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Runtime error
| import os | |
| os.system("pip install moviepy") | |
| os.system("pip install -U openmim") | |
| os.system("pip install mmengine") | |
| os.system("pip install mmcv") | |
| os.system("pip install mmdet") | |
| os.system("pip install mmpose") | |
| os.system("pip install mmaction2") | |
| import argparse | |
| import fnmatch | |
| import os.path | |
| import os.path as osp | |
| from operator import itemgetter | |
| from typing import Optional, Tuple | |
| import torch | |
| from mmengine import Config, DictAction | |
| from mmaction.apis import inference_recognizer, init_recognizer | |
| from mmaction.visualization import ActionVisualizer | |
| from mim import download | |
| import warnings | |
| warnings.filterwarnings("ignore") | |
| import gradio as gr | |
| mmaction2_models_list = [ | |
| 'slowfast-acrn_kinetics400-pretrained-r50_8xb8-8x8x1-cosine-10e_ava21-rgb', | |
| 'slowfast-acrn_kinetics400-pretrained-r50_8xb8-8x8x1-cosine-10e_ava22-rgb', | |
| 'slowonly-lfb-nl_kinetics400-pretrained-r50_8xb12-4x16x1-20e_ava21-rgb', | |
| 'slowonly-lfb-max_kinetics400-pretrained-r50_8xb12-4x16x1-20e_ava21-rgb', | |
| 'slowfast_kinetics400-pretrained-r50_8xb16-4x16x1-20e_ava21-rgb', | |
| 'slowfast_kinetics400-pretrained-r50-context_8xb16-4x16x1-20e_ava21-rgb', | |
| 'slowfast_kinetics400-pretrained-r50_8xb8-8x8x1-20e_ava21-rgb', | |
| 'slowfast_kinetics400-pretrained-r50_8xb6-8x8x1-cosine-10e_ava22-rgb', | |
| 'slowfast_kinetics400-pretrained-r50-temporal-max_8xb6-8x8x1-cosine-10e_ava22-rgb', | |
| 'slowfast_r50-k400-pre-temporal-max-focal-alpha3-gamma1_8xb6-8x8x1-cosine-10e_ava22-rgb', | |
| 'slowfast_kinetics400-pretrained-r50_8xb16-4x16x1-8e_multisports-rgb', | |
| 'slowonly_kinetics400-pretrained-r50_8xb16-4x16x1-20e_ava21-rgb', | |
| 'slowonly_kinetics700-pretrained-r50_8xb16-4x16x1-20e_ava21-rgb', | |
| 'slowonly_kinetics400-pretrained-r50-nl_8xb16-4x16x1-20e_ava21-rgb', | |
| 'slowonly_kinetics400-pretrained-r50-nl_8xb16-8x8x1-20e_ava21-rgb', | |
| 'slowonly_kinetics400-pretrained-r101_8xb16-8x8x1-20e_ava21-rgb', | |
| 'slowonly_kinetics400-pretrained-r50_8xb16-4x16x1-8e_multisports-rgb', | |
| 'vit-base-p16_videomae-k400-pre_8xb8-16x4x1-20e-adamw_ava-kinetics-rgb', | |
| 'vit-large-p16_videomae-k400-pre_8xb8-16x4x1-20e-adamw_ava-kinetics-rgb', | |
| 'c2d_r50-in1k-pre-nopool_8xb32-8x8x1-100e_kinetics400-rgb', | |
| 'c2d_r101-in1k-pre-nopool_8xb32-8x8x1-100e_kinetics400-rgb', | |
| 'c2d_r50-in1k-pre_8xb32-8x8x1-100e_kinetics400-rgb', | |
| 'c2d_r50-in1k-pre_8xb32-16x4x1-100e_kinetics400-rgb', | |
| 'c3d_sports1m-pretrained_8xb30-16x1x1-45e_ucf101-rgb', | |
| 'ircsn_ig65m-pretrained-r152_8xb12-32x2x1-58e_kinetics400-rgb', | |
| 'ircsn_ig65m-pretrained-r152-bnfrozen_8xb12-32x2x1-58e_kinetics400-rgb', | |
| 'ircsn_ig65m-pretrained-r50-bnfrozen_8xb12-32x2x1-58e_kinetics400-rgb', | |
| 'ipcsn_r152_32x2x1-180e_kinetics400-rgb', | |
| 'ircsn_r152_32x2x1-180e_kinetics400-rgb', | |
| 'ipcsn_ig65m-pretrained-r152-bnfrozen_32x2x1-58e_kinetics400-rgb', | |
| 'ipcsn_sports1m-pretrained-r152-bnfrozen_32x2x1-58e_kinetics400-rgb', | |
| 'ircsn_sports1m-pretrained-r152-bnfrozen_32x2x1-58e_kinetics400-rgb', | |
| 'i3d_imagenet-pretrained-r50-nl-dot-product_8xb8-32x2x1-100e_kinetics400-rgb', | |
| 'i3d_imagenet-pretrained-r50-nl-embedded-gaussian_8xb8-32x2x1-100e_kinetics400-rgb', | |
