![]() ![]() Height: an int32 scalar tensor indicating the current height. ![]() set_shape ( ) return resized_imageĭef _smallest_size_at_least (height, width, smallest_side ) : """Computes new shape with the smallest side equal to `smallest_side`.Ĭomputes new shape with the smallest side equal to `smallest_side` while New_height, new_width = _smallest_size_at_least (height, width, smallest_side ) convert_to_tensor (smallest_side, dtype =tf. Resized_image: A 3-D tensor containing the resized image. Smallest_side: A python integer or scalar `Tensor` indicating the size of to_float (image ) return _mean_image_subtraction (image, ) def _aspect_preserving_resize (image, smallest_side ) : """Resize images preserving the original aspect ratio. Image = _central_crop (, out_height, out_width ) Resize_side: The smallest side of the image for aspect-preserving resizing. random_flip_left_right (image ) return _mean_image_subtraction (image, ) def preprocess_for_eval (image, out_height, out_width, resize_side ) : """Preprocesses the given image for evaluation. Image = _random_crop (, out_height, out_width ) Image = _aspect_preserving_resize (image, resize_side ) random_uniform (, minval =resize_side_min, maxval =resize_side_max + 1, dtype =tf. Resize_side_max =_RESIZE_SIDE_MAX ) : """Preprocesses the given image for training. Image = preprocess_for_eval (image, out_height, out_width , Resize_side_min, resize_side_max ) else : Image = preprocess_for_train (image, out_height, out_width , Otherwise, the resize side is sampled from ![]() If `is_training` is `False`, this value is Resize_side_max: The upper bound for the smallest side of the image forĪspect-preserving resizing. If `is_training` is `False`, then this value Resize_side_min: The lower bound for the smallest side of the image forĪspect-preserving resizing. Out_width: The width of the image after preprocessing. Out_height: The height of the image after preprocessing. Resize_side_max =_RESIZE_SIDE_MAX ) : """Preprocesses the given image. _RESIZE_SIDE_MAX = 512 def preprocess_image (image, label, is_training , per_image_standardization (resized_image ) 第三種 expand_dims (resized_image, 0 ) ) # Subtract off the mean and divide by the variance of the pixels. to_float (image ) # Resize and crop if needed. per_image_standardization (distorted_image ) def preprocess_for_eval (image, output_height, output_width ,Īdd_image_summaries = True ) : """Preprocesses the given image for evaluation. Lower = 0.2, upper = 1.8 ) # Subtract off the mean and divide by the variance of the pixels. random_brightness (distorted_image ,ĭistorted_image = tf. expand_dims (distorted_image, 0 ) ) # Because these operations are not commutative, consider randomizing # the order their operation.ĭistorted_image = tf. random_flip_left_right (distorted_image ) if add_image_summaries : random_crop (image, ) # Randomly flip the image horizontally.ĭistorted_image = tf. pad (image, ,, ] ) # Randomly crop a section of the image.ĭistorted_image = tf. expand_dims (image, 0 ) ) # Transform the image to floats. Padding: The amound of padding before and after each dimension of the image. Note that the actual resizing scale is sampled from _PADDING = 4 def preprocess_image (image, label, is_training ,Īdd_image_summaries = False ) : """Preprocesses the given image.Īdd_image_summaries: Enable image summaries.Īdd_image_summaries =add_image_summaries ) else :Īdd_image_summaries =add_image_summaries ) return image, labelĪdd_image_summaries = True ) : """Preprocesses the given image for training. div (image, 128.0 ) return image, label Is_training: `True` if we're preprocessing the image for training and Output_width: The width of the image after preprocessing. Output_height: The height of the image after preprocessing. Image: A `Tensor` representing an image of arbitrary size. Out_height = 28, out_width = 28 ) : """Preprocesses the given image. ![]()
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