def setup(self, stage=None):
matFile = loadmat(self.data_path)
data = dict()
data['landsat'] = matFile['t1_L8_clipped']
# data['landsat'] = data['landsat'].reshape((-1, self.patch_size, self.patch_size, 11))
data['landsat'] = view_as_blocks(
data['landsat'], (self.patch_size, self.patch_size, 11)).reshape(
(-1, self.patch_size, self.patch_size, 11))
data['sentinel'] = matFile['logt2_clipped']
# data['sentinel'] = data['sentinel'].view().reshape((-1, self.patch_size, self.patch_size, 3))
data['sentinel'] = view_as_blocks(
data['sentinel'], (self.patch_size, self.patch_size, 3)).reshape(
(-1, self.patch_size, self.patch_size, 3))
prior_information_image = loadmat(
r'resources/alpha_prior_test.mat')['prior']
prior_information_image = prior_information_image / prior_information_image.max(
)
data['prior_info'] = prior_information_image
# data['prior_info'] = data['prior_info'].view().reshape((-1, self.patch_size, self.patch_size))
data['prior_info'] = view_as_blocks(
data['prior_info'], (self.patch_size, self.patch_size)).reshape(
(-1, self.patch_size, self.patch_size))
self.trainDataset = CaliforniaFloodDataset(data)
data['roi'] = matFile['ROI']
# data['roi'] = data['roi'].view().reshape((-1, self.patch_size, self.patch_size))
data['roi'] = view_as_blocks(
data['roi'], (self.patch_size, self.patch_size)).reshape(
(-1, self.patch_size, self.patch_size))
self.testDataset = CaliforniaTestDataset(data)
def _wrapper(dim):
h, w = dim.shape
sobel = skimage_sobel(dim)
sobel += 1e-8 # avoid division by zero
sobel_norm = view_as_blocks(sobel, (self.sobel, self.sobel)).sum((2,3))
sum_prod = view_as_blocks((sobel * dim), (self.sobel, self.sobel)).sum((2,3))
return sum_prod / sobel_norm
def get_energy_sums(event_data):
"""
Get the energy sums of 2x2 sub regions along with their indices.
Args:
event_data: Dataframe containing event data
Returns:
Dictionary of values 'index': sum
"""
# Empty dictionary
sum_energy_index = {}
# Configuring the size of sub regions
s = 2
# We will now get 2x2 sub matrices as blocks from the electron matrix.
# Reshape each matrix into a list format.
blocks_energy = view_as_blocks(get_matrix(event_data, 'et'),
(s, s)).reshape(-1, s**2)
# We will now get 2x2 sub matrices as blocks from the index matrix.
# Reshape each matrix into a list format.
blocks_idx = view_as_blocks(get_matrix(event_data, 'index'),
(s, s)).reshape(-1, s**2)
for row, (sub, idx) in enumerate(zip(blocks_energy, blocks_idx)):
# Add all energies in 2x2 region
sum_energy = np.sum(sub)
# get the actual index of the highest postion of energy in the submatrix (2x2)
actual_idx = idx[np.argmax(sub)]
sum_energy_index[actual_idx] = sum_energy
return sum_energy_index
def iris_encode(irisImage, dr=15, dtheta=15, alpha=0.4):
"""Encodes the straightened representation of an iris with gabor wavelets.
:param irisImage: Image of an iris
:param dr: Width of image patches producing one feature
:param dtheta: Length of image patches producing one feature
:param alpha: Gabor wavelets modifier (beta parameter of Gabor wavelets becomes inverse of this number)
:return: Iris code and its mask
:rtype: tuple (ndarray, ndarray)
"""
# mean = np.mean(img)
# std = img.std()
mask = view_as_blocks(np.logical_and(100 < irisImage, irisImage < 230), (dr, dtheta))
norm_iris = (irisImage - irisImage.mean()) / irisImage.std()
patches = view_as_blocks(norm_iris, (dr, dtheta))
code = np.zeros((patches.shape[0] * 3, patches.shape[1] * 2))
code_mask = np.zeros((patches.shape[0] * 3, patches.shape[1] * 2))
for i, row in enumerate(patches):
for j, p in enumerate(row):
for k, w in enumerate([8, 16, 32]):
wavelet = gabor_convolve(p, w, alpha, 1 / alpha)
code[3 * i + k, 2 * j] = np.sum(wavelet[0])
code[3 * i + k, 2 * j + 1] = np.sum(wavelet[1])
code_mask[3 * i + k, 2 * j] = code_mask[3 * i + k, 2 * j + 1] = \
1 if mask[i, j].sum() > dr * dtheta * 3 / 4 else 0
code[code >= 0] = 1
code[code < 0] = 0
return code, code_mask
def post_changed_blocks(old_seg, new_seg):
# If we post the whole volume, we'll be overwriting blocks that haven't changed,
# wasting space in DVID (for duplicate blocks stored in the child uuid).
# Instead, we need to only post the blocks that have changed.
# So, can't just do this:
# output_service.write_subvolume(new_seg, box[0], scale)
seg_diff = (old_seg != new_seg)
block_diff = view_as_blocks(seg_diff, 3 * (block_width, ))
changed_block_map = block_diff.any(axis=(3, 4, 5)).nonzero()
changed_block_corners = box[0] + np.transpose(
changed_block_map) * block_width
changed_blocks = view_as_blocks(
new_seg, 3 * (block_width, ))[changed_block_map]
encoded_blocks = encode_labelarray_blocks(
changed_block_corners, changed_blocks)
mgr = output_service.resource_manager_client
with mgr.access_context(output_service.server, True, 1,
changed_blocks.nbytes):
post_labelmap_blocks(*output_service.instance_triple,
None,
encoded_blocks,
scale,
downres=False,
noindexing=True,
throttle=False,
is_raw=True)
def get_feature_vector(filtered_1, filtered_2):
"""As the paper denotes, this method generate the feature vector
based on the two filtered image.
:param filtered_1: the filtered image 1
:param filtered_2: the filtered image 2
:return: the feature vector
:rtype: ndarray
"""
blocks_1 = view_as_blocks(filtered_1, block_shape=(8, 8)).reshape([-1, 64])
blocks_2 = view_as_blocks(filtered_2, block_shape=(8, 8)).reshape([-1, 64])
def mad(array, axis):
return np.mean(np.abs(array - np.mean(array, axis, keepdims=True)), axis)
m_1 = blocks_1.mean(axis=-1)
m_2 = blocks_2.mean(axis=-1)
mad_1 = mad(blocks_1, axis=-1)
mad_2 = mad(blocks_2, axis=-1)
# feature_vector = np.concatenate([np.stack([m_1, mad_1], axis=1).reshape([-1]),
# np.stack([m_2, mad_2], axis=1).reshape([-1])])
feature_vector = np.stack([m_1, mad_1, m_2, mad_2], axis=1).reshape([-1])
return feature_vector
def test_crops(self, img, msk, msk_true):
n_channels = 3
if self.use_dsm:
n_channels = 4
if self.overlap:
w_img = util.view_as_windows(
img, (self.crop_size[0], self.crop_size[1], n_channels),
(self.crop_size[0] // 2, self.crop_size[1] // 2,
n_channels)).squeeze()
w_msk = util.view_as_windows(
msk, (self.crop_size[0], self.crop_size[1]),
(self.crop_size[0] // 2, self.crop_size[1] // 2))
w_msk_true = util.view_as_windows(
msk_true, (self.crop_size[0], self.crop_size[1]),
(self.crop_size[0] // 2, self.crop_size[1] // 2))
else:
w_img = util.view_as_blocks(
img,
(self.crop_size[0], self.crop_size[1], n_channels)).squeeze()
w_msk = util.view_as_blocks(msk,
(self.crop_size[0], self.crop_size[1]))
w_msk_true = util.view_as_blocks(
msk_true, (self.crop_size[0], self.crop_size[1]))
return w_img, w_msk, w_msk_true
def main():
''' Perform a two-step spectral resampling to 10nm. First wavelengths are aggregated
to approximatley 10nm, then aggregated spectra a interpolated to exactly 10nm using a
cubic piecewise interpolator.