| 'i3d_imagenet-pretrained-r50-nl-gaussian_8xb8-32x2x1-100e_kinetics400-rgb', | |
| 'i3d_imagenet-pretrained-r50_8xb8-32x2x1-100e_kinetics400-rgb', | |
| 'i3d_imagenet-pretrained-r50_8xb8-dense-32x2x1-100e_kinetics400-rgb', | |
| 'i3d_imagenet-pretrained-r50-heavy_8xb8-32x2x1-100e_kinetics400-rgb', | |
| 'mvit-small-p244_32xb16-16x4x1-200e_kinetics400-rgb_infer', | |
| 'mvit-small-p244_32xb16-16x4x1-200e_kinetics400-rgb', | |
| 'mvit-base-p244_32x3x1_kinetics400-rgb', 'mvit-large-p244_40x3x1_kinetics400-rgb', | |
| 'mvit-small-p244_k400-pre_16xb16-u16-100e_sthv2-rgb_infer', | |
| 'mvit-small-p244_k400-pre_16xb16-u16-100e_sthv2-rgb', | |
| 'mvit-base-p244_u32_sthv2-rgb', 'mvit-large-p244_u40_sthv2-rgb', | |
| 'mvit-small-p244_k400-maskfeat-pre_8xb32-16x4x1-100e_kinetics400-rgb', | |
| 'slowonly_r50_8xb16-8x8x1-256e_imagenet-kinetics400-rgb', | |
| 'r2plus1d_r34_8xb8-8x8x1-180e_kinetics400-rgb', | |
| 'r2plus1d_r34_8xb8-32x2x1-180e_kinetics400-rgb', | |
| 'slowfast_r50_8xb8-4x16x1-256e_kinetics400-rgb', | |
| 'slowfast_r50_8xb8-8x8x1-256e_kinetics400-rgb', | |
| 'slowfast_r50_8xb8-8x8x1-steplr-256e_kinetics400-rgb', | |
| 'slowfast_r101_8xb8-8x8x1-256e_kinetics400-rgb', | |
| 'slowfast_r101-r50_32xb8-4x16x1-256e_kinetics400-rgb', | |
| 'slowonly_r50_8xb16-4x16x1-256e_kinetics400-rgb', | |
| 'slowonly_r50_8xb16-8x8x1-256e_kinetics400-rgb', | |
| 'slowonly_r101_8xb16-8x8x1-196e_kinetics400-rgb', | |
| 'slowonly_imagenet-pretrained-r50_8xb16-4x16x1-steplr-150e_kinetics400-rgb', | |
| 'slowonly_imagenet-pretrained-r50_8xb16-8x8x1-steplr-150e_kinetics400-rgb', | |
| 'slowonly_r50-in1k-pre-nl-embedded-gaussian_8xb16-4x16x1-steplr-150e_kinetics400-rgb', | |
| 'slowonly_r50-in1k-pre-nl-embedded-gaussian_8xb16-8x8x1-steplr-150e_kinetics400-rgb', | |
| 'slowonly_imagenet-pretrained-r50_16xb16-4x16x1-steplr-150e_kinetics700-rgb', | |
| 'slowonly_imagenet-pretrained-r50_16xb16-8x8x1-steplr-150e_kinetics700-rgb', | |
| 'slowonly_imagenet-pretrained-r50_32xb8-8x8x1-steplr-150e_kinetics710-rgb', | |
| 'swin-tiny-p244-w877_in1k-pre_8xb8-amp-32x2x1-30e_kinetics400-rgb', | |
| 'swin-small-p244-w877_in1k-pre_8xb8-amp-32x2x1-30e_kinetics400-rgb', | |
| 'swin-base-p244-w877_in1k-pre_8xb8-amp-32x2x1-30e_kinetics400-rgb', | |
| 'swin-large-p244-w877_in22k-pre_8xb8-amp-32x2x1-30e_kinetics400-rgb', | |
| 'swin-large-p244-w877_in22k-pre_16xb8-amp-32x2x1-30e_kinetics700-rgb', | |
| 'swin-small-p244-w877_in1k-pre_32xb4-amp-32x2x1-30e_kinetics710-rgb', | |
| 'tanet_imagenet-pretrained-r50_8xb8-dense-1x1x8-100e_kinetics400-rgb', | |
| 'tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb', | |
| 'tanet_imagenet-pretrained-r50_8xb6-1x1x16-50e_sthv1-rgb', | |
| 'timesformer_divST_8xb8-8x32x1-15e_kinetics400-rgb', | |
| 'timesformer_jointST_8xb8-8x32x1-15e_kinetics400-rgb', | |
| 'timesformer_spaceOnly_8xb8-8x32x1-15e_kinetics400-rgb', | |
| 'tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb', | |
| 'tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv2-rgb', | |
| 'tin_kinetics400-pretrained-tsm-r50_1x1x8-50e_kinetics400-rgb', | |
| 'tpn-slowonly_r50_8xb8-8x8x1-150e_kinetics400-rgb', | |
| 'tpn-slowonly_imagenet-pretrained-r50_8xb8-8x8x1-150e_kinetics400-rgb', | |
| 'tpn-tsm_imagenet-pretrained-r50_8xb8-1x1x8-150e_sthv1-rgb', | |
| 'trn_imagenet-pretrained-r50_8xb16-1x1x8-50e_sthv1-rgb', | |
