Python 将灰度图像规格化为一个值
有没有一种方法可以规范化灰度图像，使其具有预定义的平均值。假设我们得到一个平均值为142的图像（最大值为250，最小值为10），我们将对其进行归一化，使其平均值为100。整个图像会变暗，最小值和最大值也会改变。实现这一点的最佳方法是什么？您可以读取灰度图像，并像MinMaxScaler sklearn操作一样直接将最小最大值应用于图像Python 将灰度图像规格化为一个值,python,image-processing,Python,Image Processing,有没有一种方法可以规范化灰度图像，使其具有预定义的平均值。假设我们得到一个平均值为142的图像（最大值为250，最小值为10），我们将对其进行归一化，使其平均值为100。整个图像会变暗，最小值和最大值也会改变。实现这一点的最佳方法是什么？您可以读取灰度图像，并像MinMaxScaler sklearn操作一样直接将最小最大值应用于图像 im = cv2.imread(file,0) ((im/255)*90)+10 输出： 或 使用来变换图像 from sklearn.preprocessin
im = cv2.imread(file,0)
((im/255)*90)+10
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler(feature_range=(10,100))
scaler.fit(im)
scaler.transform(im)
您可以读取灰度图像，并像MinMaxScaler sklearn的操作一样，将直接最小最大值应用于图像
im = cv2.imread(file,0)
((im/255)*90)+10
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler(feature_range=(10,100))
scaler.fit(im)
scaler.transform(im)
您可以使用直接乘法来实现：
import numpy as np
# Make 64 noise samples with mean=142, sigma=50
s = np.random.normal(142, 50, 64)
看起来是这样的：
array([108.96793075, 122.56604336, 52.92948494, 211.88597561,
201.75694121, 127.03699257, 169.63376672, 151.38689085,
88.50751644, 180.42261676, 148.06044352, 113.02992937,
128.68514242, 171.55551377, 182.72483817, 127.008611 ,
192.63588333, 154.29289467, 95.42692049, 168.35000286,
101.22705089, 111.03725683, 110.5901968 , 185.42039232,
144.03267223, 178.25385364, 157.65533049, 142.71266293,
123.6252744 , 136.0834919 , 157.04318249, 164.20554021,
181.80931254, 125.869104 , 65.63487663, 158.02223726,
91.04841987, 41.05017344, 175.60718802, 273.45938051,
82.37393654, 188.86067814, 154.37552451, 173.63709343,
106.49434825, 110.5845771 , 87.71306659, 150.19152982,
238.95739708, 152.91382848, 210.96731605, 154.80451484,
156.14301742, 157.31710985, 170.80328144, 122.48376855,
187.7631208 , 148.40453789, 80.31133945, 97.093196 ,
149.19437508, 140.4912321 , 278.1623903 , 131.15185917])
In [53]: s.min(), s.max(), s.mean()
Out[53]: (41.050173444195025, 278.1623902968886, 146.1319527357261)
array([ 74.56817534, 83.87354105, 36.22033645, 144.99633492,
138.06490465, 86.93306987, 116.08259764, 103.59602264,
60.56684714, 123.46554835, 101.31969138, 77.34785395,
88.06092029, 117.39767419, 125.04098847, 86.91364799,
131.82324586, 105.58463894, 65.30188552, 115.2041013 ,
69.27099036, 75.98424215, 75.67831315, 126.88559131,
98.56343498, 121.98143548, 107.8855976 , 97.66013542,
84.59838665, 93.12370728, 107.46669674, 112.36799149,
124.41448235, 86.13386849, 44.91480159, 108.13667668,
62.30562048, 28.09116875, 120.17028769, 187.13181846,
56.36955847, 129.23982374, 105.64118362, 118.82212629,
72.8754706 , 75.67446752, 60.02319476, 102.77802152,
163.52166149, 104.64092597, 144.3676842 , 105.93474729,
106.85070205, 107.65414881, 116.88291181, 83.81723932,
128.48875095, 101.55515964, 54.9580964 , 66.44213958,
102.09565552, 96.13998135, 190.35014936, 89.74892672])
In [59]: r.min(), r.max(), r.mean()
Out[59]: (28.091168752416973, 190.3501493611971, 100.0)
现在将平均值转换为100：
r = s * 100./s.mean()
现在看起来是这样的：