'''
parser = argparse.ArgumentParser()
parser.add_argument('infile', type=str)
parser.add_argument('outdir', type=str)
parser.add_argument('--verbose', action='store_true')
args = parser.parse_args()
out_image = args.outdir + '/' + os.path.basename(args.infile) + "_10nm"
hy_obj = htl.HyTools()
hy_obj.read_file(args.infile, 'envi')
if hy_obj.wavelengths.max() < 1100:
new_waves = np.arange(400, 991, 10)
else:
new_waves = np.arange(400, 2451, 10)
bins = int(np.round(10 / np.diff(hy_obj.wavelengths).mean()))
agg_waves = np.nanmean(view_as_blocks(
hy_obj.wavelengths[:(hy_obj.bands // bins) * bins], (bins, )),
axis=1)
if args.verbose:
print("Aggregating every: %s" % bins)
out_header = hy_obj.get_header()
out_header['bands'] = len(new_waves)
out_header['wavelength'] = new_waves.tolist()
out_header['fwhm'] = [10 for x in new_waves]
out_header['default bands'] = []
writer = WriteENVI(out_image, out_header)
iterator = hy_obj.iterate(by='line')
while not iterator.complete:
if (iterator.current_line % 100 == 0) and args.verbose:
print(iterator.current_line)
line = iterator.read_next()[:, :(hy_obj.bands // bins) * bins]
line = np.nanmean(view_as_blocks(line, (
1,
bins,
)), axis=(2, 3))
interpolator = interp1d(agg_waves,
line,
fill_value='extrapolate',
kind='cubic')
line = interpolator(new_waves)
writer.write_line(line, iterator.current_line)
def calculate_bleedthrough(self, control, bins, show_graphs = False) :
"""
Function that calculates the FRET bleedthrough in control situations
where no FRET should occur.
Inputs
| * control - which control sample to calculate
| * bins - size of kernels for pooling step
Returns
| * (slope, intercept) of the linear regression model fit to the
| processed control sample data.
"""
# assertions
assert control in ['no_acceptor_control', 'no_donor_control'], 'control parameter is not valid (`no_acceptor_control` or `no_donor_control` are valid options)'
if control == 'no_acceptor_control' :
channel = 0
path_to_control = self.no_acceptor_path
control_filenames = self.no_acceptor_filenames
elif control == 'no_donor_control' :
channel = 1
path_to_control = self.no_donor_path
control_filenames = self.no_donor_filenames
# Calculate bleedthrough per image
results = []
for f in control_filenames :
img = io.imread(os.path.join(path_to_control, f))
assert (img.shape[0] % bins == 0) & (img.shape[1] % bins == 0), 'Image shape is not divisible by bin number'
block_dims = [img.shape[0] // bins, img.shape[1] // bins] # // returns integer values
control_blocks = util.view_as_blocks(img[:,:,channel], block_shape=tuple(block_dims))
fret_blocks = util.view_as_blocks(img[:,:,2], block_shape = tuple(block_dims))
for m in range(control_blocks.shape[1]) :
for n in range(control_blocks.shape[0]) :
results.append( [np.mean(control_blocks[m,n]), np.mean(fret_blocks[m,n])])
if show_graphs :
plt.scatter([x[0] for x in results],
[y[1] for y in results])
# TODO : label axes
plt.show()
LR_clf = LinearRegression()
LR_clf.fit(X = np.array([x[0] for x in results]).reshape(-1,1),
y = np.array([y[1] for y in results]))
return LR_clf.coef_[0], LR_clf.intercept_
def correlated_noise_stamps(num_stamps, stamp_sz, noise_kernel, seed,
aug_noise_factor, aug_noise_factor_min,
aug_noise_factor_max):
stamp_sz = np.array(stamp_sz)
kernel_sz = np.array(noise_kernel.shape)
pad_sz = np.ceil(kernel_sz / 2.0).astype('int32') + 1
padded_stamp_sz = stamp_sz + 2 * pad_sz
noise_im_sz = padded_stamp_sz * int(ma.ceil(ma.sqrt(num_stamps)))
np.random.seed(seed)
noise_im = np.random.randn(*noise_im_sz)
noise_im = convolve(noise_im,
noise_kernel,
normalize_kernel=False,
boundary=None)
noise_stamps = view_as_blocks(noise_im, tuple(padded_stamp_sz)).reshape(
-1, *padded_stamp_sz).copy()
noise_stamps = noise_stamps[:num_stamps, pad_sz[0]:-pad_sz[0],
pad_sz[1]:-pad_sz[1]]
noise_stamps = noise_stamps.reshape(-1, stamp_sz[0], stamp_sz[1], 1)
if aug_noise_factor:
aug_noise_range = aug_noise_factor_max - aug_noise_factor_min
noise_factors = np.random.rand(
num_stamps, 1, 1, 1) * aug_noise_range + aug_noise_factor_min
noise_stamps = noise_stamps * noise_factors
return noise_stamps
def windolf_sampling(self, params, layer): a = np.abs(params) alpha = np.angle(params) if layer=='visible': rates = np.abs(self.clamp) phases = vm(alpha, a*rates / self.sigma_sq) return rates*np.exp(1j*phases) else: bessels = bessel(a - self.biases)/ self.sigma_sq custom_kernel = np.ones((self.pool_size, self.pool_size)) sum_bessels = conv(bessels, custom_kernel, mode='valid') # Downsample sum_bessels = sum_bessels[0::self.pool_stride, 0::self.pool_stride] bessel_sftmx_denom = 1.0 + sum_bessels upsampled_denom = bessel_sftmx_denom.repeat(self.pool_stride, axis=0).repeat(self.pool_stride, axis=1) hid_cat_P = bessels / upsampled_denom pool_P = 1.0 - 1.0 / bessel_sftmx_denom hid_rates, pool_rates = self._dbn_maxpool_sample_helper(hid_cat_P, pool_P) hid_phases = vm(alpha, a*hid_rates / self.sigma_sq) hid_samples = hid_rates*np.exp(1j*hid_phases) pool_phases = np.sum(imx.view_as_blocks(hid_phases, (self.pool_size, self.pool_size)), axis=(2,3)) pool_samples = pool_rates*np.exp(1j*pool_phases) return hid_samples, pool_samples
def create_bags(self, dir_list):
bag_list = []
labels_list = []
img_list = []
for dir in dir_list:
img = io.imread(dir)
if img.shape[2] == 4:
img = color.rgba2rgb(img)
bag = view_as_blocks(img, block_shape=(32, 32,
3)).reshape(-1, 32, 32, 3)
# store single cell labels
label = 1 if 'malignant' in dir else 0
# shuffle
if self.shuffle_bag:
random.shuffle(bag)
bag_list.append(bag)
labels_list.append(label)
img_list.append(img)
if self.train:
return bag_list, labels_list
else:
return bag_list, labels_list, img_list
def standard(segments, conv_map_shape, N_segments):
subsample = tuple(np.array(segments.shape) // np.array(conv_map_shape))
from skimage.util import view_as_blocks
W = segments[np.newaxis, ...] == np.arange(N_segments)[..., np.newaxis,
np.newaxis]
W = np.sum(view_as_blocks(W, (1, ) + subsample), axis=(3, 4, 5))
return W.astype(float)
def write_brick(output_service, scale, brick):
shape = np.array(brick.volume.shape)
assert (shape[0:2] == output_service.block_width).all()
assert shape[2] % output_service.block_width == 0
# Omit leading/trailing empty blocks
block_width = output_service.block_width
assert (np.array(brick.volume.shape) % block_width).all() == 0
blockwise_view = view_as_blocks( brick.volume, brick.volume.shape[0:2] + (block_width,) )
# blockwise view has shape (1,1,X/bx, bz, by, bx)
assert blockwise_view.shape[0:2] == (1,1)
blockwise_view = blockwise_view[0,0] # drop singleton axes
block_maxes = blockwise_view.max( axis=(1,2,3) )
assert block_maxes.ndim == 1
nonzero_block_indexes = np.nonzero(block_maxes)[0]
if len(nonzero_block_indexes) == 0:
return # brick is completely empty
first_nonzero_block = nonzero_block_indexes[0]
last_nonzero_block = nonzero_block_indexes[-1]
nonzero_start = (0, 0, block_width*first_nonzero_block)
nonzero_stop = ( brick.volume.shape[0:2] + (block_width*(last_nonzero_block+1),) )
nonzero_subvol = brick.volume[box_to_slicing(nonzero_start, nonzero_stop)]
nonzero_subvol = np.asarray(nonzero_subvol, order='C')
output_service.write_subvolume(nonzero_subvol, brick.physical_box[0] + nonzero_start, scale)
def digitized_array(img_array, new_dims, colour_depth):
"""[summary]
Args:
img_array (np array): hxw array of greyscale values in [0,255]
new_dims (tuple): 2x2 tuple describing the "pixellated" dimensions
to reshape the image into.
