-
Notifications
You must be signed in to change notification settings - Fork 1.2k
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
[Feature] support RTMPose Gradio app in projects (#2877)
- Loading branch information
Showing
4 changed files
with
217 additions
and
2 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,208 @@ | ||
| # Copyright (c) OpenMMLab. All rights reserved. | ||
|
|
||
| import os | ||
| from functools import partial | ||
|
|
||
| import gradio as gr | ||
|
|
||
| # prepare environment | ||
| project_path = os.path.join(os.path.dirname(os.path.abspath(__file__))) | ||
| mmpose_path = project_path.split('/projects', 1)[0] | ||
|
|
||
| os.system('python -m pip install Openmim') | ||
| os.system('python -m mim install "mmcv>=2.0.0"') | ||
| os.system('python -m mim install "mmengine>=0.9.0"') | ||
| os.system('python -m mim install "mmdet>=3.0.0"') | ||
| os.system(f'python -m mim install -e {mmpose_path}') | ||
| from mmpose.apis import MMPoseInferencer # noqa | ||
|
|
||
| models = [ | ||
| 'rtmpose | body', 'rtmo | body', 'rtmpose | face', 'dwpose | wholebody', | ||
| 'rtmw | wholebody' | ||
| ] | ||
| cached_model = {model: None for model in models} | ||
|
|
||
|
|
||
| def predict(input, | ||
| draw_heatmap=False, | ||
| model_type='body', | ||
| skeleton_style='mmpose', | ||
| input_type='image'): | ||
| """Visualize the demo images. | ||
| Using mmdet to detect the human. | ||
| """ | ||
|
|
||
| if model_type == 'rtmpose | face': | ||
| if cached_model[model_type] is None: | ||
| cached_model[model_type] = MMPoseInferencer(pose2d='face') | ||
| model = cached_model[model_type] | ||
|
|
||
| elif model_type == 'dwpose | wholebody': | ||
| if cached_model[model_type] is None: | ||
| cached_model[model_type] = MMPoseInferencer( | ||
| pose2d=os.path.join( | ||
| project_path, 'rtmpose/wholebody_2d_keypoint/' | ||
| 'rtmpose-l_8xb32-270e_coco-wholebody-384x288.py'), | ||
| pose2d_weights='https://download.openmmlab.com/mmpose/v1/' | ||
| 'projects/rtmposev1/rtmpose-l_simcc-ucoco_dw-ucoco_270e-' | ||
| '384x288-2438fd99_20230728.pth') | ||
| model = cached_model[model_type] | ||
|
|
||
| elif model_type == 'rtmw | wholebody': | ||
| if cached_model[model_type] is None: | ||
| cached_model[model_type] = MMPoseInferencer( | ||
| pose2d=os.path.join( | ||
| project_path, 'rtmpose/wholebody_2d_keypoint/' | ||
| 'rtmw-l_8xb320-270e_cocktail14-384x288.py'), | ||
| pose2d_weights='https://download.openmmlab.com/mmpose/v1/' | ||
| 'projects/rtmw/rtmw-dw-x-l_simcc-cocktail14_270e-' | ||
| '384x288-20231122.pth') | ||
| model = cached_model[model_type] | ||
|
|
||
| elif model_type == 'rtmpose | body': | ||
| if cached_model[model_type] is None: | ||
| cached_model[model_type] = MMPoseInferencer(pose2d='rtmpose-l') | ||
| model = cached_model[model_type] | ||
|
|
||
| elif model_type == 'rtmo | body': | ||
| if cached_model[model_type] is None: | ||
| cached_model[model_type] = MMPoseInferencer(pose2d='rtmo') | ||
| model = cached_model[model_type] | ||
| draw_heatmap = False | ||
|
|
||
| else: | ||
| raise ValueError | ||
|
|
||
| if input_type == 'image': | ||
|
|
||
| result = next( | ||
| model( | ||
| input, | ||
| return_vis=True, | ||
| draw_heatmap=draw_heatmap, | ||
| skeleton_style=skeleton_style)) | ||
| img = result['visualization'][0][..., ::-1] | ||
| return img | ||
|
|
||
| elif input_type == 'video': | ||
|
|
||
| for _ in model( | ||
| input, | ||
| vis_out_dir='test.mp4', | ||
| draw_heatmap=draw_heatmap, | ||
| skeleton_style=skeleton_style): | ||
| pass | ||
|
|
||
| return 'test.mp4' | ||
|
|
||
| return None | ||
|
|
||
|
|
||
| news_list = [ | ||
