app0.py

import gradio as gr
import cv2
import numpy as np
import os
import sys
from ultralytics import YOLO
from PIL import Image
import time

print("Python version:", sys.version)
print("Gradio version:", gr.__version__)


class SimpleObjectDetector:
    def __init__(self):
        """Initialize YOLO11n general object detector"""
        self.model = None
        try:
            self.model = YOLO('yolo11n.pt')
            print("✅ YOLO11n model initialization complete")
            print("📦 Can detect 80 object classes: person, car, animals, etc.")
        except Exception as e:
            import traceback 
            print(f"⚠️ Model initialization error: {e}")
            traceback.print_exc() 
            print("🔄 Running in dummy mode")

    def detect(self, image, conf_threshold=0.25):
        """Object detection process"""
        if image is None:
            return None, []
        
        if self.model is None:
            # Dummy processing
            result = image.copy()
            cv2.putText(result, "MODEL NOT FOUND", (50, 100), 
                        cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
            return result, []
        
        try:
            # Run inference
            results = self.model(image, conf=conf_threshold)
            
            detections = []
            if len(results) > 0:
                annotated = results[0].plot()
                
                # Get detection details
                for box in results[0].boxes:
                    class_id = int(box.cls[0])
                    class_name = results[0].names[class_id]
                    confidence = float(box.conf[0])
                    detections.append({
                        'class': class_name,
                        'confidence': confidence
                    })
                
                return annotated, detections
            
            return image, []
        except Exception as e:
            print(f"Detection Error: {e}")
            return image, []

# Create instance
detector = SimpleObjectDetector()

def process_image(image, conf_threshold):
    """Image processing function"""
    if image is None:
        return None, "Please upload an image"
    
    # Convert from RGB (Gradio) to BGR (OpenCV)
    if len(image.shape) == 3:
        if image.shape[2] == 4:  # If RGBA
            image = cv2.cvtColor(image, cv2.COLOR_RGBA2RGB)
        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
    
    # Run object detection
    start_time = time.time()
    result, detections = detector.detect(image, conf_threshold)
    processing_time = time.time() - start_time
    
    # Convert back to RGB
    if result is not None:
        result = cv2.cvtColor(result, cv2.COLOR_BGR2RGB)
    
    # Format detection results
    if len(detections) > 0:
        result_text = f"✅ Detected {len(detections)} object(s):\n\n"
        for i, det in enumerate(detections, 1):
            result_text += f"{i}. {det['class'].upper()} - Confidence: {det['confidence']*100:.1f}%\n"
        result_text += f"\n⏱️ Processing time: {processing_time:.2f} seconds"
    else:
        result_text = "❌ No objects detected\n\nTry:\n- Adjusting confidence threshold\n- Using a clearer image\n- Getting closer to objects"
    
    return result, result_text

def flip_image(image):
    """Flip image horizontally"""
    if image is None:
        return None
    
    if isinstance(image, Image.Image):
        image = np.array(image)
    
    return cv2.flip(image, 1)

def rotate_image(image, angle):
    """Rotate image by specified angle"""
    if image is None:
        return None
    
    if isinstance(image, Image.Image):
        image = np.array(image)
    
    height, width = image.shape[:2]
    center = (width // 2, height // 2)
    rotation_matrix = cv2.getRotationMatrix2D(center, angle, 1.0)
    rotated = cv2.warpAffine(image, rotation_matrix, (width, height))
    
    return rotated

def adjust_brightness_contrast(image, bright_val, contrast_val):
    """Adjust image brightness and contrast"""
    if image is None:
        return None
    
    if isinstance(image, Image.Image):
        image = np.array(image)
    
    img_float = image.astype(np.float32) / 255.0
    adjusted = img_float * contrast_val + (bright_val - 1.0)
    adjusted = np.clip(adjusted, 0, 1)
    adjusted = (adjusted * 255).astype(np.uint8)
    
    return adjusted

def generate_test_image():
    """Generate test image with sample objects"""
    img = np.ones((480, 640, 3), dtype=np.uint8) * 230
    
    # Draw sample shapes
    cv2.rectangle(img, (100, 150), (200, 300), (50, 50, 200), -1)  # "car-like" shape
    cv2.circle(img, (400, 200), 50, (200, 50, 50), -1)  # circle
    
    cv2.putText(img, "TEST IMAGE", (200, 50), 
               cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2)
    cv2.putText(img, "Click 'Detect Objects' to test", (150, 400),
               cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 0), 2)
    
    return img

# Create Gradio Interface
with gr.Blocks(title="YOLO11n Object Detection - Smartphone Friendly", theme=gr.themes.Soft()) as demo:
    gr.Markdown("# 📱🔍 YOLO11n Object Detection")
    gr.Markdown("### Detect 80 types of objects: people, vehicles, animals, and more!")
    