| 'trn_imagenet-pretrained-r50_8xb16-1x1x8-50e_sthv2-rgb', | |
| 'tsm_imagenet-pretrained-r50_8xb16-1x1x8-50e_kinetics400-rgb', | |
| 'tsm_imagenet-pretrained-r50_8xb16-1x1x8-100e_kinetics400-rgb', | |
| 'tsm_imagenet-pretrained-r50_8xb16-1x1x16-50e_kinetics400-rgb', | |
| 'tsm_imagenet-pretrained-r50_8xb16-dense-1x1x8-50e_kinetics400-rgb', | |
| 'tsm_imagenet-pretrained-r50-nl-embedded-gaussian_8xb16-1x1x8-50e_kinetics400-rgb', | |
| 'tsm_imagenet-pretrained-r50-nl-dot-product_8xb16-1x1x8-50e_kinetics400-rgb', | |
| 'tsm_imagenet-pretrained-r50-nl-gaussian_8xb16-1x1x8-50e_kinetics400-rgb', | |
| 'tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb', | |
| 'tsm_imagenet-pretrained-r50_8xb16-1x1x8-50e_sthv2-rgb', | |
| 'tsm_imagenet-pretrained-r50_8xb16-1x1x16-50e_sthv2-rgb', | |
| 'tsn_imagenet-pretrained-r50_8xb32-1x1x3-100e_kinetics400-rgb', | |
| 'tsn_imagenet-pretrained-r50_8xb32-1x1x5-100e_kinetics400-rgb', | |
| 'tsn_imagenet-pretrained-r50_8xb32-1x1x8-100e_kinetics400-rgb', | |
| 'tsn_imagenet-pretrained-r50_8xb32-dense-1x1x5-100e_kinetics400-rgb', | |
| 'tsn_imagenet-pretrained-r101_8xb32-1x1x8-100e_kinetics400-rgb', | |
| 'tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb', | |
| 'tsn_imagenet-pretrained-dense161_8xb32-1x1x3-100e_kinetics400-rgb', | |
| 'tsn_imagenet-pretrained-swin-transformer_8xb32-1x1x3-100e_kinetics400-rgb', | |
| 'tsn_imagenet-pretrained-swin-transformer_32xb8-1x1x8-50e_kinetics400-rgb', | |
| 'tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb', | |
| 'tsn_imagenet-pretrained-r50_8xb32-1x1x16-50e_sthv2-rgb', | |
| 'uniformer-small_imagenet1k-pre_16x4x1_kinetics400-rgb', | |
| 'uniformer-base_imagenet1k-pre_16x4x1_kinetics400-rgb', | |
| 'uniformer-base_imagenet1k-pre_32x4x1_kinetics400-rgb', | |
| 'uniformerv2-base-p16-res224_clip_8xb32-u8_kinetics400-rgb', | |
| 'uniformerv2-base-p16-res224_clip-kinetics710-pre_8xb32-u8_kinetics400-rgb', | |
| 'uniformerv2-large-p14-res224_clip-kinetics710-pre_u8_kinetics400-rgb', | |
| 'uniformerv2-large-p14-res224_clip-kinetics710-pre_u16_kinetics400-rgb', | |
| 'uniformerv2-large-p14-res224_clip-kinetics710-pre_u32_kinetics400-rgb', | |
| 'uniformerv2-large-p14-res336_clip-kinetics710-pre_u32_kinetics400-rgb', | |
| 'uniformerv2-base-p16-res224_clip-kinetics710-pre_8xb32-u8_kinetics600-rgb', | |
| 'uniformerv2-large-p14-res224_clip-kinetics710-pre_u8_kinetics600-rgb', | |
| 'uniformerv2-large-p14-res224_clip-kinetics710-pre_u16_kinetics600-rgb', | |
| 'uniformerv2-large-p14-res224_clip-kinetics710-pre_u32_kinetics600-rgb', | |
| 'uniformerv2-large-p14-res336_clip-kinetics710-pre_u32_kinetics600-rgb', | |
| 'uniformerv2-base-p16-res224_clip-pre_8xb32-u8_kinetics700-rgb', | |
| 'uniformerv2-base-p16-res224_clip-kinetics710-pre_8xb32-u8_kinetics700-rgb', | |
| 'uniformerv2-large-p14-res224_clip-kinetics710-pre_u8_kinetics700-rgb', | |
| 'uniformerv2-large-p14-res224_clip-kinetics710-pre_u16_kinetics700-rgb', | |
| 'uniformerv2-large-p14-res224_clip-kinetics710-pre_u32_kinetics700-rgb', | |
| 'uniformerv2-large-p14-res336_clip-kinetics710-pre_u32_kinetics700-rgb', | |
| 'uniformerv2-base-p16-res224_clip-pre_u8_kinetics710-rgb', | |
| 'uniformerv2-large-p14-res224_clip-pre_u8_kinetics710-rgb', | |
| 'uniformerv2-large-p14-res336_clip-pre_u8_kinetics710-rgb', | |
| 'uniformerv2-base-p16-res224_clip-kinetics710-kinetics-k400-pre_16xb32-u8_mitv1-rgb', | |
