def get_saliency_tile_mean(im, min_max=None, vis_order=None):
"""
Gets the mean lab averages per tile
"""
if vis_order == 'bgr':
lidx = [2, 1, 0]
else:
lidx = [0, 1, 2]
layers = scale_rgb(im[0], min_max, lidx)
# Transpose the image to RGB
layers = layers.transpose(1, 2, 0)
# Perform RGB to CIE Lab color space conversion
layers = rgb2rgbcie(layers)
# Compute Lab average values
lm = layers[:, :, 0].mean(axis=0).mean()
am = layers[:, :, 1].mean(axis=0).mean()
bm = layers[:, :, 2].mean(axis=0).mean()
lab_means = (lm, am, bm)
return None, lab_means
def test_rgbcie2rgb_conversion(self, channel_axis):
rgb = np.moveaxis(self.colbars_array,
source=-1,
destination=channel_axis)
round_trip = rgbcie2rgb(rgb2rgbcie(rgb, channel_axis=channel_axis),
channel_axis=channel_axis)
# only roundtrip test, we checked rgb2rgbcie above already
assert_almost_equal(round_trip, rgb)
def test_rgb2rgbcie_conversion(self):
gt = np.array([[[0.1488856, 0.18288098, 0.19277574],
[0.01163224, 0.16649536, 0.18948516],
[0.12259182, 0.03308008, 0.17298223],
[-0.01466154, 0.01669446, 0.16969164]],
[[0.16354714, 0.16618652, 0.0230841],
[0.02629378, 0.1498009, 0.01979351],
[0.13725336, 0.01638562, 0.00329059], [0., 0., 0.]]])
assert_almost_equal(rgb2rgbcie(self.colbars_array), gt)
def test_rgb2rgbcie_conversion(self):
gt = np.array([[[ 0.1488856 , 0.18288098, 0.19277574],
[ 0.01163224, 0.16649536, 0.18948516],
[ 0.12259182, 0.03308008, 0.17298223],
[-0.01466154, 0.01669446, 0.16969164]],
[[ 0.16354714, 0.16618652, 0.0230841 ],
[ 0.02629378, 0.1498009 , 0.01979351],
[ 0.13725336, 0.01638562, 0.00329059],
[ 0. , 0. , 0. ]]])
assert_almost_equal(rgb2rgbcie(self.colbars_array), gt)
def saliency(i_info, parameter_object, i_sect, j_sect, n_rows, n_cols):
"""
References:
Federico Perazzi, Philipp Krahenbul, Yael Pritch, Alexander Hornung. Saliency Filters. (2012).
Contrast Based Filtering for Salient Region Detection. IEEE CVPR, Providence, Rhode Island, USA, June 16-21.
https://graphics.ethz.ch/~perazzif/saliency_filters/
Ming-Ming Cheng, Niloy J. Mitra, Xiaolei Huang, Philip H. S. Torr, Shi-Min Hu. (2015).
Global Contrast based Salient Region detection. IEEE TPAMI.
"""
# min_max = sputilities.get_layer_min_max(i_info)
min_max = [(parameter_object.image_min, parameter_object.image_max)] * 3
if parameter_object.vis_order == 'bgr':
lidx = [2, 1, 0]
else:
lidx = [0, 1, 2]
# Read the section.
layers = i_info.read(bands2open=[1, 2, 3],
i=i_sect,
j=j_sect,
rows=n_rows,
cols=n_cols,
d_type='float32')
layers = scale_rgb(layers, min_max, lidx)
# Transpose the image to RGB
layers = layers.transpose(1, 2, 0)
# Perform RGB to CIE Lab color space conversion
layers = rgb2rgbcie(layers)
# Compute Lab average values
# lm = layers[:, :, 0].mean(axis=0).mean()
# am = layers[:, :, 1].mean(axis=0).mean()
# bm = layers[:, :, 2].mean(axis=0).mean()
lm = parameter_object.lab_means[0]
am = parameter_object.lab_means[1]
bm = parameter_object.lab_means[2]
return np.uint8(
rescale_intensity(
(layers[:, :, 0] - lm)**2. + (layers[:, :, 1] - am)**2. +
(layers[:, :, 2] - bm)**2.,
in_range=(-1, 1),
out_range=(0, 255)))
def test_rgb2rgbcie_conversion(self, channel_axis):
gt = np.array([[[0.1488856, 0.18288098, 0.19277574],
[0.01163224, 0.16649536, 0.18948516],
[0.12259182, 0.03308008, 0.17298223],
[-0.01466154, 0.01669446, 0.16969164]],
[[0.16354714, 0.16618652, 0.0230841],
[0.02629378, 0.1498009, 0.01979351],
[0.13725336, 0.01638562, 0.00329059], [0., 0., 0.]]])