array([108.96793075, 122.56604336, 52.92948494, 211.88597561,
201.75694121, 127.03699257, 169.63376672, 151.38689085,
88.50751644, 180.42261676, 148.06044352, 113.02992937,
128.68514242, 171.55551377, 182.72483817, 127.008611 ,
192.63588333, 154.29289467, 95.42692049, 168.35000286,
101.22705089, 111.03725683, 110.5901968 , 185.42039232,
144.03267223, 178.25385364, 157.65533049, 142.71266293,
123.6252744 , 136.0834919 , 157.04318249, 164.20554021,
181.80931254, 125.869104 , 65.63487663, 158.02223726,
91.04841987, 41.05017344, 175.60718802, 273.45938051,
82.37393654, 188.86067814, 154.37552451, 173.63709343,
106.49434825, 110.5845771 , 87.71306659, 150.19152982,
238.95739708, 152.91382848, 210.96731605, 154.80451484,
156.14301742, 157.31710985, 170.80328144, 122.48376855,
187.7631208 , 148.40453789, 80.31133945, 97.093196 ,
149.19437508, 140.4912321 , 278.1623903 , 131.15185917])
In [53]: s.min(), s.max(), s.mean()
Out[53]: (41.050173444195025, 278.1623902968886, 146.1319527357261)
array([ 74.56817534, 83.87354105, 36.22033645, 144.99633492,
138.06490465, 86.93306987, 116.08259764, 103.59602264,
60.56684714, 123.46554835, 101.31969138, 77.34785395,
88.06092029, 117.39767419, 125.04098847, 86.91364799,
131.82324586, 105.58463894, 65.30188552, 115.2041013 ,
69.27099036, 75.98424215, 75.67831315, 126.88559131,
98.56343498, 121.98143548, 107.8855976 , 97.66013542,
84.59838665, 93.12370728, 107.46669674, 112.36799149,
124.41448235, 86.13386849, 44.91480159, 108.13667668,
62.30562048, 28.09116875, 120.17028769, 187.13181846,
56.36955847, 129.23982374, 105.64118362, 118.82212629,
72.8754706 , 75.67446752, 60.02319476, 102.77802152,
163.52166149, 104.64092597, 144.3676842 , 105.93474729,
106.85070205, 107.65414881, 116.88291181, 83.81723932,
128.48875095, 101.55515964, 54.9580964 , 66.44213958,
102.09565552, 96.13998135, 190.35014936, 89.74892672])
In [59]: r.min(), r.max(), r.mean()
Out[59]: (28.091168752416973, 190.3501493611971, 100.0)
您可以使用直接乘法来实现：
import numpy as np
# Make 64 noise samples with mean=142, sigma=50
s = np.random.normal(142, 50, 64)
看起来是这样的：
array([108.96793075, 122.56604336, 52.92948494, 211.88597561,
201.75694121, 127.03699257, 169.63376672, 151.38689085,
88.50751644, 180.42261676, 148.06044352, 113.02992937,
128.68514242, 171.55551377, 182.72483817, 127.008611 ,
192.63588333, 154.29289467, 95.42692049, 168.35000286,
101.22705089, 111.03725683, 110.5901968 , 185.42039232,
144.03267223, 178.25385364, 157.65533049, 142.71266293,
123.6252744 , 136.0834919 , 157.04318249, 164.20554021,
181.80931254, 125.869104 , 65.63487663, 158.02223726,
91.04841987, 41.05017344, 175.60718802, 273.45938051,
82.37393654, 188.86067814, 154.37552451, 173.63709343,
106.49434825, 110.5845771 , 87.71306659, 150.19152982,
238.95739708, 152.91382848, 210.96731605, 154.80451484,
156.14301742, 157.31710985, 170.80328144, 122.48376855,
187.7631208 , 148.40453789, 80.31133945, 97.093196 ,
149.19437508, 140.4912321 , 278.1623903 , 131.15185917])
In [53]: s.min(), s.max(), s.mean()