Returns:
array: new array of greyscale values in original image resolution
"""
original_dims = img_array.shape
nx, ny = new_dims
dh = int(img_array.shape[1] / nx) # new number of rows
dw = int(img_array.shape[0] / ny) # new number of cols
print(f'Original image array shape {original_dims}')
print(f'window shape = {dw} wide, {dh} high')
blocks = view_as_blocks(img_array, (dw, dh))
colours = np.array(set_color_array(colour_depth))
for i in range(blocks.shape[0]):
for j in range(blocks.shape[1]):
mean_gs_value = int(np.mean(blocks[i, j]))
nearest_value_idx = (np.abs(colours - mean_gs_value)).argmin()
blocks[i, j] = nearest_value_idx
return np.swapaxes(blocks, 1, 2).reshape(original_dims)
def rando_scrambo_array(img_array, new_dims, colour_depth):
"""[summary]
Args:
img_array (np array): hxw array of greyscale values in [0,255]
new_dims (tuple): 2x2 tuple describing the "pixellated" dimensions
to reshape the image into.
Returns:
array: new array of greyscale values in original image resolution
"""
t0 = time()
original_dims = img_array.shape
nx, ny = new_dims
dh = int(img_array.shape[1] / nx) # new number of rows
dw = int(img_array.shape[0] / ny) # new number of cols
blocks = view_as_blocks(img_array, (dw, dh))
colours = set_color_array(colour_depth)
blocks_updated = update_block_vals(blocks, colours)
out = np.swapaxes(blocks_updated, 1, 2).reshape(original_dims)
t1 = time()
tt = t1 - t0
# print(f'rando time = {tt:.1f}')
return out
def convert_image_to_stack_of_tiles(image, tile_height, tile_width):
'''
Converts an image to a stack of tiles to be fed to the DL models
Observations:
- if (image.shape % tile_height != 0) or (image.shape % tile_width != 0)
the right and bottom remainder will be cropped so that
(image.shape % tile_height == 0) and (image.shape % tile_width == 0)
input:
- image: the image that will be converted as a numpy array
- tile_height:
- tile_width:
Output:
np.array((n, h, w, c)),
n: N° of tiles
h: tile height
w: tile width
c: N° of channels (always 3, as rgb and hsv are used)
'''
shape = image.shape
imageCopy = image[:shape[0] - shape[0] % tile_height, :shape[1] -
shape[1] % tile_width, :3]
imageCopy = util.view_as_blocks(imageCopy,
block_shape=(tile_height, tile_width, 3))
imageCopy = imageCopy.reshape(
shape[0] // tile_height * shape[1] // tile_width, tile_height,
tile_width, 3)
return imageCopy
def encodeInfo(img, msg):
img = img.copy()
color = img[:, :, color_index]
blocks = view_as_blocks(color, block_shape=(N, N))
w = blocks.shape[1]
h = blocks.shape[0]
r_max = w * h
encBlockList = []
iter = 0
for bit in msg:
block_number = random.randrange(r_max - 1)
while (block_number in encBlockList):
block_number = random.randrange(r_max - 1)
iter += 1
if iter > 0.9 * w * h:
print("err")
break
i = block_number // w
j = block_number % w
encBlockList.append(block_number)
block = blocks[i, j]
coefs = dct(dct(block, axis=0, norm='ortho'), axis=1, norm='ortho')
while not is_valid_coeff(coefs, bit, P) or (bit != get_bit(block)):
coefs = change_coeffs(coefs, bit)
block = round_to_byte(
idct(idct(coefs, axis=0, norm='ortho'), axis=1, norm='ortho'))
color[i * N:(i + 1) * N, j * N:(j + 1) * N] = block
img[:, :, color_index] = color
return img, encBlockList
def extract_patches_from_raster():
count = 0
for raster_file in Path('./world_map').glob('**/*.tif'):
data = gr.from_file(str(raster_file))
raster_blocks = view_as_blocks(data.raster, (225, 225))
for i in range(raster_blocks.shape[0]):
for j in range(raster_blocks.shape[1]):
raster_data = raster_blocks[i, j]
src = cv2.pyrDown(raster_data,
dstsize=(raster_data.shape[1] // 2,
raster_data.shape[0] // 2))
data_out_downsampled = gr.GeoRaster(
src,
data.geot,
nodata_value=data.nodata_value,
projection=data.projection,
datatype=data.datatype,
)
data_out_downsampled.to_tiff(
'./data_downsampled_blurred/data_q' + str(count) + str(i) +
str(j))
data_out = gr.GeoRaster(
raster_data,
data.geot,
nodata_value=data.nodata_value,
projection=data.projection,
datatype=data.datatype,
)
data_out.to_tiff('./data/data_q' + str(count) + str(i) +
str(j))
count += 1
def chop_to_blocks(data, shape):
"""Subdivides the current image and returns an array of images
with the dims `shape`
Args
----
shape (tuple : ints) : the dims of the subdivided images
Returns
-------
matrix of (j, i, 1, shape[0], shape[1])
"""
# Make sure there are not multiple strides in the color ch direction
assert shape[-1] == data.shape[-1]
# Drop parts of the image that cant be captured by an integer number of
# strides
_split_factor = np.floor(np.divide(data.shape, shape)).astype(int)
_img_lims = (_split_factor * shape)
#print("Can only preserve up to pix: ", _img_lims)
_data = np.ascontiguousarray(
data[:_img_lims[0], :_img_lims[1], :_img_lims[2]])
return view_as_blocks(_data, shape)
def deserialize_uint64_blocks(compressed_blocks, shape):
"""
Reconstitute a volume that was serialized with serialize_uint64_blocks(), above.
NOTE: If the volume is not 64-px aligned, then the output will NOT be C-contiguous.
