The equalized image [2]_ has a roughly linear cumulative distribution function
for each pixel neighborhood. The local version [3]_ of the histogram
equalization emphasizes every local greylevel variations.
.. [2] http://en.wikipedia.org/wiki/Histogram_equalization
.. [3] http://en.wikipedia.org/wiki/Adaptive_histogram_equalization
"""
from skimage import exposure
from skimage.filter import rank
ima = data.camera()
# equalize globally and locally
glob = exposure.equalize(ima) * 255
loc = rank.equalize(ima, disk(20))
# extract histogram for each image
hist = np.histogram(ima, bins=np.arange(0, 256))
glob_hist = np.histogram(glob, bins=np.arange(0, 256))
loc_hist = np.histogram(loc, bins=np.arange(0, 256))
plt.figure(figsize=(10, 10))
plt.subplot(321)
plt.imshow(ima, cmap=plt.cm.gray, interpolation='nearest')
plt.axis('off')
plt.subplot(322)
plt.plot(hist[1][:-1], hist[0], lw=2)
plt.title('histogram of grey values')
plt.subplot(323)
plt.imshow(glob, cmap=plt.cm.gray, interpolation='nearest')
ax_cdf.plot(bins, img_cdf, 'r')
return ax_img, ax_hist, ax_cdf
# Load an example image
img = img_as_ubyte(data.moon())
# Contrast stretching
p2 = np.percentile(img, 2)
p98 = np.percentile(img, 98)
img_rescale = exposure.equalize_hist(img)
# Equalization
selem = disk(30)
img_eq = rank.equalize(img, selem=selem)
# Display results
f, axes = plt.subplots(2, 3, figsize=(8, 4))
ax_img, ax_hist, ax_cdf = plot_img_and_hist(img, axes[:, 0])
ax_img.set_title('Low contrast image')
ax_hist.set_ylabel('Number of pixels')
ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_rescale, axes[:, 1])
ax_img.set_title('Global equalise')
ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_eq, axes[:, 2])
ax_img.set_title('Local equalize')
ax_cdf.set_ylabel('Fraction of total intensity')
# Display cumulative distribution
img_cdf, bins = exposure.cumulative_distribution(img, bins)
ax_cdf.plot(bins, img_cdf, "r")
return ax_img, ax_hist, ax_cdf
# Load an example image
img = img_as_ubyte(data.moon())
# Global equalize
img_rescale = exposure.equalize_hist(img)
# Equalization
selem = disk(30)
img_eq = rank.equalize(img, selem=selem)
# Display results
f, axes = plt.subplots(2, 3, figsize=(8, 5))
ax_img, ax_hist, ax_cdf = plot_img_and_hist(img, axes[:, 0])
ax_img.set_title("Low contrast image")
ax_hist.set_ylabel("Number of pixels")
ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_rescale, axes[:, 1])
ax_img.set_title("Global equalise")
ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_eq, axes[:, 2])
ax_img.set_title("Local equalize")
ax_cdf.set_ylabel("Fraction of total intensity")
for each pixel neighborhood. The local version [3]_ of the histogram
equalization emphasizes every local gray-level variations.
.. [2] http://en.wikipedia.org/wiki/Histogram_equalization
.. [3] http://en.wikipedia.org/wiki/Adaptive_histogram_equalization
"""
from skimage import exposure
from skimage.filter import rank
noisy_image = img_as_ubyte(data.camera())
# equalize globally and locally
glob = exposure.equalize(noisy_image) * 255
loc = rank.equalize(noisy_image, disk(20))
# extract histogram for each image
hist = np.histogram(noisy_image, bins=np.arange(0, 256))
glob_hist = np.histogram(glob, bins=np.arange(0, 256))
loc_hist = np.histogram(loc, bins=np.arange(0, 256))
plt.figure(figsize=(10, 10))
plt.subplot(321)
plt.imshow(noisy_image, interpolation='nearest')
plt.axis('off')
plt.subplot(322)
plt.plot(hist[1][:-1], hist[0], lw=2)
plt.title('Histogram of gray values')
The equalized image [2]_ has a roughly linear cumulative distribution function
for each pixel neighborhood. The local version [3]_ of the histogram
equalization emphasizes every local greylevel variations.