| 'uniformerv2-large-p16-res224_clip-kinetics710-kinetics-k400-pre_u8_mitv1-rgb', | |
| 'uniformerv2-large-p16-res336_clip-kinetics710-kinetics-k400-pre_u8_mitv1-rgb', | |
| 'vit-base-p16_videomae-k400-pre_16x4x1_kinetics-400', | |
| 'vit-large-p16_videomae-k400-pre_16x4x1_kinetics-400', | |
| 'vit-small-p16_videomaev2-vit-g-dist-k710-pre_16x4x1_kinetics-400', | |
| 'vit-base-p16_videomaev2-vit-g-dist-k710-pre_16x4x1_kinetics-400', | |
| 'x3d_s_13x6x1_facebook-kinetics400-rgb', | |
| 'x3d_m_16x5x1_facebook-kinetics400-rgb', | |
| 'tsn_r18_8xb320-64x1x1-100e_kinetics400-audio-feature', | |
| 'bmn_2xb8-400x100-9e_activitynet-feature', | |
| 'bsn_400x100_1xb16_20e_activitynet_feature (cuhk_mean_100)', | |
| 'clip4clip_vit-base-p32-res224-clip-pre_8xb16-u12-5e_msrvtt-9k-rgb', | |
| '2s-agcn_8xb16-joint-u100-80e_ntu60-xsub-keypoint-2d', | |
| '2s-agcn_8xb16-bone-u100-80e_ntu60-xsub-keypoint-2d', | |
| '2s-agcn_8xb16-joint-motion-u100-80e_ntu60-xsub-keypoint-2d', | |
| '2s-agcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d', | |
| '2s-agcn_8xb16-joint-u100-80e_ntu60-xsub-keypoint-3d', | |
| '2s-agcn_8xb16-bone-u100-80e_ntu60-xsub-keypoint-3d', | |
| '2s-agcn_8xb16-joint-motion-u100-80e_ntu60-xsub-keypoint-3d', | |
| '2s-agcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-3d', | |
| 'slowonly_r50_8xb16-u48-240e_gym-keypoint', | |
| 'slowonly_r50_8xb16-u48-240e_gym-limb', 'slowonly_r50_8xb16-u48-240e_ntu60-xsub-keypoint', | |
| 'slowonly_r50_8xb16-u48-240e_ntu60-xsub-limb', | |
| 'slowonly_kinetics400-pretrained-r50_8xb16-u48-120e_hmdb51-split1-keypoint', | |
| 'slowonly_kinetics400-pretrained-r50_8xb16-u48-120e_ucf101-split1-keypoint', | |
| 'stgcn_8xb16-joint-u100-80e_ntu60-xsub-keypoint-2d', | |
| 'stgcn_8xb16-bone-u100-80e_ntu60-xsub-keypoint-2d', | |
| 'stgcn_8xb16-joint-motion-u100-80e_ntu60-xsub-keypoint-2d', | |
| 'stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d', | |
| 'stgcn_8xb16-joint-u100-80e_ntu60-xsub-keypoint-3d', | |
| 'stgcn_8xb16-bone-u100-80e_ntu60-xsub-keypoint-3d', | |
| 'stgcn_8xb16-joint-motion-u100-80e_ntu60-xsub-keypoint-3d', | |
| 'stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-3d', | |
| 'stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d', | |
| 'stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d', | |
| 'stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-2d', | |
| 'stgcn_8xb16-bone-motion-u100-80e_ntu120-xsub-keypoint-2d', | |
| 'stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d', | |
| 'stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d', | |
| 'stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d', | |
| 'stgcn_8xb16-bone-motion-u100-80e_ntu120-xsub-keypoint-3d', | |
| 'stgcnpp_8xb16-joint-u100-80e_ntu60-xsub-keypoint-2d', | |
| 'stgcnpp_8xb16-bone-u100-80e_ntu60-xsub-keypoint-2d', | |
| 'stgcnpp_8xb16-joint-motion-u100-80e_ntu60-xsub-keypoint-2d', | |
| 'stgcnpp_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d', | |
| 'stgcnpp_8xb16-joint-u100-80e_ntu60-xsub-keypoint-3d', | |
| 'stgcnpp_8xb16-bone-u100-80e_ntu60-xsub-keypoint-3d', | |
| 'stgcnpp_8xb16-joint-motion-u100-80e_ntu60-xsub-keypoint-3d', | |
| 'stgcnpp_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-3d' | |
| ] | |
| labelmap_list = [ | |
| 'kinetics_label_map_k400.txt', 'kinetics_label_map_k600.txt', 'kinetics_label_map_k700.txt', | |
