seq文件转化为avi文件.rar

--------------------------------------------------------------------------- Training stage 0 Sampled 12249 windows from 32077 images. Done sampling windows (time=345s). Computing lambdas... done (time=36s). Extracting features... done (time=9s). Sampled 25000 windows from 1024 images. Done sampling windows (time=19s). Extracting features... done (time=8s). Training AdaBoost: nWeak= 64 nFtrs=5120 pos=24498 neg=25000 i= 16 alpha=1.000 err=0.226 loss=1.36e-02 i= 32 alpha=1.000 err=0.222 loss=5.68e-04 i= 48 alpha=1.000 err=0.233 loss=2.34e-05 i= 64 alpha=1.000 err=0.225 loss=8.70e-07 Done training err=0.0000 fp=0.0000 fn=0.0000 (t=7.8s). Done training stage 0 (time=426s). --------------------------------------------------------------------------- Training stage 1 Sampled 25000 windows from 1152 images. Done sampling windows (time=31s). Extracting features... done (time=9s). Training AdaBoost: nWeak=256 nFtrs=5120 pos=24498 neg=50000 i= 16 alpha=1.000 err=0.369 loss=3.11e-01 i= 32 alpha=1.000 err=0.372 loss=1.56e-01 i= 48 alpha=1.000 err=0.370 loss=7.92e-02 i= 64 alpha=1.000 err=0.363 loss=4.18e-02 i= 80 alpha=1.000 err=0.367 loss=2.15e-02 i= 96 alpha=1.000 err=0.374 loss=1.10e-02 i= 112 alpha=1.000 err=0.375 loss=5.56e-03 i= 128 alpha=1.000 err=0.383 loss=2.89e-03 i= 144 alpha=1.000 err=0.368 loss=1.49e-03 i= 160 alpha=1.000 err=0.371 loss=7.70e-04 i= 176 alpha=1.000 err=0.363 loss=3.80e-04 i= 192 alpha=1.000 err=0.356 loss=1.89e-04 i= 208 alpha=1.000 err=0.359 loss=9.48e-05 i= 224 alpha=1.000 err=0.374 loss=4.85e-05 i= 240 alpha=1.000 err=0.353 loss=2.43e-05 i= 256 alpha=1.000 err=0.373 loss=1.24e-05 Done training err=0.0000 fp=0.0000 fn=0.0000 (t=37.5s). Done training stage 1 (time=79s). --------------------------------------------------------------------------- Training stage 2 Sampled 25000 windows from 2432 images. Done sampling windows (time=57s). Extracting features... done (time=9s). Training AdaBoost: nWeak=1024 nFtrs=5120 pos=24498 neg=50000 i= 16 alpha=1.000 err=0.391 loss=4.53e-01 i= 32 alpha=1.000 err=0.390 loss=2.89e-01 i= 48 alpha=1.000 err=0.396 loss=1.84e-01 i= 64 alpha=1.000 err=0.396 loss=1.18e-01 i= 80 alpha=1.000 err=0.384 loss=7.62e-02 i= 96 alpha=1.000 err=0.396 loss=4.88e-02 i= 112 alpha=1.000 err=0.404 loss=3.07e-02 i= 128 alpha=1.000 err=0.397 loss=1.94e-02 i= 144 alpha=1.000 err=0.392 loss=1.22e-02 i= 160 alpha=1.000 err=0.391 loss=7.72e-03 i= 176 alpha=1.000 err=0.393 loss=4.81e-03 i= 192 alpha=1.000 err=0.384 loss=3.05e-03 i= 208 alpha=1.000 err=0.399 loss=1.95e-03 i= 224 alpha=1.000 err=0.399 loss=1.22e-03 i= 240 alpha=1.000 