Add files

.gitattributes

@@ -1,3 +1,5 @@
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
 *.7z filter=lfs diff=lfs merge=lfs -text
 *.arrow filter=lfs diff=lfs merge=lfs -text
 *.bin filter=lfs diff=lfs merge=lfs -text

.gitignore

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+mmdet_configs/configs

.gitmodules

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+[submodule "ViTPose"]
+	path = ViTPose
+	url = https://github.com/ViTAE-Transformer/ViTPose

.pre-commit-config.yaml

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+exclude: ^(ViTPose/|mmdet_configs/configs/)
+repos:
+- repo: https://github.com/pre-commit/pre-commit-hooks
+  rev: v4.2.0
+  hooks:
+  - id: check-executables-have-shebangs
+  - id: check-json
+  - id: check-merge-conflict
+  - id: check-shebang-scripts-are-executable
+  - id: check-toml
+  - id: check-yaml
+  - id: double-quote-string-fixer
+  - id: end-of-file-fixer
+  - id: mixed-line-ending
+    args: ['--fix=lf']
+  - id: requirements-txt-fixer
+  - id: trailing-whitespace
+- repo: https://github.com/myint/docformatter
+  rev: v1.4
+  hooks:
+  - id: docformatter
+    args: ['--in-place']
+- repo: https://github.com/pycqa/isort
+  rev: 5.10.1
+  hooks:
+    - id: isort
+- repo: https://github.com/pre-commit/mirrors-mypy
+  rev: v0.812
+  hooks:
+    - id: mypy
+      args: ['--ignore-missing-imports']
+- repo: https://github.com/google/yapf
+  rev: v0.32.0
+  hooks:
+  - id: yapf
+    args: ['--parallel', '--in-place']
+- repo: https://github.com/kynan/nbstripout
+  rev: 0.5.0
+  hooks:
+    - id: nbstripout
+      args: ['--extra-keys', 'metadata.interpreter metadata.kernelspec cell.metadata.pycharm']
+- repo: https://github.com/nbQA-dev/nbQA
+  rev: 1.3.1
+  hooks:
+    - id: nbqa-isort
+    - id: nbqa-yapf

.style.yapf

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+[style]
+based_on_style = pep8
+blank_line_before_nested_class_or_def = false
+spaces_before_comment = 2
+split_before_logical_operator = true

ViTPose

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+Subproject commit 86eced957665e62fa728eea059d5fffb9b94d653

