public fiberlyzer push

functions.py

+import cv2
+import numpy as np
+import math
+import matplotlib.pyplot as plt
+import matplotlib.colors as plt_colors
+import csv
+import timeit
+import argparse
+import os
+import pywt
+from skimage.transform import resize
+from skimage.morphology import medial_axis, skeletonize, binary_opening
+import sys
+from collections import deque
+from itertools import combinations
+
+def plot_single_figure(img, size=(15,5), overlay=[]):
+    plt.figure(figsize=size)
+
+    plt.imshow(img)
+    if len(overlay) >= 1:
+        plt.imshow(overlay, alpha=0.5)
+
+    plt.show()
+    plt.close()
+
+def plot_figure(imgs, size=(15,5), overlay=None):
+    for img in imgs:
+        plt.figure(figsize=size)
+
+        plt.imshow(img)
+        if overlay:
+            plt.imshow(overlay, alpha=0.5)
+
+    plt.show()
+    plt.close()
+
+
+def quantize_binary(img, step=20, start=0, end=255):
+    val_range = np.arange(end, start, -step)
+    images_mask = []
+    for r in val_range:
+        img_mask = img.copy()
+        img_mask[img_mask >= r] = 0
+        img_mask[img_mask >= 1] = 1
+        images_mask.append(img_mask)
+    return images_mask
+
+def quantize_binary_rev(img, step=20, start=0, end=255):
+    val_range = np.arange(start, end, step)
+    images_mask = []
+    for r in val_range:
+        img_mask = img.copy()
+        img_mask[img_mask < r] = 0
+        img_mask[img_mask >= 1] = 1
+        images_mask.append(img_mask)
+    return images_mask
+
+def crop_image(img, border):
+    c_vert = border[0]
+    c_hor = border[1]
+    w = img.shape[1]
+    h = img.shape[0]
+    if h > c_vert*2 and w > c_hor*2:
+        img = img[c_vert:h - c_vert, c_hor:w - c_hor]
+    return img
+
+def save_statistical_info(data, filename, header=[]):
+    # open the file in the write mode
+    with open(filename, 'w', newline='') as f:
+        # create the csv writer
+        writer = csv.writer(f, delimiter=';')
+
+        if header:
+            writer.writerow(header)
+
+        # write a row to the csv file
+        for row in range(len(data[0])):
+            line = [col[row] for col in data]
+            writer.writerow(line)
+
+# BFS
+def findPathBwTwoPoints(img_binary, delta, points):
+    '''
+    img_binary: skeleton image
+    points: (y_start_point,x_start_point),(y_end_point,x_end_point)
+    '''
+    height, width = img_binary.shape
+
+    # The start and end point you're looking at
+    # start, end = (31, 14), (34, 51)
+
+    start, end = points
+
+
+    # Store the results of the BFS as the shortest distance to start
+    grid = np.full_like(img_binary, sys.maxsize, dtype=np.uint32)
+    grid[start[0], start[1]] = 0
+
+    # The actual BFS algorithm
+    bfs = deque([start])
+    found = False
+    while len(bfs) > 0:
+        y, x = bfs.popleft()
+        # print(y,x)
+        # We've reached the end!
+        if (y, x) == end:
+            found = True
+            break
+
+        # Look all 8 directions for a good path
+        for dy, dx in delta:
+            yy, xx = y + dy, x + dx
+            # If the next position hasn't already been looked at and it's white
+            if 0 <= xx < width and 0 <= yy < height and img_binary[yy, xx] != 0 and grid[y, x] + 1 < grid[yy, xx]:
+                grid[yy, xx] = grid[y, x] + 1
+                bfs.append((yy, xx))
+
+    if found:
+        # Now rebuild the path from the end to beginning
+        path = []
+        y, x = end
+        while grid[y, x] != 0:
+            for dy, dx in delta:
+                yy, xx = y + dy, x + dx
+                if 0 <= yy < height and 0 <= xx < width and grid[yy, xx] == grid[y, x] - 1:
+                    path.append([yy, xx])
+                    y, x = yy, xx
+        return path
+    else:
+        # print(f'No path found between {start} and {end}')
+        return 0
\ No newline at end of file