    with gr.Row():
        with gr.Column(scale=1):
            # Instructions
            gr.Markdown("""
            ## 📋 How to Use:
            1. **Upload** an image or **Take Photo** (mobile)
            2. Adjust **confidence threshold** if needed
            3. Click **🚀 Detect Objects**
            
            **Detectable Objects:**
            - 🚗 Vehicles (car, truck, bus, motorcycle, bicycle)
            - 🧍 People and body parts
            - 🐕 Animals (dog, cat, bird, horse, etc.)
            - ⚽ Sports equipment
            - 🪑 Furniture and household items
            - And 60+ more categories!
            """)
            
            # Image input
            with gr.Group():
                gr.Markdown("### 📸 Capture or Upload Image")
                image_input = gr.Image(
                    label="Input Image",
                    type="numpy",
                    sources=["upload"],
                    interactive=True
                )
                
                # Confidence threshold
                conf_slider = gr.Slider(
                    0.1, 0.9, 
                    value=0.25, 
                    step=0.05,
                    label="🎯 Confidence Threshold",
                    info="Lower = more detections (may include false positives)"
                )
                
                # Image manipulation controls
                with gr.Accordion("🔄 Image Adjustments", open=False):
                    with gr.Row():
                        flip_btn = gr.Button("🪞 Flip", size="sm")
                        rotate_90_btn = gr.Button("↪️ Rotate 90°", size="sm")
                        rotate_180_btn = gr.Button("🔄 Rotate 180°", size="sm")
                    
                    brightness = gr.Slider(0.5, 2.0, value=1.0, label="☀️ Brightness")
                    contrast = gr.Slider(0.5, 2.0, value=1.0, label="🎨 Contrast")
            
        with gr.Column(scale=1):
            # Detection results
            gr.Markdown("## 🔍 Detection Results")
            output_image = gr.Image(label="Detected Objects", interactive=False)
            
            # Detection button
            detect_btn = gr.Button(
                "🚀 Detect Objects", 
                variant="primary", 
                size="lg"
            )
            
            # Results text
            results_text = gr.Textbox(
                label="📊 Detection Details",
                lines=10,
                interactive=False
            )
    
    # Test section
    with gr.Accordion("🧪 Test & Examples", open=False):
        with gr.Row():
            test_btn = gr.Button("Generate Test Image")
        
        gr.Markdown("""
        **💡 Tips for Best Results:**
        - Use clear, well-lit photos
        - Ensure objects are not too far away
        - Avoid heavy shadows or blur
        - Try different confidence thresholds
        """)
    
    # Event handlers
    test_btn.click(
        fn=generate_test_image,
        outputs=image_input
    )
    
    # Image adjustment buttons
    flip_btn.click(
        fn=flip_image,
        inputs=image_input,
        outputs=image_input
    )
    
    rotate_90_btn.click(
        fn=lambda img: rotate_image(img, 90),
        inputs=image_input,
        outputs=image_input
    )
    
    rotate_180_btn.click(
        fn=lambda img: rotate_image(img, 180),
        inputs=image_input,
        outputs=image_input
    )
    
    # Brightness/Contrast adjustments
    brightness.change(
        fn=lambda img, b, c: adjust_brightness_contrast(img, b, c) if img is not None else None,
        inputs=[image_input, brightness, contrast],
        outputs=image_input
    )
    
    contrast.change(
        fn=lambda img, b, c: adjust_brightness_contrast(img, b, c) if img is not None else None,
        inputs=[image_input, brightness, contrast],
        outputs=image_input
    )
    
    # Main detection
    detect_btn.click(
        fn=process_image,
        inputs=[image_input, conf_slider],
        outputs=[output_image, results_text]
    )

if __name__ == "__main__":
    print("=" * 60)
    print("🚀 YOLO11n Object Detection - Smartphone Friendly")
    print("=" * 60)
    print("📦 Detects 80 object classes including:")
    print("   - People, vehicles, animals")
    print("   - Furniture, sports equipment")
    print("   - Electronics, food items, and more!")
    print("=" * 60)
    print("🌐 Access via: http://localhost:7860")
    print("📱 Mobile: Use same network with computer's IP:7860")
    print("=" * 60)
    
    try:
        demo.launch(
            server_name="0.0.0.0",
            server_port=7860,
            debug=False,
            share=True,
            show_error=True,
            max_file_size="20MB"
        )
    except Exception as e:
        print(f"❌ Launch Error: {e}")
        print("\n🔧 Troubleshooting:")
        print("1. Try different port: demo.launch(server_port=7861)")
        print("2. Check firewall settings")
        print("3. Ensure ultralytics is installed: pip install ultralytics")