app.py

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+#!/usr/bin/env python
+
+from __future__ import annotations
+
+import argparse
+import os
+import pathlib
+import subprocess
+import sys
+import tarfile
+
+if os.getenv('SYSTEM') == 'spaces':
+    import mim
+
+    mim.uninstall('mmcv-full', confirm_yes=True)
+    mim.install('mmcv-full==1.5.0', is_yes=True)
+
+    subprocess.call('pip uninstall -y opencv-python'.split())
+    subprocess.call('pip uninstall -y opencv-python-headless'.split())
+    subprocess.call('pip install opencv-python-headless==4.5.5.64'.split())
+
+import gradio as gr
+import huggingface_hub
+import numpy as np
+import torch
+import torch.nn as nn
+
+sys.path.insert(0, 'ViTPose/')
+
+from mmdet.apis import inference_detector, init_detector
+from mmpose.apis import (inference_top_down_pose_model, init_pose_model,
+                         process_mmdet_results, vis_pose_result)
+
+TOKEN = os.environ['TOKEN']
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--device', type=str, default='cpu')
+    parser.add_argument('--theme', type=str)
+    parser.add_argument('--share', action='store_true')
+    parser.add_argument('--port', type=int)
+    parser.add_argument('--disable-queue',
+                        dest='enable_queue',
+                        action='store_false')
+    return parser.parse_args()
+
+
+class DetModel:
+    def __init__(self, device: str | torch.device):
+        self.device = torch.device(device)
+        self.models = self._load_models()
+        self.model_name = 'YOLOX-l'
+
+    def _load_models(self) -> dict[str, nn.Module]:
+        model_dict = {
+            'YOLOX-tiny': {
+                'config':
+                'mmdet_configs/configs/yolox/yolox_tiny_8x8_300e_coco.py',
+                'model':
+                'https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_tiny_8x8_300e_coco/yolox_tiny_8x8_300e_coco_20211124_171234-b4047906.pth',
+            },
+            'YOLOX-s': {
+                'config':
+                'mmdet_configs/configs/yolox/yolox_s_8x8_300e_coco.py',
+                'model':
+                'https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_s_8x8_300e_coco/yolox_s_8x8_300e_coco_20211121_095711-4592a793.pth',
+            },
+            'YOLOX-l': {
+                'config':
+                'mmdet_configs/configs/yolox/yolox_l_8x8_300e_coco.py',
+                'model':
+                'https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_l_8x8_300e_coco/yolox_l_8x8_300e_coco_20211126_140236-d3bd2b23.pth',
+            },
+            'YOLOX-x': {
+                'config':
+                'mmdet_configs/configs/yolox/yolox_x_8x8_300e_coco.py',
+                'model':
+                'https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_x_8x8_300e_coco/yolox_x_8x8_300e_coco_20211126_140254-1ef88d67.pth',
+            },
+        }
+        models = {
+            key: init_detector(dic['config'], dic['model'], device=self.device)
+            for key, dic in model_dict.items()
+        }
+        return models
+
+    def set_model_name(self, name: str) -> None:
+        self.model_name = name
+
+    def detect_and_visualize(
+            self, image: np.ndarray,
+            score_threshold: float) -> tuple[list[np.ndarray], np.ndarray]:
+        out = self.detect(image)
+        vis = self.visualize_detection_results(image, out, score_threshold)
+        return out, vis
+
+    def detect(self, image: np.ndarray) -> list[np.ndarray]:
+        image = image[:, :, ::-1]  # RGB -> BGR
+        model = self.models[self.model_name]
+        out = inference_detector(model, image)
+        return out
+
+    def visualize_detection_results(
+            self,
+            image: np.ndarray,
+            detection_results: list[np.ndarray],
+            score_threshold: float = 0.3) -> np.ndarray:
+        person_det = [detection_results[0]] + [np.array([]).reshape(0, 5)]
+
+        image = image[:, :, ::-1]  # RGB -> BGR
+        model = self.models[self.model_name]
+        vis = model.show_result(image,
+                                person_det,
+                                score_thr=score_threshold,
+                                bbox_color=None,
+                                text_color=(200, 200, 200),
+                                mask_color=None)
+        return vis[:, :, ::-1]  # BGR -> RGB
+
+
+class PoseModel:
+    def __init__(self, device: str | torch.device):
+        self.device = torch.device(device)
+        self.models = self._load_models()
+        self.model_name = 'ViTPose-B (multi-task train, COCO)'
+
+    def _load_models(self) -> dict[str, nn.Module]:
+        model_dict = {
+            'ViTPose-B (single-task train)': {
+                'config':
+                'ViTPose/configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/ViTPose_base_coco_256x192.py',
+                'model': 'models/vitpose-b.pth',
+            },
+            'ViTPose-L (single-task train)': {
+                'config':
+                'ViTPose/configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/ViTPose_large_coco_256x192.py',
+                'model': 'models/vitpose-l.pth',
+            },
+            'ViTPose-B (multi-task train, COCO)': {
+                'config':
+                'ViTPose/configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/ViTPose_base_coco_256x192.py',
+                'model': 'models/vitpose-b-multi-coco.pth',
+            },
+            'ViTPose-L (multi-task train, COCO)': {
+                'config':
+                'ViTPose/configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/ViTPose_large_coco_256x192.py',
+                'model': 'models/vitpose-l-multi-coco.pth',
+            },
+        }
+        models = dict()
+        for key, dic in model_dict.items():
+            ckpt_path = huggingface_hub.hf_hub_download('hysts/ViTPose',
+                                                        dic['model'],
+                                                        use_auth_token=TOKEN)
+            model = init_pose_model(dic['config'],
+                                    ckpt_path,
+                                    device=self.device)
+            models[key] = model
+        return models
+
+    def set_model_name(self, name: str) -> None:
+        self.model_name = name
+
+    def predict_pose_and_visualize(
+        self,
+        image: np.ndarray,
+        det_results: list[np.ndarray],
+        box_score_threshold: float,
+        kpt_score_threshold: float,
+        vis_dot_radius: int,
+        vis_line_thickness: int,
+    ) -> tuple[list[dict[str, np.ndarray]], np.ndarray]:
+        out = self.predict_pose(image, det_results, box_score_threshold)
+        vis = self.visualize_pose_results(image, out, kpt_score_threshold,
+                                          vis_dot_radius, vis_line_thickness)
+        return out, vis
+
+    def predict_pose(
+            self,
+            image: np.ndarray,
+            det_results: list[np.ndarray],
+            box_score_threshold: float = 0.5) -> list[dict[str, np.ndarray]]:
+        image = image[:, :, ::-1]  # RGB -> BGR
+        model = self.models[self.model_name]
+        person_results = process_mmdet_results(det_results, 1)
+        out, _ = inference_top_down_pose_model(model,
+                                               image,
+                                               person_results=person_results,
+                                               bbox_thr=box_score_threshold,
+                                               format='xyxy')
+        return out
+
+    def visualize_pose_results(self,
+                               image: np.ndarray,
+                               pose_results: list[np.ndarray],
+                               kpt_score_threshold: float = 0.3,