"""
if (np.array(shape) % 64).any():
padding = 64 - ( np.array(shape) % 64 )
aligned_shape = shape + padding
else:
aligned_shape = shape
aligned_volume = np.empty( aligned_shape, dtype=np.uint64 )
block_view = view_as_blocks( aligned_volume, (64,64,64) )
for bi, (zi, yi, xi) in enumerate(np.ndindex(*block_view.shape[:3])):
compressed_block = compressed_blocks[bi]
# (See note above regarding recompression with LZ4)
encoded_block = lz4.frame.decompress( compressed_block )
block = decode_label_block( encoded_block )
block_view[zi,yi,xi] = block
if shape == tuple(aligned_shape):
volume = aligned_volume
else:
# Trim
volume = np.asarray(aligned_volume[box_to_slicing((0,0,0), shape)], order='C')
return volume
def Down_Sample(image, block_size, func=np.sum, cval=0):
if len(block_size) != image.ndim:
raise ValueError("`block_size` must have the same length "
"as `image.shape`.")
pad_width = []
for i in range(len(block_size)):
if block_size[i] < 1:
raise ValueError("Down-sampling factors must be >= 1. Use "
"`skimage.transform.resize` to up-sample an "
"image.")
if image.shape[i] % block_size[i] != 0:
after_width = block_size[i] - (image.shape[i] % block_size[i])
else:
after_width = 0
pad_width.append((0, after_width))
image = np.pad(image,
pad_width=pad_width,
mode='constant',
constant_values=cval)
out = view_as_blocks(image, block_size)
for i in range(len(out.shape) // 2):
out = func(out, axis=-1)
return out
def _StatisticalNaturalness(self, L_ldr, win=11):
phat1 = 4.4
phat2 = 10.1
muhat = 115.94
sigmahat = 27.99
u = np.mean(L_ldr)
# moving window standard deviation using reflected image
if self.original:
W, H = L_ldr.shape
w_extra = (11 - W % 11)
h_extra = (11 - H % 11)
# zero padding to simulate matlab's behaviour
if w_extra > 0 or h_extra > 0:
test = np.pad(L_ldr,
pad_width=((0, w_extra), (0, h_extra)),
mode='constant')
else:
test = L_ldr
# block view with fixed block size, like in the original article
view = view_as_blocks(test, block_shape=(11, 11))
sig = np.mean(np.std(view, axis=(-1, -2)))
else:
# deviation: moving window with reflected borders
sig = np.mean(generic_filter(L_ldr, np.std, size=win))
beta_mode = (phat1 - 1.) / (phat1 + phat2 - 2.)
C_0 = beta.pdf(beta_mode, phat1, phat2)
C = beta.pdf(sig / 64.29, phat1, phat2)
pc = C / C_0
B = norm.pdf(u, muhat, sigmahat)
B_0 = norm.pdf(muhat, muhat, sigmahat)
pb = B / B_0
N = pb * pc
return N
def deserialize_uint64_blocks(compressed_blocks, shape):
"""
Reconstitute a volume that was serialized with serialize_uint64_blocks(), above.
NOTE: If the volume is not 64-px aligned, then the output will NOT be C-contiguous.
"""
if (np.array(shape) % 64).any():
padding = 64 - ( np.array(shape) % 64 )
aligned_shape = shape + padding
else:
aligned_shape = shape
aligned_volume = np.empty( aligned_shape, dtype=np.uint64 )
block_view = view_as_blocks( aligned_volume, (64,64,64) )
for bi, (zi, yi, xi) in enumerate(np.ndindex(*block_view.shape[:3])):
compressed_block = compressed_blocks[bi]
# (See note above regarding recompression with LZ4)
encoded_block = lz4.frame.decompress( compressed_block )
block = decode_label_block( encoded_block )
block_view[zi,yi,xi] = block
if shape == tuple(aligned_shape):
volume = aligned_volume
else:
# Trim
volume = np.asarray(aligned_volume[box_to_slicing((0,0,0), shape)], order='C')
return volume
def __init__(self, blockArray, cellSizeX, cellSizeY):
"""Constructor from an input array, this should be a block object.
Args:
blockArray: The array of blocks, should be two dimensional.
cellSizeX: The number of pixels on the x axis of this Cell.
cellSizeY: The number of pixels on the y axis of this Cell.
"""
self._blockArray = blockArray
(block_row, block_col, block_y, block_x) = blockArray.shape
cell_shape = (cellSizeY, cellSizeX)
practice = view_as_blocks(blockArray[0,0], cell_shape)
(cell_row, cell_col, cell_y, cell_x) = practice.shape
dim1 = block_row * cell_row
dim2 = block_col * cell_col
cellArray = np.zeros((dim1, dim2, cellSizeY, cellSizeX))
# Create a positional array to show which cells belonged to which block.
# The blockNum is on a grid like below.
# 1 2 3 4 5
# 6 7 8 9 10
blockPosition = np.zeros((dim1,dim2))
blockNum = 0
for i, row in enumerate(blockArray):
for j, col in enumerate(row):
cell = view_as_blocks(col, (cellSizeY, cellSizeX))
to_add_y = cell_row * i
to_add_x = cell_col * j
for k, cRow in enumerate(cell):
for l, cCol in enumerate(cRow):
# position to put the cell into
y_position = k + to_add_y
x_position = l + to_add_x
# put cell into cellArray
cellArray[y_position, x_position,: ,:] = cCol
blockPosition[y_position, x_position] = blockNum
blockNum += 1
self.cellArray = cellArray
self.blockPosition = blockPosition
def PatchMaker(mask_3D, patch_size, window_size, nclasses, pid, datapath):
patch_labels_training=[]
patch_labels_testing=[]
window_intensities_training=[]
window_intensities_testing=[]
test_img_shape = []#np.empty((len(pid_test),2))
test_img_shape_padded = []#np.empty((len(pid_test),2))
LGE = SimpleITK.ReadImage(datapath + pid + '//' + pid + '-LGE-cropped.mhd')
scar = SimpleITK.ReadImage(datapath + pid + '//' + pid + '-scar-cropped.mhd')
# mask = SimpleITK.ReadImage(datapath + pid + '//' + pid + '-myo-cropped.mhd')
#convert a SimpleITK object into an array
LGE_3D = SimpleITK.GetArrayFromImage(LGE)
scar_3D = SimpleITK.GetArrayFromImage(scar)
# mask_3D = SimpleITK.GetArrayFromImage(myomask
#masking LGE with GT myo
LGE_3D = np.multiply(LGE_3D,mask_3D)
h_LGE = LGE_3D.shape[1]
w_LGE = LGE_3D.shape[2]
d_LGE = LGE_3D.shape[0]
test_slice = range(0,d_LGE,skip)
#make windows size and patch size evenly dvideble
if (window_size-patch_size)%2 != 0:
window_size +=1
print('window size has changed to %d '%window_size)
#calculate the amount of padding for heaght and width of a slice for patches
rem_w = w_LGE%patch_size
w_pad=patch_size-rem_w
rem_h = h_LGE%patch_size
h_pad=patch_size-rem_h
pads = (h_pad,w_pad)
for sl in test_slice:
LGE_padded_slice=numpy.lib.pad(LGE_3D[sl,:,:], ((0,h_pad),(0,w_pad)), 'constant', constant_values=(0,0))
scar_padded_slice=numpy.lib.pad(scar_3D[sl,:,:], ((0,h_pad),(0,w_pad)), 'constant', constant_values=(0,0))
LGE_patches = view_as_blocks(scar_padded_slice, block_shape = (patch_size,patch_size))
LGE_patches = numpy.reshape(LGE_patches,(LGE_patches.shape[0]*LGE_patches.shape[1],patch_size,patch_size))
#re-pad your padded image before you make your windows
padding = int((window_size - patch_size)/2)
LGE_repadded_slice = numpy.lib.pad(LGE_padded_slice, ((padding,padding),(padding,padding)), 'constant', constant_values=(0,0))
#done with the labels, now we will do our windows,
LGE_windows = view_as_windows(LGE_repadded_slice, (window_size,window_size), step=patch_size)
LGE_windows = numpy.reshape(LGE_windows,(LGE_windows.shape[0]*LGE_windows.shape[1],window_size,window_size))
#for each patches:
for p in range(0,len(LGE_patches)):
#label=int(numpy.divide(numpy.multiply(numpy.divide(numpy.sum(LGE_patches[p]),numpy.square(patch_size)),nclasses),1))
label = LGE_patches[p]
label = numpy.reshape(label, (1,1))
# if label==nclasses:
# label -=1 #mmake sure the range for the classes do not exceed the maximum
#making your window intensities a single row
intensities = numpy.reshape(LGE_windows[p],(window_size*window_size))
intensities = numpy.reshape(intensities, (1,window_size*window_size))
patch_labels_testing.append(label)
window_intensities_testing.append(intensities)
def preprocess_image(image_path):
files = os.listdir(image_path)
for f in files:
image = mpimg.imread(image_path + '/' + f)
img_gray = rgb2gray(image)
thresh = threshold_otsu(img_gray)
binary = img_gray > thresh
# Step 1: Proprocess original images into binary images
# otsu thresholding with appropriate nbins number
thresh = threshold_otsu(img_gray, nbins = 60)
binary = img_gray > thresh
binary = resize(binary, (1600, 2000))
# size of blocks
block_shape = (10, 10)
# see astronaut as a matrix of blocks (of shape block_shape)
view = view_as_blocks(binary, block_shape)
# collapse the last two dimensions in one
flatten_view = view.reshape(view.shape[0], view.shape[1], -1)