Out[53]: (41.050173444195025, 278.1623902968886, 146.1319527357261)
array([ 74.56817534, 83.87354105, 36.22033645, 144.99633492,
138.06490465, 86.93306987, 116.08259764, 103.59602264,
60.56684714, 123.46554835, 101.31969138, 77.34785395,
88.06092029, 117.39767419, 125.04098847, 86.91364799,
131.82324586, 105.58463894, 65.30188552, 115.2041013 ,
69.27099036, 75.98424215, 75.67831315, 126.88559131,
98.56343498, 121.98143548, 107.8855976 , 97.66013542,
84.59838665, 93.12370728, 107.46669674, 112.36799149,
124.41448235, 86.13386849, 44.91480159, 108.13667668,
62.30562048, 28.09116875, 120.17028769, 187.13181846,
56.36955847, 129.23982374, 105.64118362, 118.82212629,
72.8754706 , 75.67446752, 60.02319476, 102.77802152,
163.52166149, 104.64092597, 144.3676842 , 105.93474729,
106.85070205, 107.65414881, 116.88291181, 83.81723932,
128.48875095, 101.55515964, 54.9580964 , 66.44213958,
102.09565552, 96.13998135, 190.35014936, 89.74892672])
In [59]: r.min(), r.max(), r.mean()
Out[59]: (28.091168752416973, 190.3501493611971, 100.0)
现在将平均值转换为100：
r = s * 100./s.mean()
现在看起来是这样的：
array([108.96793075, 122.56604336, 52.92948494, 211.88597561,
201.75694121, 127.03699257, 169.63376672, 151.38689085,
88.50751644, 180.42261676, 148.06044352, 113.02992937,
128.68514242, 171.55551377, 182.72483817, 127.008611 ,
192.63588333, 154.29289467, 95.42692049, 168.35000286,
101.22705089, 111.03725683, 110.5901968 , 185.42039232,
144.03267223, 178.25385364, 157.65533049, 142.71266293,
123.6252744 , 136.0834919 , 157.04318249, 164.20554021,
181.80931254, 125.869104 , 65.63487663, 158.02223726,
91.04841987, 41.05017344, 175.60718802, 273.45938051,
82.37393654, 188.86067814, 154.37552451, 173.63709343,
106.49434825, 110.5845771 , 87.71306659, 150.19152982,
238.95739708, 152.91382848, 210.96731605, 154.80451484,
156.14301742, 157.31710985, 170.80328144, 122.48376855,
187.7631208 , 148.40453789, 80.31133945, 97.093196 ,
149.19437508, 140.4912321 , 278.1623903 , 131.15185917])
In [53]: s.min(), s.max(), s.mean()
Out[53]: (41.050173444195025, 278.1623902968886, 146.1319527357261)
array([ 74.56817534, 83.87354105, 36.22033645, 144.99633492,
138.06490465, 86.93306987, 116.08259764, 103.59602264,
60.56684714, 123.46554835, 101.31969138, 77.34785395,
88.06092029, 117.39767419, 125.04098847, 86.91364799,
131.82324586, 105.58463894, 65.30188552, 115.2041013 ,
69.27099036, 75.98424215, 75.67831315, 126.88559131,
98.56343498, 121.98143548, 107.8855976 , 97.66013542,
84.59838665, 93.12370728, 107.46669674, 112.36799149,
124.41448235, 86.13386849, 44.91480159, 108.13667668,
62.30562048, 28.09116875, 120.17028769, 187.13181846,
56.36955847, 129.23982374, 105.64118362, 118.82212629,
72.8754706 , 75.67446752, 60.02319476, 102.77802152,
163.52166149, 104.64092597, 144.3676842 , 105.93474729,
106.85070205, 107.65414881, 116.88291181, 83.81723932,
128.48875095, 101.55515964, 54.9580964 , 66.44213958,
102.09565552, 96.13998135, 190.35014936, 89.74892672])
In [59]: r.min(), r.max(), r.mean()
Out[59]: (28.091168752416973, 190.3501493611971, 100.0)