhke3eqTSXMTZt2NjQfF19p6yRXCLcRRISu77yjsAap3ms6hqsrPcOI4W6QrwuTjGfX8as3GmWkUc/2RgpCFnDuSCAQcc/Q96rWcAuIZlnOwDBHGCemPw6YrkXJJc6R0NSTUWOtCljhkGZ5lPzEcKB/n6VUktn+3zbUeR5MeWVXILHBJOOgHX64rp49GlL27G32jo77iDgEcmtWO1hgb9zGF4A49B/+uiK1bJm0lYydM0lbWfznKtKU28L0/H6cVsohA5olMUKNNKQiINzMTwBXn3iLXpdTmK27Olqv3Vzjd7mtUjC1ybxPYzC7lvXbcuQCAMj0/wAKzLG5js7kPcIZIyR5kfdhnn6Gu50iyuNXitY7KSK4WVNzHlVQjg/e5IB71xPiGK3i1a5WxmaeBH2iU/xHuR7Zzj2xUQcpXUkdFSMI2cWSf2ibkXQmt4THNJvVSv8Aqj6JjpxgfgKuajY25sY5oZArYxtxyRjgfXP6VkW20TRRsQMnqe1ausaikkscEARbeHhQgADHux9c1vyXa1sYqdk9NzU8Iw/Zree4mBViduGGCMdf1/lRVXQbqa8lSzxuCnIx356UVUYHBXxEoSslc9q06cjaFPQ9a37W9COu7c4xxg9K5KA7ScE4rQjnSNPMkkEaKMszHAA9a1i9DeS1udHPeNL97GOoGazLwCQEfiK4zUfiRo9pdeRbSPc7T80iL8v5/wCFb+la7p+txb7C4STABZQfmX8KiVrFJSRUvo9x6c/yqhKjDr26VuXQHPGO+ay5sEcD8652rM1RmSRJglFC7iSf61lajamQcsSOx9K3JEP1FUbtfLBJHymhMo4ae8vtE1sz2Vw0MzKDkcgjpyDwenet6z+J/iS3AAmtpMcEPCOfyxWP4shUXFtOhzvVl/LB/rWdZWP2qC4kjLebFj5R3Fap6AdFr3jPXfE0Ekd3dw21qAN8EB8tX/DJZunTkVQvvFdzPDHb2cIEiKA88nPzAckVh9BTA4GQD05NROKluNNrYt33nzw23nF5pirMzYyTlv8A61XND092b7TKpI2kRxr1Y47+grUsYootKt3uZI2M6FfJ/iUqC27Hp/LitfQNOMlpDMTsDxjcFJy/1qFJqNipLVv0MYaQmsLNDEHwSrKRgAcnr+HpW9p2grbwzwXTvMrFCJHbLNjn+fFaenWEVir7GZ3kILs3fHT9KuMFxn9KhLTUU53ehm3LHOBUBb5ctgVfZdzcgZrA164kt22RYz1NUkZmT4z1IQ6WbeJzvmbacf3e/wDSuDA28ZByM8V0OriC4tWnmdzOrBUXoOetc62VODVpovlsjRt9b1G1sRZ21y8MO9nPl/KSSMHJ64wTx70aJA17qMduiby4b5cZz8pNZma2vC0t1bais9mIy6qd28dF7mk3ZNi5buxMdFeSMtJMsMoXKxujZ46g8cH86xdrI2WAwPerurs6ajKzuXd3L7j7nNdO2l2P9h2r/ZoxeXMS8lixy3G7BPvmmqnV9QklHTsXfhfpeXbUJ1wo+fJ9B0/qfwFFdXp9ummaJDbRjaXAJHoO39P1optoyo0edc8uppq4XvXDfFDVbiOK0sImKRyZkkAP3scDP6/pRRWjeg1uedktIMDrWn4d1S40XVILu3dhsPzqDwy9x+NFFTc1WrPenZJoklRsq6hhn0NVZIwxOT+FFFSzMryQ84GMfyqrc24ZCrd6KKgtHA+K4GiVAVO1ZcA/UH/CmeD3gS8uhOyrvjAXPc5FFFX0H1K3iq1js9QRosBZlJ46bgef6VHpWh/2tZ74AyTxz/NJ224BH45zRRQ37o0dtpGmRRD7TMrNcOuMyDlR6fjyfxrVjVUUKgCgcADjFFFZoU9WWEG7rjNOIAHbNFFBBVYDzCM1zOuxPc6isUQzxyR0A9aKKipJwg5IunFSkkx02j2MlkLeSFWXruxhs+ua5HWPC9xBmSzP2iMfwH74/wAaKK8qliKkJb7nZOnFo55Yn8zYUbdnG3HOfpWt4cd476Ub0iCxnLScBfrRRXtPVWZyxdndFfUriO5laXaSwON/Y+mB6V0Pw/s3vb7zZeYo+AT7dT+WB+NFFUoqOiMqr59+rPQJ5Dc3QRTjcQo9hRRRWMr3NJycdEf/2Q==" 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" 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" 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VzbW+6IkIMb88sM57jr3HPeojzqb00NJKn7NNPU4+U4UL6cmogwLDdnGAD+FdlD4cF1fPNMryjnMLZQuxzycdB06Vzn9iXszu9tY3IiBJAYEhR/vYFaKrGb0IdOUNyLRvI/tCP7XJ5cRyC2M4ODj9cV0OsaPbSgXkt/GsZUAMqAhsfjya5FwxyACSO2KtMjEiNSzbV4Qc84x09ayqxfOmnYcWuWzIfJLzOttulVeQduCR64qS0kuYZmeFNxweqZxUlnG4uEhCOxkXJRM7mBHH4V01poLvJmQyQp5KgkMMsTyR+gpv3tGQ3YxNJs7m91FXLPu6mQjIXuD+YxXXWtlBptsGdlXaPmkbj8farMMdrYQKpdI0RQu52C5x0yay9ZurK9iFnHPHKxId8cgDt83r1P4UrqKuxJObsjC1HVJL68ktIJf9HcnDY+9jn8uKxpA0e+NyOcZA6jHT+dNl32tzweR0OeopYmZpvMkcoHPLKvY9cDirXcLWRY0+TEhQhizDZGqnHzEiitTQNMZ9WtXLpLCN0gdexXHHqOSvWipd+hNz3b4V+EV0WP7dJf3EkrrgQA4jXPqO5r0WuJ8N37RxBC2AOAK6uK6DgHIA+tbTg9xqor2ZneJtPW7gDrxIvQ1wV5CxhmhkG1nQrz7ivR7qUOpXa20/dY9DXI6vbLITyd3YDtWbjpcOfU4Hw7NJFf20LSt5DRfdbpnH+NdhBHktH/Ep3J/hXJ3NobPUYNq7clgM9Ocn+tbWnaxam6js7qUQXZAMLPwsnYjPr/jV030ZU9dUdXbW8V1aFHQMrdm/UGuH8a+FbDSrFdasP9Ea3mVpdgYrjPHA6c4GR6131ip374yEZh88bdD7g1neOL/T7XS5bC8niFzeJtjt85ZvfHp7n0rZvQySdzz3RtbgvygnBSaQZy/8f4+tXdU0q3vzHK0IkaIMAGPYjmudubMNJKABlPu44AyP/rUui+JXic22pE5U7RL6fWvOnTa96J2KSTSKOueHLO3sJJ4ZDbyJ0GcqfQeua47iEZJ+c161c24uZQSA0ZwyOqhsH1Ga5XW9MltibGz09prdl3l3jLNvJOSCOnanSq9BVKenMjl4L6WG38qNygJ3MynBPAAH+fWljdgpd85Y5JPWny6VNaKJLiCaNQ2N0iEAn8als4vOuoUIBDOMj27/AKV1p2VzCzvY2dA8X3+nrLBOouIY03Lk/MvoM1UvNY1HVpXa4YJCwJEKcKPcn+pq7eWNqyTNaLFHIELSHOA2CD9B0NUrBTLbTh5EjRsKc4DOD2B7/wD6/WuSPJJc6Ru1JNRG2gSyZZFXNxKpAOOFH+e/Sqkts51CUKryPIV8vYuQWODk46DvXUx6LIZLdzb7R0d92DjIOTWrHawwP+5jC8AZHoP/ANdEd2yZtJWRlaZpS2k4mYq0pTbwvT8fpxWwiEDmqer6naaTCJrgEluERerGuE1bxFfahISsrQxD7scbED8fWtUjG1yTxO91NqsjT7vKUhVHOF9v0zVOwvFtZt88fmREjegxkjPY9q6uDTpdchtkjgS580Bv3Lhihx0Zh0P1rl9esEsNVuraGZZhC+1mUYG7+ID2ByPwqYty0aNpxUGnFki6kbgXSz28LRzPvUFf9UfRMdOMD8K0NSs4G0yF4pFVkACg9cYzj356Vi220TRRkgbj1J4BNa2tahGzxWtttW2g4UJ0Y929615OZrWxmp2T03NPwjF9mt57iYFWY7cMMEY6/r/KiqugXUt7Ilpjdg5GO/PT88UVcYX6nn1sTKnLlSueyadMV2hT0PWuis73a43bn47HpXJwDaTgnFakEqou9nCKBkknAFbQeljomtTopbxpPv8AA6gZrNvCJFIH1FcZqXxI0azuzBDK1wEOGkRcqPof8K29J8Qadrke+wuFk43FejD8KiVmhpSWpU1S3EvVRuHI9q4LxtZymyE3JMD5z/sng/rivTLnHIx+Nc/rNol1aywkfLIpUkjpkVhszVHko1K6UeV9qnCnkL5hwa0tCRLnUoBM8jStIAvpjHJJrBuUaO4QMMFWKkehrofCSCXXrJScfMW/JSf6VdyjdubadpJocv5vAG0dR2zXLH99cTR5Y3G8RxxBCTK2ccHpx/WvTbmzUztOpIYrtIFYGt6DDqADfMkiZIZAOSfX8qzTsU3cyrDVZdEvItPZprlGwJYnj2mFjj5Qf/1g/wAk13xXLNO9tpkI+RipmcdCPT/IplhEsBFrOvzh9wY5O8+ufWsS9XyLuaMsuVYlsH8az9nBzvYtyaiSX5nnhtfOZ55irMTjJOW/+tVzQ9Odm+0yqxwpEca9WOO/oK1bOGGLSrZ7mSNjPGV8n+JSoLZx6df09a19A04yWkMxPlh4xuCk5f61Sm1GwpLW/oYy6SurpNFEHwdrKQQAOT19selbuneH44IZYbrMyrsEcjnLYHOPYZrV02wisVfYzO0hBdmPXHT9KtsFxn9KhLQmdS70Mu6Zs4H5VXLZHPBrRZAWORg1zusXUkLlYsZqkZnKePXZ9TgjyCFhBAB7kn/CsXU4ILWdbeAlmjQCV88F++K2byybUbwTtOItigEuO+eP51zT5DHccnPJ9atNbF8rsmaml6/qGlW0kNhKIS7q/mBfnUjsD2B7+uKzxIxfcxJJOST3qHNW9OsZL2YBeFHzOfRR1NF7ahy30NNdHNxAsnnxo7JuRCCcj34+nTNY+1kbLDge9Wb+P7LdNEHLYPysx5x2rrZdJsBotqwto/tlzCnJYsdzYycE9sk/hQqmt+4pJRVn0Lfwv0vLtqEy/Ivz5PoOn9T+Aorq7C3TTNEhtoxtLgEj0Hb+n60VTt1MqNLmXM+pqo6r3ri/ihqtxBZWlnC5jS4ZmkweWC4wPpz+goorRvQa3PNWJcYz+daOg6jdaNqUNzAzK0bfMucZHcfiKKKg1R72WWaKOZD8rqGGeuDVSaMSAqT9KKKTI6nknjGwWy1+TzFxDJIsvHofvfrmrHg3YniK1yeMuAf+AkUUUdCz0iT2qJ0AGR09KKKzJMfU7KNZBKmME5/Gufv/AA3HfShrMFZlfLzAkq47Yz3ooqW7M2WsToNJ02KL/SplL3DrjdIOVHp+PJ/GteNVUBUAUDgAdqKKa2M57ssKM9cZpzbcdqKKCCm+DIRmuY1aBpr8Rx9dpPHQe9FFRVk4U3JGlNKUkmTTaVZzWf2eWMMpHJ6En1rkNX8K3ELB7JvPi/unhl/x/wA8UUV5VLEVIS0e52SpxkjLvbW0gRIYRJLcnr7e2PX2qbw1Iy3cuGSNRGctJ0HPf/PaiivYteNmc17SuuhBqVzHcyNKVywON/Y+mB6V0Pw+s3vb7zpj+6i4BPt1/TA/GiitFFR0RhVbnv1Z388huboIv8RCj2FFFFZSvc1nJx0R/9k=" 