def run_pipeline_v2(image_or_path,
params=None,
metadata=None,
fix_orient=True):
params_ = params.copy()
if type(image_or_path) == str:
image_path = image_or_path
# raw image data
raw_image = get_visible_raw_image(image_path)
# metadata
metadata = get_metadata(image_path)
else:
raw_image = image_or_path.copy()
# must provide metadata
if metadata is None:
raise ValueError(
"Must provide metadata when providing image data in first argument."
)
current_stage = 'raw'
current_image = raw_image
if params_['input_stage'] == current_stage:
# linearization
linearization_table = metadata['linearization_table']
if linearization_table is not None:
print('Linearization table found. Not handled.')
# TODO
current_image = normalize(current_image, metadata['black_level'],
metadata['white_level'])
params_['input_stage'] = 'normal'
current_stage = 'normal'
if params_['output_stage'] == current_stage:
return current_image
if params_['input_stage'] == current_stage:
current_image = white_balance(current_image,
metadata['as_shot_neutral'],
metadata['cfa_pattern'])
params_['input_stage'] = 'white_balance'
current_stage = 'white_balance'
if params_['output_stage'] == current_stage:
return current_image
if params_['input_stage'] == current_stage:
current_image = demosaic(current_image,
metadata['cfa_pattern'],
output_channel_order='BGR',
alg_type=params_['demosaic_type'])
params_['input_stage'] = 'demosaic'
current_stage = 'demosaic'
if params_['output_stage'] == current_stage:
return current_image
if params_['input_stage'] == current_stage:
current_image = apply_color_space_transform(current_image,
metadata['color_matrix_1'],
metadata['color_matrix_2'])
params_['input_stage'] = 'xyz'
current_stage = 'xyz'
if params_['output_stage'] == current_stage:
return current_image
if params_['input_stage'] == current_stage:
current_image = transform_xyz_to_srgb(current_image)
params_['input_stage'] = 'srgb'
current_stage = 'srgb'
if fix_orient:
# fix image orientation, if needed (after srgb stage, ok?)
current_image = fix_orientation(current_image, metadata['orientation'])
if params_['output_stage'] == current_stage:
return current_image
if params_['input_stage'] == current_stage:
current_image = apply_gamma(current_image)
params_['input_stage'] = 'gamma'
current_stage = 'gamma'
if params_['output_stage'] == current_stage:
return current_image
if params_['input_stage'] == current_stage:
current_image = apply_tone_map(current_image)
params_['input_stage'] = 'tone'
current_stage = 'tone'
if params_['output_stage'] == current_stage:
return current_image
# invalid input/output stage!
raise ValueError(
'Invalid input/output stage: input_stage = {}, output_stage = {}'.
format(params_['input_stage'], params_['output_stage']))
'input_stage':
'raw', # options: 'raw', 'normal', 'white_balance', 'demosaic', 'xyz', 'srgb', 'gamma', 'tone'
'output_stage':
'tone', # options: 'normal', 'white_balance', 'demosaic', 'xyz', 'srgb', 'gamma', 'tone'
'save_as': 'png', # options: 'jpg', 'png', 'tif', etc.
'demosaic_type': 'EA',
'save_dtype': np.uint8
}
# processing a directory
images_dir = '../data/'
image_paths = glob.glob(os.path.join(images_dir, '*.dng'))
for image_path in image_paths:
# raw image data
raw_image = get_visible_raw_image(image_path)
# metadata
metadata = get_metadata(image_path)
# modify WB here
metadata['as_shot_neutral'] = [1., 1., 1.]
# render
output_image = run_pipeline_v2(image_path, params)
# save
output_image_path = image_path.replace(
'.dng', '_{}.'.format(params['output_stage']) + params['save_as'])
max_val = 2**16 if params['save_dtype'] == np.uint16 else 255
output_image = (output_image[..., ::-1] * max_val).astype(
def run_pipeline(image_path, params):
# raw image data
raw_image = get_visible_raw_image(image_path)
# metadata
metadata = get_metadata(image_path)
# linearization
linearization_table = metadata['linearization_table']
if linearization_table is not None:
print('Linearization table found. Not handled.')
# TODO
normalized_image = normalize(raw_image, metadata['black_level'],
metadata['white_level'])
if params['output_stage'] == 'normal':
return normalized_image
white_balanced_image = white_balance(normalized_image,
metadata['as_shot_neutral'],
metadata['cfa_pattern'])
if params['output_stage'] == 'white_balance':
return white_balanced_image
demosaiced_image = demosaic(white_balanced_image,
metadata['cfa_pattern'],
output_channel_order='BGR',
alg_type=params['demosaic_type'])
# fix image orientation, if needed
demosaiced_image = fix_orientation(demosaiced_image,
metadata['orientation'])
if params['output_stage'] == 'demosaic':
return demosaiced_image
xyz_image = apply_color_space_transform(demosaiced_image,
metadata['color_matrix_1'],
metadata['color_matrix_2'])
if params['output_stage'] == 'xyz':
return xyz_image
srgb_image = transform_xyz_to_srgb(xyz_image)
if params['output_stage'] == 'srgb':
return srgb_image
gamma_corrected_image = apply_gamma(srgb_image)
if params['output_stage'] == 'gamma':
return gamma_corrected_image
tone_mapped_image = apply_tone_map(gamma_corrected_image)
if params['output_stage'] == 'tone':
return tone_mapped_image
output_image = None
return output_image