def __init__(self): self.convert_RGB_to_HSV = ConvertColor(current='RGB', to='HSV') self.convert_HSV_to_RGB = ConvertColor(current='HSV', to='RGB') self.convert_to_float32 = ConvertDataType(to='float32') self.convert_to_uint8 = ConvertDataType(to='uint8') self.convert_to_3_channels = ConvertTo3Channels() self.random_brightness = RandomBrightness(lower=-32, upper=32, prob=0.5) self.random_contrast = RandomContrast(lower=0.5, upper=1.5, prob=0.5) self.random_saturation = RandomSaturation(lower=0.5, upper=1.5, prob=0.5) self.random_hue = RandomHue(max_delta=18, prob=0.5) self.random_channel_swap = RandomChannelSwap(prob=0.0) self.sequence1 = [ self.convert_to_3_channels, self.convert_to_float32, self.random_brightness, self.random_contrast, self.convert_to_uint8, self.convert_RGB_to_HSV, self.convert_to_float32, self.random_saturation, self.random_hue, self.convert_to_uint8, self.convert_HSV_to_RGB, self.random_channel_swap ] self.sequence2 = [ self.convert_to_3_channels, self.convert_to_float32, self.random_brightness, self.convert_to_uint8, self.convert_RGB_to_HSV, self.convert_to_float32, self.random_saturation, self.random_hue, self.convert_to_uint8, self.convert_HSV_to_RGB, self.convert_to_float32, self.random_contrast, self.convert_to_uint8, self.random_channel_swap ]
def __init__(self): self.convert_to_float32 = ConvertDataType(to='float32') self.convert_to_uint8 = ConvertDataType(to='uint8') self.convert_to_3_channels = ConvertTo3Channels() self.random_brightness = RandomBrightness(lower=-16, upper=16, prob=0.5) self.random_contrast = RandomContrast(lower=0.75, upper=1.25, prob=0.5) self.random_gamma = RandomGamma(lower=0.6, upper=1.8, prob=0.5) self.sequence1 = [self.convert_to_3_channels, self.random_gamma, self.convert_to_float32] self.sequence2 = [self.convert_to_3_channels, self.convert_to_float32, self.random_contrast]
def __init__(self, random_brightness=(-48, 48, 0.5), random_contrast=(0.5, 1.8, 0.5), random_saturation=(0.5, 1.8, 0.5), random_hue=(18, 0.5), random_flip=0.5, random_translate=((0.03, 0.5), (0.03, 0.5), 0.5), random_scale=(0.5, 2.0, 0.5), n_trials_max=3, clip_boxes=True, overlap_criterion='area', bounds_box_filter=(0.3, 1.0), bounds_validator=(0.5, 1.0), n_boxes_min=1, background=(0, 0, 0), labels_format={ 'class_id': 0, 'xmin': 1, 'ymin': 2, 'xmax': 3, 'ymax': 4 }): if (random_scale[0] >= 1) or (random_scale[1] <= 1): raise ValueError( "This sequence of transformations only makes sense if the minimum scaling factor is <1 and the maximum scaling factor is >1." ) self.n_trials_max = n_trials_max self.clip_boxes = clip_boxes self.overlap_criterion = overlap_criterion self.bounds_box_filter = bounds_box_filter self.bounds_validator = bounds_validator self.n_boxes_min = n_boxes_min self.background = background self.labels_format = labels_format # 图像变换之后保留哪些boxes self.box_filter = BoxFilter(check_overlap=True, check_min_area=True, check_degenerate=True, overlap_criterion=self.overlap_criterion, overlap_bounds=self.bounds_box_filter, min_area=16, labels_format=self.labels_format) # 训练图像是否有效 self.image_validator = ImageValidator( overlap_criterion=self.overlap_criterion, bounds=self.bounds_validator, n_boxes_min=self.n_boxes_min, labels_format=self.labels_format) # Utility distortions self.convert_RGB_to_HSV = ConvertColor(current='RGB', to='HSV') self.convert_HSV_to_RGB = ConvertColor(current='HSV', to='RGB') self.convert_to_float32 = ConvertDataType(to='float32') self.convert_to_uint8 = ConvertDataType(to='uint8') self.convert_to_3_channels = ConvertTo3Channels() # 确保所有图像3通道 # Photometric transformations self.random_brightness = RandomBrightness(lower=random_brightness[0], upper=random_brightness[1], prob=random_brightness[2]) self.random_contrast = RandomContrast(lower=random_contrast[0], upper=random_contrast[1], prob=random_contrast[2]) self.random_saturation = RandomSaturation(lower=random_saturation[0], upper=random_saturation[1], prob=random_saturation[2]) self.random_hue = RandomHue(max_delta=random_hue[0], prob=random_hue[1]) # Geometric transformations self.random_flip = RandomFlip(dim='horizontal', prob=random_flip, labels_format=self.labels_format) self.random_translate = RandomTranslate( dy_minmax=random_translate[0], dx_minmax=random_translate[1], prob=random_translate[2], clip_boxes=self.clip_boxes, box_filter=self.box_filter, image_validator=self.image_validator, n_trials_max=self.n_trials_max, background=self.background, labels_format=self.labels_format) self.random_zoom_in = RandomScale(min_factor=1.0, max_factor=random_scale[1], prob=random_scale[2], clip_boxes=self.clip_boxes, box_filter=self.box_filter, image_validator=self.image_validator, n_trials_max=self.n_trials_max, background=self.background, labels_format=self.labels_format) self.random_zoom_out = RandomScale( min_factor=random_scale[0], max_factor=1.0, prob=random_scale[2], clip_boxes=self.clip_boxes, box_filter=self.box_filter, image_validator=self.image_validator, n_trials_max=self.n_trials_max, background=self.background, labels_format=self.labels_format) # 放大 self.sequence1 = [ self.convert_to_3_channels, self.convert_to_float32, self.random_brightness, self.random_contrast, self.convert_to_uint8, self.convert_RGB_to_HSV, self.convert_to_float32, self.random_saturation, self.random_hue, self.convert_to_uint8, self.convert_HSV_to_RGB, self.random_translate, self.random_zoom_in, self.random_flip ] # 缩小 self.sequence2 = [ self.convert_to_3_channels, self.convert_to_float32, self.random_brightness, self.convert_to_uint8, self.convert_RGB_to_HSV, self.convert_to_float32, self.random_saturation, self.random_hue, self.convert_to_uint8, self.convert_HSV_to_RGB, self.convert_to_float32, self.random_contrast, self.convert_to_uint8, self.random_zoom_out, self.random_translate, self.random_flip ]
def __init__(self, resize_height, resize_width, random_brightness=(-48, 48, 0.5), random_contrast=(0.5, 1.8, 0.5), random_saturation=(0.5, 1.8, 0.5), random_hue=(18, 0.5), random_flip=0.5, min_scale=0.3, max_scale=2.0, min_aspect_ratio=0.5, max_aspect_ratio=2.0, n_trials_max=3, clip_boxes=True, overlap_criterion='area', bounds_box_filter=(0.3, 1.0), bounds_validator=(0.5, 1.0), n_boxes_min=1, background=(0, 0, 0), labels_format={'class_id': 0, 'xmin': 1, 'ymin': 2, 'xmax': 3, 'ymax': 4}): self.n_trials_max = n_trials_max self.clip_boxes = clip_boxes self.overlap_criterion = overlap_criterion self.bounds_box_filter = bounds_box_filter self.bounds_validator = bounds_validator self.n_boxes_min = n_boxes_min self.background = background self.labels_format = labels_format # Determines which boxes are kept in an image after the transformations have been applied. self.box_filter_patch = BoxFilter(check_overlap=True, check_min_area=False, check_degenerate=False, overlap_criterion=self.overlap_criterion, overlap_bounds=self.bounds_box_filter, labels_format=self.labels_format) self.box_filter_resize = BoxFilter(check_overlap=False, check_min_area=True, check_degenerate=True, min_area=16, labels_format=self.labels_format) # Determines