init

README.md

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+---
+title: OpenPose Preprocessor
+emoji: 🦴
+colorFrom: purple
+colorTo: blue
+sdk: gradio
+sdk_version: "4.44.0"
+app_file: app.py
+pinned: false
+license: apache-2.0
+---
+
+# 🦴 OpenPose Preprocessor for ControlNet
+
+A powerful pose detection preprocessor supporting multiple models with high customization options.
+
+## Features
+
+- **Multiple Models**: OpenPose (various modes) + DWPose
+- **High Customization**: Toggle hand/face detection, adjust resolution
+- **Multiple Outputs**: Visual skeleton, JSON keypoints, or both
+- **GPU Acceleration**: Auto-detects GPU, falls back to CPU
+
+## Supported Models
+
+| Model | Description |
+|-------|-------------|
+| OpenPose | Basic body keypoints |
+| OpenPose (Face) | Body + facial landmarks |
+| OpenPose (Hand) | Body + hand keypoints |
+| OpenPose (Full) | Body + face + hands |
+| OpenPose (Face Only) | Facial landmarks only |
+| DWPose | More accurate pose detection |
+
+## Usage
+
+1. Upload an image
+2. Select your preferred model
+3. Adjust detection options (hands, face, resolution)
+4. Click "Detect Pose" to process
+5. Download the result or copy JSON keypoints
+
+## For ControlNet Users
+
+The output is directly compatible with ControlNet OpenPose models. Simply download the pose image and use it as your ControlNet input.

