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@Author: Nicolo' Bonettini
@Author: Luca Bondi
@Author: Francesco Picetti
"""
import random
import numpy as np
from skimage.util import view_as_windows, view_as_blocks
# Score functions ---
def mid_intensity_high_texture(in_content):
"""
Quality function that returns higher scores for mid intensity patches with high texture levels. Empirical.
:type in_content: ndarray
:param in_content : 2D or 3D ndarray. Values are expected in [0,1] if in_content is float, in [0,255] if in_content is uint8
:return score: float
score in [0,1].
"""
if in_content.dtype == np.uint8:
in_content = in_content / 255.
mean_std_weight = .7
in_content = in_content.flatten()
mean_val = in_content.mean()
std_val = in_content.std()
ch_mean_score = -4 * mean_val ** 2 + 4 * mean_val
ch_std_score = 1 - np.exp(-2 * np.log(10) * std_val)
score = mean_std_weight * ch_mean_score + (1 - mean_std_weight) * ch_std_score
return score
def count_patches(in_size, patch_size, patch_stride):
"""
Compute the number of patches
:param in_size:
:param patch_size:
:param patch_stride:
:return:
"""
win_indices_shape = (((np.array(in_size) - np.array(patch_size))
// np.array(patch_stride)) + 1)
return int(np.prod(win_indices_shape))
class PatchExtractor:
def __init__(self, dim, offset=None, stride=None, rand=None, function=None, threshold=None,
num=None, indexes=None):
"""
N-dimensional patch extractor
Args:
:param in_content : ndarray
the content to process as a numpy array of ndim dimensions
:param dim : tuple
patch_array dimensions as a tuple of ndim elements
Named args:
:param offset : tuple
the offsets along each axis as a tuple of ndim elements
:param stride : tuple
the stride of each axis as a tuple of ndim elements
:param rand : bool
randomize patch_array order. Mutually exclusive with function_handler
:param function : function
patch quality function handler. Mutually exclusive with rand
:param threshold: float
minimum quality threshold
:param num : int
maximum number of returned patch_array. Mutually exclusive with indexes
:param indexes : list|ndarray
explicitly return corresponding patch indexes (function_handler or C order used to index patch_array).
Mutually exclusive with num
:return ndarray: patch_array
array of patch_array
if rand==False and function_handler==None and num==None and indexes==None:
patch_array.ndim = 2 * in_content.ndim
else:
patch_array.ndim = 1 + in_content.ndim
"""
# Arguments parser ---
if not isinstance(dim, tuple):
raise ValueError('dim must be a tuple')
self.dim = dim
ndim = len(dim)
self.ndim = ndim
if offset is None:
offset = tuple([0] * ndim)
if not isinstance(offset, tuple):
raise ValueError('offset must be a tuple')
if len(offset) != ndim:
raise ValueError('offset must a tuple of length {:d}'.format(ndim))
self.offset = offset
if stride is None:
stride = dim
if not isinstance(stride, tuple):
raise ValueError('stride must be a tuple')
if len(stride) != ndim:
raise ValueError('stride must a tuple of length {:d}'.format(ndim))
self.stride = stride
if rand is not None and function is not None:
raise ValueError('rand and function cannot be set at the same time')
if rand is None:
rand = False
if not isinstance(rand, bool):
raise ValueError('rand must be a boolean')
self.rand = rand
if function is not None and not callable(function):
raise ValueError('function must be a function handler')
self.function_handler = function
if threshold is None:
threshold = 0.0
if not isinstance(threshold, float):
raise ValueError('threshold must be a float')
self.threshold = threshold
if num is not None and indexes is not None:
raise ValueError('num and indexes cannot be set at the same time')
if num is not None and not isinstance(num, int):
raise ValueError('num must be an int')
self.num = num
if indexes is not None and not isinstance(indexes, list) and not isinstance(indexes, np.ndarray):
raise ValueError('indexes must be an list or a 1d ndarray')
if indexes is not None:
indexes = np.array(indexes).flatten()
self.indexes = indexes
self.in_content_original_shape = None
self.in_content_cropped_shape = None
def extract(self, in_content):
if not isinstance(in_content, np.ndarray):
raise ValueError('in_content must be of type: ' + str(np.ndarray))
if in_content.ndim != self.ndim:
raise ValueError('in_content shape must a tuple of length {:d}'.format(self.ndim))
self.in_content_original_shape = in_content.shape
# Offset ---
for dim_idx, dim_offset in enumerate(self.offset):
dim_max = in_content.shape[dim_idx]
in_content = in_content.take(range(dim_offset, dim_max), axis=dim_idx)
