# Copyright 2017 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Preprocess images and bounding boxes for detection. We perform two sets of operations in preprocessing stage: (a) operations that are applied to both training and testing data, (b) operations that are applied only to training data for the purpose of data augmentation. A preprocessing function receives a set of inputs, e.g. an image and bounding boxes, performs an operation on them, and returns them. Some examples are: randomly cropping the image, randomly mirroring the image, randomly changing the brightness, contrast, hue and randomly jittering the bounding boxes. The preprocess function receives a tensor_dict which is a dictionary that maps different field names to their tensors. For example, tensor_dict[fields.InputDataFields.image] holds the image tensor. The image is a rank 4 tensor: [1, height, width, channels] with dtype=tf.float32. The groundtruth_boxes is a rank 2 tensor: [N, 4] where in each row there is a box with [ymin xmin ymax xmax]. Boxes are in normalized coordinates meaning their coordinate values range in [0, 1] To preprocess multiple images with the same operations in cases where nondeterministic operations are used, a preprocessor_cache.PreprocessorCache object can be passed into the preprocess function or individual operations. All nondeterministic operations except random_jitter_boxes support caching. E.g. Let tensor_dict{1,2,3,4,5} be copies of the same inputs. Let preprocess_options contain nondeterministic operation(s) excluding random_jitter_boxes. cache1 = preprocessor_cache.PreprocessorCache() cache2 = preprocessor_cache.PreprocessorCache() a = preprocess(tensor_dict1, preprocess_options, preprocess_vars_cache=cache1) b = preprocess(tensor_dict2, preprocess_options, preprocess_vars_cache=cache1) c = preprocess(tensor_dict3, preprocess_options, preprocess_vars_cache=cache2) d = preprocess(tensor_dict4, preprocess_options, preprocess_vars_cache=cache2) e = preprocess(tensor_dict5, preprocess_options) Then correspondings tensors of object pairs (a,b) and (c,d) are guaranteed to be equal element-wise, but the equality of any other object pair cannot be determined. Important Note: In tensor_dict, images is a rank 4 tensor, but preprocessing functions receive a rank 3 tensor for processing the image. Thus, inside the preprocess function we squeeze the image to become a rank 3 tensor and then we pass it to the functions. At the end of the preprocess we expand the image back to rank 4. """ import functools import inspect import sys import tensorflow as tf from tensorflow.python.ops import control_flow_ops from object_detection.core import box_list from object_detection.core import box_list_ops from object_detection.core import keypoint_ops from object_detection.core import preprocessor_cache from object_detection.core import standard_fields as fields from object_detection.utils import shape_utils def _apply_with_random_selector(x, func, num_cases, preprocess_vars_cache=None, key=''): """Computes func(x, sel), with sel sampled from [0...num_cases-1]. If both preprocess_vars_cache AND key are the same between two calls, sel will be the same value in both calls. Args: x: input Tensor. func: Python function to apply. num_cases: Python int32, number of cases to sample sel from. preprocess_vars_cache: PreprocessorCache object that records previously performed augmentations. Updated in-place. If this function is called multiple times with the same non-null cache, it will perform deterministically. key: variable identifier for preprocess_vars_cache. Returns: The result of func(x, sel), where func receives the value of the selector as a python integer, but sel is sampled dynamically. """ generator_func = functools.partial( tf.random_uniform, [], maxval=num_cases, dtype=tf.int32) rand_sel = _get_or_create_preprocess_rand_vars( generator_func, preprocessor_cache.PreprocessorCache.SELECTOR, preprocess_vars_cache, key) # Pass the real x only to one of the func calls. return control_flow_ops.merge([func( control_flow_ops.switch(x, tf.equal(rand_sel, case))[1], case) for case in range(num_cases)])[0] def _apply_with_random_selector_tuples(x, func, num_cases, preprocess_vars_cache=None, key=''): """Computes func(x, sel), with sel sampled from [0...num_cases-1]. If both preprocess_vars_cache AND key are the same between two calls, sel will be the same value in both calls. Args: x: A tuple of input tensors. func: Python function to apply. num_cases: Python int32, number of cases to sample sel from. preprocess_vars_cache: PreprocessorCache object that records previously performed augmentations. Updated in-place. If this function is called multiple times with the same non-null cache, it will perform deterministically. key: variable identifier for preprocess_vars_cache. Returns: The result of func(x, sel), where func receives the value of the selector as a python integer, but sel is sampled dynamically. """ num_inputs = len(x) generator_func = functools.partial( tf.random_uniform, [], maxval=num_cases, dtype=tf.int32) rand_sel = _get_or_create_preprocess_rand_vars( generator_func, preprocessor_cache.PreprocessorCache.SELECTOR_TUPLES, preprocess_vars_cache, key) # Pass the real x only to one of the func calls. tuples = [list() for t in x] for case in range(num_cases): new_x = [control_flow_ops.switch(t, tf.equal(rand_sel, case))[1] for t in x] output = func(tuple(new_x), case) for j in range(num_inputs): tuples[j].append(output[j]) for i in range(num_inputs): tuples[i] = control_flow_ops.merge(tuples[i])[0] return tuple(tuples) def _get_or_create_preprocess_rand_vars(generator_func, function_id, preprocess_vars_cache, key=''): """Returns a tensor stored in preprocess_vars_cache or using generator_func. If the tensor was previously generated and appears in the PreprocessorCache, the previously generated tensor will be returned. Otherwise, a new tensor is generated using generator_func and stored in the cache. Args: generator_func: A 0-argument function that generates a tensor. function_id: identifier for the preprocessing function used. preprocess_vars_cache: PreprocessorCache object that records previously performed augmentations. Updated in-place. If this function is called multiple times with the same non-null cache, it will perform deterministically. key: identifier for the variable stored. Returns: The generated tensor. """ if preprocess_vars_cache is not None: var = preprocess_vars_cache.get(function_id, key) if var is None: var = generator_func() preprocess_vars_cache.update(function_id, key, var) else: var = generator_func() return var def _random_integer(minval, maxval, seed): """Returns a random 0-D tensor between minval and maxval. Args: minval: minimum value of the random tensor. maxval: maximum value of the random tensor. seed: random seed. Returns: A random 0-D tensor between minval and maxval. """ return tf.random_uniform( [], minval=minval, maxval=maxval, dtype=tf.int32, seed=seed) # TODO(mttang): This method is needed because the current # tf.image.rgb_to_grayscale method does not support quantization. Replace with # tf.image.rgb_to_grayscale after quantization support is added. def _rgb_to_grayscale(images, name=None): """Converts one or more images from RGB to Grayscale. Outputs a tensor of the same `DType` and rank as `images`. The size of the last dimension of the output is 1, containing the Grayscale value of the pixels. Args: images: The RGB tensor to convert. Last dimension must have size 3 and should contain RGB values. name: A name for the operation (optional). Returns: The converted grayscale image(s). """ with tf.name_scope(name, 'rgb_to_grayscale', [images]) as name: images = tf.convert_to_tensor(images, name='images') # Remember original dtype to so we can convert back if needed orig_dtype = images.dtype flt_image = tf.image.convert_image_dtype(images, tf.float32) # Reference for converting between RGB and grayscale. # https://en.wikipedia.org/wiki/Luma_%28video%29 rgb_weights = [0.2989, 0.5870, 0.1140] rank_1 = tf.expand_dims(tf.rank(images) - 1, 0) gray_float = tf.reduce_sum( flt_image * rgb_weights, rank_1, keep_dims=True) gray_float.set_shape(images.get_shape()[:-1].concatenate([1])) return tf.image.convert_image_dtype(gray_float, orig_dtype, name=name) def normalize_image(image, original_minval, original_maxval, target_minval, target_maxval): """Normalizes pixel values in the image. Moves the pixel values from the current [original_minval, original_maxval] range to a the [target_minval, target_maxval] range. Args: image: rank 3 float32 tensor containing 1 image -> [height, width, channels]. original_minval: current image minimum value. original_maxval: current image maximum value. target_minval: target image minimum value. target_maxval: target image maximum value. Returns: image: image which is the same shape as input image. """ with tf.name_scope('NormalizeImage', values=[image]): original_minval = float(original_minval) original_maxval = float(original_maxval) target_minval = float(target_minval) target_maxval = float(target_maxval) image = tf.to_float(image) image = tf.subtract(image, original_minval) image = tf.multiply(image, (target_maxval - target_minval) / (original_maxval - original_minval)) image = tf.add(image, target_minval) return image def retain_boxes_above_threshold(boxes, labels, label_weights, label_confidences=None, multiclass_scores=None, masks=None, keypoints=None, threshold=0.0): """Retains boxes whose label weight is above a given threshold. If the label weight for a box is missing (represented by NaN), the box is retained. The boxes that don't pass the threshold will not appear in the returned tensor. Args: boxes: float32 tensor of shape [num_instance, 4] representing boxes location in normalized coordinates. labels: rank 1 int32 tensor of shape [num_instance] containing the object classes. label_weights: float32 tensor of shape [num_instance] representing the weight for each box. label_confidences: float32 tensor of shape [num_instance] representing the confidence for each box. multiclass_scores: (optional) float32 tensor of shape [num_instances, num_classes] representing the score for each box for each class. masks: (optional) rank 3 float32 tensor with shape [num_instances, height, width] containing instance masks. The masks are of the same height, width as the input `image`. keypoints: (optional) rank 3 float32 tensor with shape [num_instances, num_keypoints, 2]. The keypoints are in y-x normalized coordinates. threshold: scalar python float. Returns: retained_boxes: [num_retained_instance, 4] retianed_labels: [num_retained_instance] retained_label_weights: [num_retained_instance] If multiclass_scores, masks, or keypoints are not None, the function also returns: retained_multiclass_scores: [num_retained_instance, num_classes] retained_masks: [num_retained_instance, height, width] retained_keypoints: [num_retained_instance, num_keypoints, 2] """ with tf.name_scope('RetainBoxesAboveThreshold', values=[boxes, labels, label_weights]): indices = tf.where( tf.logical_or(label_weights > threshold, tf.is_nan(label_weights))) indices = tf.squeeze(indices, axis=1) retained_boxes = tf.gather(boxes, indices) retained_labels = tf.gather(labels, indices) retained_label_weights = tf.gather(label_weights, indices) result = [retained_boxes, retained_labels, retained_label_weights] if label_confidences is not None: retained_label_confidences = tf.gather(label_confidences, indices) result.append(retained_label_confidences) if multiclass_scores is not None: retained_multiclass_scores = tf.gather(multiclass_scores, indices) result.append(retained_multiclass_scores) if masks is not None: retained_masks = tf.gather(masks, indices) result.append(retained_masks) if keypoints is not None: retained_keypoints = tf.gather(keypoints, indices) result.append(retained_keypoints) return result def _flip_boxes_left_right(boxes): """Left-right flip the boxes. Args: boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4]. Boxes are in normalized form meaning their coordinates vary between [0, 1]. Each row is in the form of [ymin, xmin, ymax, xmax]. Returns: Flipped boxes. """ ymin, xmin, ymax, xmax = tf.split(value=boxes, num_or_size_splits=4, axis=1) flipped_xmin = tf.subtract(1.0, xmax) flipped_xmax = tf.subtract(1.0, xmin) flipped_boxes = tf.concat([ymin, flipped_xmin, ymax, flipped_xmax], 1) return flipped_boxes def _flip_boxes_up_down(boxes): """Up-down flip the boxes. Args: boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4]. Boxes are in normalized form meaning their coordinates vary between [0, 1]. Each row is in the form of [ymin, xmin, ymax, xmax]. Returns: Flipped boxes. """ ymin, xmin, ymax, xmax = tf.split(value=boxes, num_or_size_splits=4, axis=1) flipped_ymin = tf.subtract(1.0, ymax) flipped_ymax = tf.subtract(1.0, ymin) flipped_boxes = tf.concat([flipped_ymin, xmin, flipped_ymax, xmax], 1) return flipped_boxes def _rot90_boxes(boxes): """Rotate boxes counter-clockwise by 90 degrees. Args: boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4]. Boxes are in normalized form meaning their coordinates vary between [0, 1]. Each row is in the form of [ymin, xmin, ymax, xmax]. Returns: Rotated boxes. """ ymin, xmin, ymax, xmax = tf.split(value=boxes, num_or_size_splits=4, axis=1) rotated_ymin = tf.subtract(1.0, xmax) rotated_ymax = tf.subtract(1.0, xmin) rotated_xmin = ymin rotated_xmax = ymax rotated_boxes = tf.concat( [rotated_ymin, rotated_xmin, rotated_ymax, rotated_xmax], 1) return rotated_boxes def _flip_masks_left_right(masks): """Left-right flip masks. Args: masks: rank 3 float32 tensor with shape [num_instances, height, width] representing instance masks. Returns: flipped masks: rank 3 float32 tensor with shape [num_instances, height, width] representing instance masks. """ return masks[:, :, ::-1] def _flip_masks_up_down(masks): """Up-down flip masks. Args: masks: rank 3 float32 tensor with shape [num_instances, height, width] representing instance masks. Returns: flipped masks: rank 3 float32 tensor with shape [num_instances, height, width] representing instance masks. """ return masks[:, ::-1, :] def _rot90_masks(masks): """Rotate masks counter-clockwise by 90 degrees. Args: masks: rank 3 float32 tensor with shape [num_instances, height, width] representing instance masks. Returns: rotated masks: rank 3 float32 tensor with shape [num_instances, height, width] representing instance masks. """ masks = tf.transpose(masks, [0, 2, 1]) return masks[:, ::-1, :] def random_horizontal_flip(image, boxes=None, masks=None, keypoints=None, keypoint_flip_permutation=None, seed=None, preprocess_vars_cache=None): """Randomly flips the image and detections horizontally. The probability of flipping the image is 50%. Args: image: rank 3 float32 tensor with shape [height, width, channels]. boxes: (optional) rank 2 float32 tensor with shape [N, 4] containing the bounding boxes. Boxes are in normalized form meaning their coordinates vary between [0, 1]. Each row is in the form of [ymin, xmin, ymax, xmax]. masks: (optional) rank 3 float32 tensor with shape [num_instances, height, width] containing instance masks. The masks are of the same height, width as the input `image`. keypoints: (optional) rank 3 float32 tensor with shape [num_instances, num_keypoints, 2]. The keypoints are in y-x normalized coordinates. keypoint_flip_permutation: rank 1 int32 tensor containing the keypoint flip permutation. seed: random seed preprocess_vars_cache: PreprocessorCache object that records previously performed augmentations. Updated in-place. If this function is called multiple times with the same non-null cache, it will perform deterministically. Returns: image: image which is the same shape as input image. If boxes, masks, keypoints, and keypoint_flip_permutation are not None, the function also returns the following tensors. boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4]. Boxes are in normalized form meaning their coordinates vary between [0, 1]. masks: rank 3 float32 tensor with shape [num_instances, height, width] containing instance masks. keypoints: rank 3 float32 tensor with shape [num_instances, num_keypoints, 2] Raises: ValueError: if keypoints are provided but keypoint_flip_permutation is not. """ def _flip_image(image): # flip image image_flipped = tf.image.flip_left_right(image) return image_flipped if keypoints is not None and keypoint_flip_permutation is None: raise ValueError( 'keypoints are provided but keypoints_flip_permutation is not provided') with tf.name_scope('RandomHorizontalFlip', values=[image, boxes]): result = [] # random variable defining whether to do flip or not generator_func = functools.partial(tf.random_uniform, [], seed=seed) do_a_flip_random = _get_or_create_preprocess_rand_vars( generator_func, preprocessor_cache.PreprocessorCache.HORIZONTAL_FLIP, preprocess_vars_cache) do_a_flip_random = tf.greater(do_a_flip_random, 0.5) # flip image image = tf.cond(do_a_flip_random, lambda: _flip_image(image), lambda: image) result.append(image) # flip boxes if boxes is not None: boxes = tf.cond(do_a_flip_random, lambda: _flip_boxes_left_right(boxes), lambda: boxes) result.append(boxes) # flip masks if masks is not None: masks = tf.cond(do_a_flip_random, lambda: _flip_masks_left_right(masks), lambda: masks) result.append(masks) # flip keypoints if keypoints is not None and keypoint_flip_permutation is not None: permutation = keypoint_flip_permutation keypoints = tf.cond( do_a_flip_random, lambda: keypoint_ops.flip_horizontal(keypoints, 0.5, permutation), lambda: keypoints) result.append(keypoints) return tuple(result) def random_vertical_flip(image, boxes=None, masks=None, keypoints=None, keypoint_flip_permutation=None, seed=None, preprocess_vars_cache=None): """Randomly flips the image and detections vertically. The probability of flipping the image is 50%. Args: image: rank 3 float32 tensor with shape [height, width, channels]. boxes: (optional) rank 2 float32 tensor with shape [N, 4] containing the bounding boxes. Boxes are in normalized form meaning their coordinates vary between [0, 1]. Each row is in the form of [ymin, xmin, ymax, xmax]. masks: (optional) rank 3 float32 tensor with shape [num_instances, height, width] containing instance masks. The masks are of the same height, width as the input `image`. keypoints: (optional) rank 3 float32 tensor with shape [num_instances, num_keypoints, 2]. The keypoints are in y-x normalized coordinates. keypoint_flip_permutation: rank 1 int32 tensor containing the keypoint flip permutation. seed: random seed preprocess_vars_cache: PreprocessorCache object that records previously performed augmentations. Updated in-place. If this function is called multiple times with the same non-null cache, it will perform deterministically. Returns: image: image which is the same shape as input image. If boxes, masks, keypoints, and keypoint_flip_permutation are not None, the function also returns the following tensors. boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4]. Boxes are in normalized form meaning their coordinates vary between [0, 1]. masks: rank 3 float32 tensor with shape [num_instances, height, width] containing instance masks. keypoints: rank 3 float32 tensor with shape [num_instances, num_keypoints, 2] Raises: ValueError: if keypoints are provided but keypoint_flip_permutation is not. """ def _flip_image(image): # flip image image_flipped = tf.image.flip_up_down(image) return image_flipped if keypoints is not None and keypoint_flip_permutation is None: raise ValueError( 'keypoints are provided but keypoints_flip_permutation is not provided') with tf.name_scope('RandomVerticalFlip', values=[image, boxes]): result = [] # random variable defining whether to do flip or not generator_func = functools.partial(tf.random_uniform, [], seed=seed) do_a_flip_random = _get_or_create_preprocess_rand_vars( generator_func, preprocessor_cache.PreprocessorCache.VERTICAL_FLIP, preprocess_vars_cache) do_a_flip_random = tf.greater(do_a_flip_random, 0.5) # flip image image = tf.cond(do_a_flip_random, lambda: _flip_image(image), lambda: image) result.append(image) # flip boxes if boxes is not None: boxes = tf.cond(do_a_flip_random, lambda: _flip_boxes_up_down(boxes), lambda: boxes) result.append(boxes) # flip masks if masks is not None: masks = tf.cond(do_a_flip_random, lambda: _flip_masks_up_down(masks), lambda: masks) result.append(masks) # flip keypoints if keypoints is not None and keypoint_flip_permutation is not None: permutation = keypoint_flip_permutation keypoints = tf.cond( do_a_flip_random, lambda: keypoint_ops.flip_vertical(keypoints, 0.5, permutation), lambda: keypoints) result.append(keypoints) return tuple(result) def random_rotation90(image, boxes=None, masks=None, keypoints=None, seed=None, preprocess_vars_cache=None): """Randomly rotates the image and detections 90 degrees counter-clockwise. The probability of rotating the image is 50%. This can be combined with random_horizontal_flip and random_vertical_flip to produce an output with a uniform distribution of the eight possible 90 degree rotation / reflection combinations. Args: image: rank 3 float32 tensor with shape [height, width, channels]. boxes: (optional) rank 2 float32 tensor with shape [N, 4] containing the bounding boxes. Boxes are in normalized form meaning their coordinates vary between [0, 1]. Each row is in the form of [ymin, xmin, ymax, xmax]. masks: (optional) rank 3 float32 tensor with shape [num_instances, height, width] containing instance masks. The masks are of the same height, width as the input `image`. keypoints: (optional) rank 3 float32 tensor with shape [num_instances, num_keypoints, 2]. The keypoints are in y-x normalized coordinates. seed: random seed preprocess_vars_cache: PreprocessorCache object that records previously performed augmentations. Updated in-place. If this function is called multiple times with the same non-null cache, it will perform deterministically. Returns: image: image which is the same shape as input image. If boxes, masks, and keypoints, are not None, the function also returns the following tensors. boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4]. Boxes are in normalized form meaning their coordinates vary between [0, 1]. masks: rank 3 float32 tensor with shape [num_instances, height, width] containing instance masks. keypoints: rank 3 float32 tensor with shape [num_instances, num_keypoints, 2] """ def _rot90_image(image): # flip image image_rotated = tf.image.rot90(image) return image_rotated with tf.name_scope('RandomRotation90', values=[image, boxes]): result = [] # random variable defining whether to rotate by 90 degrees or not generator_func = functools.partial(tf.random_uniform, [], seed=seed) do_a_rot90_random = _get_or_create_preprocess_rand_vars( generator_func, preprocessor_cache.PreprocessorCache.ROTATION90, preprocess_vars_cache) do_a_rot90_random = tf.greater(do_a_rot90_random, 0.5) # flip image image = tf.cond(do_a_rot90_random, lambda: _rot90_image(image), lambda: image) result.append(image) # flip boxes if boxes is not None: boxes = tf.cond(do_a_rot90_random, lambda: _rot90_boxes(boxes), lambda: boxes) result.append(boxes) # flip masks if masks is not None: masks = tf.cond(do_a_rot90_random, lambda: _rot90_masks(masks), lambda: masks) result.append(masks) # flip keypoints if keypoints is not None: keypoints = tf.cond( do_a_rot90_random, lambda: keypoint_ops.rot90(keypoints), lambda: keypoints) result.append(keypoints) return tuple(result) def random_pixel_value_scale(image, minval=0.9, maxval=1.1, seed=None, preprocess_vars_cache=None): """Scales each value in the pixels of the image. This function scales each pixel independent of the other ones. For each value in image tensor, draws a random number between minval and maxval and multiples the values with them. Args: image: rank 3 float32 tensor contains 1 image -> [height, width, channels] with pixel values varying between [0, 255]. minval: lower ratio of scaling pixel values. maxval: upper ratio of scaling pixel values. seed: random seed. preprocess_vars_cache: PreprocessorCache object that records previously performed augmentations. Updated in-place. If this function is called multiple times with the same non-null cache, it will perform deterministically. Returns: image: image which is the same shape as input image. """ with tf.name_scope('RandomPixelValueScale', values=[image]): generator_func = functools.partial( tf.random_uniform, tf.shape(image), minval=minval, maxval=maxval, dtype=tf.float32, seed=seed) color_coef = _get_or_create_preprocess_rand_vars( generator_func, preprocessor_cache.PreprocessorCache.PIXEL_VALUE_SCALE, preprocess_vars_cache) image = tf.multiply(image, color_coef) image = tf.clip_by_value(image, 0.0, 255.0) return image def random_image_scale(image, masks=None, min_scale_ratio=0.5, max_scale_ratio=2.0, seed=None, preprocess_vars_cache=None): """Scales the image size. Args: image: rank 3 float32 tensor contains 1 image -> [height, width, channels]. masks: (optional) rank 3 float32 tensor containing masks with size [height, width, num_masks]. The value is set to None if there are no masks. min_scale_ratio: minimum scaling ratio. max_scale_ratio: maximum scaling ratio. seed: random seed. preprocess_vars_cache: PreprocessorCache object that records previously performed augmentations. Updated in-place. If this function is called multiple times with the same non-null cache, it will perform deterministically. Returns: image: image which is the same rank as input image. masks: If masks is not none, resized masks which are the same rank as input masks will be returned. """ with tf.name_scope('RandomImageScale', values=[image]): result = [] image_shape = tf.shape(image) image_height = image_shape[0] image_width = image_shape[1] generator_func = functools.partial( tf.random_uniform, [], minval=min_scale_ratio, maxval=max_scale_ratio, dtype=tf.float32, seed=seed) size_coef = _get_or_create_preprocess_rand_vars( generator_func, preprocessor_cache.PreprocessorCache.IMAGE_SCALE, preprocess_vars_cache) image_newysize = tf.to_int32( tf.multiply(tf.to_float(image_height), size_coef)) image_newxsize = tf.to_int32( tf.multiply(tf.to_float(image_width), size_coef)) image = tf.image.resize_images( image, [image_newysize, image_newxsize], align_corners=True) result.append(image) if masks is not None: masks = tf.image.resize_images( masks, [image_newysize, image_newxsize], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR, align_corners=True) result.append(masks) return tuple(result) def _augment_only_rgb_channels(image, augment_function): """Augments only the RGB slice of an image with additional channels.""" rgb_slice = image[:, :, :3] augmented_rgb_slice = augment_function(rgb_slice) image = tf.concat([augmented_rgb_slice, image[:, :, 3:]], -1) return image def random_rgb_to_gray(image, probability=0.1, seed=None, preprocess_vars_cache=None): """Changes the image from RGB to Grayscale with the given probability. Args: image: rank 3 float32 tensor contains 1 image -> [height, width, channels] with pixel values varying between [0, 255]. probability: the probability of returning a grayscale image. The probability should be a number between [0, 1]. seed: random seed. preprocess_vars_cache: PreprocessorCache object that records previously performed augmentations. Updated in-place. If this function is called multiple times with the same non-null cache, it will perform deterministically. Returns: image: image which is the same shape as input image. """ def _image_to_gray(image): image_gray1 = _rgb_to_grayscale(image) image_gray3 = tf.image.grayscale_to_rgb(image_gray1) return image_gray3 with tf.name_scope('RandomRGBtoGray', values=[image]): # random variable defining whether to change to grayscale or not generator_func = functools.partial(tf.random_uniform, [], seed=seed) do_gray_random = _get_or_create_preprocess_rand_vars( generator_func, preprocessor_cache.PreprocessorCache.RGB_TO_GRAY, preprocess_vars_cache) image = tf.cond( tf.greater(do_gray_random, probability), lambda: image, lambda: _augment_only_rgb_channels(image, _image_to_gray)) return image def random_adjust_brightness(image, max_delta=0.2, seed=None, preprocess_vars_cache=None): """Randomly adjusts brightness. Makes sure the output image is still between 0 and 255. Args: image: rank 3 float32 tensor contains 1 image -> [height, width, channels] with pixel values varying between [0, 255]. max_delta: how much to change the brightness. A value between [0, 1). seed: random seed. preprocess_vars_cache: PreprocessorCache object that records previously performed augmentations. Updated in-place. If this function is called multiple times with the same non-null cache, it will perform deterministically. Returns: image: image which is the same shape as input image. boxes: boxes which is the same shape as input boxes. """ with tf.name_scope('RandomAdjustBrightness', values=[image]): generator_func = functools.partial(tf.random_uniform, [], -max_delta, max_delta, seed=seed) delta = _get_or_create_preprocess_rand_vars( generator_func, preprocessor_cache.PreprocessorCache.ADJUST_BRIGHTNESS, preprocess_vars_cache) def _adjust_brightness(image): image = tf.image.adjust_brightness(image / 255, delta) * 255 image = tf.clip_by_value(image, clip_value_min=0.0, clip_value_max=255.0) return image image = _augment_only_rgb_channels(image, _adjust_brightness) return image def random_adjust_contrast(image, min_delta=0.8, max_delta=1.25, seed=None, preprocess_vars_cache=None): """Randomly adjusts contrast. Makes sure the output image is still between 0 and 255. Args: image: rank 3 float32 tensor contains 1 image -> [height, width, channels] with pixel values varying between [0, 255]. min_delta: see max_delta. max_delta: how much to change the contrast. Contrast will change with a value between min_delta and max_delta. This value will be multiplied to the current contrast of the image. seed: random seed. preprocess_vars_cache: PreprocessorCache object that records previously performed augmentations. Updated in-place. If this function is called multiple times with the same non-null cache, it will perform deterministically. Returns: image: image which is the same shape as input image. """ with tf.name_scope('RandomAdjustContrast', values=[image]): generator_func = functools.partial(tf.random_uniform, [], min_delta, max_delta, seed=seed) contrast_factor = _get_or_create_preprocess_rand_vars( generator_func, preprocessor_cache.PreprocessorCache.ADJUST_CONTRAST, preprocess_vars_cache) def _adjust_contrast(image): image = tf.image.adjust_contrast(image / 255, contrast_factor) * 255 image = tf.clip_by_value(image, clip_value_min=0.0, clip_value_max=255.0) return image image = _augment_only_rgb_channels(image, _adjust_contrast) return image def random_adjust_hue(image, max_delta=0.02, seed=None, preprocess_vars_cache=None): """Randomly adjusts hue. Makes sure the output image is still between 0 and 255. Args: image: rank 3 float32 tensor contains 1 image -> [height, width, channels] with pixel values varying between [0, 255]. max_delta: change hue randomly with a value between 0 and max_delta. seed: random seed. preprocess_vars_cache: PreprocessorCache object that records previously performed augmentations. Updated in-place. If this function is called multiple times with the same non-null cache, it will perform deterministically. Returns: image: image which is the same shape as input image. """ with tf.name_scope('RandomAdjustHue', values=[image]): generator_func = functools.partial(tf.random_uniform, [], -max_delta, max_delta, seed=seed) delta = _get_or_create_preprocess_rand_vars( generator_func, preprocessor_cache.PreprocessorCache.ADJUST_HUE, preprocess_vars_cache) def _adjust_hue(image): image = tf.image.adjust_hue(image / 255, delta) * 255 image = tf.clip_by_value(image, clip_value_min=0.0, clip_value_max=255.0) return image image = _augment_only_rgb_channels(image, _adjust_hue) return image def random_adjust_saturation(image, min_delta=0.8, max_delta=1.25, seed=None, preprocess_vars_cache=None): """Randomly adjusts saturation. Makes sure the output image is still between 0 and 255. Args: image: rank 3 float32 tensor contains 1 image -> [height, width, channels] with pixel values varying between [0, 255]. min_delta: see max_delta. max_delta: how much to change the saturation. Saturation will change with a value between min_delta and max_delta. This value will be multiplied to the current saturation of the image. seed: random seed. preprocess_vars_cache: PreprocessorCache object that records previously performed augmentations. Updated in-place. If this function is called multiple times with the same non-null cache, it will perform deterministically. Returns: image: image which is the same shape as input image. """ with tf.name_scope('RandomAdjustSaturation', values=[image]): generator_func = functools.partial(tf.random_uniform, [], min_delta, max_delta, seed=seed) saturation_factor = _get_or_create_preprocess_rand_vars( generator_func, preprocessor_cache.PreprocessorCache.ADJUST_SATURATION, preprocess_vars_cache) def _adjust_saturation(image): image = tf.image.adjust_saturation(image / 255, saturation_factor) * 255 image = tf.clip_by_value(image, clip_value_min=0.0, clip_value_max=255.0) return image image = _augment_only_rgb_channels(image, _adjust_saturation) return image def random_distort_color(image, color_ordering=0, preprocess_vars_cache=None): """Randomly distorts color. Randomly distorts color using a combination of brightness, hue, contrast and saturation changes. Makes sure the output image is still between 0 and 255. Args: image: rank 3 float32 tensor contains 1 image -> [height, width, channels] with pixel values varying between [0, 255]. color_ordering: Python int, a type of distortion (valid values: 0, 1). preprocess_vars_cache: PreprocessorCache object that records previously performed augmentations. Updated in-place. If this function is called multiple times with the same non-null cache, it will perform deterministically. Returns: image: image which is the same shape as input image. Raises: ValueError: if color_ordering is not in {0, 1}. """ with tf.name_scope('RandomDistortColor', values=[image]): if color_ordering == 0: image = random_adjust_brightness( image, max_delta=32. / 255., preprocess_vars_cache=preprocess_vars_cache) image = random_adjust_saturation( image, min_delta=0.5, max_delta=1.5, preprocess_vars_cache=preprocess_vars_cache) image = random_adjust_hue( image, max_delta=0.2, preprocess_vars_cache=preprocess_vars_cache) image = random_adjust_contrast( image, min_delta=0.5, max_delta=1.5, preprocess_vars_cache=preprocess_vars_cache) elif color_ordering == 1: image = random_adjust_brightness( image, max_delta=32. / 255., preprocess_vars_cache=preprocess_vars_cache) image = random_adjust_contrast( image, min_delta=0.5, max_delta=1.5, preprocess_vars_cache=preprocess_vars_cache) image = random_adjust_saturation( image, min_delta=0.5, max_delta=1.5, preprocess_vars_cache=preprocess_vars_cache) image = random_adjust_hue( image, max_delta=0.2, preprocess_vars_cache=preprocess_vars_cache) else: raise ValueError('color_ordering must be in {0, 1}') return image def random_jitter_boxes(boxes, ratio=0.05, seed=None): """Randomly jitter boxes in image. Args: boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4]. Boxes are in normalized form meaning their coordinates vary between [0, 1]. Each row is in the form of [ymin, xmin, ymax, xmax]. ratio: The ratio of the box width and height that the corners can jitter. For example if the width is 100 pixels and ratio is 0.05, the corners can jitter up to 5 pixels in the x direction. seed: random seed. Returns: boxes: boxes which is the same shape as input boxes. """ def random_jitter_box(box, ratio, seed): """Randomly jitter box. Args: box: bounding box [1, 1, 4]. ratio: max ratio between jittered box and original box, a number between [0, 0.5]. seed: random seed. Returns: jittered_box: jittered box. """ rand_numbers = tf.random_uniform( [1, 1, 4], minval=-ratio, maxval=ratio, dtype=tf.float32, seed=seed) box_width = tf.subtract(box[0, 0, 3], box[0, 0, 1]) box_height = tf.subtract(box[0, 0, 2], box[0, 0, 0]) hw_coefs = tf.stack([box_height, box_width, box_height, box_width]) hw_rand_coefs = tf.multiply(hw_coefs, rand_numbers) jittered_box = tf.add(box, hw_rand_coefs) jittered_box = tf.clip_by_value(jittered_box, 0.0, 1.0) return jittered_box with tf.name_scope('RandomJitterBoxes', values=[boxes]): # boxes are [N, 4]. Lets first make them [N, 1, 1, 4] boxes_shape = tf.shape(boxes) boxes = tf.expand_dims(boxes, 1) boxes = tf.expand_dims(boxes, 2) distorted_boxes = tf.map_fn( lambda x: random_jitter_box(x, ratio, seed), boxes, dtype=tf.float32) distorted_boxes = tf.reshape(distorted_boxes, boxes_shape) return distorted_boxes def _strict_random_crop_image(image, boxes, labels, label_weights, label_confidences=None, multiclass_scores=None, masks=None, keypoints=None, min_object_covered=1.0, aspect_ratio_range=(0.75, 1.33), area_range=(0.1, 1.0), overlap_thresh=0.3, clip_boxes=True, preprocess_vars_cache=None): """Performs random crop. Note: Keypoint coordinates that are outside the crop will be set to NaN, which is consistent with the original keypoint encoding for non-existing keypoints. This function always crops the image and is supposed to be used by `random_crop_image` function which sometimes returns the image unchanged. Args: image: rank 3 float32 tensor containing 1 image -> [height, width, channels] with pixel values varying between [0, 1]. boxes: rank 2 float32 tensor containing the bounding boxes with shape [num_instances, 4]. Boxes are in normalized form meaning their coordinates vary between [0, 1]. Each row is in the form of [ymin, xmin, ymax, xmax]. labels: rank 1 int32 tensor containing the object classes. label_weights: float32 tensor of shape [num_instances] representing the weight for each box. label_confidences: (optional) float32 tensor of shape [num_instances] representing the confidence for each box. multiclass_scores: (optional) float32 tensor of shape [num_instances, num_classes] representing the score for each box for each class. masks: (optional) rank 3 float32 tensor with shape [num_instances, height, width] containing instance masks. The masks are of the same height, width as the input `image`. keypoints: (optional) rank 3 float32 tensor with shape [num_instances, num_keypoints, 2]. The keypoints are in y-x normalized coordinates. min_object_covered: the cropped image must cover at least this fraction of at least one of the input bounding boxes. aspect_ratio_range: allowed range for aspect ratio of cropped image. area_range: allowed range for area ratio between cropped image and the original image. overlap_thresh: minimum overlap thresh with new cropped image to keep the box. clip_boxes: whether to clip the boxes to the cropped image. preprocess_vars_cache: PreprocessorCache object that records previously performed augmentations. Updated in-place. If this function is called multiple times with the same non-null cache, it will perform deterministically. Returns: image: image which is the same rank as input image. boxes: boxes which is the same rank as input boxes. Boxes are in normalized form. labels: new labels. If label_weights, multiclass_scores, masks, or keypoints is not None, the function also returns: label_weights: rank 1 float32 tensor with shape [num_instances]. multiclass_scores: rank 2 float32 tensor with shape [num_instances, num_classes] masks: rank 3 float32 tensor with shape [num_instances, height, width] containing instance masks. keypoints: rank 3 float32 tensor with shape [num_instances, num_keypoints, 2] """ with tf.name_scope('RandomCropImage', values=[image, boxes]): image_shape = tf.shape(image) # boxes are [N, 4]. Lets first make them [N, 1, 4]. boxes_expanded = tf.expand_dims( tf.clip_by_value( boxes, clip_value_min=0.0, clip_value_max=1.0), 1) generator_func = functools.partial( tf.image.sample_distorted_bounding_box, image_shape, bounding_boxes=boxes_expanded, min_object_covered=min_object_covered, aspect_ratio_range=aspect_ratio_range, area_range=area_range, max_attempts=100, use_image_if_no_bounding_boxes=True) # for ssd cropping, each value of min_object_covered has its own # cached random variable sample_distorted_bounding_box = _get_or_create_preprocess_rand_vars( generator_func, preprocessor_cache.PreprocessorCache.STRICT_CROP_IMAGE, preprocess_vars_cache, key=min_object_covered) im_box_begin, im_box_size, im_box = sample_distorted_bounding_box new_image = tf.slice(image, im_box_begin, im_box_size) new_image.set_shape([None, None, image.get_shape()[2]]) # [1, 4] im_box_rank2 = tf.squeeze(im_box, squeeze_dims=[0]) # [4] im_box_rank1 = tf.squeeze(im_box) boxlist = box_list.BoxList(boxes) boxlist.add_field('labels', labels) if label_weights is not None: boxlist.add_field('label_weights', label_weights) if label_confidences is not None: boxlist.add_field('label_confidences', label_confidences) if multiclass_scores is not None: boxlist.add_field('multiclass_scores', multiclass_scores) im_boxlist = box_list.BoxList(im_box_rank2) # remove boxes that are outside cropped image boxlist, inside_window_ids = box_list_ops.prune_completely_outside_window( boxlist, im_box_rank1) # remove boxes that are outside image overlapping_boxlist, keep_ids = box_list_ops.prune_non_overlapping_boxes( boxlist, im_boxlist, overlap_thresh) # change the coordinate of the remaining boxes new_labels = overlapping_boxlist.get_field('labels') new_boxlist = box_list_ops.change_coordinate_frame(overlapping_boxlist, im_box_rank1) new_boxes = new_boxlist.get() if clip_boxes: new_boxes = tf.clip_by_value( new_boxes, clip_value_min=0.0, clip_value_max=1.0) result = [new_image, new_boxes, new_labels] if label_weights is not None: new_label_weights = overlapping_boxlist.get_field('label_weights') result.append(new_label_weights) if label_confidences is not None: new_label_confidences = overlapping_boxlist.get_field('label_confidences') result.append(new_label_confidences) if multiclass_scores is not None: new_multiclass_scores = overlapping_boxlist.get_field('multiclass_scores') result.append(new_multiclass_scores) if masks is not None: masks_of_boxes_inside_window = tf.gather(masks, inside_window_ids) masks_of_boxes_completely_inside_window = tf.gather( masks_of_boxes_inside_window, keep_ids) masks_box_begin = [0, im_box_begin[0], im_box_begin[1]] masks_box_size = [-1, im_box_size[0], im_box_size[1]] new_masks = tf.slice( masks_of_boxes_completely_inside_window, masks_box_begin, masks_box_size) result.append(new_masks) if keypoints is not None: keypoints_of_boxes_inside_window = tf.gather(keypoints, inside_window_ids) keypoints_of_boxes_completely_inside_window = tf.gather( keypoints_of_boxes_inside_window, keep_ids) new_keypoints = keypoint_ops.change_coordinate_frame( keypoints_of_boxes_completely_inside_window, im_box_rank1) if clip_boxes: new_keypoints = keypoint_ops.prune_outside_window(new_keypoints, [0.0, 0.0, 1.0, 1.0]) result.append(new_keypoints) return tuple(result) def random_crop_image(image, boxes, labels, label_weights, label_confidences=None, multiclass_scores=None, masks=None, keypoints=None, min_object_covered=1.0, aspect_ratio_range=(0.75, 1.33), area_range=(0.1, 1.0), overlap_thresh=0.3, clip_boxes=True, random_coef=0.0, seed=None, preprocess_vars_cache=None): """Randomly crops the image. Given the input image and its bounding boxes, this op randomly crops a subimage. Given a user-provided set of input constraints, the crop window is resampled until it satisfies these constraints. If