Create app.py

app.py

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+import gradio as gr
+import cv2
+import numpy as np
+from PIL import Image
+import torch
+from transformers import DetrImageProcessor, DetrForObjectDetection
+from collections import defaultdict
+import time
+import psutil
+import os
+
+# Load DETR model (optimized for CPU)
+print("Loading DETR model...")
+processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
+model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
+model.eval()
+
+# COCO class labels
+COCO_CLASSES = [
+    'N/A', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
+    'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'N/A',
+    'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse',
+    'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'N/A', 'backpack',
+    'umbrella', 'N/A', 'N/A', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis',
+    'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove',
+    'skateboard', 'surfboard', 'tennis racket', 'bottle', 'N/A', 'wine glass',
+    'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich',
+    'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake',
+    'chair', 'couch', 'potted plant', 'bed', 'N/A', 'dining table', 'N/A',
+    'N/A', 'toilet', 'N/A', 'tv', 'laptop', 'mouse', 'remote', 'keyboard',
+    'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'N/A',
+    'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush'
+]
+
+def get_available_memory():
+    """Get available system memory in GB"""
+    return psutil.virtual_memory().available / (1024 ** 3)
+
+def auto_adjust_confidence(image_size, num_objects_hint=None):
+    """Dynamically adjust confidence based on image complexity"""
+    pixels = image_size[0] * image_size[1]
+    
+    # Base confidence on image size
+    if pixels < 500000:  # Small image
+        base_confidence = 0.6
+    elif pixels < 2000000:  # Medium image
+        base_confidence = 0.65
+    else:  # Large image
+        base_confidence = 0.7
+    
+    return base_confidence
+
+def auto_calculate_frame_interval(total_frames, video_duration, available_memory_gb):
+    """Dynamically calculate optimal frame interval based on video properties and system resources"""
+    
+    # Base calculations
+    fps = total_frames / video_duration if video_duration > 0 else 30
+    
+    # Memory-based adjustment
+    if available_memory_gb < 2:
+        memory_factor = 3
+    elif available_memory_gb < 4:
+        memory_factor = 2
+    else:
+        memory_factor = 1
+    
+    # Duration-based adjustment
+    if video_duration < 10:
+        duration_factor = 1
+    elif video_duration < 30:
+        duration_factor = 2
+    elif video_duration < 60:
+        duration_factor = 3
+    else:
+        duration_factor = 4
+    
+    # Calculate optimal frames to process
+    target_frames = min(150, max(30, total_frames // (memory_factor * duration_factor)))
+    
+    # Calculate interval
+    interval = max(1, total_frames // target_frames)
+    
+    return interval, target_frames
+
+def detect_objects(image, confidence_threshold=None):
+    """Detect objects in a single image with dynamic confidence"""
+    # Convert to RGB if needed
+    if isinstance(image, np.ndarray):
+        image = Image.fromarray(image)
+    
+    # Auto-adjust confidence if not provided
+    if confidence_threshold is None:
+        confidence_threshold = auto_adjust_confidence(image.size)
+    
+    # Prepare image
+    inputs = processor(images=image, return_tensors="pt")
+    
+    # Inference
+    with torch.no_grad():
+        outputs = model(**inputs)
+    
+    # Post-process
+    target_sizes = torch.tensor([image.size[::-1]])
+    results = processor.post_process_object_detection(
+        outputs, target_sizes=target_sizes, threshold=confidence_threshold
+    )[0]
+    
+    return results, image, confidence_threshold
+
+def draw_boxes(image, results):
+    """Draw bounding boxes on image"""
+    img_array = np.array(image)
+    
+    detections = []
+    object_counts = defaultdict(int)
+    
+    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
+        box = [round(i, 2) for i in box.tolist()]
+        label_name = COCO_CLASSES[label.item()]
+        
+        if label_name != 'N/A':
+            # Draw rectangle
+            x1, y1, x2, y2 = map(int, box)
+            cv2.rectangle(img_array, (x1, y1), (x2, y2), (0, 255, 0), 2)
+            
+            # Add label
+            label_text = f"{label_name}: {score:.2f}"
+            cv2.putText(img_array, label_text, (x1, y1-10),
+                       cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
+            
+            detections.append(f"{label_name} ({score:.2%})")
+            object_counts[label_name] += 1
+    
+    return img_array, detections, object_counts
+
+def process_static_image(image):
+    """Process static image mode with auto-detection"""
+    if image is None:
+        return None, "Please upload an image"
+    
+    start_time = time.time()
+    
+    # Detect objects with auto-adjusted confidence
+    results, pil_image, used_confidence = detect_objects(image, confidence_threshold=None)
+    
+    # Draw boxes
+    annotated_image, detections, object_counts = draw_boxes(pil_image, results)
+    
+    processing_time = time.time() - start_time
+    
+    # Create detailed summary
+    if detections:
+        summary = f"### 🎯 Detection Results\n\n"
+        summary += f"**Found {len(detections)} objects in {processing_time:.2f} seconds**\n\n"
+        summary += f"*Auto-adjusted confidence threshold: {used_confidence:.2f}*\n\n"
+        summary += "#### Detected Objects:\n"
+        
+        # Group by object type
+        for obj_name, count in sorted(object_counts.items(), key=lambda x: x[1], reverse=True):
+            summary += f"- **{obj_name}**: {count} instance(s)\n"
+        
+        summary += f"\n#### All Detections:\n"
+        for i, d in enumerate(detections, 1):
+            summary += f"{i}. {d}\n"
+    else:
+        summary = f"### ⚠️ No objects detected\n\n"
+        summary += f"*Confidence threshold used: {used_confidence:.2f}*\n\n"
+        summary += "Try uploading a different image with more visible objects."