+                               vis_dot_radius: int = 4,
+                               vis_line_thickness: int = 1) -> np.ndarray:
+        image = image[:, :, ::-1]  # RGB -> BGR
+        model = self.models[self.model_name]
+        vis = vis_pose_result(model,
+                              image,
+                              pose_results,
+                              kpt_score_thr=kpt_score_threshold,
+                              radius=vis_dot_radius,
+                              thickness=vis_line_thickness)
+        return vis[:, :, ::-1]  # BGR -> RGB
+
+
+def set_example_image(example: list) -> dict:
+    return gr.Image.update(value=example[0])
+
+
+def extract_tar() -> None:
+    if pathlib.Path('mmdet_configs/configs').exists():
+        return
+    with tarfile.open('mmdet_configs/configs.tar') as f:
+        f.extractall('mmdet_configs')
+
+
+def main():
+    args = parse_args()
+
+    extract_tar()
+
+    det_model = DetModel(device=args.device)
+    pose_model = PoseModel(device=args.device)
+
+    css = '''
+h1#title {
+  text-align: center;
+}
+'''
+
+    with gr.Blocks(theme=args.theme, css=css) as demo:
+        gr.Markdown('''<h1 id="title">ViTPose</h1>
+
+This is an unofficial demo for [https://github.com/ViTAE-Transformer/ViTPose](https://github.com/ViTAE-Transformer/ViTPose).'''
+                    )
+
+        with gr.Box():
+            gr.Markdown('## Step 1')
+            with gr.Row():
+                with gr.Column():
+                    with gr.Row():
+                        input_image = gr.Image(label='Input Image',
+                                               type='numpy')
+                    with gr.Row():
+                        detector_name = gr.Dropdown(list(
+                            det_model.models.keys()),
+                                                    value=det_model.model_name,
+                                                    label='Detector')
+                    with gr.Row():
+                        detect_button = gr.Button(value='Detect')
+                        det_preds = gr.Variable()
+                with gr.Column():
+                    detection_visualization = gr.Image(
+                        label='Detection Result', type='numpy')
+                    vis_det_score_threshold = gr.Slider(
+                        0,
+                        1,
+                        step=0.05,
+                        value=0.5,
+                        label='Visualization Score Threshold')
+                    redraw_det_button = gr.Button(value='Redraw')
+
+            with gr.Row():
+                paths = sorted(pathlib.Path('images').rglob('*.jpg'))
+                example_images = gr.Dataset(components=[input_image],
+                                            samples=[[path.as_posix()]
+                                                     for path in paths])
+
+        with gr.Box():
+            gr.Markdown('## Step 2')
+            with gr.Row():
+                with gr.Column():
+                    with gr.Row():
+                        pose_model_name = gr.Dropdown(
+                            list(pose_model.models.keys()),
+                            value=pose_model.model_name,
+                            label='Pose Model')
+                    det_score_threshold = gr.Slider(
+                        0,
+                        1,
+                        step=0.05,
+                        value=0.5,
+                        label='Box Score Threshold')
+                    with gr.Row():
+                        predict_button = gr.Button(value='Predict')
+                        pose_preds = gr.Variable()
+                with gr.Column():
+                    pose_visualization = gr.Image(label='Result', type='numpy')
+                    vis_kpt_score_threshold = gr.Slider(
+                        0,
+                        1,
+                        step=0.05,
+                        value=0.3,
+                        label='Visualization Score Threshold')
+                    vis_dot_radius = gr.Slider(1,
+                                               10,
+                                               step=1,
+                                               value=4,
+                                               label='Dot Radius')
+                    vis_line_thickness = gr.Slider(1,
+                                                   10,
+                                                   step=1,
+                                                   value=2,
+                                                   label='Line Thickness')
+                    redraw_pose_button = gr.Button(value='Redraw')
+
+        gr.Markdown(
+            '<center><img src="https://visitor-badge.glitch.me/badge?page_id=hysts.vitpose" alt="visitor badge"/></center>'
+        )
+
+        detector_name.change(fn=det_model.set_model_name,
+                             inputs=[
+                                 detector_name,
+                             ],
+                             outputs=None)
+        detect_button.click(fn=det_model.detect_and_visualize,
+                            inputs=[
+                                input_image,
+                                vis_det_score_threshold,
+                            ],
+                            outputs=[
+                                det_preds,
+                                detection_visualization,
+                            ])
+        redraw_det_button.click(fn=det_model.visualize_detection_results,
+                                inputs=[
+                                    input_image,
+                                    det_preds,
+                                    vis_det_score_threshold,
+                                ],
+                                outputs=[
+                                    detection_visualization,
+                                ])
+
+        pose_model_name.change(fn=pose_model.set_model_name,
+                               inputs=[
+                                   pose_model_name,
+                               ],
+                               outputs=None)
+        predict_button.click(fn=pose_model.predict_pose_and_visualize,
+                             inputs=[
+                                 input_image,
+                                 det_preds,
+                                 det_score_threshold,
+                                 vis_kpt_score_threshold,
+                                 vis_dot_radius,
+                                 vis_line_thickness,
+                             ],
+                             outputs=[
+                                 pose_preds,
+                                 pose_visualization,
+                             ])
+        redraw_pose_button.click(fn=pose_model.visualize_pose_results,
+                                 inputs=[
+                                     input_image,
+                                     pose_preds,
+                                     vis_kpt_score_threshold,
+                                     vis_dot_radius,
+                                     vis_line_thickness,
+                                 ],
+                                 outputs=[
+                                     pose_visualization,
+                                 ])
+
+        example_images.click(fn=set_example_image,
+                             inputs=[
+                                 example_images,
+                             ],
+                             outputs=[
+                                 input_image,
+                             ])
+
+    demo.launch(
+        enable_queue=args.enable_queue,
+        server_port=args.port,
+        share=args.share,
+    )
+
+
+if __name__ == '__main__':
+    main()

