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71cf8b5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 | import numpy as np
from scipy.ndimage import label, find_objects
def split_connected_components(labels, label_value, offset, min_volume=400, top_n=6):
"""
Split the region with the specified label_value into multiple connected components and reassign labels.
Parameters:
labels (np.ndarray): Input label array
label_value (int): The label value to split
offset (int): Offset used to generate new label values
min_volume (int): Minimum volume to retain connected components
top_n (int): Retain the top-n connected components by volume
Returns:
np.ndarray: Relabeled array
"""
# Get a binary mask where the label is equal to label_value
binary_mask = (labels == label_value)
structure = np.array([[[0, 0, 0],
[0, 1, 0],
[0, 0, 0]],
[[0, 1, 0],
[1, 1, 1],
[0, 1, 0]],
[[0, 0, 0],
[0, 1, 0],
[0, 0, 0]]], dtype=int)
# Use scipy.ndimage.label to mark connected components
labeled_array, num_features = label(binary_mask, structure=structure)
# Create new_labels as a copy of the input labels
new_labels = labels.copy()
# Get the volume of all connected components
volumes = [np.sum(labeled_array == i) for i in range(1, num_features + 1)]
# Get indices of the top-n connected components by volume
top_n_indices = np.argsort(volumes)[-top_n:][::-1]
top_n_volumes_labels = [(volumes[i], i + 1) for i in top_n_indices] # Note that component indices start from 1
# Iterate through all connected components in descending order of volume and reassign labels to avoid conflicts
current_label = offset
for volume, i in top_n_volumes_labels:
region_mask = (labeled_array == i)
if volume >= min_volume:
new_labels[region_mask] = current_label
current_label += 1
else:
new_labels[region_mask] = 0
return new_labels
def remove_small_connected_components(prediction, min_volume, label_values):
"""
Remove small connected components and set them as background.
Parameters:
prediction (np.ndarray): Model output predictions
min_volume (int): Minimum volume to retain connected components
label_values (list): List of label values to process
Returns:
np.ndarray: Processed prediction array
"""
new_prediction = prediction.copy()
# Define the connectivity structure for identifying connected components
structure = np.array([[[0, 0, 0],
[0, 1, 0],
[0, 0, 0]],
[[0, 1, 0],
[1, 1, 1],
[0, 1, 0]],
[[0, 0, 0],
[0, 1, 0],
[0, 0, 0]]], dtype=int)
for index, label_value in enumerate(label_values):
print(f"Processing label {label_value}:")
# Get binary mask for the specified label
binary_mask = (prediction == label_value)
minimum = min_volume[index]
labeled_array, num_features = label(binary_mask, structure=structure)
# Get slices of each connected component
slices = find_objects(labeled_array)
retained_sizes = []
removed_sizes = []
# Iterate through each connected component and remove those smaller than the minimum volume
for i, slice_ in enumerate(slices):
region_size = np.sum(labeled_array[slice_] == (i + 1))
if region_size <= minimum:
removed_sizes.append(region_size)
new_prediction[labeled_array == (i + 1)] = 0
else:
retained_sizes.append(region_size)
# Print the sizes of retained and removed regions
if retained_sizes:
print(f" Retained regions sizes: {retained_sizes}")
if removed_sizes:
print(f" Removed regions sizes: {removed_sizes}")
return new_prediction
def refine_labels(label1, label2):
"""
Refine label2 based on label1 by adjusting foreground and background regions.
Parameters:
label1 (np.ndarray): The reference label.
label2 (np.ndarray): The label to be refined.
Returns:
np.ndarray: Refined label.
"""
fixed_label2 = label2.copy()
# Regions that are background in label1 but foreground in label2
bg_to_fg_mask = (label1 == 0) & (label2 > 0)
fixed_label2[bg_to_fg_mask] = 0
return fixed_label2
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