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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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gcnBA4A/3hxUFxo0+k2cUl5EUmlJJRiDhR24+uapvm0Rmlyu51NxfW1m92szk2ttEmyJMDe5DYGfThQfxrjIWIj38j6cUTM0iqob5T1FQzP0hj5PenGCih1KjmzUXT557JrqOPCh8cjjb3I9s4qtMsaTCKN1JK/MwGBmuv8C373VsdIFuHIXCszcNuP3enH3ifpWRrXk6d4qm8uK3nW2fy5EWMeWSOCAO+PXuRn2qYOalsVOMORNPUwYYftNxDb7gu9sM1LqtlLYXjQs24EDa3QMK7TSvBsN+6308p8lwWCKpXeSTz7DGOlUtf8I30a+bbym6ijXAR2+ZR14z1puspT30Fy2i01qcvoxiXUoDcSeXHu5f04rX1Pw/Ege8F/B5UhLA465PQYzmuddd2VweOoq4UlO2JEdgq8YyfmxjgetKqnzJpii1y2ZVWB5HYQBpAnJwO1Ptbie3uCyAdMfMucVZso5BOlsqMwkGWRM7myOM+3NdLa+HjJKGmDxIIlwQRksc5+mP61L10ZN7GXptlLfTxON4kPLyntjocfUH866W3tLbSrUNK6JtHzSNxk1ftLWK2hWOJQAihcnqQPWsjxBJazoLd3UlSS4Kn5OOOfXBouooSTm7GFq2pLqss1vCAY4vmiOOXI6msYtmIqG4bkip9JuDYXolK+YM7So5zWvqumQRw21wF8t5yziPuVz1PoM9PxrSN3K1gcVy3vqjH0+YiXYTglQiKFzuJNFami6Ux1m2ZvmhBMgY9Rtx/UrRUyi7kqdkfVetWkV9pF3bTxrJHJEwKsMjpx+teJa/oMlsum6hon+ivCrB2hyp3hiQc59CB07V7pcuBA/P8Nea3bBDPan7m8kD0qo6aluXQ4/xJrut2OnWGqys9zI+YLhrpBt3feTbtwem78vyw2+IetywtBAlpA0nAkjQ7h9Mkj9K6bxSjXPhS/tMEmLE6enynJ/8dzXl1jazXtyILZS8jKzBR1O1Sxx+ANXcnc9E8F3F3cQXVjPchriLe4zJvBLdiRnvnP1q/qkTwyzlQTFIo2Z5C45rj/AFz5HiGJM8TRsnt0yP5V6NfKJomjPBxlaymrminy2KEpa40/7PBceSSm1JAAdvHvXB6lbw299Eb+5TbET96IsGwcbTj6dfcda6+3jkljVRGMDgljwcew71RuNOcXjC4XfCynBzgE5HUZ571K6MvRcyJ9A1m1u1iidPKdl+QEYDAcYz6+1aWq6Xb33lStCJGizgMexGDXO3NsFeYqBmMcY6AFcU3Q/E0kWLfUmJAbaJvT61jKm1eUSlJJ2MzxHoFlY2JmilME3REB3Bv/r1yRxAvOSzdPevU76Bbm6UuqeWQdkqrng9RntWD4ihW0hh06GxSeAoSoKtlT6g+tVSrP4d2KpS05jk7XUJre0MMLeXvOXZTgnjAH8/zp8bthpJCxdzksTkmo30yS2USTRTIu7GXQgZ/GrVmhluIUwCpcZHqO9daaSuzCzbsbOgeMLzTklt5YhNbQplFJwyjsAf8/hVW41XVdcnbzMiJgStvEOPqfXjNaM2n210zRWwhhcqS7464IPP5H86oWnm2El4uSXKiENGSPvcZyMYGD/TvxyR5JLmRu1JOxTtZYrK4jMUe65lBwSuFjHf6nir728j2m1QkspBdtgBwzEY+76ZJ/CtH+wXklglNvsYAq0meQPXBrajt4LfiGILwBx6D/8AXTSu7kyaSsjL0vS1tJ/OYo0pTbwvT8e/HFa0ki28DyynCqMml2qGzjFc7rGur/ag0pVHlsCsznrkjIA/StNkYpOTsYN/4pv7uWQQP5EHI2qBnHuahtNXgSXfqMD3O5wzhm+90H8hTptAnlu1j09RKX6Kp5Jz2FZBhlaQqRnbwxHIXnFEWp7GkoOm7MstcHzBOVC7mLbVGAM9hV2+vmuJUbfnACrk9AKzJ/mYKP4RTogTKM4471unZmO6Oy8PbY4Zbhz/ALIPoOp/p+VFZmhXL3ky2cYyoOfl78/40UWTdzjrYh05cqVz6ev7nMG1CPcmvPtWyt2XBJzXWaiSAcE7frxXMaiik56CrnFKOhvTldmbNEt1bywyfdlQo30Iwa8t8PPJp/ieyDHa8V0qP/31hv0zXeTeJ9Jtbgwy3agqcEgFsfkK5bVNIkv9ba+0iSJ7aZlkEocYVj1yOucgnpWCdtWdCTuS+MrB/D/igXNqNkcjC4hx0BzyPz/Qiu+tpkvrOG7i+5KgdfbPauXv7C+1zQ7lpXNxcRS+ZbY7jowGex6/gKg8PXs2k6VFBJOokkm/1Umf3Snrkdex/E/Wk2rFKDenY6tolUkoAoYkn61FPCJIyrcg1Fpd82o2/mtEY2GM45GfY96tjjrU3E9Dlbq3maSaHLmTgfKOo55NYNnbR3101sC73BmKmGFMttA+Z9xIXj0r0Ca1UztOpIZl2n3rnNY8Opduklqwtym8kxJgsT7j/PNVHQpu5R0nWHsmKRQyyWHniGISEbz2J446g+1Ra94tklne10uEEo20zOM4I9KfYoIBHZTqFMZGzjg4rnrtRb3c8e9dyuSxH51j7ODnexblJRJb7z54bTzy88xVmY4ySS1XdC0t3b7TMCQAfLjXqxx39BWrZQW8Wk281xJGWnTYIf4wQCwbHXHUfl61saBpxktIZSdgeMbgpOX+tWp2jyimldv0M7T7VrmeR7RckYK7gNpBJ4b2x6c1sxaVuNyt4FZXKbSox05wB2Ga0NO0+KxV9hZ3kILMx646fpVtlXGcfhURWmopzu9DIumbOBn6VXJyORitNkDMcisTV7hrdysQBbrk9qtGQ+SXbwBmvKtUlM+o3MpOd0rH9a6+/wBba3tpJCTvIKr65NcO5JOT1qkrDR0+meLpdM04x28Ie9lSSOW5k5Khvu7T2Iyaj8EWq6hrSwypvh8pzKP9nGP5kVzdbfh641Cz+0S2DiMNGVZ8DOcHAFS9IuxVnJnT614GwGl0yYqw58uTkH8etcTcxy2i7JUZHOQcj8DVm31i+t7tLlLud5QQSWkJ3ex9RXda7aWElqTbxxNLdKArFt5O4/e/Drn2oUpRdt7kvlSuyv8AC3Svma/nX5V+bJ9B0/qfwFFdbYW6aZosNtGNpcAn2H+cUVo7dTOlR51zy6neajMgT7w5HGOc15X8UNZubGxgt7cmP7UzB3B52rjj8c/pRRXRWMqW55a2Sm0Y9eK1fD93LYXsAnRjbyEbkfIDKTjI/Xn2oorA60exWgji2qmFTACgcACpbm3SUbjjPp60UVmJ7jIox0GMfyokTFFFIRGfQVGyADIH4UUUDMnUrOMSCVSOTnp36f1rA1Dw3Df/APHqm2cPl5wSVcfietFFS3Zmq1ib+k6bFHi5mDPOy7QZByi9wPqcn8a2IkCqFTCgcADtRRQtjObuywoz1IzSttA7UUU0QVGA8wgGuc1uKSS/SOIZLDOewHrRRU1ZOEHJF04qUkmJc6PY3FoLeeFHGM7sYYH1B6iuN1fwlc22ZLFjcRf3D98f40UV5FHEVIS33OydOLRgRW8skvlBG35xtxzmtTw9PIk89sNiBlyTIcbce/brRRXtS1Vmc0G4vmRQuEiWeV4ctEvQnuf8K6r4eWb3t8ZpifJi4Gfbr+mB+NFFXaxz1feWvVnoE0n2m5CL/EQo9hRRRWb3NqknCyR//9k=" 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" 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afFJdxMk8xzsJ6KO3HrmrcubQytZ3NjfdWnhpLq5vCzTJsggwoCjpnpk8c1gRjbHnkfQ9aSdzLFGAc4OF9hUU0m1VhTlj1pxgo3HUm528jYXSJ5dOa9ERUE7hkcbOmR+NUJ1hWURxSKTt+YgEDP611/ga+udQiOjpBEyhAFZiRuJI4Ptz+lYl5LbWPieaSOGC6ghk8tlEYEbgcHA/ke/X2qYOak9C5qHKmnqYMv3Qo6/xVWJALcc9K7j+wl1i8+14dIXYERqoG5SBgDHT61zd3oV2t1OsNldbEcjhSwGD64HFWqsZvQmVOUNyjaqGmiDMFBYAsewzXS6voFpsa8GoqI35VmG7PsMda5Zhuypz71bKSnbEiOwVeMZPzYxwPWorX5lZija2pXW3eSRhAGkCc8L2+lPs557e5LLt6YJZc4/+vViyVxOlsqMwkGWRM7myOM+3NdNa+H/MkVpg8SiJQCCMljnP0x/Wpfvbk3sZOl2NzfXSSFmBbBMmPu4zg/mP1rqbWyt9LtQzui7QN0rcf/qq7aW8NtCkcQACKFyepA9axfE1xayxfZWkRnT55EI+5xxz64Jo0SEk5uyMXWdca8lmggRTajjcVzuPrWK6gIRkYJzxV7Q5oI5RDdZ2SDJPHr71d1XT7a2uUhhDLuBc88gNyv6Y/Orjdy5bDcPc5rmXp0pEjISwYqERVH3iTRWpoelSf2vatKySRKTJuU5wVxx7clfzopSTuQpNH1dfWkN9ZS2tzGskUqFWVxkH8K8K8Q6B9nkt2sMW0ttK4Jij2tnjBLD0xxxxzXvTOFQnPavOPE0arqM2BhZDn8acbrU0ctbCaJfXmtaQ8eozNLewnKyMoHyYHHHXnPX1rmPFt9qumwwXGnyLFIkoSRZAu0huASW6DP061q6bM1tP8hxT/E9muq6PdxgZleI7Fx/EBkfqBTt1RLep5Prur6xqLBdVuncDlYwQEH0C8fjXZbGisI3TLRS28ZQnkDAB/CvNl5HWvYWtkj02KzHHlRKqn6DFKaui4y5dStcb7rThbW921udoVJFAJA9Oa5Jo7TSGubid83URDQncV3NuIK4HY7ffrXQ2qPLEqiNeOCzHg49vWsjVtIuGvFMnzQBgxAx83fB/Goi9mabcyNXRdXiuPKiuwsdxIueuQ3tn1q/q+lW+oCOR4RIYt2AT2Iwa55rdH84/dKj5QBxhlweKZofiWSH/AEfU2J2naJe6/X1rGVNr3olcyvYxPEOhQ6bF50U6x5yFhxuyfY1zbfuRk5LN3PevUr+1S6uB5q5hkUqJIx1U9s9cVh+JUS1jh0+KxSaAoSoKMCh9qqnWbtHdiqUvtdDkbbUJ7e2EcTmP5t5dThs4AHP4U6Fike45yxzTG014AHmjlRd2CXUgZ9KtWsZmuIUwCGYAj1HeutOyuzC2tja8P+L7rTUlgkhWW3ij3KCfmHsD9e1VLjVdV1yd/MB8oqSLeIcfU+vGa0rmwgvGaO2EEUu073IxuwQRnH0NUbBpNPkvAxIlZPJVk7buM59MH8q448klzo6GpXSKVpcRWUyeSm66mGNxXCoO/wBTxVuSCV7UKoSWU/O2wA/MxGOnHGSfwrU/sF5JLeUwbGAKtJnkDPXBrXW2hgOIIgvAGR6CmlrcibSSSMvS9MFpN5zMplKbeF6fj39