二值化

參考網站
當只想要物體的形狀或面積，可先將histogram分成兩個值0(白)與255(黑)方便後續的輪廓描繪與計算。
這邊來學習scikit-image上面的例子:

這裡測試

import matplotlib.pyplot as plt

from skimage import data
from skimage.filters import try_all_threshold

img = data.page()

fig, ax = try_all_threshold(img, figsize=(10, 8), verbose=False)
plt.show()

from skimage.filters import threshold_mean


image = data.camera()
thresh = threshold_mean(image)
binary = image > thresh

fig, axes = plt.subplots(ncols=2, figsize=(8, 3))
ax = axes.ravel()

ax[0].imshow(image, cmap=plt.cm.gray)
ax[0].set_title('Original image')

ax[1].imshow(binary, cmap=plt.cm.gray)
ax[1].set_title('Result')

for a in ax:
    a.axis('off')

plt.show()

Local thresholding

增加local threshold的設定，補足左半部較暗區的文字與白紙的區隔。

from skimage.filters import threshold_otsu, threshold_local


image = data.page()

global_thresh = threshold_otsu(image)
binary_global = image > global_thresh

block_size = 35
local_thresh = threshold_local(image, block_size, offset=10)
binary_local = image > local_thresh

fig, axes = plt.subplots(nrows=3, figsize=(7, 8))
ax = axes.ravel()
plt.gray()

ax[0].imshow(image)
ax[0].set_title('Original')

ax[1].imshow(binary_global)
ax[1].set_title('Global thresholding')

ax[2].imshow(binary_local)
ax[2].set_title('Local thresholding')

for a in ax:
    a.axis('off')

plt.show()

Edge detection

參考往站

如何擷取物體邊緣的輪廓在鄰域處理也是很常用的方法，從youtube的動圖可已清楚了解mask矩陣是怎麼作用的。接下來測試scikit-image裡面幾個演算法範例：

import numpy as np
import matplotlib.pyplot as plt

from skimage.data import camera
from skimage.filters import roberts, sobel, scharr, prewitt


image = camera()
edge_roberts = roberts(image)
edge_sobel = sobel(image)

fig, ax = plt.subplots(ncols=2, sharex=True, sharey=True,
                       figsize=(8, 4))

ax[0].imshow(edge_roberts, cmap=plt.cm.gray)
ax[0].set_title('Roberts Edge Detection')

ax[1].imshow(edge_sobel, cmap=plt.cm.gray)
ax[1].set_title('Sobel Edge Detection')

for a in ax:
    a.axis('off')

plt.tight_layout()
plt.show()

Canny edge detector

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

from skimage import feature


# Generate noisy image of a square
im = np.zeros((128, 128))
im[32:-32, 32:-32] = 1

im = ndi.rotate(im, 15, mode='constant')
im = ndi.gaussian_filter(im, 4)
im += 0.2 * np.random.random(im.shape)

# Compute the Canny filter for two values of sigma
edges1 = feature.canny(im)
edges2 = feature.canny(im, sigma=3)

# display results
fig, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3, figsize=(8, 3),
                                    sharex=True, sharey=True)

ax1.imshow(im, cmap=plt.cm.gray)
ax1.axis('off')
ax1.set_title('noisy image', fontsize=20)

ax2.imshow(edges1, cmap=plt.cm.gray)
ax2.axis('off')
ax2.set_title('Canny filter, $\sigma=1$', fontsize=20)

ax3.imshow(edges2, cmap=plt.cm.gray)
ax3.axis('off')
ax3.set_title('Canny filter, $\sigma=3$', fontsize=20)

fig.tight_layout()

plt.show()

Canny 這個演算法用了多次的邊緣擷取函數，還可將雜質濾除～