within 100 trials it is unable to find a valid crop, the original image is returned. See the Args section for a description of the input constraints. Both input boxes and returned Boxes are in normalized form (e.g., lie in the unit square [0, 1]). This function will return the original image with probability random_coef. Note: Keypoint coordinates that are outside the crop will be set to NaN, which is consistent with the original keypoint encoding for non-existing keypoints. Args: image: rank 3 float32 tensor contains 1 image -> [height, width, channels] with pixel values varying between [0, 1]. boxes: rank 2 float32 tensor containing the bounding boxes with shape [num_instances, 4]. Boxes are in normalized form meaning their coordinates vary between [0, 1]. Each row is in the form of [ymin, xmin, ymax, xmax]. labels: rank 1 int32 tensor containing the object classes. label_weights: float32 tensor of shape [num_instances] representing the weight for each box. label_confidences: (optional) float32 tensor of shape [num_instances]. representing the confidence for each box. multiclass_scores: (optional) float32 tensor of shape [num_instances, num_classes] representing the score for each box for each class. masks: (optional) rank 3 float32 tensor with shape [num_instances, height, width] containing instance masks. The masks are of the same height, width as the input `image`. keypoints: (optional) rank 3 float32 tensor with shape [num_instances, num_keypoints, 2]. The keypoints are in y-x normalized coordinates. min_object_covered: the cropped image must cover at least this fraction of at least one of the input bounding boxes. aspect_ratio_range: allowed range for aspect ratio of cropped image. area_range: allowed range for area ratio between cropped image and the original image. overlap_thresh: minimum overlap thresh with new cropped image to keep the box. clip_boxes: whether to clip the boxes to the cropped image. random_coef: a random coefficient that defines the chance of getting the original image. If random_coef is 0, we will always get the cropped image, and if it is 1.0, we will always get the original image. seed: random seed. preprocess_vars_cache: PreprocessorCache object that records previously performed augmentations. Updated in-place. If this function is called multiple times with the same non-null cache, it will perform deterministically. Returns: image: Image shape will be [new_height, new_width, channels]. boxes: boxes which is the same rank as input boxes. Boxes are in normalized form. labels: new labels. If label_weights, multiclass_scores, masks, or keypoints is not None, the function also returns: label_weights: rank 1 float32 tensor with shape [num_instances]. multiclass_scores: rank 2 float32 tensor with shape [num_instances, num_classes] masks: rank 3 float32 tensor with shape [num_instances, height, width] containing instance masks. keypoints: rank 3 float32 tensor with shape [num_instances, num_keypoints, 2] """ def strict_random_crop_image_fn(): return _strict_random_crop_image( image, boxes, labels, label_weights, label_confidences=label_confidences, multiclass_scores=multiclass_scores, masks=masks, keypoints=keypoints, min_object_covered=min_object_covered, aspect_ratio_range=aspect_ratio_range, area_range=area_range, overlap_thresh=overlap_thresh, clip_boxes=clip_boxes, preprocess_vars_cache=preprocess_vars_cache) # avoids tf.cond to make faster RCNN training on borg. See b/140057645. if random_coef < sys.float_info.min: result = strict_random_crop_image_fn() else: generator_func = functools.partial(tf.random_uniform, [], seed=seed) do_a_crop_random = _get_or_create_preprocess_rand_vars( generator_func, preprocessor_cache.PreprocessorCache.CROP_IMAGE, preprocess_vars_cache) do_a_crop_random = tf.greater(do_a_crop_random, random_coef) outputs = [image, boxes, labels] if label_weights is not None: outputs.append(label_weights) if label_confidences is not None: outputs.append(label_confidences) if multiclass_scores is not None: outputs.append(multiclass_scores) if masks is not None: outputs.append(masks) if keypoints is not None: outputs.append(keypoints) result = tf.cond(do_a_crop_random, strict_random_crop_image_fn, lambda: tuple(outputs)) return result def random_pad_image(image, boxes, keypoints=None, min_image_size=None, max_image_size=None, pad_color=None, seed=None, preprocess_vars_cache=None): """Randomly pads the image. This function randomly pads the image with zeros. The final size of the padded image will be between min_image_size and max_image_size. if min_image_size is smaller than the input image size, min_image_size will be set to the input image size. The same for max_image_size. The input image will be located at a uniformly random location inside the padded image. The relative location of the boxes to the original image will remain the same. Args: image: rank 3 float32 tensor containing 1 image -> [height, width, channels] with pixel values varying between [0, 1]. boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4]. Boxes are in normalized form meaning their coordinates vary between [0, 1]. Each row is in the form of [ymin, xmin, ymax, xmax]. keypoints: (optional) rank 3 float32 tensor with shape [N, num_keypoints, 2]. The keypoints are in y-x normalized coordinates. min_image_size: a tensor of size [min_height, min_width], type tf.int32. If passed as None, will be set to image size [height, width]. max_image_size: a tensor of size [max_height, max_width], type tf.int32. If passed as None, will be set to twice the image [height * 2, width * 2]. pad_color: padding color. A rank 1 tensor of [channels] with dtype= tf.float32. if set as None, it will be set to average color of the input image. seed: random seed. preprocess_vars_cache: PreprocessorCache object that records previously performed augmentations. Updated in-place. If this function is called multiple times with the same non-null cache, it will perform deterministically. Returns: image: Image shape will be [new_height, new_width, channels]. boxes: boxes which is the same rank as input boxes. Boxes are in normalized form. if keypoints is not None, the function also returns: keypoints: rank 3 float32 tensor with shape [N, num_keypoints, 2] """ if pad_color is None: pad_color = tf.reduce_mean(image, axis=[0, 1]) image_shape = tf.shape(image) image_height = image_shape[0] image_width = image_shape[1] if max_image_size is None: max_image_size = tf.stack([image_height * 2, image_width * 2]) max_image_size = tf.maximum(max_image_size, tf.stack([image_height, image_width])) if min_image_size is None: min_image_size = tf.stack([image_height, image_width]) min_image_size = tf.maximum(min_image_size, tf.stack([image_height, image_width])) target_height = tf.cond( max_image_size[0] > min_image_size[0], lambda: _random_integer(min_image_size[0], max_image_size[0], seed), lambda: max_image_size[0]) target_width = tf.cond( max_image_size[1] > min_image_size[1], lambda: _random_integer(min_image_size[1], max_image_size[1], seed), lambda: max_image_size[1]) offset_height = tf.cond( target_height > image_height, lambda: _random_integer(0, target_height - image_height, seed), lambda: tf.constant(0, dtype=tf.int32)) offset_width = tf.cond( target_width > image_width, lambda: _random_integer(0, target_width - image_width, seed), lambda: tf.constant(0, dtype=tf.int32)) gen_func = lambda: (target_height, target_width, offset_height, offset_width) params = _get_or_create_preprocess_rand_vars( gen_func, preprocessor_cache.PreprocessorCache.PAD_IMAGE, preprocess_vars_cache) target_height, target_width, offset_height, offset_width = params new_image = tf.image.pad_to_bounding_box( image, offset_height=offset_height, offset_width=offset_width, target_height=target_height, target_width=target_width) # Setting color of the padded pixels image_ones = tf.ones_like(image) image_ones_padded = tf.image.pad_to_bounding_box( image_ones, offset_height=offset_height, offset_width=offset_width, target_height=target_height, target_width=target_width) image_color_padded = (1.0 - image_ones_padded) * pad_color new_image += image_color_padded # setting boxes new_window = tf.to_float( tf.stack([ -offset_height, -offset_width, target_height - offset_height, target_width - offset_width ])) new_window /= tf.to_float( tf.stack([image_height, image_width, image_height, image_width])) boxlist = box_list.BoxList(boxes) new_boxlist = box_list_ops.change_coordinate_frame(boxlist, new_window) new_boxes = new_boxlist.get() result = [new_image, new_boxes] if keypoints is not None: new_keypoints = keypoint_ops.change_coordinate_frame(keypoints, new_window) result.append(new_keypoints) return tuple(result) def