.. [2] http://en.wikipedia.org/wiki/Histogram_equalization
.. [3] http://en.wikipedia.org/wiki/Adaptive_histogram_equalization
"""
from skimage import exposure
from skimage.filter import rank
ima = data.camera()
# equalize globally and locally
glob = exposure.equalize(ima) * 255
loc = rank.equalize(ima, disk(20))
# extract histogram for each image
hist = np.histogram(ima, bins=np.arange(0, 256))
glob_hist = np.histogram(glob, bins=np.arange(0, 256))
loc_hist = np.histogram(loc, bins=np.arange(0, 256))
plt.figure(figsize=(10, 10))
plt.subplot(321)
plt.imshow(ima, cmap=plt.cm.gray, interpolation='nearest')
plt.axis('off')
plt.subplot(322)
plt.plot(hist[1][:-1], hist[0], lw=2)
plt.title('histogram of grey values')
plt.subplot(323)
plt.imshow(glob, cmap=plt.cm.gray, interpolation='nearest')
#load the image image = nh.getImage(image_source) image = nh.RGBtoGray(image) image = image.astype(np.uint8) #blur image to get rid of most of the noise blur_kernel = (blur, blur) image = cv2.blur(image, blur_kernel) #image = np.where(image > 100, 255,1) #image = threshold_adaptive(image, 21,offset=0) #perform local histogram equalization to emphasize edges selem = disk(histogram_equalization) image_eq = rank.equalize(image, selem=selem) #add half of the processed and half of the original image to further separate #regions where there is structure from the background image = ratio * image_eq + (1 - ratio) * image #find a favorable threshold using otsu thresholding and modify it by t_mod threshold = nh.otsuThreshold(image) - offset #threshold and save image image = np.where(image > threshold, 1.0, 0.0) image = np.where(image > 0, 0, 1) #image = opening(image,disk(3)) #image = closing(image,disk(4)) image = remove_small_objects(image.astype(bool),\
for each pixel neighborhood. The local version [3]_ of the histogram
equalization emphasizes every local gray-level variations.
.. [2] http://en.wikipedia.org/wiki/Histogram_equalization
.. [3] http://en.wikipedia.org/wiki/Adaptive_histogram_equalization
"""
from skimage import exposure
from skimage.filter import rank
noisy_image = img_as_ubyte(data.camera())
# equalize globally and locally
glob = exposure.equalize(noisy_image) * 255
loc = rank.equalize(noisy_image, disk(20))
# extract histogram for each image
hist = np.histogram(noisy_image, bins=np.arange(0, 256))
glob_hist = np.histogram(glob, bins=np.arange(0, 256))
loc_hist = np.histogram(loc, bins=np.arange(0, 256))
plt.figure(figsize=(10, 10))
plt.subplot(321)
plt.imshow(noisy_image, interpolation='nearest')
plt.axis('off')
plt.subplot(322)
plt.plot(hist[1][:-1], hist[0], lw=2)
plt.title('Histogram of gray values')
image = nh.getImage(image_source) image = nh.RGBtoGray(image) image = image.astype(np.uint8) #blur image to get rid of most of the noise blur_kernel = (blur,blur) image = cv2.blur(image, blur_kernel) #image = np.where(image > 100, 255,1) #image = threshold_adaptive(image, 21,offset=0) #perform local histogram equalization to emphasize edges selem = disk(histogram_equalization) image_eq = rank.equalize(image, selem=selem) #add half of the processed and half of the original image to further separate #regions where there is structure from the background image = ratio*image_eq + (1-ratio)*image #find a favorable threshold using otsu thresholding and modify it by t_mod threshold = nh.otsuThreshold(image)-offset #threshold and save image image = np.where(image > threshold,1.0,0.0) image = np.where(image > 0,0,1) #image = opening(image,disk(3)) #image = closing(image,disk(4))
