app.py

import gradio as gr
from PIL import Image, ImageDraw, ImageFont
import cv2
import numpy as np
import pandas as pd
import tempfile
import sys
import os
from huggingface_hub import hf_hub_download

print("="*60)
print("Setting up RF-DETR SoccerNet Model...")
print("="*60)

repo_id = "julianzu9612/RFDETR-Soccernet"

try:
    # Download inference.py
    print("\nDownloading inference.py...")
    inference_path = hf_hub_download(repo_id=repo_id, filename="inference.py")
    
    # Read the file
    with open(inference_path, 'r') as f:
        inference_code = f.read()
    
    print("\n🔧 Patching inference.py...")
    print("   Changing: RFDETRBase() → RFDETRLarge()")
    
    # THE FIX: Replace RFDETRBase with RFDETRLarge
    inference_code = inference_code.replace(
        'from rfdetr import RFDETRBase',
        'from rfdetr import RFDETRLarge'
    )
    inference_code = inference_code.replace(
        'self.model = RFDETRBase()',
        'self.model = RFDETRLarge()'
    )
    
    # Save the patched version
    with open(inference_path, 'w') as f:
        f.write(inference_code)
    print("✓ Patched inference.py successfully!")
    
    # Download weights
    print("\nDownloading model weights...")
    weights_path = hf_hub_download(repo_id=repo_id, filename="weights/checkpoint_best_regular.pth")
    print(f"✓ Downloaded weights")
    
    # Setup environment
    cache_dir = os.path.dirname(inference_path)
    
    if cache_dir not in sys.path:
        sys.path.insert(0, cache_dir)
    
    original_dir = os.getcwd()
    os.chdir(cache_dir)
    
    # Create weights directory structure
    weights_dir = os.path.join(cache_dir, "weights")
    os.makedirs(weights_dir, exist_ok=True)
    
    expected_weights = os.path.join(weights_dir, "checkpoint_best_regular.pth")
    if not os.path.exists(expected_weights):
        import shutil
        shutil.copy(weights_path, expected_weights)
        print(f"✓ Weights copied to: {expected_weights}")
    
    print("\n" + "="*60)
    print("Initializing RF-DETR SoccerNet Model...")
    print("="*60)
    
    # Import and initialize the patched model
    from inference import RFDETRSoccerNet
    
    detector = RFDETRSoccerNet()
    print("\n✅ Model loaded successfully!")
    
    os.chdir(original_dir)
    
except Exception as e:
    print(f"\n❌ Error: {e}")
    import traceback
    traceback.print_exc()
    raise

# Helper functions for Gradio
def draw_detections_on_image(image, df):
    """Draw bounding boxes on PIL image"""
    draw = ImageDraw.Draw(image)
    
    try:
        font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf", 16)
    except:
        font = ImageFont.load_default()
    
    colors = {
        'ball': (255, 0, 0),
        'player': (0, 255, 0),
        'referee': (255, 255, 0),
        'goalkeeper': (0, 0, 255)
    }
    
    for _, row in df.iterrows():
        x1, y1, x2, y2 = row['x1'], row['y1'], row['x2'], row['y2']
        class_name = row['class_name']
        conf = row['confidence']
        color = colors.get(class_name, (255, 255, 255))
        
        draw.rectangle([x1, y1, x2, y2], outline=color, width=3)
        
        text = f"{class_name}: {conf:.2f}"
        bbox = draw.textbbox((x1, y1-20), text, font=font)
        draw.rectangle([bbox[0]-2, bbox[1]-2, bbox[2]+2, bbox[3]+2], fill=color)
        draw.text((x1, y1-20), text, fill=(0, 0, 0), font=font)
    
    return image

def process_image_interface(image, confidence_threshold):
    """Process image with the model"""
    if image is None:
        return None, pd.DataFrame()
    
    try:
        # Save temporary image
        temp_path = tempfile.mktemp(suffix='.jpg')
        Image.fromarray(image if isinstance(image, np.ndarray) else np.array(image)).save(temp_path)
        