random_absolute_pad_image(image, boxes, max_height_padding, max_width_padding, pad_color=None, seed=None, preprocess_vars_cache=None): """Randomly pads the image by small absolute amounts. As random_pad_image above, but the padding is of size [0, max_height_padding] or [0, max_width_padding] instead of padding to a fixed size of max_height_padding for all images. Args: image: rank 3 float32 tensor containing 1 image -> [height, width, channels] with pixel values varying between [0, 1]. boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4]. Boxes are in normalized form meaning their coordinates vary between [0, 1]. Each row is in the form of [ymin, xmin, ymax, xmax]. max_height_padding: a scalar tf.int32 tensor denoting the maximum amount of height padding. The padding will be chosen uniformly at random from [0, max_height_padding). max_width_padding: a scalar tf.int32 tensor denoting the maximum amount of width padding. The padding will be chosen uniformly at random from [0, max_width_padding). pad_color: padding color. A rank 1 tensor of [3] with dtype=tf.float32. if set as None, it will be set to average color of the input image. seed: random seed. preprocess_vars_cache: PreprocessorCache object that records previously performed augmentations. Updated in-place. If this function is called multiple times with the same non-null cache, it will perform deterministically. Returns: image: Image shape will be [new_height, new_width, channels]. boxes: boxes which is the same rank as input boxes. Boxes are in normalized form. """ min_image_size = tf.shape(image)[:2] max_image_size = min_image_size + tf.to_int32( [max_height_padding, max_width_padding]) return random_pad_image(image, boxes, min_image_size=min_image_size, max_image_size=max_image_size, pad_color=pad_color, seed=seed, preprocess_vars_cache=preprocess_vars_cache) def random_crop_pad_image(image, boxes, labels, label_weights, label_confidences=None, multiclass_scores=None, min_object_covered=1.0, aspect_ratio_range=(0.75, 1.33), area_range=(0.1, 1.0), overlap_thresh=0.3, clip_boxes=True, random_coef=0.0, min_padded_size_ratio=(1.0, 1.0), max_padded_size_ratio=(2.0, 2.0), pad_color=None, seed=None, preprocess_vars_cache=None): """Randomly crops and pads the image. Given an input image and its bounding boxes, this op first randomly crops the image and then randomly pads the image with background values. Parameters min_padded_size_ratio and max_padded_size_ratio, determine the range of the final output image size. Specifically, the final image size will have a size in the range of min_padded_size_ratio * tf.shape(image) and max_padded_size_ratio * tf.shape(image). Note that these ratios are with respect to the size of the original image, so we can't capture the same effect easily by independently applying RandomCropImage followed by RandomPadImage. Args: image: rank 3 float32 tensor containing 1 image -> [height, width, channels] with pixel values varying between [0, 1]. boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4]. Boxes are in normalized form meaning their coordinates vary between [0, 1]. Each row is in the form of [ymin, xmin, ymax, xmax]. labels: rank 1 int32 tensor containing the object classes. label_weights: rank 1 float32 containing the label weights. label_confidences: rank 1 float32 containing the label confidences. multiclass_scores: (optional) float32 tensor of shape [num_instances, num_classes] representing the score for each box for each class. min_object_covered: the cropped image must cover at least this fraction of at least one of the input bounding boxes. aspect_ratio_range: allowed range for aspect ratio of cropped image. area_range: allowed range for area ratio between cropped image and the original image. overlap_thresh: minimum overlap thresh with new cropped image to keep the box. clip_boxes: whether to clip the boxes to the cropped image. random_coef: a random coefficient that defines the chance of getting the original image. If random_coef is 0, we will always get the cropped image, and if it is 1.0, we will always get the original image. min_padded_size_ratio: min ratio of padded image height and width to the input image's height and width. max_padded_size_ratio: max ratio of padded image height and width to the input image's height and width. pad_color: padding color. A rank 1 tensor of [3] with dtype=tf.float32. if set as None, it will be set to average color of the randomly cropped image. seed: random seed. preprocess_vars_cache: PreprocessorCache object that records previously performed augmentations. Updated in-place. If this function is called multiple times with the same non-null cache, it will perform deterministically. Returns: padded_image: padded image. padded_boxes: boxes which is the same rank as input boxes. Boxes are in normalized form. cropped_labels: cropped labels. if label_weights is not None also returns: cropped_label_weights: cropped label weights. if multiclass_scores is not None also returns: cropped_multiclass_scores: cropped_multiclass_scores. """ image_size = tf.shape(image) image_height = image_size[0] image_width = image_size[1] result = random_crop_image( image=image, boxes=boxes, labels=labels, label_weights=label_weights, label_confidences=label_confidences, multiclass_scores=multiclass_scores, min_object_covered=min_object_covered, aspect_ratio_range=aspect_ratio_range, area_range=area_range, overlap_thresh=overlap_thresh, clip_boxes=clip_boxes, random_coef=random_coef, seed=seed, preprocess_vars_cache=preprocess_vars_cache) cropped_image, cropped_boxes, cropped_labels = result[:3] min_image_size = tf.to_int32( tf.to_float(tf.stack([image_height, image_width])) * min_padded_size_ratio) max_image_size = tf.to_int32( tf.to_float(tf.stack([image_height, image_width])) * max_padded_size_ratio) padded_image, padded_boxes = random_pad_image( cropped_image, cropped_boxes, min_image_size=min_image_size, max_image_size=max_image_size, pad_color=pad_color, seed=seed, preprocess_vars_cache=preprocess_vars_cache) cropped_padded_output = (padded_image, padded_boxes, cropped_labels) index = 3 if label_weights is not None: cropped_label_weights = result[index] cropped_padded_output += (cropped_label_weights,) index += 1 if label_confidences is not None: cropped_label_confidences = result[index] cropped_padded_output += (cropped_label_confidences,) index += 1 if multiclass_scores is not None: cropped_multiclass_scores = result[index] cropped_padded_output += (cropped_multiclass_scores,) return cropped_padded_output def random_crop_to_aspect_ratio(image, boxes, labels, label_weights, label_confidences=None, multiclass_scores=None, masks=None, keypoints=None, aspect_ratio=1.0, overlap_thresh=0.3, clip_boxes=True, seed=None, preprocess_vars_cache=None): """Randomly crops an image to the specified aspect ratio. Randomly crops the a portion of the image such that the crop is of the specified aspect ratio, and the crop is as large as possible. If the specified aspect ratio is larger than the aspect ratio of the image, this op will randomly remove rows from the top and bottom of the image. If the specified aspect ratio is less than the aspect ratio of the image, this op will randomly remove cols from the left and right of the image. If the specified aspect ratio is the same as the aspect ratio of the image, this op will return the image. Args: image: rank 3 float32 tensor contains 1 image -> [height, width, channels] with pixel values varying between [0, 1]. boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4]. Boxes are in normalized form meaning their coordinates vary between [0, 1]. Each row is in the form of [ymin, xmin, ymax, xmax]. labels: rank 1 int32 tensor containing the object classes. label_weights: float32 tensor of shape [num_instances] representing the weight for each box. label_confidences: (optional) float32 tensor of shape [num_instances] representing the confidence for each box. multiclass_scores: (optional) float32 tensor of shape [num_instances, num_classes] representing the score for each box for each class. masks: (optional) rank 3 float32 tensor with shape [num_instances, height, width] containing instance masks. The masks are of the same height, width as the input `image`. keypoints: (optional) rank 3 float32 tensor with shape [num_instances, num_keypoints, 2]. The keypoints are in y-x normalized coordinates. aspect_ratio: the aspect ratio of cropped image. overlap_thresh: minimum overlap thresh with new cropped image to keep the box. clip_boxes: whether to clip the boxes to the cropped image. seed: random seed. preprocess_vars_cache: PreprocessorCache object that records previously performed augmentations. Updated in-place. If this function is called multiple times with the same non-null cache, it will