K2UBVfmJNIYwGBxiuX8S+JZbKc2ljtEi/6xyM4PoK1Rhq2VPFWt3iXn2a2doYccsvBY/WsKO+Bld7rfMJCN7FiWbHvXTahCmoWqrIXSSVVYPsxuHGDjH8q5e8sp7W9ktpI3TymwyuOR9amE1PQ3nSdOzuSQzLFdo5UBcg7fTBzVie9kuL0yO2Wckk1nTbjJ8van23ykyN2roi9Tn6HX+GwIluLt/4iFGfQdf8+1FUNAupr6SO0+9g547jPT+QooST1Zx1sQ6cuVK59R3Fz+7wpHHUmuH8QjfNuyWIrpL04DEMduPXFc7qKBhkcD0rSUEo6G0JamMg5Bq3JdeXZyybGcxoWCJyTgdB71hX+vadptx5NzcqrDqOpH1xVrTNWsNRybK5jl4zgHB/Kuc6LHk1/GYr6ZSCMncBtxjPb+les6dK13ptrdMADJEr4HuKw/Gnh37fCLy12pLEDvBH3l/+tU/hbU0gsYNMu5UaaIbVdc7eSdq59cfypNlWurmsY1UkooUMST9ajmh8yMhjkGrUiZ+lMXjrSTA5W5gnaWWIFzKMAbR1HqawrC3j1K6eHLNKZirRQruYqBy+Tx6Dr3/PvprRTO06lgWUKa5zVfDonlR7JhbbQ27ylwWzjuKcXbcpu5U0bWJLNQI4ZJLAz+TD5hAZh6+nr7VDr3iySad7XS4QSjbTM4zgj0qTTx5ASynUAxkbPQ4rnrxRbXc8e9cq5LEH15rL2cHO9i25KJJfefNDaGcvPMVYscZJJaruh6W7n7TOCQFPlxr1Jx39BWtYwW8Wk28tzJGWnTZ5P8QIBYNjrjqPy9a2dA08yWkMpPlh4xuCk5f61Sm1HlFNK7foZlhatcTyPZrkghhuA2EEnhvbHpzWzFpW43K3gVlcoVKjHTnHsM1padp8Vir7CzvIQWYnrjp+lWmVcZ/T1qEtNSZzu9DIuixOB+VVzyORitMoCxyOaydUm+zk+Xgt6VaMypq9+NPsZpyASi8DPfsPzxXlcsjSytJIxZ2JLE9ya6nxRevNYhWY/NKMj8DXJ96vYpHW6B4vGj2kTNbG8vY3ba05BVExxg9c5/QY+lfwxH/bPiILfZmFz5jTMTyTgnP54rm81teGpr6zuJJ7EhMxsjOVzjPT8cgVL0TsOzk0jpdc8DBVebTZGBHPlMc5+h9a4m6WWABHR0yAfmGM1cTV9QhuluReTtODks0hP4H29q7vWrawl09TBFCZbpAFyQ33sc/h1z7UlKUdN7ibildlX4XaXlmv5lwq/Nk+g6f1P4CiuusLZNM0WG1jG0uoJ9h/nFFaOxlRpc6531PQL+eNU+8ORkY5zXC+NNZk0nRLm6jUCXhI888k4z+HWiiuqpsY09zxCWaSdpJJWZ3kOWZjkk1NpV5cafdR3Fu7Iyt17GiiuY7Ee2abdLfWNvcjAEsYbHpms+fQrV7gurNGrEkxqflyepA7Z7+tFFZsV7PQ1EG0BDyvYk8ilkQD0xRRQIiPtTGQDkD8KKKBmTqVnGJBMpHJz079KwNQ8Nw3/wDx6ptmD/POCSrj8T1ooqXozVaxOg0nTYo8XU4Z52XapkHKL3H4nJ/GtiJVUBUAUDgAdqKKEZzd2ywoyOcZpTtxziiinYgptjzCM1zesrJLqCxwjcSMk9lHrRRUVZOEHJGlOKlJJi3WkWNzaCC4hVxjO7GGB9Qe1cZq/hO4tiZLBjcR/wBw/fH+NFFeVSxFSEtzrnTi0YEUEjyiLaQ5O3aeDn0rV8PTyRXU1sQiBhyZeNpHr6de9FFey1dWZywbi7oo3Qh+0SSRbmQdz3bP8q6n4eWj31950x/cxcDPt1P5YH40UVaVjGq+Za9WegTym4ugi/xEKPYUUUVlJu5rUk4WSP/Z" 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" + } + ], + "frames_discarded_reasons": { + "near_duplicate_phash": 297, + "low_motion_no_face": 1007, + "total_discarded": 7040 + } +} \ No newline at end of file diff --git a/solution.py b/solution.py new file mode 100644 index 0000000..a646439 --- /dev/null +++ b/solution.py @@ -0,0 +1,388 @@ + +""" +video_compression.py (solution.py) +Sentio Mind · Project 2 · Smart Behavioral Video Compression +""" + +import cv2 +import json +import base64 +import subprocess +import time +import os +import numpy as np +from pathlib import Path +from PIL import Image +import imagehash + +# --------------------------------------------------------------------------- +# CONFIG (Strictly matching the assignment rules) +# --------------------------------------------------------------------------- +VIDEO_IN = Path(r"C:\\Users\\vinee\\Downloads\\Class_8_cctv_video_1.mov") +VIDEO_OUT = Path("compressed_output_1.mp4") +REPORT_HTML_OUT = Path("compression_report_1.html") +SEGMENTS_JSON_OUT = Path("segments_kept_1.json") + +PHASH_THRESHOLD = 0.95 # Step 1: drop frame if > 95% similar +MOTION_KEEP_THRESH = 0.15 # Step 4: Keep if > 0.15 +MOTION_DISCARD_THRESH = 0.05 # Step 2: Discard if < 0.05 +CONTEXT_EVERY_SEC = 3 # Step 5: Keep 1 frame every 3 seconds +OUTPUT_FPS = 12 +OUTPUT_CRF = 28 + + +# --------------------------------------------------------------------------- +# PERCEPTUAL HASH +# --------------------------------------------------------------------------- + +def compute_phash(frame: np.ndarray) -> str: + """Compute a perceptual hash using Extreme Downsampling for speed.""" + small = cv2.resize(frame, (32, 32), interpolation=cv2.INTER_NEAREST) + rgb_frame = cv2.cvtColor(small, cv2.COLOR_BGR2RGB) + pil_img = Image.fromarray(rgb_frame) + + hash_obj = imagehash.phash(pil_img) + return ''.join(['1' if b else '0' for b in hash_obj.hash.flatten()]) + +def phash_similarity(h1: str, h2: str) -> float: + if not h1 or not h2 or len(h1) != len(h2): + return 0.0 + hamming_distance = sum(c1 != c2 for c1, c2 in zip(h1, h2)) + return 1.0 - (hamming_distance / len(h1)) + + +# --------------------------------------------------------------------------- +# MOTION SCORE +# --------------------------------------------------------------------------- + +def compute_motion_score(prev_gray, curr_gray: np.ndarray) -> float: + """Extreme Downscale (80x60) + Lower Iterations makes flow lightning fast.""" + if prev_gray is None: + return 0.0 + + prev_tiny = cv2.resize(prev_gray, (80, 60), interpolation=cv2.INTER_NEAREST) + curr_tiny = cv2.resize(curr_gray, (80, 60), interpolation=cv2.INTER_NEAREST) + + flow = cv2.calcOpticalFlowFarneback( + prev_tiny, curr_tiny, None, + pyr_scale=0.5, levels=2, winsize=11, + iterations=2, poly_n=5, poly_sigma=1.1, 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: + """Shrunk further + high scaleFactor to double processing speed.""" + small = cv2.resize(frame, (160, 120), interpolation=cv2.INTER_NEAREST) + gray = cv2.cvtColor(small, cv2.COLOR_BGR2GRAY) + eq_gray = cv2.equalizeHist(gray) + + faces = cascade.detectMultiScale( + eq_gray, scaleFactor=1.3, minNeighbors=3, minSize=(10, 10) + ) + return len(faces) > 0 + + +# --------------------------------------------------------------------------- +# FRAME KEEP DECISION +# --------------------------------------------------------------------------- + +def should_keep_frame(frame: np.ndarray, + prev_frame, + prev_kept_hash: str, + last_kept_time_sec: float, + current_time_sec: float, + cascade) -> tuple: + + # 1. Context frames skip all heavy math instantly + if (current_time_sec - last_kept_time_sec) >= CONTEXT_EVERY_SEC: + return True, "context_frame", 0.0, False + + # 2. SPEED HACK: Discard instantly if duplicate. Skips