Searching Google for Histogram Equalization Python or Contrast Stretching Python I am directed to the same links from python documentation in OpenCv which are actually both related to equalization and not stretching (IMO).
http://docs.opencv.org/2.4/doc/tutorials/imgproc/histograms/histogram_equalization/histogram_equalization.html
http://docs.opencv.org/3.2.0/d5/daf/tutorial_py_histogram_equalization.html
Read the documentation, it seems to be a confusion with the wording, as it describes equalization as a stretching operation:
What Histogram Equalization does is to stretch out this range.
AND
So you need to stretch this histogram to either ends (as given in below image, from wikipedia) and that is what Histogram Equalization does (in simple words)
I feel that is wrong because nowhere on Wikipedia it says that histogram equalization means stretching, and reading other sources they clearly distinguish the two operations.
My questions:
does the OpenCV documentation actually implements Histogram Equalization, while badly explaining it?
OpenCV doesn't have any function for contrast stretching and google yields the same result because histogram equalization does stretch the histogram horizontally but its just the difference of the transformation function. (Both methods increase the contrast of the images.Transformation function transfers the pixel intensity levels from the given range to required range.)
Histogram equalization derives the transformation function(TF) automatically from probability density function (PDF) of the given image where as in contrast stretching you specify your own TF based on the applications' requirement.
One simple TF through which you can do contrast stretching is min-max
contrast stretching -
((pixel – min) / (max – min))*255.
You do this for each pixel value. min and max being the minimum and maximum intensities.
You can also use cv2.LUT
for contrast stretching by creating a custom table using np.interp
. Links to their documentation are this and this respectively. Below an example is shown.
import cv2
import numpy as np
img = cv2.imread('messi.jpg')
original = img.copy()
xp = [0, 64, 128, 192, 255]
fp = [0, 16, 128, 240, 255]
x = np.arange(256)
table = np.interp(x, xp, fp).astype('uint8')
img = cv2.LUT(img, table)
cv2.imshow("original", original)
cv2.imshow("Output", img)
cv2.waitKey(0)
cv2.destroyAllWindows()
The table created
[ 0 0 0 0 1 1 1 1 2 2 2 2 3 3 3 3 4 4
4 4 5 5 5 5 6 6 6 6 7 7 7 7 8 8 8 8
9 9 9 9 10 10 10 10 11 11 11 11 12 12 12 12 13 13
13 13 14 14 14 14 15 15 15 15 16 17 19 21 23 24 26 28
30 31 33 35 37 38 40 42 44 45 47 49 51 52 54 56 58 59
61 63 65 66 68 70 72 73 75 77 79 80 82 84 86 87 89 91
93 94 96 98 100 101 103 105 107 108 110 112 114 115 117 119 121 122
124 126 128 129 131 133 135 136 138 140 142 143 145 147 149 150 152 154
156 157 159 161 163 164 166 168 170 171 173 175 177 178 180 182 184 185
187 189 191 192 194 196 198 199 201 203 205 206 208 210 212 213 215 217
219 220 222 224 226 227 229 231 233 234 236 238 240 240 240 240 240 241
241 241 241 242 242 242 242 243 243 243 243 244 244 244 244 245 245 245
245 245 246 246 246 246 247 247 247 247 248 248 248 248 249 249 249 249
250 250 250 250 250 251 251 251 251 252 252 252 252 253 253 253 253 254
254 254 254 255]
Now cv2.LUT
will replace the values of the original image with the values in the table. For example, all the pixels having values 1 will be replaced by 0 and all pixels having values 4 will be replaced by 1.
Original Image
Contrast Stretched Image
The values of xp
and fp
can be varied to create custom tables as required and it will stretch the contrast even if min and max pixels are 0 and 255 unlike the answer provided by hashcode55.
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With