Update tempo.txt

tempo.txt

@@ -1,61 +1,51 @@
 import cv2
 import numpy as np
 
-def extend_line_to_image_edges(x1, y1, x2, y2, img_width, img_height):
-    # Calculate the slope (m) and intercept (c) of the line
-    if x2 != x1:
-        m = (y2 - y1) / (x2 - x1)
-        c = y1 - m * x1
-    else:
-        # The line is vertical
-        m = None
+def detect_half_circles(binary_image, threshold=80):
+    # Find contours in the binary image
+    contours, _ = cv2.findContours(binary_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
     
-    # Find intersection points with image borders
-    points = []
-    
-    # Intersection with the left border (x=0)
-    if m is not None:
-        y_at_left = c
-        if 0 <= y_at_left <= img_height:
-            points.append((0, int(y_at_left)))
-
-    # Intersection with the right border (x=img_width)
-    if m is not None:
-        y_at_right = m * img_width + c
-        if 0 <= y_at_right <= img_height:
-            points.append((img_width, int(y_at_right)))
+    detected_shapes = []
 
-    # Intersection with the top border (y=0)
-    if m is not None:
-        x_at_top = -c / m
-        if 0 <= x_at_top <= img_width:
-            points.append((int(x_at_top), 0))
-    else:
-        # The line is vertical and intersects the top border if y1=0 or y2=0
-        if y1 == 0:
-            points.append((x1, 0))
-        elif y2 == 0:
-            points.append((x2, 0))
+    for contour in contours:
+        # Approximate the contour
+        epsilon = 0.04 * cv2.arcLength(contour, True)
+        approx = cv2.approxPolyDP(contour, epsilon, True)
         
-    # Intersection with the bottom border (y=img_height)
-    if m is not None:
-        x_at_bottom = (img_height - c) / m
-        if 0 <= x_at_bottom <= img_width:
-            points.append((int(x_at_bottom), img_height))
-    else:
-        # The line is vertical and intersects the bottom border if y1=img_height or y2=img_height
-        if y1 == img_height:
-            points.append((x1, img_height))
-        elif y2 == img_height:
-            points.append((x2, img_height))
+        # Check for a half-circle shape
+        if len(approx) >= 5:
+            # Fit an ellipse to the contour
+            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
+                
+                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(binary_image, (x, y), (x + w, y + h), (255, 255, 255), 2)
+    
+    return binary_image, detected_shapes
 
-    # Choose the two valid intersection points that extend the line
-    if len(points) >= 2:
-        extended_points = points[:2]
-    else:
-        extended_points = points
+# Example usage
+# Load the binary masked image (make sure it's a binary image)
+binary_image = cv2.imread('path_to_your_image.png', cv2.IMREAD_GRAYSCALE)
 
-    return extended_points
+# Threshold the image to make sure it's binary
+_, binary_image = cv2.threshold(binary_image, 127, 255, cv2.THRESH_BINARY)
 
-# Example usage
-img = np.zeros((500, 500, 3),
+# Detect half-circles and draw rectangles
+output_image, detected_shapes = detect_half_circles(binary_image)
+
+# Show the result
+cv2.imshow('Detected Half-Circles', output_image)
+cv2.waitKey(0)
+cv2.destroyAllWindows()