In [2]:
import numpy as np
from skimage import exposure,data
In [3]:
image =data.camera()*1.0
用 numpy 包计算直方图
In [4]:
hist1=np.histogram(image, bins=2)
用 skimage 计算直方图
In [5]:
hist2=exposure.histogram(image, nbins=2)
In [6]:
print(hist1)
(array([ 93585, 168559]), array([ 0. , 127.5, 255. ]))
In [7]:
print(hist2)
(array([ 93585, 168559]), array([ 63.75, 191.25]))
n, bins, patches = plt.hist(arr, bins=10, normed=0, facecolor='black', edgecolor='black',alpha=1，histtype='bar')hist 的参数非常多，但常用的就这六个，只有第一个是必须的，后面四个可选。
arr: 需要计算直方图的一维数组。
bins: 直方图的柱数，可选项，默认为10。
normed: 是否将得到的直方图向量归一化，默认为0。
facecolor: 直方图颜色。
edgecolor: 直方图边框颜色。
alpha: 透明度。
histtype: 直方图类型，‘bar’, ‘barstacked’, ‘step’, ‘stepfilled’ 。
返回值 ：
n: 直方图向量，是否归一化由参数 normed 设定。
bins: 返回各个 bin 的区间范围。
patches: 返回每个 bin 里面包含的数据，是一个 list 。
In [8]:
%matplotlib inline
In [9]:
from skimage import data
import matplotlib.pyplot as plt
In [10]:
img=data.camera()
In [11]:
plt.figure("hist")
arr=img.flatten()
<Figure size 640x480 with 0 Axes>
原代码
In [12]:
# n, bins, patches = plt.hist(arr, bins=256, normed=1,edgecolor='None',facecolor='red')
n, bins, patches = plt.hist(arr, bins=256,edgecolor='None',facecolor='red')
plt.show()
In [13]:
from skimage import data
import matplotlib.pyplot as plt
In [14]:
img=data.chelsea()
ar=img[:,:,0].flatten()
# plt.hist(ar, bins=256, normed=1,facecolor='r',edgecolor='r')
plt.hist(ar, bins=256, facecolor='r',edgecolor='r')
ag=img[:,:,1].flatten()
# plt.hist(ag, bins=256, normed=1, facecolor='g',edgecolor='g')
plt.hist(ag, bins=256, facecolor='g',edgecolor='g')
ab=img[:,:,2].flatten()
# ,host
# plt.hist(ab, bins=256, normed=1, facecolor='b',edgecolor='b')
plt.hist(ab, bins=256, facecolor='b',edgecolor='b')
plt.show()
其中，在 plt.hist(ab, bins=256, normed=1, facecolor='b',edgecolor='b',host=1)
加一个参数 hold=1，表示可以叠加。
In [15]:
from skimage import data,exposure
import matplotlib.pyplot as plt
In [16]:
img=data.moon()
plt.figure("hist",figsize=(8,8))
Out[16]:
<Figure size 800x800 with 0 Axes>
<Figure size 800x800 with 0 Axes>
原始图像，原始图像直方图：
In [17]:
arr=img.flatten()
plt.subplot(221)
plt.imshow(img,plt.cm.gray) #原始图像
plt.subplot(222)
# plt.hist(arr, bins=256, normed=1,edgecolor='None',facecolor='red') #原始图像直方图
plt.hist(arr, bins=256, edgecolor='None',facecolor='red') #原始图像直方图
Out[17]:
(array([2.4000e+02, 0.0000e+00, 6.0000e+01, 3.6000e+01, 0.0000e+00,
6.0000e+01, 5.2000e+01, 0.0000e+00, 5.2000e+01, 0.0000e+00,
6.0000e+01, 8.0000e+01, 0.0000e+00, 4.0000e+00, 4.4000e+01,
0.0000e+00, 5.6000e+01, 0.0000e+00, 8.8000e+01, 7.2000e+01,
0.0000e+00, 8.0000e+01, 7.6000e+01, 0.0000e+00, 5.2000e+01,
6.8000e+01, 0.0000e+00, 6.4000e+01, 0.0000e+00, 8.8000e+01,
8.0000e+01, 0.0000e+00, 7.2000e+01, 4.0000e+01, 0.0000e+00,
7.2000e+01, 3.6000e+01, 0.0000e+00, 4.8000e+01, 6.4000e+01,
0.0000e+00, 6.4000e+01, 4.8000e+01, 0.0000e+00, 2.8000e+01,
7.6000e+01, 0.0000e+00, 8.4000e+01, 7.6000e+01, 8.8000e+01,
0.0000e+00, 8.0000e+01, 1.0800e+02, 0.0000e+00, 5.2000e+01,
7.6000e+01, 9.2000e+01, 0.0000e+00, 8.8000e+01, 1.0000e+02,