+    
+    return annotated_image, summary
+
+def process_video(video_path, progress=gr.Progress()):
+    """Process video mode with full auto-adjustment"""
+    if video_path is None:
+        return None, "Please upload a video"
+    
+    progress(0, desc="Analyzing video...")
+    
+    cap = cv2.VideoCapture(video_path)
+    
+    # Get video properties
+    fps = int(cap.get(cv2.CAP_PROP_FPS))
+    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    duration = total_frames / fps if fps > 0 else 0
+    
+    # Get available memory
+    available_memory = get_available_memory()
+    
+    # Auto-calculate optimal frame interval
+    frame_interval, estimated_frames = auto_calculate_frame_interval(
+        total_frames, duration, available_memory
+    )
+    
+    progress(0.1, desc=f"Processing video (sampling every {frame_interval} frames)...")
+    
+    # Auto-adjust confidence based on video properties
+    frame_size = width * height
+    confidence_threshold = auto_adjust_confidence((width, height))
+    
+    # Output video writer
+    output_path = "output_video.mp4"
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+    out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
+    
+    frame_count = 0
+    processed_count = 0
+    object_tracker = defaultdict(int)
+    start_time = time.time()
+    
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+        
+        # Update progress
+        if frame_count % 30 == 0:
+            progress_pct = (frame_count / total_frames) * 0.8 + 0.1
+            progress(progress_pct, desc=f"Processing frame {frame_count}/{total_frames}")
+        
+        # Process every nth frame
+        if frame_count % frame_interval == 0:
+            # Convert BGR to RGB
+            rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            
+            # Detect objects
+            results, _, _ = detect_objects(rgb_frame, confidence_threshold)
+            
+            # Draw boxes
+            for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
+                box = [round(i, 2) for i in box.tolist()]
+                label_name = COCO_CLASSES[label.item()]
+                
+                if label_name != 'N/A':
+                    x1, y1, x2, y2 = map(int, box)
+                    cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
+                    
+                    label_text = f"{label_name}: {score:.2f}"
+                    cv2.putText(frame, label_text, (x1, y1-10),
+                               cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
+                    
+                    object_tracker[label_name] += 1
+            
+            processed_count += 1
+        
+        out.write(frame)
+        frame_count += 1
+    
+    cap.release()
+    out.release()
+    
+    processing_time = time.time() - start_time
+    
+    progress(1.0, desc="Complete!")
+    
+    # Create detailed summary
+    summary = f"### 🎬 Video Processing Complete\n\n"
+    summary += f"**Processing Time**: {processing_time:.2f} seconds\n\n"
+    summary += "#### Video Information:\n"
+    summary += f"- Duration: {duration:.2f} seconds\n"
+    summary += f"- Total frames: {total_frames}\n"
+    summary += f"- FPS: {fps}\n"
+    summary += f"- Resolution: {width}x{height}\n\n"
+    
+    summary += "#### Auto-Optimization Settings:\n"
+    summary += f"- Confidence threshold: {confidence_threshold:.2f} *(auto-adjusted)*\n"
+    summary += f"- Frame interval: Every {frame_interval} frame(s) *(auto-calculated)*\n"
+    summary += f"- Frames processed: {processed_count}/{total_frames}\n"
+    summary += f"- Available memory: {available_memory:.2f} GB\n\n"
+    
+    if object_tracker:
+        summary += "### 📊 Detected Objects Across Video:\n\n"
+        for obj, count in sorted(object_tracker.items(), key=lambda x: x[1], reverse=True):
+            summary += f"- **{obj}**: {count} detection(s)\n"
+    else:
+        summary += "⚠️ No objects detected in the video.\n"
+        summary += "This might be due to low lighting, fast motion, or absence of recognizable objects."