| 'kinetics_label_map_k710.txt', 'diving48_label_map.txt', 'gym_label_map.txt', | |
| 'hmdb51_label_map.txt', 'jester_label_map.txt', 'mit_label_map.txt', | |
| 'mmit_label_map.txt', 'multisports_label_map.txt', 'skeleton_label_map_gym99.txt', | |
| 'skeleton_label_map_ntu60.txt', 'sthv1_label_map.txt', 'sthv2_label_map.txt', 'ucf101_label_map.txt', | |
| ] | |
| def parse_args(): | |
| parser = argparse.ArgumentParser(description='MMAction2 demo') | |
| parser.add_argument('--config', help='test config file path') | |
| parser.add_argument('--checkpoint', help='checkpoint file/url') | |
| parser.add_argument('--video', help='video file/url or rawframes directory') | |
| parser.add_argument('--label', help='label file') | |
| parser.add_argument( | |
| '--cfg-options', | |
| nargs='+', | |
| action=DictAction, | |
| help='override some settings in the used config, the key-value pair ' | |
| 'in xxx=yyy format will be merged into config file. For example, ' | |
| "'--cfg-options model.backbone.depth=18 model.backbone.with_cp=True'") | |
| parser.add_argument( | |
| '--device', type=str, default='cuda:0', help='CPU/CUDA device option') | |
| parser.add_argument( | |
| '--fps', | |
| default=30, | |
| type=int, | |
| help='specify fps value of the output video when using rawframes to ' | |
| 'generate file') | |
| parser.add_argument( | |
| '--font-scale', | |
| default=None, | |
| type=float, | |
| help='font scale of the text in output video') | |
| parser.add_argument( | |
| '--font-color', | |
| default='white', | |
| help='font color of the text in output video') | |
| parser.add_argument( | |
| '--target-resolution', | |
| nargs=2, | |
| default=None, | |
| type=int, | |
| help='Target resolution (w, h) for resizing the frames when using a ' | |
| 'video as input. If either dimension is set to -1, the frames are ' | |
| 'resized by keeping the existing aspect ratio') | |
| parser.add_argument('--out-filename', default=None, help='output filename') | |
| args = parser.parse_args() | |
| return args | |
| def get_output( | |
| video_path: str, | |
| out_filename: str, | |
| data_sample: str, | |
| labels: list, | |
| fps: int = 30, | |
| font_scale: Optional[str] = None, | |
| font_color: str = 'white', | |
| target_resolution: Optional[Tuple[int]] = None, | |
| ) -> None: | |
| """Get demo output using ``moviepy``. | |
| This function will generate video file or gif file from raw video or | |
| frames, by using ``moviepy``. For more information of some parameters, | |
| you can refer to: https://github.com/Zulko/moviepy. | |
| Args: | |
| video_path (str): The video file path. | |
| out_filename (str): Output filename for the generated file. | |
| datasample (str): Predicted label of the generated file. | |
| labels (list): Label list of current dataset. | |
| fps (int): Number of picture frames to read per second. Defaults to 30. | |
| font_scale (float): Font scale of the text. Defaults to None. | |
| font_color (str): Font color of the text. Defaults to ``white``. | |
| target_resolution (Tuple[int], optional): Set to | |
| (desired_width desired_height) to have resized frames. If | |
| either dimension is None, the frames are resized by keeping | |
| the existing aspect ratio. Defaults to None. | |
| """ | |
| if video_path.startswith(('http://', 'https://')): | |
