Duplicate from Gradio-Blocks/ViTPose

Co-authored-by: hysts <hysts@users.noreply.huggingface.co>

.gitattributes

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+*.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
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*tfevents* 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

README.md

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+---
+title: ViTPose
+emoji: 📊
+colorFrom: yellow
+colorTo: indigo
+sdk: gradio
+sdk_version: 3.21.0
+app_file: app.py
+pinned: false
+duplicated_from: Gradio-Blocks/ViTPose
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference

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 pathlib
+import tarfile
+
+import gradio as gr
+
+from model import AppDetModel, AppPoseModel
+
+DESCRIPTION = '# [ViTPose](https://github.com/ViTAE-Transformer/ViTPose)'
+
+
+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')
+
+
+extract_tar()
+
+det_model = AppDetModel()
+pose_model = AppPoseModel()
+
+with gr.Blocks(css='style.css') as demo:
+    gr.Markdown(DESCRIPTION)
+
+    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(
+                        label='Detector',
+                        choices=list(det_model.MODEL_DICT.keys()),
+                        value=det_model.model_name)
+                with gr.Row():
+                    detect_button = gr.Button('Detect')
+                    det_preds = gr.Variable()
+            with gr.Column():
+                with gr.Row():
+                    detection_visualization = gr.Image(
+                        label='Detection Result',
+                        type='numpy',
+                        elem_id='det-result')
+                with gr.Row():
+                    vis_det_score_threshold = gr.Slider(
+                        label='Visualization Score Threshold',
+                        minimum=0,
+                        maximum=1,
+                        step=0.05,
+                        value=0.5)
+                with gr.Row():
+                    redraw_det_button = gr.Button(value='Redraw')
+
+        with gr.Row():
+            paths = sorted(pathlib.Path('images').rglob('*.jpg'))
+            example_images = gr.Examples(examples=[[path.as_posix()]
+                                                   for path in paths],
+                                         inputs=input_image)
+
+    with gr.Box():
+        gr.Markdown('## Step 2')
+        with gr.Row():
+            with gr.Column():
+                with gr.Row():
+                    pose_model_name = gr.Dropdown(
+                        label='Pose Model',
+                        choices=list(pose_model.MODEL_DICT.keys()),
+                        value=pose_model.model_name)
+                det_score_threshold = gr.Slider(label='Box Score Threshold',
+                                                minimum=0,
+                                                maximum=1,
+                                                step=0.05,
+                                                value=0.5)
+                with gr.Row():
+                    predict_button = gr.Button('Predict')
+                    pose_preds = gr.Variable()
+            with gr.Column():
+                with gr.Row():
+                    pose_visualization = gr.Image(label='Result',
+                                                  type='numpy',
+                                                  elem_id='pose-result')
+                with gr.Row():
+                    vis_kpt_score_threshold = gr.Slider(
+                        label='Visualization Score Threshold',
+                        minimum=0,
+                        maximum=1,
+                        step=0.05,
+                        value=0.3)
+                with gr.Row():
+                    vis_dot_radius = gr.Slider(label='Dot Radius',
+                                               minimum=1,
+                                               maximum=10,
+                                               step=1,
+                                               value=4)
+                with gr.Row():
+                    vis_line_thickness = gr.Slider(label='Line Thickness',
+                                                   minimum=1,
+                                                   maximum=10,
+                                                   step=1,
+                                                   value=2)
+                with gr.Row():
+                    redraw_pose_button = gr.Button('Redraw')
+
+    detector_name.change(fn=det_model.set_model,
+                         inputs=detector_name,
+                         outputs=None)
+    detect_button.click(fn=det_model.run,
+                        inputs=[
+                            detector_name,
+                            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,
+                           inputs=pose_model_name,
+                           outputs=None)
+    predict_button.click(fn=pose_model.run,
+                         inputs=[
+                             pose_model_name,
+                             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)
+
+demo.queue(api_open=False).launch()

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

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mmdet_configs/README.md

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+`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