err=0.387 loss=7.61e-04 i= 256 alpha=1.000 err=0.396 loss=4.78e-04 i= 272 alpha=1.000 err=0.387 loss=3.00e-04 i= 288 alpha=1.000 err=0.389 loss=1.90e-04 i= 304 alpha=1.000 err=0.389 loss=1.21e-04 i= 320 alpha=1.000 err=0.392 loss=7.49e-05 i= 336 alpha=1.000 err=0.401 loss=4.71e-05 i= 352 alpha=1.000 err=0.390 loss=2.98e-05 i= 368 alpha=1.000 err=0.398 loss=1.85e-05 i= 384 alpha=1.000 err=0.391 loss=1.15e-05 i= 400 alpha=1.000 err=0.394 loss=7.05e-06 i= 416 alpha=1.000 err=0.387 loss=4.33e-06 i= 432 alpha=1.000 err=0.381 loss=2.68e-06 i= 448 alpha=1.000 err=0.396 loss=1.65e-06 i= 464 alpha=1.000 err=0.379 loss=1.02e-06 i= 480 alpha=1.000 err=0.390 loss=6.35e-07 i= 496 alpha=1.000 err=0.389 loss=3.92e-07 i= 512 alpha=1.000 err=0.389 loss=2.44e-07 i= 528 alpha=1.000 err=0.392 loss=1.51e-07 i= 544 alpha=1.000 err=0.391 loss=9.46e-08 i= 560 alpha=1.000 err=0.395 loss=5.95e-08 i= 576 alpha=1.000 err=0.386 loss=3.69e-08 i= 592 alpha=1.000 err=0.396 loss=2.28e-08 i= 608 alpha=1.000 err=0.390 loss=1.44e-08 i= 624 alpha=1.000 err=0.394 loss=8.87e-09 i= 640 alpha=1.000 err=0.388 loss=5.48e-09 i= 656 alpha=1.000 err=0.389 loss=3.40e-09 i= 672 alpha=1.000 err=0.380 loss=2.10e-09 i= 688 alpha=1.000 err=0.382 loss=1.30e-09 i= 704 alpha=1.000 err=0.389 loss=7.95e-10 i= 720 alpha=1.000 err=0.396 loss=4.75e-10 i= 736 alpha=1.000 err=0.384 loss=2.91e-10 i= 752 alpha=1.000 err=0.397 loss=1.77e-10 i= 768 alpha=1.000 err=0.383 loss=1.10e-10 i= 784 alpha=1.000 err=0.394 loss=6.77e-11 i= 800 alpha=1.000 err=0.392 loss=4.15e-11 i= 816 alpha=1.000 err=0.382 loss=2.54e-11 i= 832 alpha=1.000 err=0.402 loss=1.58e-11 i= 848 alpha=1.000 err=0.386 loss=9.62e-12 i= 864 alpha=1.000 err=0.391 loss=5.86e-12 i= 880 alpha=1.000 err=0.400 loss=3.64e-12 i= 896 alpha=1.000 err=0.390 loss=2.20e-12 i= 912 alpha=1.000 err=0.394 loss=1.37e-12 i= 928 alpha=1.000 err=0.382 loss=8.24e-13 i= 944 alpha=1.000 err=0.387 loss=5.07e-13 i= 960 alpha=1.000 err=0.388 loss=3.12e-13 i= 976 alpha=1.000 err=0.392 loss=1.93e-13 i= 992 alpha=1.000 err=0.384 loss=1.19e-13 i=1008 alpha=1.000 err=0.390 loss=7.26e-14 i=1024 alpha=1.000 err=0.387 loss=4.47e-14 Done training err=0.0000 fp=0.0000 fn=0.0000 (t=141.2s). Done training stage 2 (time=209s). --------------------------------------------------------------------------- Training stage 3 Sampled 25000 windows from 23360 images. Done sampling windows (time=502s). Extracting features... done (time=9s). Training AdaBoost: nWeak=4096 nFtrs=5120 pos=24498 neg=50000 i= 16 alpha=1.000 err=0.408 loss=6.14e-01 i= 32 alpha=1.000 err=0.424 loss=4.47e-01 i= 48 alpha=1.000 err=0.408 loss=3.29e-01 