1.0000e+02, 0.0000e+00, 1.0400e+02, 0.0000e+00, 1.3200e+02,
1.0800e+02, 0.0000e+00, 1.4800e+02, 1.2400e+02, 1.6800e+02,
1.6000e+02, 1.6000e+02, 0.0000e+00, 2.1200e+02, 2.0800e+02,
1.8000e+02, 2.0000e+02, 2.6400e+02, 2.1600e+02, 2.2000e+02,
3.1200e+02, 2.2400e+02, 2.7200e+02, 2.9600e+02, 2.8400e+02,
3.1600e+02, 2.5200e+02, 5.3600e+02, 3.6800e+02, 4.0800e+02,
3.7200e+02, 8.2400e+02, 3.5200e+02, 5.9600e+02, 4.5600e+02,
8.6000e+02, 9.1600e+02, 4.0000e+02, 8.8000e+02, 9.0800e+02,
5.8000e+02, 1.8600e+03, 1.4160e+03, 2.5640e+03, 2.0360e+03,
3.3920e+03, 5.9280e+03, 5.7240e+03, 1.1964e+04, 1.1436e+04,
1.6256e+04, 1.7772e+04, 2.0324e+04, 2.1444e+04, 1.7484e+04,
2.3296e+04, 1.6144e+04, 1.2096e+04, 1.1748e+04, 9.4080e+03,
9.0200e+03, 5.0800e+03, 5.3160e+03, 2.7760e+03, 1.3640e+03,
2.0560e+03, 8.7200e+02, 1.2600e+03, 8.6800e+02, 3.7600e+02,
5.5200e+02, 1.7600e+02, 4.3200e+02, 1.9200e+02, 1.5600e+02,
2.5200e+02, 1.1200e+02, 1.3600e+02, 1.1600e+02, 1.0400e+02,
8.8000e+01, 1.1600e+02, 1.0000e+02, 8.8000e+01, 6.0000e+01,
1.0800e+02, 7.6000e+01, 1.1600e+02, 8.8000e+01, 7.6000e+01,
6.8000e+01, 9.2000e+01, 7.2000e+01, 0.0000e+00, 4.4000e+01,
6.4000e+01, 5.6000e+01, 0.0000e+00, 1.1200e+02, 7.2000e+01,
7.6000e+01, 0.0000e+00, 9.6000e+01, 5.2000e+01, 0.0000e+00,
0.0000e+00, 3.2000e+01, 4.4000e+01, 0.0000e+00, 4.8000e+01,
0.0000e+00, 7.6000e+01, 2.8000e+01, 0.0000e+00, 3.6000e+01,
3.6000e+01, 0.0000e+00, 4.4000e+01, 0.0000e+00, 2.8000e+01,
0.0000e+00, 2.0000e+01, 5.2000e+01, 0.0000e+00, 2.0000e+01,
0.0000e+00, 3.2000e+01, 0.0000e+00, 1.2000e+01, 0.0000e+00,
2.0000e+01, 0.0000e+00, 1.2000e+01, 0.0000e+00, 1.2000e+01,
0.0000e+00, 1.2000e+01, 0.0000e+00, 2.0000e+01, 0.0000e+00,
4.0000e+00, 0.0000e+00, 2.8000e+01, 0.0000e+00, 8.0000e+00,
0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.6000e+01,
0.0000e+00, 8.0000e+00, 0.0000e+00, 1.2000e+01, 0.0000e+00,
0.0000e+00, 1.2000e+01, 0.0000e+00, 2.0000e+01, 0.0000e+00,
2.8000e+01, 0.0000e+00, 0.0000e+00, 2.0000e+01, 0.0000e+00,
1.2000e+01, 0.0000e+00, 0.0000e+00, 3.6000e+01, 0.0000e+00,
4.8000e+01, 0.0000e+00, 0.0000e+00, 2.4000e+01, 0.0000e+00,
2.4000e+01, 0.0000e+00, 3.6000e+01, 0.0000e+00, 0.0000e+00,
2.4000e+01, 0.0000e+00, 1.6000e+01, 0.0000e+00, 0.0000e+00,
1.2000e+01, 0.0000e+00, 1.6000e+01, 0.0000e+00, 0.0000e+00,
4.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
4.0000e+00]),
array([ 0. , 0.99609375, 1.9921875 , 2.98828125,
3.984375 , 4.98046875, 5.9765625 , 6.97265625,
7.96875 , 8.96484375, 9.9609375 , 10.95703125,
11.953125 , 12.94921875, 13.9453125 , 14.94140625,
15.9375 , 16.93359375, 17.9296875 , 18.92578125,
19.921875 , 20.91796875, 21.9140625 , 22.91015625,
23.90625 , 24.90234375, 25.8984375 , 26.89453125,
27.890625 , 28.88671875, 29.8828125 , 30.87890625,
31.875 , 32.87109375, 33.8671875 , 34.86328125,
35.859375 , 36.85546875, 37.8515625 , 38.84765625,
39.84375 , 40.83984375, 41.8359375 , 42.83203125,
43.828125 , 44.82421875, 45.8203125 , 46.81640625,
47.8125 , 48.80859375, 49.8046875 , 50.80078125,
51.796875 , 52.79296875, 53.7890625 , 54.78515625,