+    
+    return output_path, summary
+
+# Create Gradio interface
+with gr.Blocks(title="AI Object Recognition System", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""
+    # 🤖 AI Object Recognition System
+    ### Intelligent Auto-Adjusting Detection & Tracking
+    
+    This system **automatically optimizes** detection parameters based on:
+    - Image/video size and complexity
+    - Available system resources
+    - Video duration and frame rate
+    
+    **No manual tuning required!**
+    """)
+    
+    with gr.Tabs():
+        # Static Image Tab
+        with gr.Tab("📸 Static Mode - Image Detection"):
+            gr.Markdown("""
+            ### Automatic Image Analysis
+            Upload any image and the system will:
+            - Auto-adjust confidence thresholds
+            - Detect all visible objects
+            - Provide detailed statistics
+            """)
+            
+            with gr.Row():
+                with gr.Column():
+                    static_input = gr.Image(type="numpy", label="Upload Image")
+                    static_btn = gr.Button("🔍 Auto-Detect Objects", variant="primary", size="lg")
+                    gr.Markdown("*The system will automatically optimize detection settings*")
+                
+                with gr.Column():
+                    static_output = gr.Image(label="Detected Objects")
+                    static_summary = gr.Markdown(label="Detection Results")
+            
+            static_btn.click(
+                fn=process_static_image,
+                inputs=[static_input],
+                outputs=[static_output, static_summary]
+            )
+            
+            gr.Examples(
+                examples=[],
+                inputs=static_input,
+                label="Try these examples (upload your own images)"
+            )
+        
+        # Dynamic Video Tab
+        with gr.Tab("🎥 Dynamic Mode - Video Detection"):
+            gr.Markdown("""
+            ### Automatic Video Analysis
+            Upload a video and the system will:
+            - Auto-calculate optimal frame sampling
+            - Adjust confidence based on video quality
+            - Optimize for available CPU resources
+            - Track objects across frames
+            
+            **Supports videos of any length!** The system automatically scales processing.
+            """)
+            
+            with gr.Row():
+                with gr.Column():
+                    video_input = gr.Video(label="Upload Video")
+                    video_btn = gr.Button("🎬 Auto-Process Video", variant="primary", size="lg")
+                    gr.Markdown("""
+                    *The system will automatically:*
+                    - Analyze video properties
+                    - Calculate optimal frame sampling
+                    - Adjust detection thresholds
+                    - Monitor system resources
+                    """)
+                
+                with gr.Column():
+                    video_output = gr.Video(label="Processed Video with Detections")
+                    video_summary = gr.Markdown(label="Processing Results")
+            
+            video_btn.click(
+                fn=process_video,
+                inputs=[video_input],
+                outputs=[video_output, video_summary]
+            )
+    
+    gr.Markdown("""
+    ---
+    ## 🧠 How Auto-Adjustment Works
+    
+    ### Image Mode:
+    - **Small images** (< 500K pixels): Lower confidence threshold for more detections
+    - **Large images** (> 2M pixels): Higher threshold to reduce false positives
+    
+    ### Video Mode:
+    - **Short videos** (< 10s): Process more frames for detail
+    - **Long videos** (> 60s): Smart sampling to maintain performance
+    - **Memory-aware**: Adjusts based on available RAM
+    - **Quality-adaptive**: Balances speed vs accuracy automatically
+    
+    ### 📊 Technical Details:
+    - **Model**: DETR ResNet-50 (Detection Transformer)
+    - **Dataset**: COCO (80+ object categories)
+    - **Optimization**: CPU-friendly with intelligent resource management
+    - **Supported Objects**: People, vehicles, animals, furniture, electronics, food, and more
+    
+    ### 💡 Tips:
+    - The system works best with clear, well-lit images/videos
+    - All adjustments happen automatically - just upload and click!
+    - Processing time varies based on video length and system resources
+    """)
+
+if __name__ == "__main__":
+    demo.launch()