def plot_components(components,
patches_size=(DEFAULT_PATCHE_SIZE, DEFAULT_PATCHE_SIZE)):
import cv2
for i, comp in enumerate(components):
c_img = (255 * minmaxnormalization(
comp.reshape(patches_size[0], patches_size[1], 3))).astype('uint8')
imwrite('c%d.png' % i, c_img)
def frequency_tuned_saliency(image):
"""
Frequency Tuned Saliency.
Find the Euclidean distance between
the Lab pixel vector in a Gaussian filtered image
with the average Lab vector for the input image.
R. Achanta, S. Hemami, F. Estrada and S. Susstrunk,
Frequency-tuned Salient Region
Detection, IEEE International Conference on Computer
Vision and Pattern Recognition
Args:
image (numpy.array): an image.
Returns:
a 2d image saliency map.
"""
image = cv2.cvtColor(image, cv2.COLOR_BGR2LAB)
#mean of each channel
means = []
for c in range(image.shape[2]):
means.append(image[:, :, c].mean())
means = numpy.asarray(means)
image = cv2.medianBlur(image, 9)
dist = (image - means) ** 2
print("mean color is %s" % means)
salmap = numpy.zeros((dist.shape[0], dist.shape[1]))
for i in range(dist.shape[0]):
for j in range(dist.shape[1]):
salmap[i][j] = numpy.sqrt(dist[i][j].sum())
return minmaxnormalization(salmap)
patches = extract_patches_2d(image, patches_size)
patches = numpy.reshape(patches, (patches.shape[0], -1))
encoded = []
for s in gen_even_slices(len(patches), 100):
encoded.extend(encoder.transform(patches[s]))
return numpy.asarray(encoded)
def plot_components(components,
patches_size=(DEFAULT_PATCHE_SIZE, DEFAULT_PATCHE_SIZE)):
import cv2
for i, comp in enumerate(components):
c_img = (255 * minmaxnormalization(
comp.reshape(patches_size[0], patches_size[1], 3))).astype('uint8')
imwrite('c%d.png' % i, c_img)
if __name__ == '__main__':
from cv2 import imread, imwrite
import sys
image = imread(sys.argv[1])
sal = dictionary_saliency(image, 'ica', True)
sal = 255 * minmaxnormalization(sal)
imwrite('saliency_ica.png', sal.astype('uint8'))
del image
del sal
#image = imread(sys.argv[1])
#sal = dictionary_saliency(image, 'kmeans')
#imwrite('saliency_kmeans.jpg', (255 * sal).astype('uint8'))
def plot_components(components, patches_size=(DEFAULT_PATCHE_SIZE,DEFAULT_PATCHE_SIZE)):
import cv2
for i, comp in enumerate(components):
c_img = (255 * minmaxnormalization(comp.reshape(patches_size[0],patches_size[1],3))).astype('uint8')
imwrite('c%d.png'%i, c_img)
def image_to_components_space(image, encoder, patches_size=(DEFAULT_PATCHE_SIZE,DEFAULT_PATCHE_SIZE)):
patches = extract_patches_2d(image, patches_size)
patches = numpy.reshape(patches, (patches.shape[0],-1))
encoded = []
for s in gen_even_slices(len(patches), 100):
encoded.extend(encoder.transform(patches[s]))
return numpy.asarray(encoded)
def plot_components(components, patches_size=(DEFAULT_PATCHE_SIZE,DEFAULT_PATCHE_SIZE)):
import cv2
for i, comp in enumerate(components):
c_img = (255 * minmaxnormalization(comp.reshape(patches_size[0],patches_size[1],3))).astype('uint8')
imwrite('c%d.png'%i, c_img)
if __name__ == '__main__':
from cv2 import imread, imwrite
import sys
image = imread(sys.argv[1])
sal = dictionary_saliency(image, 'ica', True)
sal = 255 * minmaxnormalization(sal)
imwrite('saliency_ica.png', sal.astype('uint8'))
del image
del sal
#image = imread(sys.argv[1])
#sal = dictionary_saliency(image, 'kmeans')
#imwrite('saliency_kmeans.jpg', (255 * sal).astype('uint8'))