55.78125 , 56.77734375, 57.7734375 , 58.76953125,
59.765625 , 60.76171875, 61.7578125 , 62.75390625,
63.75 , 64.74609375, 65.7421875 , 66.73828125,
67.734375 , 68.73046875, 69.7265625 , 70.72265625,
71.71875 , 72.71484375, 73.7109375 , 74.70703125,
75.703125 , 76.69921875, 77.6953125 , 78.69140625,
79.6875 , 80.68359375, 81.6796875 , 82.67578125,
83.671875 , 84.66796875, 85.6640625 , 86.66015625,
87.65625 , 88.65234375, 89.6484375 , 90.64453125,
91.640625 , 92.63671875, 93.6328125 , 94.62890625,
95.625 , 96.62109375, 97.6171875 , 98.61328125,
99.609375 , 100.60546875, 101.6015625 , 102.59765625,
103.59375 , 104.58984375, 105.5859375 , 106.58203125,
107.578125 , 108.57421875, 109.5703125 , 110.56640625,
111.5625 , 112.55859375, 113.5546875 , 114.55078125,
115.546875 , 116.54296875, 117.5390625 , 118.53515625,
119.53125 , 120.52734375, 121.5234375 , 122.51953125,
123.515625 , 124.51171875, 125.5078125 , 126.50390625,
127.5 , 128.49609375, 129.4921875 , 130.48828125,
131.484375 , 132.48046875, 133.4765625 , 134.47265625,
135.46875 , 136.46484375, 137.4609375 , 138.45703125,
139.453125 , 140.44921875, 141.4453125 , 142.44140625,
143.4375 , 144.43359375, 145.4296875 , 146.42578125,
147.421875 , 148.41796875, 149.4140625 , 150.41015625,
151.40625 , 152.40234375, 153.3984375 , 154.39453125,
155.390625 , 156.38671875, 157.3828125 , 158.37890625,
159.375 , 160.37109375, 161.3671875 , 162.36328125,
163.359375 , 164.35546875, 165.3515625 , 166.34765625,
167.34375 , 168.33984375, 169.3359375 , 170.33203125,
171.328125 , 172.32421875, 173.3203125 , 174.31640625,
175.3125 , 176.30859375, 177.3046875 , 178.30078125,
179.296875 , 180.29296875, 181.2890625 , 182.28515625,
183.28125 , 184.27734375, 185.2734375 , 186.26953125,
187.265625 , 188.26171875, 189.2578125 , 190.25390625,
191.25 , 192.24609375, 193.2421875 , 194.23828125,
195.234375 , 196.23046875, 197.2265625 , 198.22265625,
199.21875 , 200.21484375, 201.2109375 , 202.20703125,
203.203125 , 204.19921875, 205.1953125 , 206.19140625,
207.1875 , 208.18359375, 209.1796875 , 210.17578125,
211.171875 , 212.16796875, 213.1640625 , 214.16015625,
215.15625 , 216.15234375, 217.1484375 , 218.14453125,
219.140625 , 220.13671875, 221.1328125 , 222.12890625,
223.125 , 224.12109375, 225.1171875 , 226.11328125,
227.109375 , 228.10546875, 229.1015625 , 230.09765625,
231.09375 , 232.08984375, 233.0859375 , 234.08203125,
235.078125 , 236.07421875, 237.0703125 , 238.06640625,
239.0625 , 240.05859375, 241.0546875 , 242.05078125,
243.046875 , 244.04296875, 245.0390625 , 246.03515625,
247.03125 , 248.02734375, 249.0234375 , 250.01953125,
251.015625 , 252.01171875, 253.0078125 , 254.00390625,
255. ]),
<BarContainer object of 256 artists>)
均衡化图像，均衡化直方图：
In [18]:
img1=exposure.equalize_hist(img)
arr1=img1.flatten()
plt.subplot(223)
plt.imshow(img1,plt.cm.gray) #均衡化图像
plt.subplot(224)
# plt.hist(arr1, bins=256, normed=1,edgecolor='None',facecolor='red') #均衡化直方图
plt.hist(arr1, bins=256, edgecolor='None',facecolor='red') #均衡化直方图
plt.show()