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5d2091e07da6b74a6cd694e895b653485f7ce9d5d17738a415ca77a56940b989
+size 3389440

model.py

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+from __future__ import annotations
+
+import os
+import pathlib
+import shlex
+import subprocess
+import sys
+
+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.run(shlex.split('pip uninstall -y opencv-python'))
+    subprocess.run(shlex.split('pip uninstall -y opencv-python-headless'))
+    subprocess.run(shlex.split('pip install opencv-python-headless==4.5.5.64'))
+
+import huggingface_hub
+import numpy as np
+import torch
+import torch.nn as nn
+
+app_dir = pathlib.Path(__file__).parent
+submodule_dir = app_dir / 'ViTPose'
+sys.path.insert(0, submodule_dir.as_posix())
+
+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)
+
+HF_TOKEN = os.getenv('HF_TOKEN')
+
+
+class DetModel:
+    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',
+        },
+    }
+
+    def __init__(self):
+        self.device = torch.device(
+            'cuda:0' if torch.cuda.is_available() else 'cpu')
+        self._load_all_models_once()
+        self.model_name = 'YOLOX-l'
+        self.model = self._load_model(self.model_name)
+
+    def _load_all_models_once(self) -> None:
+        for name in self.MODEL_DICT:
+            self._load_model(name)
+
+    def _load_model(self, name: str) -> nn.Module:
+        dic = self.MODEL_DICT[name]
+        return init_detector(dic['config'], dic['model'], device=self.device)
+
+    def set_model(self, name: str) -> None:
+        if name == self.model_name:
+            return
+        self.model_name = name
+        self.model = self._load_model(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
+        out = inference_detector(self.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)] * 79
+
+        image = image[:, :, ::-1]  # RGB -> BGR
+        vis = self.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 AppDetModel(DetModel):
+    def run(self, model_name: str, image: np.ndarray,
+            score_threshold: float) -> tuple[list[np.ndarray], np.ndarray]:
+        self.set_model(model_name)
+        return self.detect_and_visualize(image, score_threshold)
+
+
+class PoseModel:
+    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',
+        },
+    }
+
+    def __init__(self):
+        self.device = torch.device(
+            'cuda:0' if torch.cuda.is_available() else 'cpu')
+        self.model_name = 'ViTPose-B (multi-task train, COCO)'
+        self.model = self._load_model(self.model_name)
+
+    def _load_all_models_once(self) -> None:
+        for name in self.MODEL_DICT:
+            self._load_model(name)
+
+    def _load_model(self, name: str) -> nn.Module:
+        dic = self.MODEL_DICT[name]
+        ckpt_path = huggingface_hub.hf_hub_download('hysts/ViTPose',
+                                                    dic['model'],
+                                                    use_auth_token=HF_TOKEN)
+        model = init_pose_model(dic['config'], ckpt_path, device=self.device)
+        return model
+
+    def set_model(self, name: str) -> None:
+        if name == self.model_name:
+            return
+        self.model_name = name
+        self.model = self._load_model(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
+        person_results = process_mmdet_results(det_results, 1)
+        out, _ = inference_top_down_pose_model(self.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
+        vis = vis_pose_result(self.model,
+                              image,
+                              pose_results,
+                              kpt_score_thr=kpt_score_threshold,
+                              radius=vis_dot_radius,
+                              thickness=vis_line_thickness)
+        return vis[:, :, ::-1]  # BGR -> RGB
+
+
+class AppPoseModel(PoseModel):
+    def run(
+        self, model_name: str, 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]:
+        self.set_model(model_name)
+        return self.predict_pose_and_visualize(image, det_results,
+                                               box_score_threshold,
+                                               kpt_score_threshold,
+                                               vis_dot_radius,
+                                               vis_line_thickness)

requirements.txt

@@ -0,0 +1,9 @@
+mmcv-full==1.5.0
+mmdet==2.24.1
+mmpose==0.25.1
+numpy==1.23.5
+opencv-python-headless==4.5.5.64
+openmim==0.1.5
+timm==0.5.4
+torch==1.11.0
+torchvision==0.12.0

style.css

@@ -0,0 +1,15 @@
+h1 {
+  text-align: center;
+}
+div#det-result {
+  max-width: 600px;
+  max-height: 600px;
+}
+div#pose-result {
+  max-width: 600px;
+  max-height: 600px;
+}
+img#visitor-badge {
+  display: block;
+  margin: auto;
+}