实例化效果图
代码
from scipy.ndimage.morphology import distance_transform_edt
import numpy as np
import cv2 as cv
import matplotlib.pyplot as plt
from scipy import ndimage
from skimage.segmentation import find_boundaries
import glob
import tqdm
import os
import gdalTools
from skimage.morphology import square, dilation
def get_labels():
"""Load the mapping that associates classes with label colors
Returns:
np.ndarray with dimensions (13, 3)
"""
return np.asarray(
[
[0, 0, 0],
[0, 200, 0],
[150, 250, 0],
[150, 200, 150],
[200, 0, 200],
[150, 0, 250],
[150, 150, 250],
[250, 200, 0],
[200, 200, 0],
[100, 56, 250],
[250, 150, 56],
[200, 0, 0],
[250, 0, 150],
[200, 150, 150],
[250, 150, 125],
[0, 0, 200],
[0, 100, 50],
[0, 150, 200],
[0, 200, 250],
[255, 255, 255],
# [255, 255, 0],
[250, 128, 114],
[255, 20, 147],
[105, 139, 34],
[139, 58, 58],
[255,69,0],
[255,165,0],
[128,128,0],
[64,224,208]
]
)
def decode_segmap(label_mask, n_classes):
"""Decode segmentation class labels into a color image
Args:
label_mask (np.ndarray): an (M,N) array of integer values denoting
the class label at each spatial location.
plot (bool, optional): whether to show the resulting color image
in a figure.
Returns:
(np.ndarray, optional): the resulting decoded color image.
"""
label_colours = get_labels()
color_length = len(label_colours)
r = label_mask.copy()
g = label_mask.copy()
b = label_mask.copy()
for ll in range(0, n_classes):
if ll < color_length:
r[label_mask == ll] = label_colours[ll, 0]
g[label_mask == ll] = label_colours[ll, 1]
b[label_mask == ll] = label_colours[ll, 2]
else:
lll = ll%(color_length-1)
if lll == 0:
lll = 13
r[label_mask == ll] = label_colours[lll, 0]
g[label_mask == ll] = label_colours[lll, 1]
b[label_mask == ll] = label_colours[lll, 2]
rgb = np.zeros((label_mask.shape[0], label_mask.shape[1], 3))
rgb[:, :, 0] = r
rgb[:, :, 1] = g
rgb[:, :, 2] = b
return rgb.astype(np.uint8)
def makedir(path):
if not os.path.exists(path):
os.makedirs(path)
if __name__ == '__main__':
imgPath = 'test.tif'
poly = cv.imread(imgPath, 0)
labeled_array, num_features = ndimage.label(poly)
decode = decode_segmap(labeled_array, n_classes=num_features)