images/README.md

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+These images are from the following public domain:
+
+- https://www.pexels.com/photo/women-in-active-wear-balancing-their-body-while-leaning-by-the-doorway-5770445/
+- https://www.pexels.com/photo/woman-balancing-her-body-on-a-handstand-using-one-hand-5770708/
+- https://www.pexels.com/photo/persons-in-black-shirt-and-pants-690598/
+- https://www.pexels.com/photo/photo-of-woman-doing-a-ballet-dance-1164975/
+- https://www.pexels.com/photo/beautiful-woman-in-a-red-dress-wearing-red-lipstick-7909580/
+- https://www.pexels.com/photo/girl-in-red-jacket-riding-bicycle-5792907/
+- https://www.pexels.com/photo/woman-wearing-a-white-gown-walking-on-grass-field-8574605/

mmdet_configs/LICENSE

@@ -0,0 +1,203 @@
+Copyright 2018-2023 OpenMMLab. All rights reserved.
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
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+
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mmdet_configs/README.md

@@ -0,0 +1,2 @@
+`configs.tar` is a tarball of https://github.com/open-mmlab/mmdetection/tree/v2.24.1/configs.
+The license file of the mmdetection is also included in this directory.

mmdet_configs/configs.tar

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5d2091e07da6b74a6cd694e895b653485f7ce9d5d17738a415ca77a56940b989
+size 3389440

requirements.txt

@@ -0,0 +1,9 @@
+mmcv-full==1.5.0
+mmdet==2.24.1
+mmpose==0.25.1
+numpy==1.22.4
+opencv-python-headless==4.5.5.64
+openmim==0.1.5
+timm==0.5.4
+torch==1.11.0
+torchvision==0.12.0