我有一些单色图像(黑白而不是灰度)和一些形状怪异的物体。我正在尝试使用 python27、PIL、scipy 和 numpy 以及以下方法提取每个对象:
我看过 http://www.scipy.org/Cookbook/Watershed 和 http://scikits-image.org/docs/dev/auto_examples/plot_contours.html ，它们确实有效，但我特别希望边界框是矩形的，以确保任何“稍微断开”的位都包含在边界框中。理想情况下，要处理断开连接的位(例如左下角的 Blob )，我会有某种阈值控制。关于什么工具箱最适合这个的任何想法？
最佳答案
这使用 Joe Kington's find_paws function 。
import numpy as np
import scipy.ndimage as ndimage
import scipy.spatial as spatial
import scipy.misc as misc
import matplotlib.pyplot as plt
import matplotlib.patches as patches
class BBox(object):
def __init__(self, x1, y1, x2, y2):
'''
(x1, y1) is the upper left corner,
(x2, y2) is the lower right corner,
with (0, 0) being in the upper left corner.
'''
if x1 > x2: x1, x2 = x2, x1
if y1 > y2: y1, y2 = y2, y1
self.x1 = x1
self.y1 = y1
self.x2 = x2
self.y2 = y2
def taxicab_diagonal(self):
'''
Return the taxicab distance from (x1,y1) to (x2,y2)
'''
return self.x2 - self.x1 + self.y2 - self.y1
def overlaps(self, other):
'''
Return True iff self and other overlap.
'''
return not ((self.x1 > other.x2)
or (self.x2 < other.x1)
or (self.y1 > other.y2)
or (self.y2 < other.y1))
def __eq__(self, other):
return (self.x1 == other.x1
and self.y1 == other.y1
and self.x2 == other.x2
and self.y2 == other.y2)
def find_paws(data, smooth_radius = 5, threshold = 0.0001):
# https://stackoverflow.com/questions/4087919/how-can-i-improve-my-paw-detection
"""Detects and isolates contiguous regions in the input array"""
# Blur the input data a bit so the paws have a continous footprint
data = ndimage.uniform_filter(data, smooth_radius)
# Threshold the blurred data (this needs to be a bit > 0 due to the blur)
thresh = data > threshold
# Fill any interior holes in the paws to get cleaner regions...
filled = ndimage.morphology.binary_fill_holes(thresh)
# Label each contiguous paw
coded_paws, num_paws = ndimage.label(filled)
# Isolate the extent of each paw
# find_objects returns a list of 2-tuples: (slice(...), slice(...))
# which represents a rectangular box around the object
data_slices = ndimage.find_objects(coded_paws)
return data_slices
def slice_to_bbox(slices):
for s in slices:
dy, dx = s[:2]
yield BBox(dx.start, dy.start, dx.stop+1, dy.stop+1)
def remove_overlaps(bboxes):
'''
Return a set of BBoxes which contain the given BBoxes.
When two BBoxes overlap, replace both with the minimal BBox that contains both.
'''
# list upper left and lower right corners of the Bboxes
corners = []
# list upper left corners of the Bboxes
ulcorners = []
# dict mapping corners to Bboxes.
bbox_map = {}
for bbox in bboxes:
ul = (bbox.x1, bbox.y1)
lr = (bbox.x2, bbox.y2)
bbox_map[ul] = bbox
bbox_map[lr] = bbox
ulcorners.append(ul)
corners.append(ul)
corners.append(lr)
# Use a KDTree so we can find corners that are nearby efficiently.
tree = spatial.KDTree(corners)
new_corners = []
for corner in ulcorners:
bbox = bbox_map[corner]
# Find all points which are within a taxicab distance of corner
indices = tree.query_ball_point(
corner, bbox_map[corner].taxicab_diagonal(), p = 1)
for near_corner in tree.data[indices]:
near_bbox = bbox_map[tuple(near_corner)]
if bbox != near_bbox and bbox.overlaps(near_bbox):
# Expand both bboxes.
# Since we mutate the bbox, all references to this bbox in
# bbox_map are updated simultaneously.
bbox.x1 = near_bbox.x1 = min(bbox.x1, near_bbox.x1)
bbox.y1 = near_bbox.y1 = min(bbox.y1, near_bbox.y1)
bbox.x2 = near_bbox.x2 = max(bbox.x2, near_bbox.x2)
bbox.y2 = near_bbox.y2 = max(bbox.y2, near_bbox.y2)
return set(bbox_map.values())
if __name__ == '__main__':
fig = plt.figure()
ax = fig.add_subplot(111)
data = misc.imread('image.png')
im = ax.imshow(data)
data_slices = find_paws(255-data, smooth_radius = 20, threshold = 22)
bboxes = remove_overlaps(slice_to_bbox(data_slices))
for bbox in bboxes:
xwidth = bbox.x2 - bbox.x1
ywidth = bbox.y2 - bbox.y1
p = patches.Rectangle((bbox.x1, bbox.y1), xwidth, ywidth,
fc = 'none', ec = 'red')
ax.add_patch(p)
plt.show()
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关于python - 使用python在单色图像中围绕 Blob 的矩形边界框，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/9525313/