From 9cb3b6c4f1ecb1667f29121379a074c81000dccd Mon Sep 17 00:00:00 2001
From: s224389 <s224389@student.dtu.dk>
Date: Mon, 20 Jan 2025 13:42:51 +0100
Subject: [PATCH] Delete sato_test.py
---
sato_test.py | 142 ---------------------------------------------------
1 file changed, 142 deletions(-)
delete mode 100644 sato_test.py
diff --git a/sato_test.py b/sato_test.py
deleted file mode 100644
index 9774a3f..0000000
--- a/sato_test.py
+++ /dev/null
@@ -1,142 +0,0 @@
-import time
-import cv2
-import numpy as np
-import matplotlib.pyplot as plt
-from skimage.morphology import skeletonize
-from skimage.filters import gaussian, sato
-from skimage.feature import canny
-from skimage.graph import route_through_array
-
-#### Helper functions ####
-
-def load_image(path, type):
- """
- Load an image in either gray or color mode (then convert color to gray).
- """
- if type == 'gray':
- img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
- if img is None:
- raise FileNotFoundError(f"Could not read {path}")
- elif type == 'color':
- img = cv2.imread(path, cv2.IMREAD_COLOR)
- if img is None:
- raise FileNotFoundError(f"Could not read {path}")
- else:
- img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
- else:
- raise ValueError("type must be 'gray' or 'color'")
- return img
-
-def downscale(img, points, scale_percent):
- """
- Downsample `img` to `scale_percent` size and scale the given points accordingly.
- Returns (downsampled_img, (scaled_seed, scaled_target)).
- """
- if scale_percent == 100:
- return img, (tuple(points[0]), tuple(points[1]))
- else:
- # Compute new dimensions
- width = int(img.shape[1] * scale_percent / 100)
- height = int(img.shape[0] * scale_percent / 100)
- new_dimensions = (width, height)
-
- # Downsample
- downsampled_img = cv2.resize(img, new_dimensions, interpolation=cv2.INTER_AREA)
-
- # Scaling factors
- scale_x = width / img.shape[1]
- scale_y = height / img.shape[0]
-
- # Scale the points (x, y)
- seed_xy = tuple(points[0])
- target_xy = tuple(points[1])
- scaled_seed_xy = (int(seed_xy[0] * scale_x), int(seed_xy[1] * scale_y))
- scaled_target_xy = (int(target_xy[0] * scale_x), int(target_xy[1] * scale_y))
-
- return downsampled_img, (scaled_seed_xy, scaled_target_xy)
-
-## NO LONGER INVERSE (NOT 1/...)
-def compute_cost(image, sigma=1.0, epsilon=1e-1):
- """
- Smooth the image, run Canny edge detection, then invert the edge map into a cost image.
- """
-
- smoothed_img = gaussian(image, sigma=sigma)
- canny_img = sato(smoothed_img)
-
- canny_thresh = canny_img > 0.08
-
- skeleton = skeletonize(canny_thresh)
-
- cost_img = 1 /(skeleton + epsilon) # Invert edges: higher cost where edges are stronger
- return cost_img, skeleton
-
-def backtrack_pixels_on_image(img_color, path_coords, bgr_color=(0, 0, 255)):
- """
- Color the path on the (already converted BGR) image in the specified color.
- `path_coords` should be a list of (row, col) or (y, x).
- """
- for (row, col) in path_coords:
- img_color[row, col] = bgr_color
- return img_color
-
-#### Main Script ####
-
-def main():
- # Define input parameters
- image_path = './tests/slice_60_volQ.png'
- image_type = 'gray' # 'gray' or 'color'
- downscale_factor = 100 # % of original size
- points_path = './tests/LiveWireEndPoints.npy'
-
- # Load image
- image = load_image(image_path, image_type)
-
- # Load seed and target points
- points = np.int0(np.round(np.load(points_path))) # shape: (2, 2), i.e. [[x_seed, y_seed], [x_target, y_target]]
-
- # Downscale image and points
- scaled_image, scaled_points = downscale(image, points, downscale_factor)
- seed, target = scaled_points # Each is (x, y)
-
- # Convert to row,col for scikit-image (which uses (row, col) = (y, x))
- seed_rc = (seed[1], seed[0])
- target_rc = (target[1], target[0])
-
- # Compute cost image
- cost_image, canny_img = compute_cost(scaled_image)
-
- # Find path using route_through_array
- # route_through_array expects: route_through_array(image, start, end, fully_connected=True/False)
- start_time = time.time()
- path_rc, cost = route_through_array(
- cost_image,
- start=seed_rc,
- end=target_rc,
- fully_connected=True
- )
- end_time = time.time()
-
- print(f"Elapsed time for pathfinding: {end_time - start_time:.3f} seconds")
-
- # Convert single-channel image to BGR for coloring
- color_img = cv2.cvtColor(scaled_image, cv2.COLOR_GRAY2BGR)
-
- # Draw path. `path_rc` is a list of (row, col).
- # If you want to mark it in red, do (0,0,255) because OpenCV uses BGR format.
- color_img = backtrack_pixels_on_image(color_img, path_rc, bgr_color=(0, 0, 255))
-
- # Display results
- plt.figure(figsize=(20, 8))
- plt.subplot(1, 2, 1)
- plt.title("Canny Edges")
- plt.imshow(canny_img, cmap='gray')
-
- plt.subplot(1, 2, 2)
- plt.title("Path from Seed to Target")
- # Convert BGR->RGB for pyplot
- plt.imshow(color_img[..., ::-1])
- plt.show()
-
-if __name__ == "__main__":
- main()
--
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