原图：
效果图：
from fast_slic import Slic
from fast_slic.avx2 import SlicAvx2
from skimage import segmentation, color
import matplotlib.pyplot as plt
import imutils
import cv2
image = cv2.imread("fish.jpg")
image = imutils.resize(image, width=300)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
slic = SlicAvx2(num_components=12, compactness=10, subsample_stride=1)
n_clusters = len(slic.slic_model.clusters) # The cluster information of superpixels.
# use pre-defined centroids
centers_w = [20, 70, 120, 170, 220, 270] * 2
centers_h = [40] * 6 + [80] * 6
new_clusters = slic.slic_model.clusters.copy()
print(len(new_clusters))
for k in range(0, len(new_clusters)):
cluster = new_clusters[k]
cluster['yx'] = (centers_h[k], centers_w[k])
cluster['number'] = k
slic.slic_model.clusters = new_clusters # The cluster information of superpixels.
# print('after center assignment', slic.slic_model.clusters)
# iterate only once
slic_result = slic.iterate(image, 1) # Cluster Map
# plot results
fig, ax_arr = plt.subplots(1, 2)
ax1, ax2 = ax_arr.ravel()
# show centers in image
for cluster in new_clusters:
label = cluster['number']
center = cluster['yx']
#cv2.circle(image, (int(center[1]), int(center[0])), 3, [255, 0, 255], -1)
#cv2.putText(image, str(label), (int(center[1]), int(center[0])), cv2.FONT_HERSHEY_PLAIN, 2, [255, 0, 255], 2)
ax1.imshow(segmentation.mark_boundaries(image, slic_result))
ax2.imshow(slic_result)
plt.show()
another sample:
这回是需要一个mask来指定哪些区域不画裂纹
fastslic虽然很快，但是处理mask的函数要自己写，同时没有outline
"""
-*- coding: utf-8 -*-
author： Hao Hu
@date 2022/3/6 3:40 PM
"""
import numpy as np
import os
# Much faster than the standard class
from fast_slic.avx2 import SlicAvx2
import maskslic as seg
import os.path as osp
from tqdm import tqdm
import cv2
from skimage import segmentation
"""超像素由一系列位置相邻且颜色、亮度、纹理等特征相似的像素点组成的小区域。
这些小区域大多保留了进一步进行图像分割的有效信息，且一般不会破坏图像中物体的边界信息，
用少量的超像素代替大量像素表达图像特征，降低了图像处理的复杂度，
一般作为分割算法的预处理步骤。"""
def get_ori_list(ori_folder):
img_list = os.listdir(ori_folder)
ori_list = []
check_list = ['copper', 'bg', 'check', 'dust']
for img_name in img_list:
flag = 0
for sample in check_list:
if sample in img_name:
flag = 1
break
if flag == 0:
ori_list.append(osp.join(ori_folder, img_name))
if len(ori_list) > 50:
break
return ori_list
def use_mask_to_get_two_areas(mask,end_result,image):
"""通过将指定mask来指定不画裂纹的区域"""
where_0 = np.where(mask == False)
end_result[where_0] = image[where_0]/255
return end_result
def use_fast_slic_to_get_edge(img_path,mask_path,end_path):
image = cv2.imread(img_path)
# image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
mask_image = cv2.imread(mask_path)
img_R = mask_image[:, :, 0]
mask = img_R > 220
# use fast slic
slic = SlicAvx2(num_components=8000, compactness=10)
slic_result = slic.iterate(image, 10) # Cluster Map
end_result = segmentation.mark_boundaries(image, slic_result, outline_color=(0, 1, 1))
end_result = use_mask_to_get_two_areas(mask,end_result,image)
cv2.imwrite(end_path,end_result*255)
if __name__ == '__main__':
ori_folder = '/Users/huhao/Documents/GitHub/real-time-semantic-segmentation/PCB/pcb_small'
end_folder = '/Users/huhao/Documents/GitHub/real-time-semantic-segmentation/PCB/slic_result'
ori_list = get_ori_list(ori_folder)
for img_path in tqdm(ori_list):
end_path = osp.join(end_folder, osp.basename(img_path[:-4])+'_end_result.jpg')
mask_path = osp.join(ori_folder, osp.basename(img_path[:-4]) + '_bg_mask.jpg')
use_fast_slic_to_get_edge(img_path, mask_path, end_path)
os.system("cp {} {}".format(img_path,end_folder))
boundaries.py
import numpy as np
from scipy import ndimage as ndi
from ..morphology import dilation, erosion, square
from ..util import img_as_float, view_as_windows
from ..color import gray2rgb
def _find_boundaries_subpixel(label_img):
"""See ``find_boundaries(..., mode='subpixel')``.