| raise NotImplementedError | |
| # init visualizer | |
| out_type = 'gif' if osp.splitext(out_filename)[1] == '.gif' else 'video' | |
| visualizer = ActionVisualizer() | |
| visualizer.dataset_meta = dict(classes=labels) | |
| text_cfg = {'colors': font_color} | |
| if font_scale is not None: | |
| text_cfg.update({'font_sizes': font_scale}) | |
| visualizer.add_datasample( | |
| out_filename, | |
| video_path, | |
| data_sample, | |
| draw_pred=True, | |
| draw_gt=False, | |
| text_cfg=text_cfg, | |
| fps=fps, | |
| out_type=out_type, | |
| out_path=osp.join('demo', out_filename), | |
| target_resolution=target_resolution) | |
| def clear_folder(folder_path): | |
| import shutil | |
| for filename in os.listdir(folder_path): | |
| file_path = os.path.join(folder_path, filename) | |
| try: | |
| if os.path.isfile(file_path) or os.path.islink(file_path): | |
| os.unlink(file_path) | |
| elif os.path.isdir(file_path): | |
| shutil.rmtree(file_path) | |
| except Exception as e: | |
| print(f"Failed to delete {file_path}. Reason: {e}") | |
| print(f"Clear {folder_path} successfully.") | |
| def download_cfg_checkpoint_model_name(model_name): | |
| clear_folder("./checkpoint") | |
| download(package='mmaction2', | |
| configs=[model_name], | |
| dest_root='./checkpoint') | |
| def download_test_video(): | |
| # Images | |
| torch.hub.download_url_to_file( | |
| 'https://user-images.githubusercontent.com/59380685/267197615-0e372587-9f42-428a-8f3b-e4e6f17e8b1a.mp4', | |
| 'demo.mp4') | |
| torch.hub.download_url_to_file( | |
| 'https://user-images.githubusercontent.com/59380685/267197620-56ee9562-ba3a-4ac4-977a-6df1cd693c39.mp4', | |
| 'zelda.mp4') | |
| torch.hub.download_url_to_file( | |
| 'https://user-images.githubusercontent.com/59380685/267197784-b8bff32a-6655-4777-a3f4-49070d480a76.mp4', | |
| 'test_video_structuralize.mp4') | |
| torch.hub.download_url_to_file( | |
| 'https://user-images.githubusercontent.com/59380685/267197798-9f88e0b9-1889-494a-a886-2e1e9ed43327.mp4', | |
| 'shaowei.mp4') | |
| torch.hub.download_url_to_file( | |
| 'https://user-images.githubusercontent.com/59380685/267197804-953056d5-1351-4c5c-8459-f4e8f6815836.mp4', | |
| 'demo_skeleton.mp4') | |
| torch.hub.download_url_to_file( | |
| 'https://user-images.githubusercontent.com/59380685/267197812-b4be4451-b694-4717-b8cf-545e36e506c1.mp4', | |
| 'cxk.mp4') | |
| torch.hub.download_url_to_file( | |
| 'https://user-images.githubusercontent.com/59380685/267234378-2cddbf73-9e5d-45a9-b3d3-56aa2b994abe.mp4', | |
| 'baoguo.mp4') | |
| def download_label_map_txt(): | |
| torch.hub.download_url_to_file( | |
| 'https://github.com/isLinXu/issues/files/12579936/ucf101_label_map.txt', | |
| 'ucf101_label_map.txt') | |
| torch.hub.download_url_to_file( | |
| 'https://github.com/isLinXu/issues/files/12579940/gym_label_map.txt', | |
| 'gym_label_map.txt') | |
| torch.hub.download_url_to_file( | |
| 'https://github.com/isLinXu/issues/files/12579943/diving48_label_map.txt', | |
| 'diving48_label_map.txt') | |
| torch.hub.download_url_to_file( | |
| 'https://github.com/isLinXu/issues/files/12579947/hmdb51_label_map.txt', | |
| 'hmdb51_label_map.txt') | |
| torch.hub.download_url_to_file( | |
| 'https://github.com/isLinXu/issues/files/12579949/jester_label_map.txt', | |
| 'jester_label_map.txt') | |
| torch.hub.download_url_to_file( | |