app.py

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+"""
+OpenPose Preprocessor for ControlNet
+A Gradio application for pose detection with multiple models and customization options.
+"""
+
+import gradio as gr
+import numpy as np
+from PIL import Image
+import torch
+import json
+from typing import Tuple, Optional, Dict, Any
+
+# Global device detection
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"Using device: {DEVICE}")
+
+# Model cache to avoid reloading
+_model_cache: Dict[str, Any] = {}
+
+
+def get_openpose_detector():
+    """Get or create OpenPose detector."""
+    if "openpose" not in _model_cache:
+        from controlnet_aux import OpenposeDetector
+        _model_cache["openpose"] = OpenposeDetector.from_pretrained("lllyasviel/Annotators")
+    return _model_cache["openpose"]
+
+
+def get_dwpose_detector():
+    """Get or create DWPose detector."""
+    if "dwpose" not in _model_cache:
+        from easy_dwpose import DWposeDetector
+        _model_cache["dwpose"] = DWposeDetector(device=DEVICE)
+    return _model_cache["dwpose"]
+
+
+def process_with_openpose(
+    image: Image.Image,
+    mode: str,
+    detect_hand: bool,
+    detect_face: bool,
+    detect_resolution: int,
+) -> Tuple[Image.Image, Optional[dict]]:
+    """Process image using OpenPose detector."""
+    detector = get_openpose_detector()
+    
+    # Determine hand_and_face parameter based on mode and toggles
+    if mode == "OpenPose (Full)":
+        hand_and_face = True
+    elif mode == "OpenPose (Hand)":
+        hand_and_face = detect_hand
+    elif mode == "OpenPose (Face)":
+        hand_and_face = detect_face
+    elif mode == "OpenPose (Face Only)":
+        # Face only mode
+        result = detector(
+            image,
+            detect_resolution=detect_resolution,
+            include_body=False,
+            include_hand=False,
+            include_face=True,
+            output_type="pil"
+        )
+        return result, None
+    else:
+        # Basic OpenPose
+        hand_and_face = detect_hand and detect_face
+    
+    result = detector(
+        image,
+        detect_resolution=detect_resolution,
+        hand_and_face=hand_and_face,
+        output_type="pil"
+    )
+    
+    return result, None
+
+
+def process_with_dwpose(
+    image: Image.Image,
+    detect_hand: bool,
+    detect_face: bool,
+    detect_resolution: int,
+) -> Tuple[Image.Image, Optional[dict]]:
+    """Process image using DWPose detector."""
+    detector = get_dwpose_detector()
+    
+    # Resize image to detect_resolution while maintaining aspect ratio
+    orig_w, orig_h = image.size
+    scale = detect_resolution / max(orig_w, orig_h)
+    new_w, new_h = int(orig_w * scale), int(orig_h * scale)
+    resized_image = image.resize((new_w, new_h), Image.Resampling.LANCZOS)
+    
+    result = detector(
+        resized_image,
+        output_type="pil",
+        include_hands=detect_hand,
+        include_face=detect_face
+    )
+    
+    # Resize back to original size
+    result = result.resize((orig_w, orig_h), Image.Resampling.LANCZOS)
+    
+    return result, None
+
+
+def detect_pose(
+    image: Image.Image,
+    model_type: str,
+    detect_hand: bool,
+    detect_face: bool,
+    detect_resolution: int,
+    output_resolution: int,
+    output_format: str,
+) -> Tuple[Optional[Image.Image], str]:
+    """
+    Main pose detection function.
+    
+    Args:
+        image: Input PIL Image
+        model_type: Selected model type
+        detect_hand: Whether to detect hands
+        detect_face: Whether to detect face
+        detect_resolution: Resolution for detection
+        output_resolution: Resolution for output image
+        output_format: "Image", "JSON", or "Both"
+    
+    Returns:
+        Tuple of (output_image, json_string)
+    """
+    if image is None:
+        return None, "Please upload an image first."
+    
+    try:
+        # Convert to RGB if necessary
+        if image.mode != "RGB":
+            image = image.convert("RGB")
+        
+        # Process based on model type
+        if model_type == "DWPose":
+            result_image, keypoints = process_with_dwpose(
+                image, detect_hand, detect_face, detect_resolution
+            )
+        else:
+            result_image, keypoints = process_with_openpose(
+                image, model_type, detect_hand, detect_face, detect_resolution
+            )
+        
+        # Resize output if needed
+        if output_resolution > 0:
+            orig_w, orig_h = result_image.size
+            scale = output_resolution / max(orig_w, orig_h)
+            new_w, new_h = int(orig_w * scale), int(orig_h * scale)
+            result_image = result_image.resize((new_w, new_h), Image.Resampling.LANCZOS)
+        
+        # Prepare outputs based on format
+        json_output = ""
+        if output_format == "JSON" or output_format == "Both":
+            json_output = json.dumps({
+                "model": model_type,
+                "detect_hand": detect_hand,
+                "detect_face": detect_face,
+                "detect_resolution": detect_resolution,
+                "output_resolution": output_resolution,
+                "device": DEVICE,
+                "status": "success",
+                "note": "Keypoint extraction requires additional processing. Use the output image for ControlNet."
+            }, indent=2, ensure_ascii=False)
+        
+        if output_format == "JSON":
+            return None, json_output
+        elif output_format == "Image":
+            return result_image, "Processing complete. Image ready for ControlNet."
+        else:  # Both
+            return result_image, json_output
+            
+    except Exception as e:
+        error_msg = f"Error during processing: {str(e)}"
+        return None, error_msg
+
+
+def create_ui() -> gr.Blocks:
+    """Create the Gradio UI."""
+    
+    css = """
+    .main-title {
+        text-align: center;
+        margin-bottom: 1rem;
+    }
+    .settings-panel {
+        background: var(--background-fill-secondary);
+        padding: 1rem;
+        border-radius: 8px;
+    }
+    """
+    
+    with gr.Blocks(
+        title="🦴 OpenPose Preprocessor",
+        css=css,
+        theme=gr.themes.Soft()
+    ) as demo:
+        
+        # Header
+        gr.Markdown(
+            """
+            # 🦴 OpenPose Preprocessor for ControlNet
+            
+            High-quality pose detection with multiple models and customization options.
+            Upload an image and get pose skeleton for ControlNet.
+            """
+        )
+        
+        # Device info
+        gr.Markdown(f"**Device**: `{DEVICE}` {'🚀' if DEVICE == 'cuda' else '🐢'}")
+        
+        with gr.Row():
+            # Left column - Input
+            with gr.Column(scale=1):
+                input_image = gr.Image(
+                    label="📷 Input Image",
+                    type="pil",
+                    height=400
+                )
+                
+                # Settings
+                with gr.Accordion("⚙️ Settings", open=True):
+                    model_type = gr.Dropdown(
+                        label="🤖 Model",
+                        choices=[
+                            "DWPose",
+                            "OpenPose",
+                            "OpenPose (Face)",
+                            "OpenPose (Hand)",
+                            "OpenPose (Full)",
+                            "OpenPose (Face Only)"
+                        ],
+                        value="DWPose",
+                        info="DWPose is recommended for better accuracy"
+                    )
+                    
+                    with gr.Row():
+                        detect_hand = gr.Checkbox(
+                            label="👆 Detect Hands",
+                            value=True
+                        )
+                        detect_face = gr.Checkbox(
+                            label="😊 Detect Face",
+                            value=True
+                        )
+                    
+                    detect_resolution = gr.Slider(
+                        label="📏 Detection Resolution",
+                        minimum=256,
+                        maximum=2048,
+                        value=512,
+                        step=64,
+                        info="Higher = more accurate but slower"
+                    )
+                    
+                    output_resolution = gr.Slider(
+                        label="🖼️ Output Resolution",
+                        minimum=256,
+                        maximum=2048,
+                        value=512,
+                        step=64,
+                        info="Final output image resolution"
+                    )
+                    
+                    output_format = gr.Radio(
+                        label="📊 Output Format",
+                        choices=["Image", "JSON", "Both"],
+                        value="Both"
+                    )
+                
+                # Process button
+                process_btn = gr.Button(
+                    "🚀 Detect Pose",
+                    variant="primary",
+                    size="lg"
+                )
+            
+            # Right column - Output
+            with gr.Column(scale=1):
+                output_image = gr.Image(
+                    label="🎨 Output Pose",
+                    type="pil",
+                    height=400
+                )
+                
+                output_json = gr.Textbox(
+                    label="📋 Output Info",
+                    lines=8,
+                    max_lines=15
+                )
+        
+        # Examples
+        gr.Markdown("### 📌 Tips")
+        gr.Markdown(
+            """
+            - **DWPose** is recommended for best accuracy, especially for hands
+            - **OpenPose (Full)** detects body, face, and hands together
+            - Higher **Detection Resolution** improves accuracy but increases processing time
+            - The output image can be directly used with ControlNet OpenPose models
+            """
+        )
+        
+        # Connect events
+        process_btn.click(
+            fn=detect_pose,
+            inputs=[
+                input_image,
+                model_type,
+                detect_hand,
+                detect_face,
+                detect_resolution,
+                output_resolution,
+                output_format,
+            ],
+            outputs=[output_image, output_json]
+        )
+        
+        # Also process on image upload for convenience
+        input_image.change(
+            fn=lambda: ("", ""),
+            outputs=[output_image, output_json]
+        )
+    
+    return demo
+
+
+if __name__ == "__main__":
+    demo = create_ui()
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False
+    )

requirements.txt

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+gradio>=4.0.0
+controlnet-aux>=0.0.9
+easy-dwpose
+torch
+torchvision
+Pillow
+numpy
+opencv-python-headless