# resampling the image by taking either the `mean`,
# the `max` or the `median` value of each blocks.
mean_view = np.mean(flatten_view, axis=2)
image = mean_view
seed = np.copy(mean_view)
seed[1:-1, 1:-1] = image.max()
mask = image
# fill holes (i.e. isolated, dark spots) in an image using morphological reconstruction by erosion
# plt.title("Filled dark spots by morphological reconstruction")
filled = reconstruction(seed, mask, method='erosion')
plt.figure()
plt.axis('off')
plt.imshow(filled, cmap=cm.Greys_r)
plt.savefig("./enhanced_image/" + f)
# Step 2: Label Junctions
denoised = denoise_wavelet(filled, multichannel=True)
image = filled
coords1 = corner_peaks(corner_harris(image), min_distance=5)
coords_subpix1 = corner_subpix(image, coords1, window_size=13)
image = denoised
coords2 = corner_peaks(corner_harris(image), min_distance=5)
coords_subpix2 = corner_subpix(image, coords2, window_size=13)
fig, ax = plt.subplots()
ax.imshow(image, interpolation='nearest', cmap=plt.cm.gray)
ax.plot(coords_subpix1[:, 1], coords_subpix1[:, 0], '+r', markersize=15)
ax.plot(coords_subpix2[:, 1], coords_subpix2[:, 0], '+r', markersize=15)
plt.axis('off')
plt.savefig("./preprocessed_image/" + f)
def neighbourhoodify(image, nbhd=10):
avgs = np.zeros(map(lambda x: int(x / nbhd), image.shape))
blocks = view_as_blocks(image, (nbhd, nbhd))
for row in range(0, len(blocks)):
for col in range(0, len(blocks[row])):
mode = np.bincount(blocks[row,col].ravel()).argmax()
avgs[row, col] = mode
return avgs
def retrieve_message(img, length):
img = img[:, :, 2]
width, height = np.shape(img)
width -= width % n
height -= height % n
img = img[:width, :height]
blocks = view_as_blocks(img, block_shape=(n, n))
h = blocks.shape[1]
return [retrieve_bit(blocks[index // h, index % h]) for index in range(length)]
def updatePixels(self, tlc, shape, props, **pixelBlocks):
p = pixelBlocks['raster_pixels']
m = pixelBlocks['raster_mask']
if self.func is None:
b = resize(p, shape, order=0, preserve_range=True)
else:
blockSizes = tuple(np.divide(p.shape, shape))
b = np.ma.masked_array(view_as_blocks(p, blockSizes),
view_as_blocks(~m.astype('b1'), blockSizes))
for i in range(len(b.shape) // 2):
b = self.func(b, axis=-1)
b = b.data
d = self.padding
pixelBlocks['output_pixels'] = b.astype(props['pixelType'], copy=False)
pixelBlocks['output_mask'] = resize(m, shape, order=0, preserve_range=True).astype('u1', copy=False)
return pixelBlocks
def get_aggregated(self, nagg_x=2, nagg_y=2):
if USE_SKIMAGE:
new_lons = view_as_blocks(self.lons, (nagg_x, nagg_y)).mean(axis=2).mean(axis=2)
new_lats = view_as_blocks(self.lats, (nagg_x, nagg_y)).mean(axis=2).mean(axis=2)
else:
nx, ny = self.lons.shape
new_lons, new_lats = np.zeros((nx // nagg_x, ny // nagg_y)), np.zeros((nx // nagg_x, ny // nagg_y))
for i in range(0, nx, nagg_x):
for j in range(0, ny, nagg_y):
i1 = i // nagg_x
j1 = j // nagg_y
new_lons[i1, j1] = np.mean(self.lons[i:i + nagg_x, j:j + nagg_y])
new_lats[i1, j1] = np.mean(self.lons[i:i + nagg_x, j:j + nagg_y])
return BasemapInfo(lons=new_lons, lats=new_lats, bmp=self.basemap)
def np_extract_patches(img):
orig = np.array(img.shape[:2])
new = patch_s[0] * np.ceil(orig / patch_s[0]).astype(int)
points = new - orig
img = np.pad(img, [(0, points[0]), (0, points[1]), (0, 0)],
mode='constant')
patches = view_as_blocks(img, tuple(patch_s)).astype(np.float32)
patches = patches.reshape(-1, *patch_s)
return patches
def _process(dstl_image):
# Make sure there are not multiple strides in the color ch direction
assert block_shape[-1] == dstl_image.data.shape[-1]
# Drop parts of the image that cant be captured by an integer number of
# strides
_split_factor = np.floor(np.divide(dstl_image.data.shape,
block_shape)).astype(int)
_img_lims = (_split_factor * block_shape)
print("Can only preserve up to pix: ", _img_lims)
_data = np.ascontiguousarray(
dstl_image._data[:_img_lims[0], :_img_lims[1], :_img_lims[2]])
blocks = view_as_blocks(_data, block_shape)
ystride, xstride, _ = block_shape
jmx, imx, *_ = blocks.shape
# Transform the features of an image into the coordinates of the new
# blocks
collection = __transform_and_collect_features(dstl_image, block_shape,
blocks.shape)
for j in range(jmx):
for i in range(imx):
block_tag = "{}_{}".format(i, j)
block_id = "{}__{}".format(dstl_image.image_id, block_tag)
# Save the image
block_img_path = os.path.join(image_save_dir,
block_id + '.png')
skio.imsave(block_img_path, blocks[j, i, 0, ...])
# Save annotations
# write the image path and x,y coor of bounding boxes to
# the csv file
# has format:
# path_to_image, x1, y1, x2, y2, label
try:
block_features = collection[block_tag]
except KeyError:
# if no features in an image, just append blank position
row = [block_img_path, '', '', '', '', '']
annotation_writer.writerow(row)
else:
# for each features, append its locations and label of
# the features
for label, features in block_features.items():
for coor in features:
row = [block_img_path, *coor, label]
# output to file
annotation_writer.writerow(row)
def aggregate_array(in_arr, nagg_x=2, nagg_y=2):
"""
:type in_arr: numpy.ndarray
:type nagg_y: int
"""
from skimage.util import view_as_blocks
return view_as_blocks(in_arr, (nagg_x, nagg_y)).mean(axis=2).mean(axis=2)
def embed_message(orig, msg):
changed = orig.copy()
blocks = view_as_blocks(changed, block_shape=(n, n))
h = blocks.shape[1]
for index, bit in enumerate(msg):
i = index // h
j = index % h
block = blocks[i, j]
changed[i * n:(i + 1) * n, j * n:(j + 1) * n] = embed_bit(block, bit)
return changed
def test_random_blocks(self):
volume = self._gen_test_volume()
blocks = view_as_blocks(volume, (64, 64, 64)).reshape(-1, 64, 64, 64)
encoded = encode_mask_blocks(blocks, [(1, 2, 3)] * len(blocks), 17)
decoded, corners, label = decode_mask_blocks(encoded)
assert label == 17
assert (np.array(corners) == (1, 2, 3)).all()
assert (np.array(decoded) == np.array(blocks)).all()
def build_cells(g_magn, theta):
phi = max_theta // nbins
cells_g_magn = view_as_blocks(g_magn, (cellh, cellh)).reshape(
(imgh // cellh)**2, cellh**2)
cells_theta = view_as_blocks(theta, (cellh, cellh)).reshape(
(imgh // cellh)**2, cellh**2)
cells_bin = np.zeros(((imgh // cellh)**2, nbins))
idx = cells_theta // phi
ratio = (cells_theta - idx * phi) / phi
rr = np.arange(cells_bin.shape[0]).reshape(-1, 1)
cells_bin[rr, idx] = cells_g_magn * (1 - ratio)
idx += 1
idx[idx == nbins] = 0
cells_bin[rr, idx] += cells_g_magn * ratio
return cells_bin.reshape(((imgh // cellh), (imgh // cellh), nbins))
def create_grid(self, height, width):
"""
Выполняет разбиение изображения на блоки размера (height, width)
"""
self.block_height = height
self.block_width = width
# копируем область изображения, которая покрывается заданными блоками
h, w = self.image.shape
self.nrows = h // self.block_height
self.ncols = w // self.block_width
self.image_clone = self.image[:self.block_height * self.nrows, :self.block_width * self.ncols].copy()
# разбиваем скопированную часть изображения на блоки
self.image_blocks = view_as_blocks(self.image_clone, (self.block_height, self.block_width))
def extractBlocks(npatches,blocksize=4):
side_length = np.sqrt(npatches)
print "side_length,patches",side_length
if not side_length.is_integer():
print "ERROR: no integer side length!"