| 'https://github.com/isLinXu/issues/files/12579951/kinetics_label_map_k400.txt', | |
| 'kinetics_label_map_k400.txt') | |
| torch.hub.download_url_to_file( | |
| 'https://github.com/isLinXu/issues/files/12579952/kinetics_label_map_k600.txt', | |
| 'kinetics_label_map_k600.txt') | |
| torch.hub.download_url_to_file( | |
| 'https://github.com/isLinXu/issues/files/12579953/kinetics_label_map_k700.txt', | |
| 'kinetics_label_map_k700.txt') | |
| torch.hub.download_url_to_file( | |
| 'https://github.com/isLinXu/issues/files/12579954/kinetics_label_map_k710.txt', | |
| 'kinetics_label_map_k710.txt') | |
| torch.hub.download_url_to_file( | |
| 'https://github.com/isLinXu/issues/files/12579955/mit_label_map.txt', | |
| 'mit_label_map.txt') | |
| torch.hub.download_url_to_file( | |
| 'https://github.com/isLinXu/issues/files/12579957/mmit_label_map.txt', | |
| 'mmit_label_map.txt') | |
| torch.hub.download_url_to_file( | |
| 'https://github.com/isLinXu/issues/files/12579960/multisports_label_map.txt', | |
| 'multisports_label_map.txt') | |
| torch.hub.download_url_to_file( | |
| 'https://github.com/isLinXu/issues/files/12579961/skeleton_label_map_ntu60.txt', | |
| 'mmit_label_map.txt') | |
| torch.hub.download_url_to_file( | |
| 'https://github.com/isLinXu/issues/files/12579960/multisports_label_map.txt', | |
| 'multisports_label_map.txt') | |
| torch.hub.download_url_to_file( | |
| 'https://github.com/isLinXu/issues/files/12579961/skeleton_label_map_ntu60.txt', | |
| 'skeleton_label_map_ntu60.txt') | |
| torch.hub.download_url_to_file( | |
| 'https://github.com/isLinXu/issues/files/12579962/skeleton_label_map_gym99.txt', | |
| 'skeleton_label_map_gym99.txt') | |
| torch.hub.download_url_to_file( | |
| 'https://github.com/isLinXu/issues/files/12579965/sthv1_label_map.txt', | |
| 'sthv1_label_map.txt') | |
| torch.hub.download_url_to_file( | |
| 'https://github.com/isLinXu/issues/files/12579967/sthv2_label_map.txt', | |
| 'sthv2_label_map.txt') | |
| def mmaction_inference(video, mmaction2_models, device, label, out_filename): | |
| args = parse_args() | |
| path = "./checkpoint" | |
| if not os.path.exists(path): | |
| os.makedirs(path) | |
| download_cfg_checkpoint_model_name(mmaction2_models) | |
| config = [f for f in os.listdir(path) if fnmatch.fnmatch(f, "*.py")][0] | |
| config = path + "/" + config | |
| checkpoint = [f for f in os.listdir(path) if fnmatch.fnmatch(f, "*.pth")][0] | |
| checkpoint = path + "/" + checkpoint | |
| # args setting | |
| args.config = config | |
| args.checkpoint = checkpoint | |
| args.video = video | |
| args.device = device | |
| args.label = label | |
| args.out_filename = out_filename | |
| cfg = Config.fromfile(args.config) | |
| if args.cfg_options is not None: | |
| cfg.merge_from_dict(args.cfg_options) | |
| # Build the recognizer from a config file and checkpoint file/url | |
| model = init_recognizer(cfg, args.checkpoint, device=args.device) | |
| pred_result = inference_recognizer(model, args.video) | |
| pred_scores = pred_result.pred_scores.item.tolist() | |
| score_tuples = tuple(zip(range(len(pred_scores)), pred_scores)) | |
| score_sorted = sorted(score_tuples, key=itemgetter(1), reverse=True) | |
| top5_label = score_sorted[:5] | |
| labels = open(args.label).readlines() | |
| labels = [x.strip() for x in labels] | |
| results = [(labels[k[0]], k[1]) for k in top5_label] | |