        # Process with model
        df = detector.process_image(temp_path, confidence_threshold=confidence_threshold)
        
        # Draw detections
        img = Image.open(temp_path)
        annotated_img = draw_detections_on_image(img, df)
        
        # Cleanup
        os.remove(temp_path)
        
        return annotated_img, df
        
    except Exception as e:
        print(f"Error processing image: {e}")
        import traceback
        traceback.print_exc()
        return None, pd.DataFrame()

def process_video_interface(video, confidence_threshold, frame_skip, max_frames):
    """Process video with the model"""
    if video is None:
        return None, pd.DataFrame()
    
    try:
        max_frames_val = int(max_frames) if max_frames > 0 else None
        
        # Process video
        print(f"Processing video with confidence={confidence_threshold}, frame_skip={frame_skip}, max_frames={max_frames_val}")
        df = detector.process_video(
            video,
            confidence_threshold=confidence_threshold,
            frame_skip=int(frame_skip),
            max_frames=max_frames_val
        )
        
        # Create annotated video
        cap = cv2.VideoCapture(video)
        fps = int(cap.get(cv2.CAP_PROP_FPS))
        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
        
        output_path = tempfile.mktemp(suffix='.mp4')
        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
        out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
        
        frame_num = 0
        while cap.isOpened():
            ret, frame = cap.read()
            if not ret:
                break
            
            # Get detections for this frame
            frame_detections = df[df['frame'] == frame_num]
            
            if not frame_detections.empty:
                rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
                pil_img = Image.fromarray(rgb_frame)
                annotated_pil = draw_detections_on_image(pil_img, frame_detections)
                frame = cv2.cvtColor(np.array(annotated_pil), cv2.COLOR_RGB2BGR)
            
            out.write(frame)
            frame_num += 1
        
        cap.release()
        out.release()
        
        return output_path, df
        
    except Exception as e:
        print(f"Error processing video: {e}")
        import traceback
        traceback.print_exc()
        return None, pd.DataFrame()

# Create Gradio interface
with gr.Blocks(title="⚽ Soccer Object Detection", theme=gr.themes.Soft()) as demo:
    gr.Markdown("""
    # ⚽ Soccer Object Detection with RF-DETR
    
    Professional-grade object detection for soccer videos using RF-DETR-Large model.
    
    ### Model: [julianzu9612/RFDETR-Soccernet](https://huggingface.co/julianzu9612/RFDETR-Soccernet)
    - **Architecture**: RF-DETR-Large (128M parameters)
    - **Performance**: 85.7% mAP@50, 49.8% mAP
    - **Dataset**: SoccerNet-Tracking 2023 (42,750 images)
    - **Classes**: Ball, Player, Referee, Goalkeeper
    """)
    
    with gr.Tab("📸 Image Detection"):
        gr.Markdown("### Upload a soccer image to detect objects")
        
        with gr.Row():
            with gr.Column():
                image_input = gr.Image(label="Upload Soccer Image", type="numpy")
                image_confidence = gr.Slider(
                    minimum=0.1, 
                    maximum=1.0, 
                    value=0.5, 
                    step=0.05, 
                    label="Confidence Threshold",
                    info="Lower values detect more objects but may include false positives"
                )
                image_button = gr.Button("🔍 Detect Objects", variant="primary", size="lg")
            
            with gr.Column():
                image_output = gr.Image(label="Detected Objects")
        
        image_detections = gr.Dataframe(
            label="Detection Results",
            wrap=True,
            interactive=False
        )
        
        image_button.click(
            fn=process_image_interface,
            inputs=[image_input, image_confidence],
            outputs=[image_output, image_detections]
        )
        
        gr.Examples(
            examples=[],
            inputs=image_input,
            label="Example Images (Upload your own!)"
        )
    
    with gr.Tab("🎥 Video Detection"):
        gr.Markdown("### Upload a soccer video to track objects frame by frame")
        
