Create part3.py

part3.py

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+import numpy as np
+import cv2
+from segment_anything import sam_model_registry, SamPredictor
+import matplotlib.pyplot as plt
+import io
+from PIL import Image
+
+class SAMAnalyzer:
+    def __init__(self, model_path="sam_vit_h_4b8939.pth"):
+        self.model_path = model_path
+        self.sam = None
+        self.predictor = None
+        self.initialize_sam()
+
+    def initialize_sam(self):
+        """Initialize SAM model"""
+        try:
+            self.sam = sam_model_registry["vit_h"](checkpoint=self.model_path)
+            self.predictor = SamPredictor(self.sam)
+            print("SAM model initialized successfully")
+        except Exception as e:
+            print(f"Error initializing SAM model: {e}")
+            raise
+
+    def process_image(self, image_data):
+        """Process uploaded image using SAM"""
+        try:
+            # Convert uploaded image to numpy array
+            if isinstance(image_data, (str, bytes)):
+                if isinstance(image_data, str):
+                    image = cv2.imread(image_data)
+                else:
+                    nparr = np.frombuffer(image_data, np.uint8)
+                    image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+                image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+            else:
+                image = np.array(Image.open(image_data))
+
+            # Segment farmland
+            print("Segmenting farmland...")
+            farmland_mask = self.segment_farmland(image)
+
+            # Calculate vegetation index
+            print("Calculating vegetation index...")
+            veg_index = self.calculate_vegetation_index(image, farmland_mask)
+
+            # Analyze health
+            print("Analyzing crop health...")
+            health_analysis = self.analyze_crop_health(veg_index, farmland_mask)
+
+            # Create visualization
+            print("Generating visualization...")
+            viz_plot = self.create_visualization(image, farmland_mask, veg_index)
+
+            return veg_index, health_analysis, viz_plot
+
+        except Exception as e:
+            print(f"Error processing image: {e}")
+            return None, None, None
+
+    def segment_farmland(self, image):
+        """Segment farmland using SAM2"""
+        self.predictor.set_image(image)
+
+        # Generate automatic mask proposals
+        h, w = image.shape[:2]
+        input_point = np.array([[w//2, h//2]])
+        input_label = np.array([1])
+
+        masks, scores, logits = self.predictor.predict(
+            point_coords=input_point,
+            point_labels=input_label,
+            multimask_output=True
+        )
+
+        # Select best mask
+        best_mask = masks[scores.argmax()]
+        return best_mask
+
+    def calculate_vegetation_index(self, image, mask):
+        """Calculate vegetation index using RGB"""
+        r, g, b = image[:,:,0], image[:,:,1], image[:,:,2]
+
+        numerator = (2 * g.astype(float) - r.astype(float) - b.astype(float))
+        denominator = (2 * g.astype(float) + r.astype(float) + b.astype(float))
+        denominator[denominator == 0] = 1e-10
+
+        veg_index = numerator / denominator
+        veg_index = (veg_index + 1) / 2
+        veg_index = veg_index * mask
+
+        return veg_index
+
+    def analyze_crop_health(self, veg_index, mask):
+        """Analyze crop health based on vegetation index"""
+        valid_pixels = veg_index[mask > 0]
+        if len(valid_pixels) == 0:
+            return {
+                'average_index': 0,
+                'health_distribution': {
+                    'low_vegetation': 0,
+                    'moderate_vegetation': 0,
+                    'high_vegetation': 0
+                },
+                'overall_health': 'No vegetation detected'
+            }
+
+        avg_index = np.mean(valid_pixels)
+        health_categories = {
+            'low_vegetation': np.sum((valid_pixels <= 0.3)) / len(valid_pixels),
+            'moderate_vegetation': np.sum((valid_pixels > 0.3) & (valid_pixels <= 0.6)) / len(valid_pixels),
+            'high_vegetation': np.sum((valid_pixels > 0.6)) / len(valid_pixels)
+        }
+
+        return {
+            'average_index': avg_index,
+            'health_distribution': health_categories,
+            'overall_health': 'Healthy' if avg_index > 0.5 else 'Needs attention'
+        }
+
+    def create_visualization(self, image, mask, veg_index):
+        """Create visualization of results"""
+        fig = plt.figure(figsize=(15, 5))
+
+        # Original image with mask overlay
+        plt.subplot(131)
+        plt.imshow(image)
+        plt.imshow(mask, alpha=0.3, cmap='gray')
+        plt.title('Segmented Farmland')
+        plt.axis('off')
+
+        # Vegetation index heatmap
+        plt.subplot(132)
+        plt.imshow(veg_index, cmap='RdYlGn')
+        plt.colorbar(label='Vegetation Index')
+        plt.title('Vegetation Index')
+        plt.axis('off')
+
+        # Health classification
+        plt.subplot(133)
+        health_mask = np.zeros_like(veg_index)
+        health_mask[veg_index <= 0.3] = 1
+        health_mask[(veg_index > 0.3) & (veg_index <= 0.6)] = 2
+        health_mask[veg_index > 0.6] = 3
+        health_mask = health_mask * mask
+        plt.imshow(health_mask, cmap='viridis')
+        plt.colorbar(ticks=[1, 2, 3], 
+                    label='Vegetation Levels',
+                    boundaries=np.arange(0.5, 4.5),
+                    values=[1, 2, 3])
+        plt.title('Vegetation Levels')
+        plt.axis('off')
+
+        plt.tight_layout()
+        
+        # Save plot to buffer
+        buf = io.BytesIO()
+        plt.savefig(buf, format='png', bbox_inches='tight')
+        buf.seek(0)
+        plt.close()
+        
+        return buf
+
+    def format_analysis_text(self, health_analysis):
+        """Format health analysis results as text"""
+        return f"""
+        🌿 Vegetation Analysis Results:
+        
+        📊 Average Vegetation Index: {health_analysis['average_index']:.2f}
+        
+        🌱 Vegetation Distribution:
+        • Low Vegetation: {health_analysis['health_distribution']['low_vegetation']*100:.1f}%
+        • Moderate Vegetation: {health_analysis['health_distribution']['moderate_vegetation']*100:.1f}%
+        • High Vegetation: {health_analysis['health_distribution']['high_vegetation']*100:.1f}%
+        
+        📋 Overall Health Status: {health_analysis['overall_health']}
+        
+        Note: Analysis uses SAM2 for farmland segmentation
+        """