sys.exit(1)
else:
side_length = int(side_length)
m = np.arange(0,npatches).reshape((side_length,side_length))
blocks = view_as_blocks(m,(blocksize,blocksize))
blocks = np.reshape(blocks, (blocks.shape[0]*blocks.shape[1],blocksize*blocksize))
print "Extracting ",blocks.shape[0]," blocks:",blocksize,"X",blocksize
return blocks
def block_mask_to_px_mask(block_mask, block_width):
"""
Given a mask array with block-resolution (each item represents 1 block),
upscale it to pixel-resolution.
"""
px_mask_shape = block_width*np.array(block_mask.shape)
px_mask = np.zeros( px_mask_shape, dtype=np.bool )
# In this 6D array, the first 3 axes address the block index,
# and the last 3 axes address px within the block
px_mask_blockwise = view_as_blocks(px_mask, (block_width, block_width, block_width))
assert px_mask_blockwise.shape[0:3] == block_mask.shape
assert px_mask_blockwise.shape[3:6] == (block_width, block_width, block_width)
# Now we can broadcast into it from the block mask
px_mask_blockwise[:] = block_mask[:, :, :, None, None, None]
return px_mask
def threshold_local(frame, shape, llim=0.01, ulim=0.99):
'''
threshold_local
Trim a percentile of values from non-overlapping blocks in 2d array.
'''
tmp = frame.copy()
nullmask = (frame != 0) & (frame != np.nan)
rowblocks = view_as_blocks(tmp, shape).reshape((-1,np.prod(shape)))
vrange = np.sort(rowblocks, axis=1)
llimIdx, ulimIdx = round(llim*np.prod(shape)), round(ulim*np.prod(shape))
thresh_low = vrange[ ..., [llimIdx] ]
thresh_high = vrange[ ..., [ulimIdx] ]
rowblocks[..., (rowblocks < thresh_low) | (rowblocks > thresh_high)] = -1
return nullmask & (tmp != -1)
def agg_blocks_skimage_improved(data, block_shape: tuple, func=np.nanmean, pad_value=np.nan):
"""
Modified from skimage.measure.block_reduce
:param data:
:param block_shape:
:param func:
:param pad_value:
"""
from skimage.util import view_as_blocks
assert data.ndim == len(block_shape), "Block should have the same number of dimensions as the input array(ndim={}).".format(data.ndim)
if data.ndim == 1:
data.shape = data.shape + (1, )
block_shape = block_shape + (1, )
pad_width = []
for i in range(data.ndim):
if block_shape[i] < 1:
raise ValueError("Down-sampling factors must be >= 1. Use "
"`skimage.transform.resize` to up-sample an "
"image.")
if data.shape[i] % block_shape[i] != 0:
after_width = block_shape[i] - (data.shape[i] % block_shape[i])
else:
after_width = 0
pad_width.append((0, after_width))
image = np.pad(data, pad_width=pad_width, mode='constant', constant_values=pad_value)
out = view_as_blocks(image, block_shape)
result_shape = out.shape[:-2]
out = out.reshape((-1, ) + block_shape)
@jit(nopython=True)
def wrap(*args):
return func(*args)
return np.array([wrap(chunk) for chunk in out]).reshape(result_shape).squeeze()
def aggregate_array(in_arr, nagg_x=2, nagg_y=2):
"""
:type in_arr: numpy.ndarray
:type nagg_y: int
"""
from skimage.util import view_as_blocks
if nagg_x == 1 and nagg_y == 1:
return in_arr
print(in_arr.shape, nagg_x, nagg_y)
assert in_arr.shape[0] % nagg_x == 0
assert in_arr.shape[1] % nagg_y == 0
return view_as_blocks(in_arr, (nagg_x, nagg_y)).mean(axis=2).mean(axis=2)
def Feature_Extractor_Fn(vid,num_frames,frame_no,new_shape=(360,480),step=80, radius=45):
"""Extract Daisy feature for a frame of video """
if frame_no<num_frames-1:
frame = vid.get_data(frame_no)
frame_resized=resize(frame, new_shape)
frame_gray= rgb2gray(frame_resized)
daisy_desc = daisy(frame_gray,step=step, radius=radius)
daisy_1D=np.ravel(daisy_desc)
"""Extract Daisy feature for a patch from the frame of video """
N=4
step_glove=int(step/N)
radius_glove=int(radius/N)
patch_shape_x=int(new_shape[0]/N)
patch_shape_y=int(new_shape[1]/N)
patchs_arr = view_as_blocks(frame_gray, (patch_shape_x,patch_shape_y))
patch_num_row=patchs_arr.shape[0]
patch_num_col=patchs_arr.shape[1]
final_daisy_length=daisy(patchs_arr[0,0,:,:],step=step_glove, radius=radius_glove).size
patch_daisy_arr=np.zeros((patch_num_row,patch_num_col,final_daisy_length))
for i in xrange(patch_num_row):
for k in xrange(patch_num_col):
patch=patchs_arr[i,k,:,:]
patch_daisy_desc = daisy(patch,step=step_glove, radius=radius_glove)
patch_daisy_1D=np.ravel(patch_daisy_desc)
patch_daisy_arr[i,k,:]=patch_daisy_1D
#sift = cv2.xfeatures2d.SIFT_create()
# (sift_kps, sift_descs) = sift.detectAndCompute(frame, None)
# print("# kps: {}, descriptors: {}".format(len(sift_kps), sift_descs.shape))
# surf = cv2.xfeatures2d.SURF_create()
# (surf_kps, surf_descs) = surf.detectAndCompute(frame, None)
# print("# kps: {}, descriptors: {}".format(len(surf_kps), surf_descs.shape))
else:
print("Frame number is larger than the length of video")
# return (daisy_1D,surf_descs,sift_descs)
return patch_daisy_arr,daisy_1D
def get_switches(current,maxpool,maxpool_shape):
# print("getting switches")
current=np.array(current)
size = maxpool_shape[0]
switches = np.zeros((current.shape[0],current.shape[1],int(current.shape[2]/size)*size,int(current.shape[3]/size)*size))
# print("before shape",switches.shape)
for k1idx, k1 in enumerate(current):
for k2idx, k2 in enumerate(k1):
# print("before", k2[:4,:4])
while k2.shape[0]%size!=0:
k2=k2[:-1,:-1]
blocks = view_as_blocks(k2,maxpool_shape)
for x in blocks:
for y in x:
idx = np.unravel_index(y.argmax(), y.shape)
y.fill(0)
y[idx] = 1
# print("after",k2[:4,:4])
switches[k1idx,k2idx,:,:]=k2[:]
# print("after shape",switches.shape)
return switches
def serialize_uint64_blocks(volume):
"""
Compress and serialize a volume of uint64.