img = np.moveaxis(self.colbars_array,
source=-1,
destination=channel_axis)
out = rgb2rgbcie(img, channel_axis=channel_axis)
out = np.moveaxis(out, source=channel_axis, destination=-1)
assert_almost_equal(out, gt)
def extract_color_descriptors(image, space=None, verbose=False):
"""
Extract color descriptors of the image
Parameters
----------
image:
space:
None, equivalent to rgb
rgb
hsv
xyz
rgbcie
Returns
-------
Descriptors
"""
if space == 'hsv':
image = color.rgb2hsv(image)
elif space == 'xyz':
image = color.rgb2xyz(image)
elif space == 'rgbcie':
image = color.rgb2rgbcie(image)
elif space == 'gray' or space == 'grey':
image = color.rgb2gray(image)
gen = get_patch(image, size=1)
descs = []
for patch, coord in gen:
if verbose and coord[1] % 5 == 0 and coord[0] == 0:
print 'computed up to %d, %d' % coord
desc = patch.flatten()
desc = np.concatenate((desc, np.array(coord)))
descs.append(desc)
return np.array(descs)
def trans(self, img):
rst = color.rgb2rgbcie(img)
np.maximum(rst, 0, out=rst)
print('============', rst.min(axis=(0, 1)), rst.max(axis=(0, 1)))
rst *= 255 / 50 * 255
return rst.astype(np.uint8)
def test_rgbcie2rgb_dtype(self):
img = rgb2rgbcie(self.colbars_array).astype('float64')
img32 = img.astype('float32')
assert rgbcie2rgb(img).dtype == img.dtype
assert rgbcie2rgb(img32).dtype == img32.dtype
def test_rgbcie2rgb_conversion(self):
# only roundtrip test, we checked rgb2rgbcie above already
assert_almost_equal(rgbcie2rgb(rgb2rgbcie(self.colbars_array)),
self.colbars_array)
def test_rgbcie2rgb_conversion(self):
# only roundtrip test, we checked rgb2rgbcie above already
assert_almost_equal(rgbcie2rgb(rgb2rgbcie(self.colbars_array)),
self.colbars_array)
def __call__(self, img):
img = np.asarray(img, np.uint8)
img = color.rgb2rgbcie(img)
return img
def rgb2rgbcie(self,imageArray):
return color.rgb2rgbcie(imageArray)
#%%
def rgb2ycbcr(im_rgb):
im_rgb = im_rgb.astype(np.float32)
im_ycrcb = cv2.cvtColor(im_rgb, cv2.COLOR_RGB2YCR_CB)
im_ycbcr = im_ycrcb[:,:,(0,2,1)].astype(np.float32)
im_ycbcr[:,:,0] = (im_ycbcr[:,:,0]*(235-16)+16)/255.0 #to [16/255, 235/255]
im_ycbcr[:,:,1:] = (im_ycbcr[:,:,1:]*(240-16)+16)/255.0 #to [16/255, 240/255]
return im_ycbcr
#%%
path_img = "./201208172_T-12-58-58_Dive_01_041.jpg"
img = imread(path_img)
img = img/255
img_hsv = color.rgb2hsv(img)
img_lab = color.rgb2lab(img)
img_hed = color.rgb2hed(img)
img_rgbcie = color.rgb2rgbcie(img)
img_xyz = color.rgb2xyz(img)
img_yuv = color.rgb2yuv(img)
img_yiq = color.rgb2yiq(img)
img_ycbcr = color.rgb2ycbcr(img)
img1 = color.ycbcr2rgb(img_ycbcr)
#%%
#%%
fig, ax = plt.subplots(2, 4, figsize=(15,8))
ax[0, 0].imshow(img)
ax[0, 0].set_title("RGB image")
ax[0, 1].imshow(img_hsv)
ax[0, 1].set_title("HSV image")
ax[0, 2].imshow(img_lab)
