From af30f8698c48dfe01be3b76f7b5558d78e0826ba Mon Sep 17 00:00:00 2001 From: xujinling Date: Mon, 13 Jun 2022 15:17:35 +0800 Subject: [PATCH] add6 Signed-off-by: xujinling --- README.md | 14 ++++++-------- result1.png | Bin 51214 -> 12867 bytes result2.png | Bin 156870 -> 91084 bytes 3 files changed, 6 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 00496dd..2bb0c7f 100644 --- a/README.md +++ b/README.md @@ -9,14 +9,14 @@ Author: Jinling xu An action classification operator generates labels of human activities (with corresponding scores) and extracts features for the input video. It transforms the video into frames and loads pre-trained models by model names. -This operator has implemented pre-trained models from [TimeSformer](https://arxiv.org/abs/2102.05095) +This operator has implemented pre-trained models from [VideoSwinTransformer](https://arxiv.org/abs/2106.13230) and maps vectors with labels.
## Code Example -Use the pretrained TimeSformer model ('timesformer_k400_8x224') +Use the pretrained VideoSwinTransformer model ('swin_tiny_patch244_window877_kinetics400_1k') to classify and generate a vector for the given video path './archery.mp4' ([download](https://dl.fbaipublicfiles.com/pytorchvideo/projects/archery.mp4)). *Write the pipeline in simplified style*: @@ -25,9 +25,8 @@ to classify and generate a vector for the given video path './archery.mp4' ([dow import towhee ( - towhee.dc(['./demo_video.mp4']) - .video_decode.ffmpeg(sample_type='uniform_temporal_subsample', args={'num_samples': 4}) - .runas_op(func=lambda x: [y for y in x]) + towhee.glob('./archery.mp4') + .video_decode.ffmpeg() .action_classification.video_swin_transformer(model_name='swin_tiny_patch244_window877_kinetics400_1k') .show() ) @@ -42,9 +41,8 @@ import towhee import towhee ( - towhee.dc['path']('./demo_video.mp4') - .video_decode.ffmpeg['path', 'frames'](sample_type='uniform_temporal_subsample', args={'num_samples': 4}) - .runas_op['frames', 'frames'](func=lambda x: [y for y in x]) + towhee.glob['path']('./archery.mp4') + .video_decode.ffmpeg['path', 'frames']() .action_classification.video_swin_transformer['frames', ('labels', 'scores', 'features')](model_name='swin_tiny_patch244_window877_kinetics400_1k') .select['path', 'labels', 'scores', 'features']() .show(formatter={'path': 'video_path'}) diff --git a/result1.png b/result1.png index d019f6dff48ef56f5d7c6c565871a18834e62de9..e231f9726e8930d2fa836e26f56109978364d510 100644 GIT binary patch literal 12867 zcmb7rWn7ct+xAESK@bF_yGt4*1?fh*1?iNI5rUwI)JB&y(lJ69j+71sX<^g|=^-)l z8Tx;o_j$j)FCVVm_t<_r@9Q|vx2*lW(Qo{f+l3+7l&Me5U4dX58Mp6Klg)XeH0Dp)87c~R`ueOKHFbsJ( zAOeN~2*vuh0xodT*LUmPr~a;6G~0S}jr(DB*)#Sy4ax;1m%JuCHf|xZAloPJ?q#5aQ^Xl8RjDbSK7PF;}Klr%1B>C3HV( znh0KQ6GI)AKo6Cp7H@P)0ei~a=sUn;tO;DGvU64g65P}7J@X}OuD3eh#53|AJWiKN zuH{)Ds7c|yuGX3n5wskA7sArdn;V|fMkZ*~*;alE7bxan81`@Jf~VSD$jObS7Ob63 zu61v@xQqsktRLR)4u(y)p2?2~O6gR*hC8{3c``r;rurs7bguWMuzOd9z*iRR%)cD9 zd0Skp{rukhrgZ*hYhXQ81Qe}UM6d4{CMEXS$?d4HlEPAAXP6yvvsc&?o3Oh7N2Gb! z*2nMYY^^2r(@UE2dSS$I(3oS%9YH=a%{trd^Uw~w z3HbH>D;0JTb4)@Eu4I@C-Zwy)0X_bO;lQkBUP?)w4v}m5MChRyf;&UIg1K)$Tit3J zFC0*uCMeft|3GY4WROdH(_}M%n!0AzAyc~9`omIPbA1}XcVP0t;TPH{3=a1v5CEx^ zXx@fUL{{@wJ#A(Tz-D{&bN!azr4a`9K?ys}urVMzl?6A&Ib}s0E5Z>f8bgO4fpT2u zWYJJ~I>u=UYJT_0uxVo<$cj~W;JdKB{9&tiahZ@#+5m8F;+K6@E2hnS5F%r=Yzf}? 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