Notes
-----
This function puts in an empty row and column between each *actual*
row and column of the image, for a corresponding shape of ``2s - 1``
for every image dimension of size ``s``. These "interstitial" rows
and columns are filled as ``True`` if they separate two labels in
`label_img`, ``False`` otherwise.
I used ``view_as_windows`` to get the neighborhood of each pixel.
Then I check whether there are two labels or more in that
neighborhood.
"""
ndim = label_img.ndim
max_label = np.iinfo(label_img.dtype).max
label_img_expanded = np.zeros([(2 * s - 1) for s in label_img.shape],
label_img.dtype)
pixels = (slice(None, None, 2), ) * ndim
label_img_expanded[pixels] = label_img
edges = np.ones(label_img_expanded.shape, dtype=bool)
edges[pixels] = False
label_img_expanded[edges] = max_label
windows = view_as_windows(np.pad(label_img_expanded, 1,
mode='constant', constant_values=0),
(3,) * ndim)
boundaries = np.zeros_like(edges)
for index in np.ndindex(label_img_expanded.shape):
if edges[index]:
values = np.unique(windows[index].ravel())
if len(values) > 2: # single value and max_label
boundaries[index] = True
return boundaries
def find_boundaries(label_img, connectivity=1, mode='thick', background=0):
"""Return bool array where boundaries between labeled regions are True.
Parameters
----------
label_img : array of int or bool
An array in which different regions are labeled with either different
integers or boolean values.
connectivity : int in {1, ..., `label_img.ndim`}, optional
A pixel is considered a boundary pixel if any of its neighbors
has a different label. `connectivity` controls which pixels are
considered neighbors. A connectivity of 1 (default) means
pixels sharing an edge (in 2D) or a face (in 3D) will be
considered neighbors. A connectivity of `label_img.ndim` means
pixels sharing a corner will be considered neighbors.
mode : string in {'thick', 'inner', 'outer', 'subpixel'}
How to mark the boundaries:
- thick: any pixel not completely surrounded by pixels of the
same label (defined by `connectivity`) is marked as a boundary.
This results in boundaries that are 2 pixels thick.
- inner: outline the pixels *just inside* of objects, leaving
background pixels untouched.
- outer: outline pixels in the background around object
boundaries. When two objects touch, their boundary is also
marked.
- subpixel: return a doubled image, with pixels *between* the
original pixels marked as boundary where appropriate.
background : int, optional
For modes 'inner' and 'outer', a definition of a background
label is required. See `mode` for descriptions of these two.
Returns
-------
boundaries : array of bool, same shape as `label_img`
A bool image where ``True`` represents a boundary pixel. For
`mode` equal to 'subpixel', ``boundaries.shape[i]`` is equal
to ``2 * label_img.shape[i] - 1`` for all ``i`` (a pixel is
inserted in between all other pairs of pixels).
