Create plant_health.py

services/plantation/plant_health.py

@@ -0,0 +1,62 @@
+# services/plantation/plant_health.py
+import cv2
+import numpy as np
+import os
+
+def compute_vari(frame):
+    """
+    Compute VARI (Visible Atmospherically Resistant Index) to assess plant health.
+    Args:
+        frame: Input frame (numpy array)
+    Returns:
+        dict: Detection results with numbered labels for unhealthy areas
+        numpy array: Annotated frame
+    """
+    # Convert to RGB
+    frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+    r, g, b = cv2.split(frame_rgb)
+
+    # Compute VARI: (Green - Red) / (Green + Red - Blue)
+    denominator = (g.astype(float) + r.astype(float) - b.astype(float))
+    denominator[denominator == 0] = 1e-10  # Avoid division by zero
+    vari = (g.astype(float) - r.astype(float)) / denominator
+
+    # Threshold to find unhealthy areas (VARI < 0 indicates poor health)
+    unhealthy_mask = vari < 0
+    contours, _ = cv2.findContours(unhealthy_mask.astype(np.uint8), 
+                                   cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+    detections = []
+    line_counter = 1  # Initialize counter for numbered labels
+
+    # Process unhealthy areas
+    for contour in contours:
+        area = cv2.contourArea(contour)
+        if area < 50:  # Filter small areas
+            continue
+        x, y, w, h = cv2.boundingRect(contour)
+        x_min, y_min, x_max, y_max = x, y, x + w, y + h
+
+        # Add numbered label
+        detection_label = f"Line {line_counter} - Unhealthy Vegetation"
+        detections.append({
+            "label": detection_label,
+            "coordinates": [x_min, y_min, x_max, y_max]
+        })
+
+        # Draw bounding box and label
+        color = (255, 0, 0)  # Red for unhealthy areas
+        cv2.rectangle(frame, (x_min, y_min), (x_max, y_max), color, 2)
+        cv2.putText(frame, detection_label, (x_min, y_min - 10), 
+                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
+        
+        line_counter += 1
+
+    return {"detections": detections, "frame": frame}
+
+def process_plant_health(frame):
+    """
+    Wrapper function for integration with app.py.
+    """
+    result = compute_vari(frame)
+    return result["detections"], result["frame"]