def __getitem__(self, index):
# read image and simulate Wishart noise
img = cv2.imread(self.files_path[index]).astype(np.float32)
# print(f'file path {self.files_path[index]}')
img = img[:256, :256, :] # ensure can be divede by 32
img[img < 1] += 1 # avoid zero value
if self.augments:
img = self.augments(torch.from_numpy(img))
if isinstance(img, torch.Tensor):
img = img.numpy()
img, noise = simulate.generate_Wishart_noise_from_img(img, self.ENL)
# pauli_img = psr.rgb_by_c3(img, type='sinclair')
# pauli_noise = psr.rgb_by_c3(noise, type='sinclair')
# cv2.imwrite(osp.join(_TMP_PATH, 'pauli_img.jpg'), cv2.cvtColor((255*pauli_img).astype(np.uint8), cv2.COLOR_RGB2BGR))
# cv2.imwrite(osp.join(_TMP_PATH, 'pauli_noise.jpg'), cv2.cvtColor((255*pauli_noise).astype(np.uint8), cv2.COLOR_RGB2BGR))
# hoekman decomposition
img = psr.Hokeman_decomposition(img)
noise = psr.Hokeman_decomposition(noise)
# log transform
if self.log:
img = np.log(img) + self.log_compensation
noise = np.log(noise) + self.log_compensation
# to pytorch
img = torch.from_numpy(img)
noise = torch.from_numpy(noise)
return img, noise, self.files_path[index]
def hoekman(src_path: str, norm=False) -> None:
''' Hoekman decomposition and save to file, the hoekman_and_norm() is an older version of this func, and is a subset of this func
@in -src_path -source path, where should contains 'C3' folder
@in -norm -normalize or not, default: false
'''
if 'C3' in os.listdir(src_path):
# read C3 file
print(f'hoekman on dir (norm={norm}): {src_path}', end='')
c3 = psr.read_c3(osp.join(src_path, 'C3'))
h = psr.Hokeman_decomposition(c3)
dst_path = osp.join(src_path, 'Hoekman')
fu.mkdir_if_not_exist(dst_path)
# np.save(osp.join(dst_path, 'ori'), h) # save the unnormalized file
# normalize
if norm:
for ii in range(9):
h[ii, :, :] = psr.min_max_contrast_median_map(
10 * np.log10(h[ii, :, :]))
# cv2.imwrite(osp.join(dst_path, f'{ii}.jpg'), (h[0, :, :]*255).astype(np.uint8))
# plt.hist() can take very long time to process a 2D array, but little time to process a 1D array, so flatten the array if possible
# plt.hist(h[ii, :, :].flatten())
# plt.savefig(osp.join(dst_path, f'log-hist-{ii}.jpg'))
# save to file
if norm:
np.save(osp.join(dst_path, 'normed'), h)
else:
np.save(osp.join(dst_path, 'unnormed'), h)
print('\tdone')
else:
raise ValueError('wrong src path')
def __getitem__(self, index: int):
label, mask = self.get_label_and_mask(index)
# get the file path
label_path = self.labels_path[index]
label_dir = osp.split(label_path)[0]
# two date time display format
if osp.isfile(osp.join(label_dir, 'pin.txt')): # mode 1
re_exp = r'20\d{6}'
elif osp.isfile(osp.join(label_dir, 'pin-2.txt')): # mode 2
re_exp = r'\d{2}[A-Z][a-z]{2}\d{4}'
else:
raise ValueError('unknown mode of label format')
[a_time, b_time] = re.findall(re_exp, label_path)
file_path = []
file_path.append(
osp.join(label_dir.replace('label', 'data'), a_time, 'C3'))
file_path.append(
osp.join(label_dir.replace('label', 'data'), b_time, 'C3'))
# get the file data
slice_idx = re.search(r'-\d{4}-', label_path)
if slice_idx is None:
raise ValueError('can not find the wave code')
slice_idx = int(slice_idx.group()[1:5])
file = []
for ii in range(2):
tmp = psr.read_c3(osp.join(file_path[ii], str(slice_idx)))
tmp = psr.Hokeman_decomposition(tmp)
# for jj in range(9):
# tmp[jj, :, :] = psr.min_max_contrast_median_map(10*np.log10(tmp[jj, :, :]))
tmp = np.log10(tmp)
file.append(tmp)
if self.time_shuffle and np.random.binomial(1, 0.5):
return torch.from_numpy(file[0]), torch.from_numpy(
file[1]), label, mask
else:
return torch.from_numpy(file[1]), torch.from_numpy(
file[0]), label, mask
def hoekman_and_norm(src_path: str) -> None:
''' hoekman decomposition, normalization as pauliRGB, and save to file
@in -src_path -source path, where should contains 'C3' folder
'''
if 'C3' in os.listdir(src_path):
print(f'hoekman and norm on dir: {src_path}', end='')
c3 = psr.read_c3(osp.join(src_path, 'C3'))
h = psr.Hokeman_decomposition(c3)
dst_path = osp.join(src_path, 'Hoekman')
fu.mkdir_if_not_exist(dst_path)
# np.save(osp.join(dst_path, 'ori'), h) # save the unnormalized file
for ii in range(9):
h[ii, :, :] = psr.min_max_contrast_median_map(
10 * np.log10(h[ii, :, :]))
# cv2.imwrite(osp.join(dst_path, f'{ii}.jpg'), (h[0, :, :]*255).astype(np.uint8))
# plt.hist() can take very long time to process a 2D array, but little time to process a 1D array, so flatten the array if possible
# plt.hist(h[ii, :, :].flatten())
# plt.savefig(osp.join(dst_path, f'log-hist-{ii}.jpg'))
np.save(osp.join(dst_path, 'normed'), h)
print('\tdone')
else:
raise ValueError('wrong src path')