Add files

.gitignore

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+images

app.py

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+#!/usr/bin/env python
+
+from __future__ import annotations
+
+import argparse
+import os
+import pathlib
+import subprocess
+import tarfile
+
+if os.environ.get('SYSTEM') == 'spaces':
+    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 mediapipe as mp
+import numpy as np
+
+mp_drawing = mp.solutions.drawing_utils
+mp_drawing_styles = mp.solutions.drawing_styles
+mp_pose = mp.solutions.pose
+
+TITLE = 'MediaPipe Human Pose Estimation'
+DESCRIPTION = 'https://google.github.io/mediapipe/'
+ARTICLE = None
+
+TOKEN = os.environ['TOKEN']
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--theme', type=str)
+    parser.add_argument('--live', action='store_true')
+    parser.add_argument('--share', action='store_true')
+    parser.add_argument('--port', type=int)
+    parser.add_argument('--disable-queue',
+                        dest='enable_queue',
+                        action='store_false')
+    parser.add_argument('--allow-flagging', type=str, default='never')
+    parser.add_argument('--allow-screenshot', action='store_true')
+    return parser.parse_args()
+
+
+def load_sample_images() -> list[pathlib.Path]:
+    image_dir = pathlib.Path('images')
+    if not image_dir.exists():
+        image_dir.mkdir()
+        dataset_repo = 'hysts/input-images'
+        filenames = ['002.tar']
+        for name in filenames:
+            path = huggingface_hub.hf_hub_download(dataset_repo,
+                                                   name,
+                                                   repo_type='dataset',
+                                                   use_auth_token=TOKEN)
+            with tarfile.open(path) as f:
+                f.extractall(image_dir.as_posix())
+    return sorted(image_dir.rglob('*.jpg'))
+
+
+def run(image: np.ndarray, model_complexity: int, enable_segmentation: bool,
+        min_detection_confidence: float, background_color: str) -> np.ndarray:
+    with mp_pose.Pose(
+            static_image_mode=True,
+            model_complexity=model_complexity,
+            enable_segmentation=enable_segmentation,
+            min_detection_confidence=min_detection_confidence) as pose:
+        results = pose.process(image)
+
+    res = image[:, :, ::-1].copy()
+    if enable_segmentation:
+        if background_color == 'white':
+            bg_color = 255
+        elif background_color == 'black':
+            bg_color = 0
+        elif background_color == 'green':
+            bg_color = (0, 255, 0)
+        else:
+            raise ValueError
+
+        if results.segmentation_mask is not None:
+            res[results.segmentation_mask <= 0.1] = bg_color
+        else:
+            res[:] = bg_color
+
+    mp_drawing.draw_landmarks(res,
+                              results.pose_landmarks,
+                              mp_pose.POSE_CONNECTIONS,
+                              landmark_drawing_spec=mp_drawing_styles.
+                              get_default_pose_landmarks_style())
+
+    return res[:, :, ::-1]
+
+
+def main():
+    args = parse_args()
+
+    model_complexities = list(range(3))
+    background_colors = ['white', 'black', 'green']
+
+    image_paths = load_sample_images()
+    examples = [[
+        path.as_posix(), model_complexities[1], True, 0.5, background_colors[0]
+    ] for path in image_paths]
+
+    gr.Interface(
+        run,
+        [
+            gr.inputs.Image(type='numpy', label='Input'),
+            gr.inputs.Radio(model_complexities,
+                            type='index',
+                            default=model_complexities[1],
+                            label='Model Complexity'),
+            gr.inputs.Checkbox(default=True, label='Enable Segmentation'),
+            gr.inputs.Slider(0,
+                             1,
+                             step=0.05,
+                             default=0.5,
+                             label='Minimum Detection Confidence'),
+            gr.inputs.Radio(background_colors,
+                            type='value',
+                            default=background_colors[0],
+                            label='Background Color'),
+        ],
+        gr.outputs.Image(type='numpy', label='Output'),
+        examples=examples,
+        title=TITLE,
+        description=DESCRIPTION,
+        article=ARTICLE,
+        theme=args.theme,
+        allow_screenshot=args.allow_screenshot,
+        allow_flagging=args.allow_flagging,
+        live=args.live,
+    ).launch(
+        enable_queue=args.enable_queue,
+        server_port=args.port,
+        share=args.share,
+    )
+
+
+if __name__ == '__main__':
+    main()

requirements.txt

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+mediapipe==0.8.9.1
+numpy==1.22.3
+opencv-python-headless==4.5.5.64