Examples
--------
>>> labels = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
... [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
... [0, 0, 0, 0, 0, 5, 5, 5, 0, 0],
... [0, 0, 1, 1, 1, 5, 5, 5, 0, 0],
... [0, 0, 1, 1, 1, 5, 5, 5, 0, 0],
... [0, 0, 1, 1, 1, 5, 5, 5, 0, 0],
... [0, 0, 0, 0, 0, 5, 5, 5, 0, 0],
... [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
... [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=np.uint8)
>>> find_boundaries(labels, mode='thick').astype(np.uint8)
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 1, 1, 1, 1, 1, 0, 1, 1, 0],
[0, 1, 1, 0, 1, 1, 0, 1, 1, 0],
[0, 1, 1, 1, 1, 1, 0, 1, 1, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
>>> find_boundaries(labels, mode='inner').astype(np.uint8)
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 0, 1, 0, 0],
[0, 0, 1, 0, 1, 1, 0, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
>>> find_boundaries(labels, mode='outer').astype(np.uint8)
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 0, 0, 1, 0],
[0, 1, 0, 0, 1, 1, 0, 0, 1, 0],
[0, 1, 0, 0, 1, 1, 0, 0, 1, 0],
[0, 1, 0, 0, 1, 1, 0, 0, 1, 0],
[0, 0, 1, 1, 1, 1, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
>>> labels_small = labels[::2, ::3]
>>> labels_small
array([[0, 0, 0, 0],
[0, 0, 5, 0],
[0, 1, 5, 0],
[0, 0, 5, 0],
[0, 0, 0, 0]], dtype=uint8)
>>> find_boundaries(labels_small, mode='subpixel').astype(np.uint8)
array([[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 1, 1, 0],
[0, 0, 0, 1, 0, 1, 0],
[0, 1, 1, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 0],
[0, 1, 1, 1, 0, 1, 0],
[0, 0, 0, 1, 0, 1, 0],
[0, 0, 0, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
>>> bool_image = np.array([[False, False, False, False, False],
... [False, False, False, False, False],
... [False, False, True, True, True],
... [False, False, True, True, True],
... [False, False, True, True, True]],
... dtype=bool)
>>> find_boundaries(bool_image)
array([[False, False, False, False, False],
[False, False, True, True, True],
[False, True, True, True, True],
[False, True, True, False, False],
[False, True, True, False, False]])
"""
if label_img.dtype == 'bool':
label_img = label_img.astype(np.uint8)
ndim = label_img.ndim
selem = ndi.generate_binary_structure(ndim, connectivity)
if mode != 'subpixel':
boundaries = dilation(label_img, selem) != erosion(label_img, selem)
if mode == 'inner':
foreground_image = (label_img != background)
boundaries &= foreground_image
elif mode == 'outer':
max_label = np.iinfo(label_img.dtype).max
background_image = (label_img == background)
selem = ndi.generate_binary_structure(ndim, ndim)
inverted_background = np.array(label_img, copy=True)
inverted_background[background_image] = max_label
adjacent_objects = ((dilation(label_img, selem) !=
erosion(inverted_background, selem)) &
~background_image)
boundaries &= (background_image | adjacent_objects)
return boundaries
else:
boundaries = _find_boundaries_subpixel(label_img)
return boundaries
def mark_boundaries(image, label_img, color=(0, 1, 0),
outline_color=None, mode='outer', background_label=0):
"""Return image with boundaries between labeled regions highlighted.
Parameters
----------
image : (M, N[, 3]) array
Grayscale or RGB image.
label_img : (M, N) array of int
Label array where regions are marked by different integer values.
color : length-3 sequence, optional
RGB color of boundaries in the output image.
outline_color : length-3 sequence, optional
RGB color surrounding boundaries in the output image. If None, no
outline is drawn.
mode : string in {'thick', 'inner', 'outer', 'subpixel'}, optional
The mode for finding boundaries.
background_label : int, optional
Which label to consider background (this is only useful for
modes ``inner`` and ``outer``).
Returns
-------
marked : (M, N, 3) array of float
An image in which the boundaries between labels are
superimposed on the original image.
See Also
--------
find_boundaries
"""
marked = img_as_float(image, force_copy=True)
if marked.ndim == 2:
marked = gray2rgb(marked)
if mode == 'subpixel':
# Here, we want to interpose an extra line of pixels between
# each original line - except for the last axis which holds
# the RGB information. ``ndi.zoom`` then performs the (cubic)
# interpolation, filling in the values of the interposed pixels
marked = ndi.zoom(marked, [2 - 1/s for s in marked.shape[:-1]] + [1],
mode='mirror')
boundaries = find_boundaries(label_img, mode=mode,
background=background_label)
boundaries = dilation(boundaries, square(10))
if outline_color is not None:
outlines = dilation(boundaries, square(3))
marked[outlines] = outline_color
marked[boundaries] = color
#marked[boundaries] = image[boundaries]/255
return marked