Update services/plantation/missing_patch_check.py

services/plantation/missing_patch_check.py

@@ -1,63 +1,47 @@
-# services/plantation/missing_patch_check.py
 import cv2
 import numpy as np
-from ultralytics import YOLO
 import os
 
-# Load YOLOv8n model
-BASE_DIR = os.path.dirname(os.path.abspath(__file__))
-MODEL_PATH = os.path.join(BASE_DIR, "../../models/yolov8n.pt")
-model = YOLO(MODEL_PATH)
-
-def detect_missing_patches(frame):
+def process_missing_patches(frame):
     """
-    Detect missing plantation patches in a frame using YOLOv8n.
+    Detect missing patches of plants using contour analysis.
     Args:
         frame: Input frame (numpy array)
     Returns:
-        dict: Detection results with numbered labels
-        numpy array: Annotated frame
+        list: List of missing patches
+        numpy array: Annotated frame with numbered labels
     """
-    # Run YOLOv8 inference
-    results = model(frame)
+    # Convert to grayscale
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    # Apply thresholding to detect plant areas (simplified)
+    _, plant_mask = cv2.threshold(gray, 100, 255, cv2.THRESH_BINARY_INV)
+    contours, _ = cv2.findContours(plant_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
 
     detections = []
     line_counter = 1  # Initialize counter for numbered labels
 
-    # Process detections
-    for r in results:
-        for box in r.boxes:
-            conf = float(box.conf[0])
-            if conf < 0.5:
-                continue
-            cls = int(box.cls[0])
-            label = model.names[cls]
-            if label != "missing_patch":  # Assuming "missing_patch" class exists
-                continue
-            xyxy = box.xyxy[0].cpu().numpy()
-            x_min, y_min, x_max, y_max = map(int, xyxy)
-
-            # Add numbered label
-            detection_label = f"Line {line_counter} - Missing Patch (Conf: {conf:.2f})"
-            detections.append({
-                "label": detection_label,
-                "confidence": conf,
-                "coordinates": [x_min, y_min, x_max, y_max]
-            })
-
-            # Draw bounding box and label
-            color = (139, 69, 19)  # Brown for missing patches
-            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_missing_patches(frame):
-    """
-    Wrapper function for integration with app.py.
-    """
-    result = detect_missing_patches(frame)
-    return result["detections"], result["frame"]
+    # Identify gaps (missing patches) by analyzing contour areas
+    for contour in contours:
+        area = cv2.contourArea(contour)
+        if area < 500:  # Filter small gaps
+            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} - Missing Patch"
+        detections.append({
+            "type": "missing_patch",
+            "label": detection_label,
+            "coordinates": [x_min, y_min, x_max, y_max]
+        })
+
+        # Draw bounding box and label
+        color = (139, 69, 19)  # Brown for missing patches
+        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, frame