        with gr.Row():
            with gr.Column():
                video_input = gr.Video(label="Upload Soccer Video")
                video_confidence = gr.Slider(
                    minimum=0.1,
                    maximum=1.0,
                    value=0.5,
                    step=0.05,
                    label="Confidence Threshold"
                )
                video_frame_skip = gr.Slider(
                    minimum=1,
                    maximum=10,
                    value=5,
                    step=1,
                    label="Frame Skip",
                    info="Process every Nth frame (higher = faster but less detections)"
                )
                video_max_frames = gr.Number(
                    value=300,
                    label="Max Frames to Process",
                    info="Set to 0 to process entire video (300 frames ≈ 10 seconds at 30 FPS)"
                )
                
                gr.Markdown("""
                #### ⚡ Performance Tips:
                - **CPU**: 2-3 FPS (slow) - Use frame_skip=5 and limit frames
                - **GPU**: 12-30 FPS (fast) - Can process full videos
                - **Quick test**: Use 300 frames with frame_skip=5
                """)
                
                video_button = gr.Button("🎬 Process Video", variant="primary", size="lg")
            
            with gr.Column():
                video_output = gr.Video(label="Annotated Video")
        
        video_detections = gr.Dataframe(
            label="Detection Results",
            wrap=True,
            interactive=False
        )
        
        video_button.click(
            fn=process_video_interface,
            inputs=[video_input, video_confidence, video_frame_skip, video_max_frames],
            outputs=[video_output, video_detections]
        )
    
    with gr.Tab("ℹ️ About"):
        gr.Markdown("""
        ## About This Model
        
        ### 🎯 Detected Classes
        
        | Class | Color | Precision | Description |
        |-------|-------|-----------|-------------|
        | 🔴 Ball | Red | 78.5% | Soccer ball detection |
        | 🟢 Player | Green | 91.3% | Field players from both teams |
        | 🟡 Referee | Yellow | 85.2% | Match officials |
        | 🔵 Goalkeeper | Blue | 88.9% | Specialized goalkeeper detection |
        
        ### 📊 Model Performance
        
        - **mAP@50**: 85.7%
        - **mAP**: 49.8%
        - **mAP@75**: 52.0%
        - **Parameters**: 128M
        - **Training Time**: ~14 hours on NVIDIA A100 40GB
        
        ### 🎓 Training Details
        
        - **Dataset**: SoccerNet-Tracking 2023
        - **Images**: 42,750 annotated images
        - **Source**: Professional soccer broadcasts
        - **Input Resolution**: 1280x1280 pixels
        - **Optimizer**: AdamW (lr=1e-4)
        
        ### 💡 Best Practices
        
        1. **Confidence Threshold**: 
           - Use 0.5 for general detection
           - Use 0.7+ for high-precision applications
        
        2. **Video Quality**:
           - Works best on 720p+ broadcast footage
           - Standard broadcast camera angles preferred
        
        3. **Frame Processing**:
           - frame_skip=1: Every frame (best accuracy, slow)
           - frame_skip=5: Every 5th frame (good balance)
           - frame_skip=10: Every 10th frame (fast, lower accuracy)
        
        ### 🚨 Limitations
        
        - Optimized for professional broadcast footage
        - May have reduced accuracy in poor lighting
        - Small balls may be missed when heavily occluded
        - Camera angle dependency
        
        ### 📚 Use Cases
        
        - **Sports Analytics**: Player tracking, formation analysis
        - **Broadcast Enhancement**: Automatic highlighting, statistics overlay
        - **Research**: Tactical analysis, computer vision benchmarking
        - **Video Analytics**: Automated video processing pipelines
        
        ### 🔗 Links
        
        - [Model on Hugging Face](https://huggingface.co/julianzu9612/RFDETR-Soccernet)
        - [SoccerNet Dataset](https://www.soccer-net.org/)
        - [RF-DETR Paper](https://arxiv.org/abs/2304.08069)
        
        ### 📄 Citation
        
        ```bibtex
        @misc{rfdetr-soccernet-2025,
          title={RF-DETR SoccerNet: High-Performance Soccer Object Detection},
          author={Computer Vision Research Team},
          year={2025},
          publisher={Hugging Face},
          url={https://huggingface.co/julianzu9612/rf-detr-soccernet}
        }
        ```
        
        ---
        
        **License**: Apache 2.0
        """)

print("\n" + "="*60)
print("🚀 Launching Gradio Interface...")
print("="*60)

demo.launch()