Preconditions:
- volume.dtype == np.uint64
- volume.ndim == 3
NOTE: If volume.shape is NOT divisible by 64, the input will be copied and padded.
Returns compressed_blocks, where the blocks are a flat list, in scan-order
"""
assert volume.dtype == np.uint64
assert volume.ndim == 3
if (np.array(volume.shape) % 64).any():
padding = 64 - ( np.array(volume.shape) % 64 )
aligned_shape = volume.shape + padding
aligned_volume = np.zeros( aligned_shape, dtype=np.uint64 )
aligned_volume[box_to_slicing((0,0,0), volume.shape)] = volume
else:
aligned_volume = volume
assert (np.array(aligned_volume.shape) % 64 == 0).all()
block_view = view_as_blocks( aligned_volume, (64,64,64) )
compressed_blocks = []
for zi, yi, xi in np.ndindex(*block_view.shape[:3]):
block = block_view[zi,yi,xi].copy('C')
encoded_block = encode_label_block(block)
# We compress AGAIN, with LZ4, because this seems to provide
# an additional 2x size reduction for nearly no slowdown.
compressed_block = lz4.frame.compress( encoded_block )
compressed_blocks.append( compressed_block )
del block
return compressed_blocks
def _clahe(image, ntiles_x, ntiles_y, clip_limit, nbins=128):
ntiles_x = min(ntiles_x, MAX_REG_X)
ntiles_y = min(ntiles_y, MAX_REG_Y)
ntiles_y = max(ntiles_x, 2)
ntiles_x = max(ntiles_y, 2)
if clip_limit == 1.0:
return image # is OK, immediately returns original image.
map_array = np.zeros((ntiles_y, ntiles_x, nbins), dtype=int)
y_res = image.shape[0] - image.shape[0] % ntiles_y
x_res = image.shape[1] - image.shape[1] % ntiles_x
image = image[: y_res, : x_res]
x_size = image.shape[1] / ntiles_x # Actual size of contextual regions
y_size = image.shape[0] / ntiles_y
n_pixels = x_size * y_size
if clip_limit > 0.0: # Calculate actual cliplimit
clip_limit = int(clip_limit * (x_size * y_size) / nbins)
if clip_limit < 1:
clip_limit = 1
else:
clip_limit = NR_OF_GREY # Large value, do not clip (AHE)
bin_size = 1 + NR_OF_GREY / nbins
aLUT = np.arange(NR_OF_GREY)
aLUT /= bin_size
img_blocks = view_as_blocks(image, (y_size, x_size))
# Calculate greylevel mappings for each contextual region
for y in range(ntiles_y):
for x in range(ntiles_x):
sub_img = img_blocks[y, x]
hist = aLUT[sub_img.ravel()]
hist = np.bincount(hist)
hist = np.append(hist, np.zeros(nbins - hist.size, dtype=int))
hist = clip_histogram(hist, clip_limit)
hist = map_histogram(hist, 0, NR_OF_GREY - 1, n_pixels)
map_array[y, x] = hist
# Interpolate greylevel mappings to get CLAHE image
ystart = 0
for y in range(ntiles_y + 1):
xstart = 0
if y == 0: # special case: top row
ystep = y_size / 2.0
yU = 0
yB = 0
elif y == ntiles_y: # special case: bottom row
ystep = y_size / 2.0
yU = ntiles_y - 1
yB = yU
else: # default values
ystep = y_size
yU = y - 1
yB = yB + 1
for x in range(ntiles_x + 1):
if x == 0: # special case: left column
xstep = x_size / 2.0
xL = 0
xR = 0
elif x == ntiles_x: # special case: right column
xstep = x_size / 2.0
xL = ntiles_x - 1
xR = xL
else: # default values
xstep = x_size
xL = x - 1
xR = xL + 1
mapLU = map_array[yU, xL]
mapRU = map_array[yU, xR]
mapLB = map_array[yB, xL]
mapRB = map_array[yB, xR]
xslice = np.arange(xstart, xstart + xstep)
yslice = np.arange(ystart, ystart + ystep)
interpolate(image, xslice, yslice,
mapLU, mapRU, mapLB, mapRB, aLUT)
xstart += xstep # set pointer on next matrix */
ystart += ystep
return image
def _clahe(image, ntiles_x, ntiles_y, clip_limit, nbins=128):
"""Contrast Limited Adaptive Histogram Equalization.
Parameters
----------
image : array-like
Input image.
ntiles_x : int, optional
Number of tile regions in the X direction. Ranges between 2 and 16.
ntiles_y : int, optional
Number of tile regions in the Y direction. Ranges between 2 and 16.
clip_limit : float, optional
Normalized clipping limit (higher values give more contrast).
nbins : int, optional
Number of gray bins for histogram ("dynamic range").
Returns
-------
out : ndarray
Equalized image.
The number of "effective" greylevels in the output image is set by `nbins`;
selecting a small value (eg. 128) speeds up processing and still produce
an output image of good quality. The output image will have the same
minimum and maximum value as the input image. A clip limit smaller than 1
results in standard (non-contrast limited) AHE.
"""
ntiles_x = min(ntiles_x, MAX_REG_X)
ntiles_y = min(ntiles_y, MAX_REG_Y)
if clip_limit == 1.0:
return image # is OK, immediately returns original image.
h_inner = image.shape[0] - image.shape[0] % ntiles_y
w_inner = image.shape[1] - image.shape[1] % ntiles_x
# make the tile size divisible by 2
h_inner -= h_inner % (2 * ntiles_y)
w_inner -= w_inner % (2 * ntiles_x)
orig_shape = image.shape
width = w_inner // ntiles_x # Actual size of contextual regions
height = h_inner // ntiles_y
if h_inner != image.shape[0]:
ntiles_y += 1
if w_inner != image.shape[1]:
ntiles_x += 1
if h_inner != image.shape[1] or w_inner != image.shape[0]:
h_pad = height * ntiles_y - image.shape[0]
w_pad = width * ntiles_x - image.shape[1]
image = pad(image, ((0, h_pad), (0, w_pad)), mode='reflect')
h_inner, w_inner = image.shape
bin_size = 1 + NR_OF_GREY / nbins
lut = np.arange(NR_OF_GREY)
lut //= bin_size
img_blocks = view_as_blocks(image, (height, width))
map_array = np.zeros((ntiles_y, ntiles_x, nbins), dtype=int)
n_pixels = width * height
if clip_limit > 0.0: # Calculate actual cliplimit
clip_limit = int(clip_limit * (width * height) / nbins)
if clip_limit < 1:
clip_limit = 1
else:
clip_limit = NR_OF_GREY # Large value, do not clip (AHE)
# Calculate greylevel mappings for each contextual region
for y in range(ntiles_y):
for x in range(ntiles_x):
sub_img = img_blocks[y, x]
hist = lut[sub_img.ravel()]
hist = np.bincount(hist)
hist = np.append(hist, np.zeros(nbins - hist.size, dtype=int))
hist = clip_histogram(hist, clip_limit)
hist = map_histogram(hist, 0, NR_OF_GREY - 1, n_pixels)
map_array[y, x] = hist
# Interpolate greylevel mappings to get CLAHE image
ystart = 0
for y in range(ntiles_y + 1):
xstart = 0
if y == 0: # special case: top row
ystep = height / 2.0
yU = 0
yB = 0
elif y == ntiles_y: # special case: bottom row
ystep = height / 2.0
yU = ntiles_y - 1
yB = yU
else: # default values
ystep = height
yU = y - 1
yB = yB + 1
for x in range(ntiles_x + 1):
if x == 0: # special case: left column
xstep = width / 2.0
xL = 0
xR = 0
elif x == ntiles_x: # special case: right column
xstep = width / 2.0
xL = ntiles_x - 1
xR = xL
else: # default values
xstep = width
xL = x - 1
xR = xL + 1
mapLU = map_array[yU, xL]
mapRU = map_array[yU, xR]
mapLB = map_array[yB, xL]
mapRB = map_array[yB, xR]
xslice = np.arange(xstart, xstart + xstep)
yslice = np.arange(ystart, ystart + ystep)
interpolate(image, xslice, yslice,
mapLU, mapRU, mapLB, mapRB, lut)
xstart += xstep # set pointer on next matrix */
ystart += ystep
if image.shape != orig_shape:
image = image[:orig_shape[0], :orig_shape[1]]
return image
from matplotlib import pyplot as plt import matplotlib.cm as cm from skimage import data from skimage import color from skimage.util import view_as_blocks # get astronaut from skimage.data in grayscale l = color.rgb2gray(data.astronaut()) # size of blocks block_shape = (4, 4) # see astronaut as a matrix of blocks (of shape block_shape) view = view_as_blocks(l, block_shape) # collapse the last two dimensions in one flatten_view = view.reshape(view.shape[0], view.shape[1], -1) # resampling the image by taking either the `mean`, # the `max` or the `median` value of each blocks. mean_view = np.mean(flatten_view, axis=2) max_view = np.max(flatten_view, axis=2) median_view = np.median(flatten_view, axis=2) # display resampled images fig, axes = plt.subplots(2, 2, figsize=(8, 8), sharex=True, sharey=True) ax = axes.ravel() l_resized = ndi.zoom(l, 2, order=3)
def block_reduce(image, block_size, func=np.sum, cval=0):
"""Down-sample image by applying function to local blocks.
