【超像素分割|超像素分割, 并获取每一个分区】参考、学习自Greatpyimagesearch
from skimage.segmentation import slic
from skimage.segmentation import mark_boundaries
from skimage.util import img_as_float
import matplotlib.pyplot as plt
import numpy as np
import cv2# args
args = {"image": './hand_0.png'}# load the image and apply SLIC and extract (approximately)
# the supplied number of segments
image = cv2.imread(args["image"])
segments = slic(img_as_float(image), n_segments=100, sigma=5)# show the output of SLIC
fig = plt.figure('Superpixels')
ax = fig.add_subplot(1, 1, 1)
ax.imshow(mark_boundaries(img_as_float(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)), segments))
plt.axis("off")
plt.show()
print("segments:\n", segments)
print("np.unique(segments):", np.unique(segments))
# loop over the unique segment values
for (i, segVal) in enumerate(np.unique(segments)):
# construct a mask for the segment
print("[x] inspecting segment {}, for {}".format(i, segVal))
mask = np.zeros(image.shape[:2], dtype="uint8")
mask[segments == segVal] = 255# show the masked region
cv2.imshow("Mask", mask)
cv2.imshow("Applied", np.multiply(image, cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR) > 0))
cv2.waitKey(0)
转载于:https://www.cnblogs.com/ZhengPeng7/p/8986404.html
