Update services/plantation/plant_count.py

services/plantation/plant_count.py

@@ -1,67 +1,45 @@
 import cv2
 import numpy as np
-import logging
-from typing import List, Dict, Tuple
+from typing import List, Tuple, Dict, Any
 
-# Setup logging
-logging.basicConfig(
-    filename="app.log",
-    level=logging.INFO,
-    format="%(asctime)s - %(levelname)s - %(message)s"
-)
-
-def process_plants(frame: np.ndarray) -> Tuple[List[Dict], np.ndarray]:
+def process_plants(frame: np.ndarray) -> Tuple[List[Dict[str, Any]], np.ndarray]:
     """
-    Process a frame to count plants/trees using color-based segmentation.
+    Detect and count plants in the frame using color segmentation.
     Args:
-        frame: Input frame (BGR numpy array)
+        frame: Input frame as a numpy array.
     Returns:
-        Tuple: (List of detection dictionaries, annotated frame)
+        Tuple of (list of detections, annotated frame).
     """
-    try:
-        # Convert frame to HSV for better color segmentation
-        hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
-
-        # Define range for green color (trees/plants)
-        lower_green = np.array([35, 40, 40])
-        upper_green = np.array([85, 255, 255])
-        mask = cv2.inRange(hsv, lower_green, upper_green)
-
-        # Morphological operations to reduce noise
-        kernel = np.ones((5, 5), np.uint8)
-        mask = cv2.erode(mask, kernel, iterations=1)
-        mask = cv2.dilate(mask, kernel, iterations=1)
-
-        # Find contours of green regions (trees)
-        contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-
-        detections = []
-        for idx, contour in enumerate(contours):
-            # Filter small contours (noise)
-            area = cv2.contourArea(contour)
-            if area < 300:  # Minimum area threshold
-                continue
-
-            # Get bounding box
-            x, y, w, h = cv2.boundingRect(contour)
-            x_min, y_min, x_max, y_max = x, y, x + w, y + h
-
-            # Add detection
-            detections.append({
-                "type": "plant",
-                "label": f"Plant {idx + 1}",
-                "box": [x_min, y_min, x_max, y_max],
-                "confidence": 0.9  # Simulated confidence
-            })
-
-            # Draw bounding box and label on frame
-            cv2.rectangle(frame, (x_min, y_min), (x_max, y_max), (0, 255, 0), 2)
-            cv2.putText(frame, f"Plant {idx + 1}", (x_min, y_min - 10),
-                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
-
-        logging.info(f"Detected {len(detections)} plants in frame.")
-        return detections, frame
-
-    except Exception as e:
-        logging.error(f"Error in plant counting: {str(e)}")
-        return [], frame
+    # Convert to HSV color space for plant detection
+    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
+    
+    # Define range for green color (plants)
+    lower_green = np.array([35, 50, 50])
+    upper_green = np.array([85, 255, 255])
+    mask = cv2.inRange(hsv, lower_green, upper_green)
+    
+    # Morphological operations to clean up the mask
+    kernel = np.ones((5, 5), np.uint8)
+    mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
+    
+    # Find contours of plants
+    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    
+    detections = []
+    for i, contour in enumerate(contours):
+        area = cv2.contourArea(contour)
+        if area < 200:  # Ignore small contours
+            continue
+        
+        x, y, w, h = cv2.boundingRect(contour)
+        x_min, y_min, x_max, y_max = x, y, x + w, y + h
+        
+        # Label as a plant
+        label = f"Plant {i+1}"
+        detections.append({
+            "box": [x_min, y_min, x_max, y_max],
+            "label": label,
+            "type": "plant"
+        })
+    
+    return detections, frame