remove name=main scripts

ripplemapper/contour.py

@@ -3,12 +3,10 @@
 
 import cv2
 import numpy as np
-from matplotlib import pyplot as plt
 from scipy.spatial import cKDTree
 from skimage import measure
 
-from ripplemapper.image import detect_edges, preprocess_image, process_edges
-from ripplemapper.io import load_tif
+from ripplemapper.image import detect_edges, process_edges
 
 __all__ = ["find_contours", "compute_recursive_midpoints", "extend_contour", "combine_contours", "smooth_contour", "distance_map", "neighbors", "a_star", "get_next_node", "find_boundaries", "find_bump_limits", "smooth_bumps", "average_boundaries"]
 
@@ -312,76 +310,3 @@ def average_boundaries(self, contour_a = None, contour_b = None, iterations: int
  self.contours.append(RippleContour(midpoints_a, "averaged", self))
  if save_both:
  self.contours.append(RippleContour(midpoints_b, "averaged", self))
-
-
-if __name__ == "__main__":
- # Load and preprocess the image
- image_path = "/mnt/h/1_00369.tif"
- files, img_data = load_tif(image_path)
- gray_image = preprocess_image(img_data, roi_y=[400, 900])
-
- # Detect edges and process them
- edges_gray = detect_edges(gray_image.squeeze())
- edges_cleaned = process_edges(edges_gray)
-
- # Find contours and approximate them
- contours = find_contours(edges_cleaned, level=0.5)
- blank_image = np.zeros(edges_cleaned.shape, dtype=np.uint8)
- cont1 = np.flip(contours[0]).astype(np.int32)
- cont2 = np.flip(contours[1]).astype(np.int32)
- contour = combine_contours(cont1, cont2)
-
- poly1_vertices = measure.approximate_polygon(contours[0], tolerance=1)
- poly2_vertices = measure.approximate_polygon(contours[1], tolerance=1)
- poly1 = smooth_contour(poly1_vertices)
- poly2 = smooth_contour(poly2_vertices)
- plt.plot(poly1[:, 1], poly1[:, 0], 'r-')
- plt.plot(poly2[:, 1], poly2[:, 0], 'b-')
- ax = plt.gca()
- ax.set_ylim(ax.get_ylim()[::-1])
-
- img = cv2.drawContours(blank_image, [contour], 0, (255, 255, 255), -1)
- plt.figure()
- plt.imshow(img)
-
-
- # distance_map = distance_map(img)
-
- # start = (np.argmax(distance_map[:,0]), 0) # Highest value on the left boundary
- # goal = (np.argmax(distance_map[:, -1]), distance_map.shape[1] - 1) # Highest value on the right boundary, for simplicity
- # path = a_star(start, goal, distance_map)
-
- # # To visualize the path on the distance map for verification
- # import matplotlib.pyplot as pltw
-
- # plt.imshow(distance_map, cmap='gray')
- # plt.plot(start[1], start[0], 'ro')
- # plt.plot(goal[1], goal[0], 'go')
- # plt.figure()
- # print(path[0:5])
- # plt.plot([p[1] for p in path], [p[0] for p in path], 'r-') # x and y are swapped for plotting
- # plt.figure()
- # plt.plot([distance_map[p[0], p[1]] for p in path], 'r-')
-
- # midpoint_vertices_a, midpoint_vertices_b = compute_recursive_midpoints(poly1_vertices, poly2_vertices, iterations=3)
-
- # # Print or further process the midpoint vertices as needed
- # plt.figure()
- # plt.imshow(edges_cleaned)
-
- # # plot the original image and all our paths:
- # plt.figure(figsize=(24,16))
- # plt.imshow(gray_image.squeeze(), cmap='gray')
- # plt.plot(cont1[:, 0], cont1[:, 1], 'r-')
- # plt.plot(cont2[:, 0], cont2[:, 1], 'b-')
- # plt.plot(midpoint_vertices_a[:, 1], midpoint_vertices_a[:, 0], 'g-')
- # plt.plot(midpoint_vertices_b[:, 1], midpoint_vertices_b[:, 0], 'y-')
- # plt.plot([p[1] for p in path], [p[0] for p in path], 'c-') # x and y are swapped for plotting
- # plt.legend(['Upper Contour', 'Lower Contour', 'Midpoints forward', 'Midpoints Reverse', 'A* Path through volume'])
-
-
- # fig = plt.figure()
- # plt.plot(cont1[:, 0], cont1[:, 1], 'r-')
- # ax=plt.gca()
- # ax.set_ylim(ax.get_ylim()[::-1])
- plt.show()

ripplemapper/image.py

@@ -102,25 +102,3 @@ def process_edges(edges_gray: np.ndarray, sigma: float=0) -> np.ndarray:
  if sigma > 0:
  edges_cleaned = filters.gaussian(edges_cleaned, sigma=sigma)
  return edges_closed
-
-
-if __name__ == "__main__":
- from matplotlib import pyplot as plt
-
- from ripplemapper.analyse import (add_a_star_contours,
- add_boundary_contours,
- add_chan_vese_contours)
- from ripplemapper.io import load_dir_to_obj
-
- x = load_dir_to_obj('/mnt/h/Downloads/D16H10/high_speed/', roi_x=[400,900], skip=25, start=200, end=250)
- # calculate divergence of image
- # img = cv2.GaussianBlur(np.sum(np.abs(np.gradient(x[1].image)), axis=0)/np.max(x[1].image), (11,11), 0)*cv2.GaussianBlur(1-(x[1].image/np.max(x[1].image)), (11,11), 1)
- # img = img/np.max(img)
- # x[1].image = img
- add_boundary_contours(x[1], sigma=2)
- add_a_star_contours(x[1])
- add_chan_vese_contours(x[1], use_gradients=True)
- x[1].plot()
-
-
- plt.show()