all optical/face math. + current_hash = compute_phash(frame) + if prev_kept_hash and phash_similarity(current_hash, prev_kept_hash) >= PHASH_THRESHOLD: + return False, "discarded_duplicate", 0.0, False + + # 3. Convert to grayscale exactly once to save redundant processing + curr_small = cv2.resize(frame, (160, 120), interpolation=cv2.INTER_NEAREST) + curr_gray = cv2.cvtColor(curr_small, cv2.COLOR_BGR2GRAY) + + if prev_frame is not None: + prev_small = cv2.resize(prev_frame, (160, 120), interpolation=cv2.INTER_NEAREST) + prev_gray = cv2.cvtColor(prev_small, cv2.COLOR_BGR2GRAY) + else: + prev_gray = None + + motion_score = compute_motion_score(prev_gray, curr_gray) + + # 4. If motion is high enough, keep it instantly. Skip face detection. + if motion_score >= MOTION_KEEP_THRESH: + return True, "motion_above_threshold", motion_score, False + + # 5. Only run the heavy face detector if the motion is low/medium + face_found = has_face(frame, cascade) + + if face_found: + if motion_score > MOTION_DISCARD_THRESH: + return True, "face_and_motion", motion_score, True + else: + return True, "face_detected", motion_score, True + + return False, "discarded_static", motion_score, False + + +# --------------------------------------------------------------------------- +# THUMBNAIL HELPER +# --------------------------------------------------------------------------- + +def frame_to_b64_thumb(frame: np.ndarray, width: int = 200) -> str: + 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 (THE ULTIMATE RAM PIPE OPTIMIZATION) +# --------------------------------------------------------------------------- + +def write_frames_to_video(kept_frames: list, output_path: Path, + fps: float, frame_size: tuple): + if len(kept_frames) == 0: + print("\n❌ ERROR: 0 frames were kept!") + return + + print(f"\n➔ Encoding {len(kept_frames)} frames instantly via Direct RAM Pipe...") + + # Skips OpenCV VideoWriter completely to avoid choking the hard drive with a 3K AVI file + cmd = [ + "ffmpeg", "-y", + "-f", "rawvideo", + "-vcodec", "rawvideo", + "-s", f"{frame_size[0]}x{frame_size[1]}", + "-pix_fmt", "bgr24", + "-r", str(fps), + "-i", "-", # Read directly from Python's RAM (stdin) + "-c:v", "libx264", + "-preset", "ultrafast", # Maximum encoding speed + "-crf", str(OUTPUT_CRF), + str(output_path) + ] + + # Open FFmpeg silently in the background + proc = subprocess.Popen(cmd, stdin=subprocess.PIPE, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) + + # Blast the raw bytes straight from RAM to FFmpeg + for frame in kept_frames: + proc.stdin.write(frame.tobytes()) + + proc.stdin.close() + proc.wait() + + +# --------------------------------------------------------------------------- +# HTML REPORT +# --------------------------------------------------------------------------- + +def generate_compression_report(segments: list, stats: dict, output_path: Path): + grid_html = "" + for seg in segments: + b64 = seg.get("thumbnail_b64", "") + reason = seg.get("reason_kept", "Unknown") + sec = seg.get("start_sec", 0.0) + seg_id = seg.get("segment_id", "?") + + grid_html += f""" +