| '2023-8-1: We support [DWPose](https://arxiv.org/pdf/2307.15880.pdf).', | ||
| '2023-9-25: We release an alpha version of RTMW model, the technical ' | ||
| 'report will be released soon.', | ||
| '2023-12-11: Update RTMW models, the online version is the RTMW-l with ' | ||
| '70.1 mAP on COCO-Wholebody.', | ||
| '2023-12-13: We release an alpha version of RTMO (One-stage RTMPose) ' | ||
| 'models.', | ||
| ] | ||
|
|
||
| with gr.Blocks() as demo: | ||
|
|
||
| with gr.Tab('Upload-Image'): | ||
| input_img = gr.Image(type='numpy') | ||
| button = gr.Button('Inference', variant='primary') | ||
| hm = gr.Checkbox(label='draw-heatmap', info='Whether to draw heatmap') | ||
| model_type = gr.Dropdown([ | ||
| 'rtmpose | body', 'rtmo | body', 'rtmpose | face', | ||
| 'dwpose | wholebody', 'rtmw | wholebody' | ||
| ], | ||
| label='Model | Keypoint Type', | ||
| info='Body / Face / Wholebody') | ||
|
|
||
| gr.Markdown('## News') | ||
| for news in news_list[::-1]: | ||
| gr.Markdown(news) | ||
|
|
||
| gr.Markdown('## Output') | ||
| out_image = gr.Image(type='numpy') | ||
| gr.Examples(['./tests/data/coco/000000000785.jpg'], input_img) | ||
| input_type = 'image' | ||
| button.click( | ||
| partial(predict, input_type=input_type), | ||
| [input_img, hm, model_type], out_image) | ||
|
|
||
| with gr.Tab('Webcam-Image'): | ||
| input_img = gr.Image(source='webcam', type='numpy') | ||
| button = gr.Button('Inference', variant='primary') | ||
| hm = gr.Checkbox(label='draw-heatmap', info='Whether to draw heatmap') | ||
| model_type = gr.Dropdown([ | ||
| 'rtmpose | body', 'rtmo | body', 'rtmpose | face', | ||
| 'dwpose | wholebody', 'rtmw | wholebody' | ||
| ], | ||
| label='Model | Keypoint Type', | ||
| info='Body / Face / Wholebody') | ||
|
|
||
| gr.Markdown('## News') | ||
| for news in news_list[::-1]: | ||
| gr.Markdown(news) | ||
|
|
||
| gr.Markdown('## Output') | ||
| out_image = gr.Image(type='numpy') | ||
|
|
||
| input_type = 'image' | ||
| button.click( | ||
| partial(predict, input_type=input_type), | ||
| [input_img, hm, model_type], out_image) | ||
|
|
||
| with gr.Tab('Upload-Video'): | ||
| input_video = gr.Video(type='mp4') | ||
| button = gr.Button('Inference', variant='primary') | ||
| hm = gr.Checkbox(label='draw-heatmap', info='Whether to draw heatmap') | ||
| model_type = gr.Dropdown([ | ||
| 'rtmpose | body', 'rtmo | body', 'rtmpose | face', | ||
| 'dwpose | wholebody', 'rtmw | wholebody' | ||
| ], | ||
| label='Model | Keypoint type', | ||
| info='Body / Face / Wholebody') | ||
|
|
||
| gr.Markdown('## News') | ||
| for news in news_list[::-1]: | ||
| gr.Markdown(news) | ||
|
|
||
| gr.Markdown('## Output') | ||
| out_video = gr.Video() | ||
|
|
||
| input_type = 'video' | ||
| button.click( | ||
| partial(predict, input_type=input_type), | ||
| [input_video, hm, model_type], out_video) | ||
|
|
||
| with gr.Tab('Webcam-Video'): | ||
| input_video = gr.Video(source='webcam', format='mp4') | ||
| button = gr.Button('Inference', variant='primary') | ||
| hm = gr.Checkbox(label='draw-heatmap', info='Whether to draw heatmap') | ||
| model_type = gr.Dropdown([ | ||
| 'rtmpose | body', 'rtmo | body', 'rtmpose | face', | ||
| 'dwpose | wholebody', 'rtmw | wholebody' | ||
| ], | ||
| label='Model | Keypoint Type', | ||
| info='Body / Face / Wholebody') | ||
|
|
||
| gr.Markdown('## News') | ||
| for news in news_list[::-1]: | ||
| gr.Markdown(news) | ||
|
|
||
| gr.Markdown('## Output') | ||
| out_video = gr.Video() | ||
|
|
||
| input_type = 'video' | ||
| button.click( | ||
| partial(predict, input_type=input_type), | ||
| [input_video, hm, model_type], out_video) | ||
|
|
||
| gr.close_all() | ||
| demo.queue() | ||
| demo.launch() |