| print('The top-5 labels with corresponding scores are:') | |
| for result in results: | |
| print(f'{result[0]}: ', result[1]) | |
| if args.out_filename is not None: | |
| if args.target_resolution is not None: | |
| if args.target_resolution[0] == -1: | |
| assert isinstance(args.target_resolution[1], int) | |
| assert args.target_resolution[1] > 0 | |
| if args.target_resolution[1] == -1: | |
| assert isinstance(args.target_resolution[0], int) | |
| assert args.target_resolution[0] > 0 | |
| args.target_resolution = tuple(args.target_resolution) | |
| get_output( | |
| args.video, | |
| args.out_filename, | |
| pred_result, | |
| labels, | |
| fps=args.fps, | |
| font_scale=args.font_scale, | |
| font_color=args.font_color, | |
| target_resolution=args.target_resolution) | |
| save_dir_path = "demo/" + args.out_filename | |
| if os.path.exists(save_dir_path): | |
| print(f'File saved as {save_dir_path}') | |
| return save_dir_path | |
| else: | |
| base_name = os.path.basename(args.video) | |
| print(f'File saved as {base_name}') | |
| return base_name | |
| if __name__ == '__main__': | |
| print("Downloading test video and model...") | |
| download_test_video() | |
| print("Downloading label map txt...") | |
| download_label_map_txt() | |
| input_video = gr.Video(type='mp4', label="Original video") | |
| mmaction2_models = gr.inputs.Dropdown(label="MMAction2 models", choices=[x for x in mmaction2_models_list], | |
| default='tsn_imagenet-pretrained-r50_8xb32-1x1x3-100e_kinetics400-rgb') | |
| device = gr.inputs.Radio(label="Device", choices=["cpu", "cuda:0"], default="cpu") | |
| # label = gr.inputs.Textbox(label="Label file", default="label_map/kinetics/label_map_k400.txt") | |
| label = gr.inputs.Dropdown(label="Label file", choices=[x for x in labelmap_list], default='kinetics_label_map_k400.txt') | |
| out_filename = gr.inputs.Textbox(label="Output filename", default="demo_dst.mp4") | |
| output_video = gr.Video(label="Output video") | |
| examples = [['zelda.mp4', 'tsn_imagenet-pretrained-r50_8xb32-1x1x3-100e_kinetics400-rgb', "cpu", | |
| 'kinetics_label_map_k400.txt', "demo_dst.mp4"], | |
| ['shaowei.mp4', 'slowfast_r50_8xb8-4x16x1-256e_kinetics400-rgb', "cpu", | |
| 'kinetics_label_map_k400.txt', "demo_dst.mp4"], | |
| ['cxk.mp4', 'tsn_imagenet-pretrained-r50_8xb32-1x1x3-100e_kinetics400-rgb', "cpu", | |
| 'kinetics_label_map_k400.txt', "demo_dst.mp4"] | |
| ] | |
| title = "MMAction2 web demo" | |
| description = "<div align='center'><img src='https://raw.githubusercontent.com/open-mmlab/mmaction2/main/resources/mmaction2_logo.png' width='450''/><div>" \ | |
| "<p style='text-align: center'><a href='https://github.com/open-mmlab/mmaction2'>MMAction2</a> MMAction2 是一款基于 PyTorch 开发的行为识别开源工具包，是 open-mmlab 项目的一个子项目。" \ | |
| "OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark.</p>" | |
| article = "<p style='text-align: center'><a href='https://github.com/open-mmlab/mmaction2'>MMAction2</a></p>" \ | |
| "<p style='text-align: center'><a href='https://github.com/isLinXu'>gradio build by gatilin</a></a></p>" | |
| # gradio demo | |
| iface = gr.Interface(fn=mmaction_inference, | |
| inputs=[input_video, mmaction2_models, device, label, out_filename], | |
| outputs=output_video,examples=examples, | |
| title=title, description=description, article=article) | |
| iface.launch() | |