Parameters
----------
image : ndarray
N-dimensional input image.
block_size : array_like
Array containing down-sampling integer factor along each axis.
func : callable
Function object which is used to calculate the return value for each
local block. This function must implement an ``axis`` parameter such
as ``numpy.sum`` or ``numpy.min``.
cval : float
Constant padding value if image is not perfectly divisible by the
block size.
Returns
-------
image : ndarray
Down-sampled image with same number of dimensions as input image.
Examples
--------
>>> from skimage.measure import block_reduce
>>> image = np.arange(3*3*4).reshape(3, 3, 4)
>>> image # doctest: +NORMALIZE_WHITESPACE
array([[[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]],
[[12, 13, 14, 15],
[16, 17, 18, 19],
[20, 21, 22, 23]],
[[24, 25, 26, 27],
[28, 29, 30, 31],
[32, 33, 34, 35]]])
>>> block_reduce(image, block_size=(3, 3, 1), func=np.mean)
array([[[ 16., 17., 18., 19.]]])
>>> image_max1 = block_reduce(image, block_size=(1, 3, 4), func=np.max)
>>> image_max1 # doctest: +NORMALIZE_WHITESPACE
array([[[11]],
[[23]],
[[35]]])
>>> image_max2 = block_reduce(image, block_size=(3, 1, 4), func=np.max)
>>> image_max2 # doctest: +NORMALIZE_WHITESPACE
array([[[27],
[31],
[35]]])
"""
if len(block_size) != image.ndim:
raise ValueError("`block_size` must have the same length "
"as `image.shape`.")
pad_width = []
for i in range(len(block_size)):
if block_size[i] < 1:
raise ValueError("Down-sampling factors must be >= 1. Use "
"`skimage.transform.resize` to up-sample an "
"image.")
if image.shape[i] % block_size[i] != 0:
after_width = block_size[i] - (image.shape[i] % block_size[i])
else:
after_width = 0
pad_width.append((0, after_width))
image = pad(image, pad_width=pad_width, mode='constant',
constant_values=cval)
out = view_as_blocks(image, block_size)
for i in range(len(out.shape) // 2):
out = func(out, axis=-1)
return out
def transform(self, X):
return np.mean(view_as_blocks(X, self.pool_size), axis=(4, 5, 6, 7))
def to_patches(img, patch_size=20):
"""
Assuming a 2-dimensional image, divide into patch_size * patch_size pixel patches.
"""
return view_as_blocks(img, (patch_size, patch_size))
def _clahe(image, ntiles_x, ntiles_y, clip_limit, nbins=128):
"""Contrast Limited Adaptive Histogram Equalization.
Parameters
----------
image : array-like
Input image.
ntiles_x : int, optional
Number of tile regions in the X direction. Ranges between 2 and 16.
ntiles_y : int, optional
Number of tile regions in the Y direction. Ranges between 2 and 16.
clip_limit : float, optional
Normalized clipping limit (higher values give more contrast).
nbins : int, optional
Number of gray bins for histogram ("dynamic range").
Returns
-------
out : ndarray
Equalized image.
The number of "effective" greylevels in the output image is set by `nbins`;
selecting a small value (eg. 128) speeds up processing and still produce
an output image of good quality. The output image will have the same
minimum and maximum value as the input image. A clip limit smaller than 1
results in standard (non-contrast limited) AHE.
"""
ntiles_x = min(ntiles_x, MAX_REG_X)
ntiles_y = min(ntiles_y, MAX_REG_Y)
ntiles_y = max(ntiles_x, 2)
ntiles_x = max(ntiles_y, 2)
if clip_limit == 1.0:
return image # is OK, immediately returns original image.
map_array = np.zeros((ntiles_y, ntiles_x, nbins), dtype=int)
y_res = image.shape[0] - image.shape[0] % ntiles_y
x_res = image.shape[1] - image.shape[1] % ntiles_x
image = image[: y_res, : x_res]
x_size = image.shape[1] / ntiles_x # Actual size of contextual regions
y_size = image.shape[0] / ntiles_y
n_pixels = x_size * y_size
if clip_limit > 0.0: # Calculate actual cliplimit
clip_limit = int(clip_limit * (x_size * y_size) / nbins)
if clip_limit < 1:
clip_limit = 1
else:
clip_limit = NR_OF_GREY # Large value, do not clip (AHE)
bin_size = 1 + NR_OF_GREY / nbins
aLUT = np.arange(NR_OF_GREY)
aLUT /= bin_size
img_blocks = view_as_blocks(image, (y_size, x_size))
# Calculate greylevel mappings for each contextual region
for y in range(ntiles_y):
for x in range(ntiles_x):
sub_img = img_blocks[y, x]
hist = aLUT[sub_img.ravel()]
hist = np.bincount(hist)
hist = np.append(hist, np.zeros(nbins - hist.size, dtype=int))
hist = clip_histogram(hist, clip_limit)
hist = map_histogram(hist, 0, NR_OF_GREY - 1, n_pixels)
map_array[y, x] = hist
# Interpolate greylevel mappings to get CLAHE image
ystart = 0
for y in range(ntiles_y + 1):
xstart = 0
if y == 0: # special case: top row
ystep = y_size / 2.0
yU = 0
yB = 0
elif y == ntiles_y: # special case: bottom row
ystep = y_size / 2.0
yU = ntiles_y - 1
yB = yU
else: # default values
ystep = y_size
yU = y - 1
yB = yB + 1
for x in range(ntiles_x + 1):
if x == 0: # special case: left column
xstep = x_size / 2.0
xL = 0
xR = 0
elif x == ntiles_x: # special case: right column
xstep = x_size / 2.0
xL = ntiles_x - 1
xR = xL
else: # default values
xstep = x_size
xL = x - 1
xR = xL + 1
mapLU = map_array[yU, xL]
mapRU = map_array[yU, xR]
mapLB = map_array[yB, xL]
mapRB = map_array[yB, xR]
xslice = np.arange(xstart, xstart + xstep)
yslice = np.arange(ystart, ystart + ystep)
interpolate(image, xslice, yslice,
mapLU, mapRU, mapLB, mapRB, aLUT)
xstart += xstep # set pointer on next matrix */
ystart += ystep
return image
def simplifyReward(reward, block):
return ski.view_as_blocks(reward, (block, block)).mean(axis = (2, 3)) # simplified (average) reward matrix
def blocks(self, size):
'''blocks are non-overlapping sub images'''
size = list(size)
size.append(self.channels)
return [Image(i) for i in util.view_as_blocks(self, size)]