def create_aug_pipeline_val(input_size): """Image Augmentation Pipeline for Validation/Test Set.""" p_val = Pipeline() # # Center Crop # p_val.crop_centre(probability=1, percentage_area=0.9) # Resize the image to the desired input size of the model p_val.resize(probability=1, width=input_size[0], height=input_size[1]) return p_val
def __init__(self, train=True, root='../data'): if train: self.dataset = MNIST(root=root, train=True) else: self.dataset = MNIST(root=root, train=False) self.transform = transforms.Compose( [transforms.ToTensor()] ) self.p = Pipeline() self.p.random_distortion(probability=0.5, grid_width=7, grid_height=7, magnitude=1)
def __init__(self): self.augmentor_pipeline = Pipeline() self.augmentor_pipeline.add_operation(Operations.Crop(probability=1, width=64, height=64, centre=False)) self.augmentor_pipeline.add_operation( Operations.Resize(probability=1, width=512, height=512, resample_filter="BILINEAR") ) self.imgaug_transform = iaa.Sequential( [iaa.CropToFixedSize(width=64, height=64), iaa.Scale(size=512, interpolation="linear")] ) self.solt_stream = slc.Stream( [slt.CropTransform(crop_size=(64, 64), crop_mode="r"), slt.ResizeTransform(resize_to=(512, 512))] )
def make_pipeline(imageset_path, output_dir): """returns an augmentation pipeline for a given image set""" p = Pipeline(imageset_path, output_dir) p.random_distortion(probability=0.7, grid_width=4, grid_height=4, magnitude=8) p.flip_left_right(probability=0.5) p.flip_top_bottom(probability=0.5) p.zoom(probability=0.3, min_factor=1.1, max_factor=1.4) p.rotate(probability=0.5, max_left_rotation=10, max_right_rotation=10) return p
def __init__(self): self.imgaug_transform = iaa.Sequential( [iaa.Multiply((1.5, 1.5), per_channel=False), iaa.Add((127, 127), per_channel=False)] ) self.augmentor_pipeline = Pipeline() self.augmentor_pipeline.add_operation( Operations.RandomBrightness(probability=1, min_factor=1.5, max_factor=1.5) ) self.augmentor_pipeline.add_operation(Operations.RandomContrast(probability=1, min_factor=1.5, max_factor=1.5)) self.solt_stream = slc.Stream( [ slt.ImageRandomBrightness(p=1, brightness_range=(127, 127)), slt.ImageRandomContrast(p=1, contrast_range=(1.5, 1.5)), ] )
def create_aug_pipeline_train(input_size): """Image Augmentation Pipeline for Training Set.""" p_train = Pipeline() # Random crop p_train.add_operation(CropPercentageRange(probability=1, min_percentage_area=0.8, max_percentage_area=1, centre=False)) # Rotate the image by either 90, 180, or 270 degrees randomly p_train.rotate_random_90(probability=0.5) # Flip the image along its vertical axis p_train.flip_top_bottom(probability=0.5) # Flip the image along its horizontal axis p_train.flip_left_right(probability=0.5) # Random change brightness of the image p_train.random_brightness(probability=0.5, min_factor=0.9, max_factor=1.1) # Random change saturation of the image p_train.random_color(probability=0.5, min_factor=0.9, max_factor=1.1) # Resize the image to the desired input size of the model p_train.resize(probability=1, width=input_size[0], height=input_size[1]) return p_train
png = data.train.images[i] png = np.array(png, dtype='float') pixels = png.reshape((28, 28)) image.imsave('image_no_{}.png'.format(i), pixels, cmap='gray') # In[10]: print(os.listdir()) # In[11]: from Augmentor import Pipeline # In[12]: augmentor = Pipeline('/home/asherif844/sparkNotebooks/Ch03/MNIST/images') # In[13]: augmentor.rotate(probability=0.9, max_left_rotation=25, max_right_rotation=25) # In[14]: for i in range(1, 3): augmentor.sample(10) # In[15]: xtrain = data.train.images ytrain = np.asarray(data.train.labels) xtest = data.test.images
随机20度范围内的旋转。变换时,边界外像素点的填充采用“reflect”模式。图像均被缩放为256*256大小,像素值被调整到0-1之间。 ''' # this is import from Augmentor third-party package # import Augmentor # # # create a new pipeline # file_path = "C:\\liyu\\files\\tiff\\cells" # p = Augmentor.Pipeline(file_path) from Augmentor.Pipeline import * import time # create a new pipeline file_path = "C:\\liyu\\files\\tiff\\newtest" p = Pipeline(file_path) # add operations to the pipeline # 水平翻转 p.flip_left_right(probability=0.8) # # 垂直翻转 # p.flip_top_bottom(probability=0.5) # # # 随机90, 180, 270 度旋转 # p.rotate_random_90(probability=0.75) # # # 随机20度内旋转,不变形, 四角填充黑色,图片大小不变 # p.rotate_without_crop(probability=0.5, max_left_rotation=20, max_right_rotation=20) #