[Feature] support RTMPose Gradio app in projects

configs/body_2d_keypoint/rtmpose/body8/rtmpose_body8-coco.yml

@@ -84,6 +84,7 @@ Models:
   Weights: https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-body7_pt-body7_420e-384x288-65e718c4_20230504.pth
 - Config: configs/body_2d_keypoint/rtmpose/body8/rtmpose-l_8xb256-420e_body8-384x288.py
   In Collection: RTMPose
+  Alias: rtmpose-l
   Metadata:
     Architecture: *id001
     Training Data: *id002

configs/wholebody_2d_keypoint/rtmpose/coco-wholebody/rtmpose_coco-wholebody.yml

@@ -48,7 +48,7 @@ Models:
   Metadata:
     Architecture: *id001
     Training Data: COCO-WholeBody
-  Name: rtmpose-l_8xb32-270e_coco-wholebody-384x288.py
+  Name: rtmpose-l_8xb32-270e_coco-wholebody-384x288
   Results:
   - Dataset: COCO-WholeBody
     Metrics:

mmpose/apis/inferencers/utils/default_det_models.py

@@ -7,7 +7,13 @@
 mmpose_path = get_installed_path(MODULE2PACKAGE['mmpose'])
 
 default_det_models = dict(
-    human=dict(model='rtmdet-m', weights=None, cat_ids=(0, )),
+    human=dict(
+        model=osp.join(
+            mmpose_path, '.mim', 'demo/mmdetection_cfg/'
+            'rtmdet_m_640-8xb32_coco-person.py'),
+        weights='https://download.openmmlab.com/mmpose/v1/projects/'
+        'rtmposev1/rtmdet_m_8xb32-100e_coco-obj365-person-235e8209.pth',
+        cat_ids=(0, )),
     face=dict(
         model=osp.join(mmpose_path, '.mim',
                        'demo/mmdetection_cfg/yolox-s_8xb8-300e_coco-face.py'),

projects/rtmpose/app.py

@@ -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()