Adaptive alignment of a histogram in Python
I am trying to implement adaptive alignment of a histogram in python. I take an image and split it into smaller areas and then apply a traditional histogram to it. Then I combine the smaller images into one and get the final resulting image. The final image looks very blocky and has different contrast levels for each individual region. Is there a way I could maintain consistent contrast for each individual image so that it looks like a single image instead of small images stitched together.
import cv2
import numpy as np
from matplotlib import pyplot as plt
from scipy.misc import imsave
from scipy import ndimage
from scipy import misc
import scipy.misc
import scipy
import image_slicer
from image_slicer import join
from PIL import Image
img = 'watch.png'
num_tiles = 25
tiles = image_slicer.slice(img, num_tiles)
for tile in tiles:
img = scipy.misc.imread(tile.filename)
hist,bins = np.histogram(img.flatten(),256,[0,256])
cdf = hist.cumsum()
cdf_normalized = cdf *hist.max()/ cdf.max()
plt.plot(cdf_normalized, color = 'g')
plt.hist(img.flatten(),256,[0,256], color = 'g')
plt.xlim([0,256])
plt.legend(('cdf','histogram'), loc = 'upper left')
cdf_m = np.ma.masked_equal(cdf,0)
cdf_o = (cdf_m - cdf_m.min())*255/(cdf_m.max()-cdf_m.min())
cdf = np.ma.filled(cdf_o,0).astype('uint8')
img3 = cdf[img]
cv2.imwrite(tile.filename,img3)
tile.image = Image.open(tile.filename
image = join(tiles)
image.save('watch-join.png')
+3
source to share
1 answer
I looked at the actual algorithm and came up with the following implementation. I'm sure there is a better way to do this. Any suggestions are greatly appreciated.
import numpy as np
import cv2
img = cv2.imread('watch.png',0)
print img
img_size=img.shape
print img_size
img_mod = np.zeros((600, 800))
for i in range(0,img_size[0]-30):
for j in range(0,img_size[1]-30):
kernel = img[i:i+30,j:j+30]
for k in range(0,30):
for l in range(0,30):
element = kernel[k,l]
rank = 0
for m in range(0,30):
for n in range(0,30):
if(kernel[k,l]>kernel[m,n]):
rank = rank + 1
img_mod[i,j] = ((rank * 255 )/900)
im = np.array(img_mod, dtype = np.uint8)
cv2.imwrite('target.png',im)
0
source to share