Update tempo.txt

tempo.txt

@@ -1,42 +1,35 @@
 import cv2
 import numpy as np
 
-def detect_half_circles(binary_image, threshold=80):
+def detect_half_circles(binary_image, threshold=0.8):
+    # Convert binary image to BGR for drawing colored shapes
+    output_image = cv2.cvtColor(binary_image, cv2.COLOR_GRAY2BGR)
+    
     # Find contours in the binary image
     contours, _ = cv2.findContours(binary_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
     
     detected_shapes = []
-    output_image = cv2.cvtColor(binary_image, cv2.COLOR_GRAY2BGR)
 
     for contour in contours:
-        # Approximate the contour
-        epsilon = 0.04 * cv2.arcLength(contour, True)
-        approx = cv2.approxPolyDP(contour, epsilon, True)
+        # Calculate the perimeter of the contour
+        perimeter = cv2.arcLength(contour, True)
         
-        # Check for a half-circle shape
-        if len(approx) >= 5:
-            # Fit an ellipse to the contour
+        # Fit an ellipse to the contour
+        if len(contour) >= 5:  # Need at least 5 points to fit an ellipse
             ellipse = cv2.fitEllipse(contour)
             (x, y), (MA, ma), angle = ellipse
             
-            # Calculate the aspect ratio
-            aspect_ratio = min(MA, ma) / max(MA, ma)
-            
-            # Check if the shape is approximately a half-circle
-            if 0.4 < aspect_ratio < 0.6:  # Adjust these values as needed
-                # Calculate the match score (this is a simplistic approach, you might need a more robust method)
-                match_score = (1 - abs(aspect_ratio - 0.5) / 0.5) * 100
+            # Check if the major axis is approximately twice the minor axis (indicating a half-circle)
+            if 0.8 < (MA / ma) < 1.2:  # This range can be adjusted based on specific requirements
+                aspect_ratio = float(MA) / ma
                 
-                if match_score >= threshold:
-                    detected_shapes.append(approx)
-                    
-                    # Draw a bounding rectangle around the detected half-circle
-                    x, y, w, h = cv2.boundingRect(contour)
-                    cv2.rectangle(output_image, (x, y), (x + w, y + h), (0, 255, 0), 2)
+                # Check if the aspect ratio is approximately 2:1 (indicating a half-circle shape)
+                if 0.4 < aspect_ratio < 0.6:  # This range can also be adjusted
+                    detected_shapes.append(contour)
                     
                     # Draw the contour with a different color
-                    cv2.drawContours(output_image, [approx], -1, (0, 0, 255), 2)
-    
+                    cv2.drawContours(output_image, [contour], -1, (0, 0, 255), 2)
+
     return output_image, detected_shapes
 
 # Example usage
@@ -46,7 +39,7 @@ binary_image = cv2.imread('path_to_your_image.png', cv2.IMREAD_GRAYSCALE)
 # Threshold the image to make sure it's binary
 _, binary_image = cv2.threshold(binary_image, 127, 255, cv2.THRESH_BINARY)
 
-# Detect half-circles and draw rectangles
+# Detect half-circles and draw contours
 output_image, detected_shapes = detect_half_circles(binary_image)
 
 # Show the result