i= 64 alpha=1.000 err=0.404 loss=2.41e-01 i= 80 alpha=1.000 err=0.407 loss=1.77e-01 i= 96 alpha=1.000 err=0.415 loss=1.29e-01 i= 112 alpha=1.000 err=0.412 loss=9.48e-02 i= 128 alpha=1.000 err=0.413 loss=6.88e-02 i= 144 alpha=1.000 err=0.403 loss=5.02e-02 i= 160 alpha=1.000 err=0.415 loss=3.68e-02 i= 176 alpha=1.000 err=0.407 loss=2.64e-02 i= 192 alpha=1.000 err=0.413 loss=1.90e-02 i= 208 alpha=1.000 err=0.412 loss=1.38e-02 i= 224 alpha=1.000 err=0.410 loss=9.94e-03 i= 240 alpha=1.000 err=0.409 loss=7.19e-03 i= 256 alpha=1.000 err=0.412 loss=5.19e-03 i= 272 alpha=1.000 err=0.404 loss=3.72e-03 i= 288 alpha=1.000 err=0.411 loss=2.65e-03 i= 304 alpha=1.000 err=0.405 loss=1.89e-03 i= 320 alpha=1.000 err=0.411 loss=1.37e-03 i= 336 alpha=1.000 err=0.414 loss=9.72e-04 i= 352 alpha=1.000 err=0.406 loss=6.99e-04 i= 368 alpha=1.000 err=0.417 loss=5.05e-04 i= 384 alpha=1.000 err=0.408 loss=3.63e-04 i= 400 alpha=1.000 err=0.404 loss=2.58e-04 i= 416 alpha=1.000 err=0.417 loss=1.84e-04 i= 432 alpha=1.000 err=0.409 loss=1.33e-04 i= 448 alpha=1.000 err=0.414 loss=9.59e-05 i= 464 alpha=1.000 err=0.418 loss=6.87e-05 i= 480 alpha=1.000 err=0.406 loss=4.96e-05 i= 496 alpha=1.000 err=0.407 loss=3.51e-05 i= 512 alpha=1.000 err=0.411 loss=2.52e-05 i= 528 alpha=1.000 err=0.418 loss=1.82e-05 i= 544 alpha=1.000 err=0.402 loss=1.28e-05 i= 560 alpha=1.000 err=0.411 loss=9.08e-06 i= 576 alpha=1.000 err=0.404 loss=6.43e-06 i= 592 alpha=1.000 err=0.411 loss=4.59e-06 i= 608 alpha=1.000 err=0.401 loss=3.21e-06 i= 624 alpha=1.000 err=0.416 loss=2.28e-06 i= 640 alpha=1.000 err=0.412 loss=1.63e-06 i= 656 alpha=1.000 err=0.405 loss=1.14e-06 i= 672 alpha=1.000 err=0.413 loss=8.13e-07 i= 688 alpha=1.000 err=0.407 loss=5.82e-07 i= 704 alpha=1.000 err=0.412 loss=4.22e-07 i= 720 alpha=1.000 err=0.411 loss=2.98e-07 i= 736 alpha=1.000 err=0.408 loss=2.13e-07 i= 752 alpha=1.000 err=0.406 loss=1.50e-07 i= 768 alpha=1.000 err=0.412 loss=1.06e-07 i= 784 alpha=1.000 err=0.410 loss=7.45e-08 i= 800 alpha=1.000 err=0.407 loss=5.25e-08 i= 816 alpha=1.000 err=0.405 loss=3.69e-08 i= 832 alpha=1.000 err=0.400 loss=2.59e-08 i= 848 alpha=1.000 err=0.415 loss=1.86e-08 i= 864 alpha=1.000 err=0.411 loss=1.31e-08 i= 880 alpha=1.000 err=0.400 loss=9.24e-09 i= 896 alpha=1.000 err=0.413 loss=6.51e-09 i= 912 alpha=1.000 err=0.411 loss=4.66e-09 i= 928 alpha=1.000 err=0.414 loss=3.32e-09 i= 944 alpha=1.000 err=0.412 loss=2.33e-09 i= 960 alpha=1.000 err=0.411 loss=1.65e-09 i= 976 alpha=1.000 err=0.396 loss=1.16e-09 i= 992 alpha=1.000 err=0.404 loss=8.31e-10 i=1008 alpha=1.000 err=0.394 loss=5.80e-10 i=1024 alpha=1.000 err=0.412 loss=4.13e