Perform the following steps to implement histogram matching:
- Let's implement the histogram-matching algorithm with the hist_matching() function, which accepts the original and the template image's cdf along with the original image:
def hist_matching(c, c_t, im):
b = np.interp(c, c_t, np.arange(256))
# find closest matches to b_t
pix_repl = {i:b[i] for i in range(256)}
# dictionary to replace the pixels
mp = np.arange(0,256)
for (k, v) in pix_repl.items():
mp[k] = v
s = im.shape
im = np.reshape(mp[im.ravel()], im.shape)
im = np.reshape(im, s)
return im
- Compute cdf of an image with the following function:
def cdf(im):
c, b = cumulative_distribution(im)
for i in range(b[0]):
c = np.insert(c, 0, 0)
for i in range(b[-1]+1, 256):
c = np.append(c, 1)
return c
- Finally, read the input and template images, compute their cdfs, and create the output image with the hist_matching() function. Plot the input, template, and output images by running the following code:
im = imread('images/goddess.png').astype(np.uint8)
im_t = imread('images/leaves.png')
im1 = np.zeros(im.shape).astype(np.uint8)
for i in range(3):
c = cdf(im[...,i])
c_t = cdf(im_t[...,i])
im1[...,i] = hist_matching(c, c_t, im[...,i])
plt.figure(figsize=(20,17))
plt.subplots_adjust(left=0, top=0.95, right=1, bottom=0, \
wspace=0.05, hspace=0.05)
plt.subplot(221), plt.imshow(im), plt.axis('off'), \
plt.title('Input Image', size=25)
plt.subplot(222), plt.imshow(im_t), plt.axis('off'), \
plt.title('Template Image', size=25)
plt.subplot(223), plt.imshow(im1[...,:3]), plt.axis('off'), \
plt.title('Output Image', size=25)
plt.show()