# Patch list ---
if self.dim == self.stride:
in_content_crop = in_content
for dim_idx in range(self.ndim):
dim_max = (in_content.shape[dim_idx] // self.dim[dim_idx]) * self.dim[dim_idx]
in_content_crop = in_content_crop.take(range(0, dim_max), axis=dim_idx)
patch_array = view_as_blocks(in_content_crop, self.dim)
else:
patch_array = view_as_windows(in_content, self.dim, self.stride)
patch_array = np.ascontiguousarray(patch_array)
patch_idx = patch_array.shape[:self.ndim]
self.in_content_cropped_shape = tuple((np.asarray(patch_idx) - 1) * np.asarray(self.stride) + np.asarray(self.dim))
# Evaluate patch_array or rand sort ---
if self.rand:
patch_array.shape = (-1,) + self.dim
random.shuffle(patch_array)
else:
if self.function_handler is not None:
patch_array.shape = (-1,) + self.dim
patch_scores = np.asarray(list(map(self.function_handler, patch_array)))
sort_idxs = np.argsort(patch_scores)[::-1]
patch_scores = patch_scores[sort_idxs]
patch_array = patch_array[sort_idxs]
patch_array = patch_array[patch_scores >= self.threshold]
if self.num is not None:
patch_array.shape = (-1,) + self.dim
patch_array = patch_array[:self.num]
if self.indexes is not None:
patch_array.shape = (-1,) + self.dim
patch_array = patch_array[self.indexes]
return patch_array
def extract_call(self, args): # TODO: verify
in_content = args.pop('in_content')
dim = args.pop('dim')
return self.extract(in_content)
def reconstruct(self, patch_array):
"""
Reconstruct the N-dim image from the patch_array that has been extracted previously
:param patch_array: array of patches as output of patch_extractor
:return:
"""
# Arguments parser ---
if not isinstance(patch_array, np.ndarray):
raise ValueError('patch_array must be of type: ' + str(np.ndarray))
ndim = patch_array.ndim // 2
# if not isinstance(patch_stride, tuple):
# raise ValueError('patch_stride must be a tuple')
# if len(patch_stride) != ndim:
# raise ValueError('patch_stride must be a tuple of length {:d}'.format(ndim))
#
# if not isinstance(image_shape, tuple):
# raise ValueError('patch_idx must be a tuple')
# if len(image_shape) != ndim:
# raise ValueError('patch_idx must be a tuple of length {:d}'.format(ndim))
patch_stride = self.stride
image_shape = self.in_content_cropped_shape
patch_shape = patch_array.shape[-ndim:]
patch_idx = patch_array.shape[:ndim]
image_shape_computed = tuple((np.array(patch_idx) - 1) * np.array(patch_stride) + np.array(patch_shape))
if not image_shape == image_shape_computed:
raise ValueError('There is something wrong with the dimensions!')
if ndim > 4:
raise ValueError('For now, it works only in 4D, sorry!')
numpatches = count_patches(image_shape, patch_shape, patch_stride)
patch_array_unwrapped = patch_array.reshape(numpatches, *patch_shape)
image_recon = np.zeros(image_shape)
norm_mask = np.zeros(image_shape)
counter = 0
for h in np.arange(0, image_shape[0] - patch_shape[0] + 1, patch_stride[0]):
if ndim > 1:
for i in np.arange(0, image_shape[1] - patch_shape[1] + 1, patch_stride[1]):
if ndim > 2:
for j in np.arange(0, image_shape[2] - patch_shape[2] + 1, patch_stride[2]):
if ndim > 3:
for k in np.arange(0, image_shape[3] - patch_shape[3] + 1, patch_stride[3]):
image_recon[h:h + patch_shape[0], i:i + patch_shape[1], j:j + patch_shape[2],
k:k + patch_shape[3]] += patch_array_unwrapped[counter, :, :, :, :]
norm_mask[h:h + patch_shape[0], i:i + patch_shape[1], j:j + patch_shape[2],
k:k + patch_shape[3]] += 1
counter += 1
else:
image_recon[h:h + patch_shape[0], i:i + patch_shape[1],
j:j + patch_shape[2]] += patch_array_unwrapped[counter, :, :, :]
norm_mask[h:h + patch_shape[0], i:i + patch_shape[1], j:j + patch_shape[2]] += 1
counter += 1
else:
image_recon[h:h + patch_shape[0], i:i + patch_shape[1]] += patch_array_unwrapped[counter, :, :]
norm_mask[h:h + patch_shape[0], i:i + patch_shape[1]] += 1
counter += 1
else:
image_recon[h:h + patch_shape[0]] += patch_array_unwrapped[counter, :]
norm_mask[h:h + patch_shape[0]] += 1
counter += 1
image_recon /= norm_mask
return image_recon
def main():
in_shape = (644, 481, 3)
dim = (120, 120, 3)
stride = (7, 90, 90, 3)
offset = (1, 0, 0, 0)
in_content = np.random.randint(256, size=in_shape).astype(np.uint8)
# args = {'in_content': in_content,
# 'dim': dim,
# 'offset': offset,
# 'stride': stride,
# }
# patch_array = patch_extractor_call(args)
pe = PatchExtractor(dim)
patch_array = pe.extract(in_content)
print('patch_array.shape = ' + str(patch_array.shape))
img_recon = pe.reconstruct(patch_array)
print('img_recon.shape = ' + str(img_recon.shape))
if __name__ == "__main__":
main()
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