形状识别与numpy/scipy(也许是分水岭)

Question

我的目标是跟踪其中包含许多不同形状的图形,并将这些形状分割为单独的图像.它是白色的黑色.我是numpy,opencv&co的新手 - 但这是我目前的想法:

• 扫描黑色像素
• 发现黑色像素 - >分水岭
• 找到分水岭边界(作为多边形路径)
• 继续搜索,但忽略已经找到的边界内的点

我不擅长这些事情,有更好的方法吗？

首先,我试图找到分水岭结果的矩形边界框(这或多或少是一个例子的拼贴画):

from numpy import *
import numpy as np
from scipy import ndimage

np.set_printoptions(threshold=np.nan)

a = np.zeros((512, 512)).astype(np.uint8) #unsigned integer type needed by watershed
y, x = np.ogrid[0:512, 0:512]
m1 = ((y-200)**2 + (x-100)**2 < 30**2)
m2 = ((y-350)**2 + (x-400)**2 < 20**2)
m3 = ((y-260)**2 + (x-200)**2 < 20**2)
a[m1+m2+m3]=1

markers = np.zeros_like(a).astype(int16)
markers[0, 0] = 1
markers[200, 100] = 2
markers[350, 400] = 3
markers[260, 200] = 4

res = ndimage.watershed_ift(a.astype(uint8), markers)
unique(res) 

B = argwhere(res.astype(uint8))
(ystart, xstart), (ystop, xstop) = B.min(0), B.max(0) + 1 
tr = a[ystart:ystop, xstart:xstop]

print tr

---

不知何故,当我使用原始数组(a)时,argwhere似乎工作,但在分水岭(res)之后它只是再次输出完整的数组.

下一步可能是找到形状周围的多边形路径,但现在边界框会很棒!

请帮忙!

Answer 1

@Hooked已经回答了你的大部分问题,但当他回答时我正在写这篇文章,所以我会发布它,希望它仍然有用......

你正试图跳过一些太多的箍.你不需要watershed_ift.

您可以scipy.ndimage.label用来区分布尔数组中的单独对象,并scipy.ndimage.find_objects查找每个对象的边界框.

让我们稍微分解一下.

import numpy as np
from scipy import ndimage
import matplotlib.pyplot as plt

def draw_circle(grid, x0, y0, radius):
    ny, nx = grid.shape
    y, x = np.ogrid[:ny, :nx]
    dist = np.hypot(x - x0, y - y0)
    grid[dist < radius] = True
    return grid

# Generate 3 circles...
a = np.zeros((512, 512), dtype=np.bool)
draw_circle(a, 100, 200, 30)
draw_circle(a, 400, 350, 20)
draw_circle(a, 200, 260, 20)

# Label the objects in the array. 
labels, numobjects = ndimage.label(a)

# Now find their bounding boxes (This will be a tuple of slice objects)
# You can use each one to directly index your data. 
# E.g. a[slices[0]] gives you the original data within the bounding box of the
# first object.
slices = ndimage.find_objects(labels)

#-- Plotting... -------------------------------------
fig, ax = plt.subplots()
ax.imshow(a)
ax.set_title('Original Data')

fig, ax = plt.subplots()
ax.imshow(labels)
ax.set_title('Labeled objects')

fig, axes = plt.subplots(ncols=numobjects)
for ax, sli in zip(axes.flat, slices):
    ax.imshow(labels[sli], vmin=0, vmax=numobjects)
    tpl = 'BBox:\nymin:{0.start}, ymax:{0.stop}\nxmin:{1.start}, xmax:{1.stop}'
    ax.set_title(tpl.format(*sli))
fig.suptitle('Individual Objects')

plt.show()

希望如何找到对象的边界框更清晰.

Answer 2

使用ndimagescipy中的库.该函数label在阈值内的每个像素块上放置唯一标记.这标识了唯一的簇(形状).从您的定义开始a:

from scipy import ndimage

image_threshold = .5
label_array, n_features =  ndimage.label(a>image_threshold)

# Plot the resulting shapes
import pylab as plt
plt.subplot(121)
plt.imshow(a)
plt.subplot(122)
plt.imshow(label_array)
plt.show()