whether the result of the transformations is a valid training image. self.image_validator = ImageValidator(overlap_criterion=self.overlap_criterion, bounds=self.bounds_validator, n_boxes_min=self.n_boxes_min, labels_format=self.labels_format) # Utility transformations self.convert_to_3_channels = ConvertTo3Channels() # Make sure all images end up having 3 channels. self.convert_RGB_to_HSV = ConvertColor(current='RGB', to='HSV') self.convert_HSV_to_RGB = ConvertColor(current='HSV', to='RGB') self.convert_to_float32 = ConvertDataType(to='float32') self.convert_to_uint8 = ConvertDataType(to='uint8') self.resize = Resize(height=resize_height, width=resize_width, box_filter=self.box_filter_resize, labels_format=self.labels_format) # Photometric transformations self.random_brightness = RandomBrightness(lower=random_brightness[0], upper=random_brightness[1], prob=random_brightness[2]) self.random_contrast = RandomContrast(lower=random_contrast[0], upper=random_contrast[1], prob=random_contrast[2]) self.random_saturation = RandomSaturation(lower=random_saturation[0], upper=random_saturation[1], prob=random_saturation[2]) self.random_hue = RandomHue(max_delta=random_hue[0], prob=random_hue[1]) # Geometric transformations self.random_flip = RandomFlip(dim='horizontal', prob=random_flip, labels_format=self.labels_format) self.patch_coord_generator = PatchCoordinateGenerator(must_match='w_ar', min_scale=min_scale, max_scale=max_scale, scale_uniformly=False, min_aspect_ratio=min_aspect_ratio, max_aspect_ratio=max_aspect_ratio) self.random_patch = RandomPatch(patch_coord_generator=self.patch_coord_generator, box_filter=self.box_filter_patch, image_validator=self.image_validator, n_trials_max=self.n_trials_max, clip_boxes=self.clip_boxes, prob=1.0, can_fail=False, labels_format=self.labels_format) # Define the processing chain self.transformations = [self.convert_to_3_channels, self.convert_to_float32, self.random_brightness, self.random_contrast, self.convert_to_uint8, self.convert_RGB_to_HSV, self.convert_to_float32, self.random_saturation, self.random_hue, self.convert_to_uint8, self.convert_HSV_to_RGB, self.random_patch, self.random_flip, self.resize]
def __init__(self, resize_height, resize_width, random_brightness=(-20, 20, 0.5), random_contrast=(0.8, 1.0, 0.5), random_saturation=(0.8, 1.8, 0.5), random_hue=(10, 0.5), random_flip=0.5, random_rotate_small=([np.pi / 40, np.pi / 30], 0.5), random_rotate_big=([np.pi / 2, np.pi, 3 * np.pi / 2], 0.5), min_scale=0.8, max_scale=1.05, min_aspect_ratio=0.8, max_aspect_ratio=1.2, n_trials_max=3, overlap_criterion='center_point', bounds_box_filter=(0.3, 1.0), bounds_validator=(0.5, 1.0), n_boxes_min=1, random_translate=((0.03, 0.05), (0.03, 0.05), 0.5), random_scale=(0.9, 1.1, 0.5), proba_no_aug=1 / 3): self.n_trials_max = n_trials_max self.overlap_criterion = overlap_criterion self.bounds_box_filter = bounds_box_filter self.bounds_validator = bounds_validator self.n_boxes_min = n_boxes_min self.proba_no_aug = proba_no_aug # the probability of not performing any transformations # Determines which boxes are kept in an image after the transformations have been applied. self.box_filter = BoxFilter(check_overlap=True, check_min_area=False, check_degenerate=False, overlap_criterion=self.overlap_criterion, overlap_bounds=self.bounds_box_filter) self.box_filter_resize = BoxFilter(check_overlap=False, check_min_area=True, check_degenerate=True, min_area=16) # Determines