+ Frame +

Seg {seg_id} @ {sec}s

+ {reason} +
+ """ + + html = f""" + + + + + Sentio Mind Compression Report + + + +
+

Smart Behavioral Video Compression Report

+ +
+
+
File Size Reduction
+
{stats.get('reduction_pct', 0)}%
+
{stats.get('original_size_mb', 0):.2f} MB → {stats.get('compressed_size_mb', 0):.2f} MB
+
+
+
Duration Comparison
+
{stats.get('compressed_duration_sec', 0):.1f}s
+
Originally {stats.get('original_duration_sec', 0):.1f}s
+
+
+
Frames Kept
+
{stats.get('frames_kept', 0)} / {stats.get('frames_original', 0)}
+
+
+
Processing Time
+
{stats.get('processing_time_sec', 0):.1f}s
+
+
+ +

Storyboard Segments

+
+ {grid_html} +
+
+ + + """ + output_path.write_text(html, encoding="utf-8") + + +# --------------------------------------------------------------------------- +# MAIN +# --------------------------------------------------------------------------- + +if __name__ == "__main__": + t_start = time.time() + + cascade = cv2.CascadeClassifier( + cv2.data.haarcascades + "haarcascade_frontalface_default.xml" + ) + + cap = cv2.VideoCapture(str(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") + + kept_frames = [] + segments = [] + prev_frame = None + prev_hash = "" + last_kept_t = -999.0 + cur_seg = None + disc_dup = 0 + disc_stat = 0 + + skip_factor = max(1, int(round(fps_in / OUTPUT_FPS))) + + frame_idx = 0 + while True: + # FAST READ: Skip useless frames instantly + ret = cap.grab() + if not ret: + break + + if frame_idx % skip_factor != 0: + frame_idx += 1 + continue + + # SLOW READ: Actually decode the pixels + ret, frame = cap.retrieve() + + if frame_idx % 1000 == 0: + print(f"Processing frame {frame_idx} / {total}...") + + ts = frame_idx / fps_in + + keep, reason, motion, face = should_keep_frame( + frame, prev_frame, prev_hash, last_kept_t, ts, cascade + ) + + if keep: + kept_frames.append(frame.copy()) + prev_hash = compute_phash(frame) + 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: + cur_seg["end_sec"] = round(ts, 2) + cur_seg["frames_in_segment"] += 1 + cur_seg["face_count_in_segment"] += 1 if face else 0 + else: + if "duplicate" in reason: + disc_dup += 1 + else: + disc_stat += 1 + + prev_frame = frame + frame_idx += 1 + + if cur_seg: + segments.append(cur_seg) + cap.release() + + 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("=======================================================") + print(f" Done in {stats['processing_time_sec']:.2f}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("=======================================================") \ No newline at end of file