in order to create synthetic input data to assess the accuracy of the method, added a function that creates a slope image

main.py

@@ -0,0 +1,38 @@
+##!/usr/bin/env python3
+import numpy as np
+import cv2
+from pathlib import Path
+import math
+
+
+def create_slope_image(image_width: int, image_height: int, p1_angle: float, p1_radius: float, bg_color: float = 0.0, fg_color: float = 1.0):
+    """
+    creates an image containing 2 regions separated by a line defined as the tangent of a point p1 on a centered circle
+
+    the semi-plane the contains the origin of the circle is colored with fg_color, while the other semi plane is assigned pixel values bg_color
+    """
+    slope_image = np.ndarray(shape=(image_height, image_width), dtype=float)
+    # work out the equation of the tangent in the form a.x + b.y + c = 0
+    c = np.array([image_height * 0.5, image_width * 0.5])
+    p1 = c + np.array([math.cos(p1_angle), math.sin(p1_angle)]) * p1_radius
+    cp1 = p1 - c
+    a = cp1[0]
+    b = cp1[1]
+    c = - p1[0] * cp1[0] - p1[1] * cp1[1]
+    x = np.tile(np.arange(image_width).reshape((1, image_width)), (image_height, 1))
+    y = np.tile(np.arange(image_height).reshape((image_height, 1)), (1, image_width))
+    # print('x=', x)
+    # print('y=', y)
+    retval, slope_image = cv2.threshold(x * a + y * b + c, 0.0, fg_color, type=cv2.THRESH_BINARY_INV)
+    # print('slope_image=', slope_image)
+    cv2.imwrite('slope.tif', slope_image)
+    return slope_image
+
+
+def main():
+    slope_image = create_slope_image(image_width=128, image_height=256, p1_angle=0.7, p1_radius=3.0)
+    cv2.imwrite(str(Path('toto.tif')), slope_image)
+
+
+if __name__ == '__main__':
+    main()