whether the result of the transformations is a valid training image. self.image_validator = ImageValidator( overlap_criterion=self.overlap_criterion, bounds=self.bounds_validator, n_boxes_min=self.n_boxes_min) # Utility transformations self.convert_to_3_channels = ConvertTo3Channels( ) # Make sure all images end up having 3 channels. self.convert_RGB_to_HSV = ConvertColor(current='RGB', to='HSV') self.convert_HSV_to_RGB = ConvertColor(current='HSV', to='RGB') self.convert_to_float32 = ConvertDataType(to='float32') self.convert_to_uint8 = ConvertDataType(to='uint8') self.resize = Resize(height=resize_height, width=resize_width, box_filter=self.box_filter_resize) # Photometric transformations self.random_brightness = RandomBrightness(lower=random_brightness[0], upper=random_brightness[1], prob=random_brightness[2]) self.random_contrast = RandomContrast(lower=random_contrast[0], upper=random_contrast[1], prob=random_contrast[2]) self.random_saturation = RandomSaturation(lower=random_saturation[0], upper=random_saturation[1], prob=random_saturation[2]) self.random_hue = RandomHue(max_delta=random_hue[0], prob=random_hue[1]) # Geometric transformations self.random_horizontal_flip = RandomFlip(dim='horizontal', prob=random_flip) self.random_vertical_flip = RandomFlip(dim='vertical', prob=random_flip) self.random_translate = RandomTranslate( dy_minmax=random_translate[0], dx_minmax=random_translate[1], prob=random_translate[2], box_filter=self.box_filter, image_validator=self.image_validator, n_trials_max=self.n_trials_max) self.random_rotate_small = RandomRotate( angles=random_rotate_small[0], prob=random_rotate_small[1], box_filter=self.box_filter, image_validator=self.image_validator, n_trials_max=self.n_trials_max) self.random_rotate_big = RandomRotate( angles=random_rotate_big[0], prob=random_rotate_big[1], box_filter=self.box_filter, image_validator=self.image_validator, n_trials_max=self.n_trials_max) self.random_zoom_in = RandomScale(min_factor=1.0, max_factor=random_scale[1], prob=random_scale[2], box_filter=self.box_filter, image_validator=self.image_validator, n_trials_max=self.n_trials_max) self.random_zoom_out = RandomScale( min_factor=random_scale[0], max_factor=random_scale[0], prob=random_scale[2], box_filter=self.box_filter, image_validator=self.image_validator, n_trials_max=self.n_trials_max) # random patch generator is not used for the moment but it could be useful in your project self.patch_coord_generator = PatchCoordinateGenerator( must_match='h_w', min_scale=min_scale, max_scale=max_scale, scale_uniformly=False, min_aspect_ratio=min_aspect_ratio, max_aspect_ratio=max_aspect_ratio) self.random_patch = RandomPatch( patch_coord_generator=self.patch_coord_generator, box_filter=self.box_filter, image_validator=self.image_validator, n_trials_max=self.n_trials_max, prob=0.5, can_fail=False) # If we zoom in, do translation before scaling. self.sequence1 = [ self.convert_to_3_channels, self.convert_to_float32, self.random_brightness, self.random_contrast, self.convert_to_uint8, self.convert_RGB_to_HSV, self.convert_to_float32, self.random_saturation, self.random_hue, self.convert_to_uint8, self.convert_HSV_to_RGB, self.random_horizontal_flip, self.random_vertical_flip, self.random_translate, self.random_rotate_big, self.random_rotate_small, self.random_zoom_in, self.random_patch, self.resize ] # If we zoom out, do translation after scaling. self.sequence2 = [ self.convert_to_3_channels, self.convert_to_float32, self.random_brightness, self.random_contrast, self.convert_to_uint8, self.convert_RGB_to_HSV, self.convert_to_float32, self.random_saturation, self.random_hue, self.convert_to_uint8, self.convert_HSV_to_RGB, self.random_horizontal_flip, self.random_vertical_flip, self.random_zoom_out, self.random_translate, self.random_rotate_big, self.random_rotate_small, self.random_patch, self.resize ] self.sequence3 = [ self.convert_to_3_channels, self.convert_to_uint8, self.random_horizontal_flip, self.random_vertical_flip, self.random_translate, self.random_rotate_big, self.random_rotate_small, self.resize ]
def __init__( self, random_brightness=(-48, 48, 0.5), random_contrast=(0.5, 1.8, 0.5), random_saturation=(0.5, 1.8, 0.5), random_hue=(18, 0.5), random_flip=0.5, # 最后一个元素表示 prob random_translate=((0.03, 0.5), (0.03, 0.5), 0.5), # 最后一个元素表示 prob random_scale=(0.5, 2.0, 0.5), # translate or scale 后的 image 如果不合格可以重复进行的最大次数 n_trials_max=3, clip_boxes=True, overlap_criterion_box_filter='area', overlap_criterion_validator='area', bounds_box_filter=(0.3, 1.0), bounds_validator=(0.5, 1.0), n_boxes_min=1, background=(0, 0, 0), labels_format=('class_id', 'xmin', 'ymin', 'xmax', 'ymax')): if (random_scale[0] >= 1) or (random_scale[1] <= 1): raise ValueError( "This sequence of transformations only makes sense" "if the minimum scaling factor is <1 and the maximum scaling factor is >1." ) self.n_trials_max = n_trials_max self.clip_boxes = clip_boxes self.overlap_criterion_box_filter = overlap_criterion_box_filter self.overlap_criterion_validator = overlap_criterion_validator self.bounds_box_filter = bounds_box_filter self.bounds_validator = bounds_validator self.n_boxes_min = n_boxes_min self.background = background self.labels_format = labels_format # Determines which boxes are kept in an image after the transformations have been applied. self.box_filter = BoxFilter( check_overlap=True, check_min_area=True, check_degenerate=True, overlap_criterion=self.overlap_criterion_box_filter, overlap_bounds=self.bounds_box_filter, min_area=16, labels_format=self.labels_format) # Determines whether the result of the transformations is a valid training image. self.image_validator = ImageValidator( overlap_criterion=self.overlap_criterion_validator, overlap_bounds=self.bounds_validator, n_boxes_min=self.n_boxes_min, labels_format=self.labels_format) # Utility distortions self.convert_RGB_to_HSV = ConvertColor(current='RGB', to='HSV') self.convert_HSV_to_RGB = ConvertColor(current='HSV', to='RGB') self.convert_to_float32 = ConvertDataType(to='float32') self.convert_to_uint8 = ConvertDataType(to='uint8') # Make sure all images end up having 3 channels. self.convert_to_3_channels = ConvertTo3Channels() # Photometric transformations self.random_brightness = RandomBrightness(lower=random_brightness[0], upper=random_brightness[1], prob=random_brightness[2]) self.random_contrast = RandomContrast(lower=random_contrast[0], upper=random_contrast[1], prob=random_contrast[2]) self.random_saturation = RandomSaturation(lower=random_saturation[0], upper=random_saturation[1], prob=random_saturation[2]) self.random_hue = RandomHue(max_delta=random_hue[0], prob=random_hue[1]) # Geometric transformations self.random_flip = RandomFlip(dim='horizontal', prob=random_flip, labels_format=self.labels_format) self.random_translate = RandomTranslate( dy_minmax=random_translate[0], dx_minmax=random_translate[1], prob=random_translate[2], clip_boxes=self.clip_boxes, box_filter=self.box_filter, image_validator=self.image_validator, n_trials_max=self.n_trials_max, background=self.background, labels_format=self.labels_format) self.random_zoom_in = RandomScale(min_factor=1.0, max_factor=random_scale[1], prob=random_scale[2], clip_boxes=self.clip_boxes, box_filter=self.box_filter, image_validator=self.image_validator, n_trials_max=self.n_trials_max, background=self.background, labels_format=self.labels_format) self.random_zoom_out = RandomScale( min_factor=random_scale[0], max_factor=1.0, prob=random_scale[2], clip_boxes=self.clip_boxes, box_filter=self.box_filter, image_validator=self.image_validator, n_trials_max=self.n_trials_max, background=self.background, labels_format=self.labels_format) # If we zoom in, do translation before scaling. self.sequence1 = [ self.convert_to_3_channels, self.convert_to_float32, self.random_brightness, self.random_contrast, self.convert_to_uint8, self.convert_RGB_to_HSV, self.convert_to_float32, self.random_saturation, self.random_hue, self.convert_to_uint8, self.convert_HSV_to_RGB, self.random_translate, self.random_zoom_in, self.random_flip ] # If we zoom out, do scaling before translation. self.sequence2 = [ self.convert_to_3_channels, self.convert_to_float32, self.random_brightness, self.random_contrast, self.convert_to_uint8, self.convert_RGB_to_HSV, self.convert_to_float32, self.random_saturation, self.random_hue, self.convert_to_uint8, self.convert_HSV_to_RGB, self.convert_to_float32, self.random_zoom_out, self.random_translate, self.random_flip ]
def __init__( self, random_brightness=(-48, 48, 0.5), random_contrast=(0.5, 1.8, 0.5), random_saturation=(0.5, 1.8, 0.5), random_hue=(18, 0.5), random_flip=0.5, random_translate=((0.03, 0.5), (0.03, 0.5), 0.5), random_scale=(0.5, 2.0, 0.5), random_gaussian_noise=(0.5, 0., 10), # gaussine noise random_poisson_noise=(0.5, 60), # poisson noise random_salt_pepper_noise=(0.5, 0.5, 0.005), # salt&pepper or impalse noise random_row_defect=(0.5, 1), # row defect random_col_defect=(0.5, 1), # col defect n_trials_max=3, clip_boxes=True, overlap_criterion='area', bounds_box_filter=(0.3, 1.0), bounds_validator=(0.5, 1.0), n_boxes_min=1, background=(0, 0, 0), labels_format={ 'class_id': 0, 'xmin': 1, 'ymin': 2, 'xmax': 3, 'ymax': 4 }): if (random_scale[0] >= 1) or (random_scale[1] <= 1): raise ValueError( "This sequence of transformations only makes sense if the minimum scaling factor is <1 and the maximum scaling factor is >1." ) self.n_trials_max = n_trials_max self.clip_boxes = clip_boxes self.overlap_criterion = overlap_criterion self.bounds_box_filter = bounds_box_filter self.bounds_validator = bounds_validator self.n_boxes_min = n_boxes_min self.background = background self.labels_format = labels_format # Determines which boxes are kept in an image after the transformations have been applied. self.box_filter = BoxFilter(check_overlap=True, check_min_area=True, check_degenerate=True, overlap_criterion=self.overlap_criterion, overlap_bounds=self.bounds_box_filter, min_area=16, labels_format=self.labels_format) # Determines whether the result of the transformations is a valid training image. self.image_validator = ImageValidator( overlap_criterion=self.overlap_criterion, bounds=self.bounds_validator, n_boxes_min=self.n_boxes_min, labels_format=self.labels_format) # Utility distortions self.convert_RGB_to_HSV = ConvertColor(current='RGB', to='HSV') self.convert_HSV_to_RGB = ConvertColor(current='HSV', to='RGB') self.convert_to_float32 = ConvertDataType(to='float32') self.convert_to_uint8 = ConvertDataType(to='uint8') self.convert_to_3_channels = ConvertTo3Channels( ) # Make sure all images end up having 3 channels. self.convert_to_1_channel = ConvertTo1Channel( ) # Make sure all images end up having 3 channels. # Photometric transformations self.random_brightness = RandomBrightness(lower=random_brightness[0], upper=random_brightness[1], prob=random_brightness[2]) self.random_contrast = RandomContrast(lower=random_contrast[0], upper=random_contrast[1], prob=random_contrast[2]) self.random_saturation = RandomSaturation(lower=random_saturation[0], upper=random_saturation[1], prob=random_saturation[2]) self.random_hue = RandomHue(max_delta=random_hue[0], prob=random_hue[1]) # Geometric transformations self.random_flip = RandomFlip(dim='horizontal', prob=random_flip, labels_format=self.labels_format) self.random_translate = RandomTranslate( dy_minmax=random_translate[0], dx_minmax=random_translate[1], prob=random_translate[2], clip_boxes=self.clip_boxes, box_filter=self.box_filter, image_validator=self.image_validator, n_trials_max=self.n_trials_max, background=self.background, labels_format=self.labels_format) self.random_zoom_in = RandomScale(min_factor=1.0, max_factor=random_scale[1], prob=random_scale[2], clip_boxes=self.clip_boxes, box_filter=self.box_filter, image_validator=self.image_validator, n_trials_max=self.n_trials_max, background=self.background, labels_format=self.labels_format) self.random_zoom_out = RandomScale( min_factor=random_scale[0], max_factor=1.0, prob=random_scale[2], clip_boxes=self.clip_boxes, box_filter=self.box_filter, image_validator=self.image_validator, n_trials_max=self.n_trials_max, background=self.background, labels_format=self.labels_format) # noises and sensor defects self.random_RowDefect = RandomRowDefect(prob=random_row_defect[0], thikness=random_row_defect[1]) self.random_col_defect = RandomColDefect(prob=random_col_defect[0], thikness=random_col_defect[1]) self.random_salt_pepper = RandomSaltPepperNoise( prob=random_salt_pepper_noise[0], salt_vs_pepper_ratio=random_salt_pepper_noise[1], percentage=random_salt_pepper_noise[2]) self.random_poisson = RandomPoissonNoise( prob=random_poisson_noise[0], Lambda=random_poisson_noise[1]) self.random_gaussian = RandomGaussianNoise( prob=random_gaussian_noise[0], mean=random_gaussian_noise[1], sigma=random_gaussian_noise[2]) # If we zoom in, do translation before scaling. self.sequence1 = [ self.convert_to_1_channel, self.convert_to_float32, self.random_brightness, self.random_contrast, # self.convert_to_uint8, # self.convert_RGB_to_HSV, # self.convert_to_float32, # self.random_saturation, # self.random_hue, self.convert_to_uint8, # self.convert_HSV_to_RGB, self.random_translate, self.random_zoom_in, self.random_flip, self.random_salt_pepper, self.random_poisson, self.random_gaussian, self.random_col_defect, self.convert_to_1_channel ] # If we zoom out, do scaling before translation. self.sequence2 = [ self.convert_to_1_channel, self.convert_to_float32, self.random_brightness, # self.convert_to_uint8, # self.convert_RGB_to_HSV, # self.convert_to_float32, # self.random_saturation, # self.random_hue, # # self.convert_to_uint8, # self.convert_HSV_to_RGB, self.convert_to_float32, self.random_contrast, self.convert_to_uint8, self.random_zoom_out, self.random_translate, self.random_flip, self.random_salt_pepper, self.random_poisson, self.random_gaussian, self.random_col_defect, self.convert_to_1_channel ]