perform deterministically. Returns: image: image which is the same rank as input image. boxes: boxes which is the same rank as input boxes. Boxes are in normalized form. labels: new labels. If label_weights, masks, keypoints, or multiclass_scores is not None, the function also returns: label_weights: rank 1 float32 tensor with shape [num_instances]. masks: rank 3 float32 tensor with shape [num_instances, height, width] containing instance masks. keypoints: rank 3 float32 tensor with shape [num_instances, num_keypoints, 2] multiclass_scores: rank 2 float32 tensor with shape [num_instances, num_classes] Raises: ValueError: If image is not a 3D tensor. """ if len(image.get_shape()) != 3: raise ValueError('Image should be 3D tensor') with tf.name_scope('RandomCropToAspectRatio', values=[image]): image_shape = tf.shape(image) orig_height = image_shape[0] orig_width = image_shape[1] orig_aspect_ratio = tf.to_float(orig_width) / tf.to_float(orig_height) new_aspect_ratio = tf.constant(aspect_ratio, dtype=tf.float32) def target_height_fn(): return tf.to_int32(tf.round(tf.to_float(orig_width) / new_aspect_ratio)) target_height = tf.cond(orig_aspect_ratio >= new_aspect_ratio, lambda: orig_height, target_height_fn) def target_width_fn(): return tf.to_int32(tf.round(tf.to_float(orig_height) * new_aspect_ratio)) target_width = tf.cond(orig_aspect_ratio <= new_aspect_ratio, lambda: orig_width, target_width_fn) # either offset_height = 0 and offset_width is randomly chosen from # [0, offset_width - target_width), or else offset_width = 0 and # offset_height is randomly chosen from [0, offset_height - target_height) offset_height = _random_integer(0, orig_height - target_height + 1, seed) offset_width = _random_integer(0, orig_width - target_width + 1, seed) generator_func = lambda: (offset_height, offset_width) offset_height, offset_width = _get_or_create_preprocess_rand_vars( generator_func, preprocessor_cache.PreprocessorCache.CROP_TO_ASPECT_RATIO, preprocess_vars_cache) new_image = tf.image.crop_to_bounding_box( image, offset_height, offset_width, target_height, target_width) im_box = tf.stack([ tf.to_float(offset_height) / tf.to_float(orig_height), tf.to_float(offset_width) / tf.to_float(orig_width), tf.to_float(offset_height + target_height) / tf.to_float(orig_height), tf.to_float(offset_width + target_width) / tf.to_float(orig_width) ]) boxlist = box_list.BoxList(boxes) boxlist.add_field('labels', labels) boxlist.add_field('label_weights', label_weights) if label_confidences is not None: boxlist.add_field('label_confidences', label_confidences) if multiclass_scores is not None: boxlist.add_field('multiclass_scores', multiclass_scores) im_boxlist = box_list.BoxList(tf.expand_dims(im_box, 0)) # remove boxes whose overlap with the image is less than overlap_thresh overlapping_boxlist, keep_ids = box_list_ops.prune_non_overlapping_boxes( boxlist, im_boxlist, overlap_thresh) # change the coordinate of the remaining boxes new_labels = overlapping_boxlist.get_field('labels') new_boxlist = box_list_ops.change_coordinate_frame(overlapping_boxlist, im_box) if clip_boxes: new_boxlist = box_list_ops.clip_to_window( new_boxlist, tf.constant([0.0, 0.0, 1.0, 1.0], tf.float32)) new_boxes = new_boxlist.get() result = [new_image, new_boxes, new_labels] new_label_weights = overlapping_boxlist.get_field('label_weights') result.append(new_label_weights) if label_confidences is not None: new_label_confidences = ( overlapping_boxlist.get_field('label_confidences')) result.append(new_label_confidences) if multiclass_scores is not None: new_multiclass_scores = overlapping_boxlist.get_field('multiclass_scores') result.append(new_multiclass_scores) if masks is not None: masks_inside_window = tf.gather(masks, keep_ids) masks_box_begin = tf.stack([0, offset_height, offset_width]) masks_box_size = tf.stack([-1, target_height, target_width]) new_masks = tf.slice(masks_inside_window, masks_box_begin, masks_box_size) result.append(new_masks) if keypoints is not None: keypoints_inside_window = tf.gather(keypoints, keep_ids) new_keypoints = keypoint_ops.change_coordinate_frame( keypoints_inside_window, im_box) if clip_boxes: new_keypoints = keypoint_ops.prune_outside_window(new_keypoints, [0.0, 0.0, 1.0, 1.0]) result.append(new_keypoints) return tuple(result) def random_pad_to_aspect_ratio(image, boxes, masks=None, keypoints=None, aspect_ratio=1.0, min_padded_size_ratio=(1.0, 1.0), max_padded_size_ratio=(2.0, 2.0), seed=None, preprocess_vars_cache=None): """Randomly zero pads an image to the specified aspect ratio. Pads the image so that the resulting image will have the specified aspect ratio without scaling less than the min_padded_size_ratio or more than the max_padded_size_ratio. If the min_padded_size_ratio or max_padded_size_ratio is lower than what is possible to maintain the aspect ratio, then this method will use the least padding to achieve the specified aspect ratio. Args: image: rank 3 float32 tensor contains 1 image -> [height, width, channels] with pixel values varying between [0, 1]. boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4]. Boxes are in normalized form meaning their coordinates vary between [0, 1]. Each row is in the form of [ymin, xmin, ymax, xmax]. masks: (optional) rank 3 float32 tensor with shape [num_instances, height, width] containing instance masks. The masks are of the same height, width as the input `image`. keypoints: (optional) rank 3 float32 tensor with shape [num_instances, num_keypoints, 2]. The keypoints are in y-x normalized coordinates. aspect_ratio: aspect ratio of the final image. min_padded_size_ratio: min ratio of padded image height and width to the input image's height and width. max_padded_size_ratio: max ratio of padded image height and width to the input image's height and width. seed: random seed. preprocess_vars_cache: PreprocessorCache object that records previously performed augmentations. Updated in-place. If this function is called multiple times with the same non-null cache, it will perform deterministically. Returns: image: image which is the same rank as input image. boxes: boxes which is the same rank as input boxes. Boxes are in normalized form. labels: new labels. If masks, or keypoints is not None, the function also returns: masks: rank 3 float32 tensor with shape [num_instances, height, width] containing instance masks. keypoints: rank 3 float32 tensor with shape [num_instances, num_keypoints, 2] Raises: ValueError: If image is not a 3D tensor. """ if len(image.get_shape()) != 3: raise ValueError('Image should be 3D tensor') with tf.name_scope('RandomPadToAspectRatio', values=[image]): image_shape = tf.shape(image) image_height = tf.to_float(image_shape[0]) image_width = tf.to_float(image_shape[1]) image_aspect_ratio = image_width / image_height new_aspect_ratio = tf.constant(aspect_ratio, dtype=tf.float32) target_height = tf.cond( image_aspect_ratio <= new_aspect_ratio, lambda: image_height, lambda: image_width / new_aspect_ratio) target_width = tf.cond( image_aspect_ratio >= new_aspect_ratio, lambda: image_width, lambda: image_height * new_aspect_ratio) min_height = tf.maximum( min_padded_size_ratio[0] * image_height, target_height) min_width = tf.maximum( min_padded_size_ratio[1] * image_width, target_width) max_height = tf.maximum( max_padded_size_ratio[0] * image_height, target_height) max_width = tf.maximum( max_padded_size_ratio[1] * image_width, target_width) max_scale = tf.minimum(max_height / target_height, max_width / target_width) min_scale = tf.minimum( max_scale, tf.maximum(min_height / target_height, min_width / target_width)) generator_func = functools.partial(tf.random_uniform, [], min_scale, max_scale, seed=seed) scale = _get_or_create_preprocess_rand_vars( generator_func, preprocessor_cache.PreprocessorCache.PAD_TO_ASPECT_RATIO, preprocess_vars_cache) target_height = tf.round(scale * target_height) target_width = tf.round(scale * target_width) new_image = tf.image.pad_to_bounding_box( image, 0, 0, tf.to_int32(target_height), tf.to_int32(target_width)) im_box = tf.stack([ 0.0, 0.0, target_height / image_height, target_width / image_width ]) boxlist = box_list.BoxList(boxes) new_boxlist = box_list_ops.change_coordinate_frame(boxlist, im_box) new_boxes = new_boxlist.get() result = [new_image, new_boxes] if masks is not None: new_masks = tf.expand_dims(masks, -1) new_masks = tf.image.pad_to_bounding_box(new_masks, 0, 0, tf.to_int32(target_height), tf.to_int32(target_width)) new_masks = tf.squeeze(new_masks, [-1]) result.append(new_masks) if keypoints is not None: new_keypoints = keypoint_ops.change_coordinate_frame(keypoints, im_box) result.append(new_keypoints) return tuple(result) def random_black_patches(image, max_black_patches=10, probability=0.5, size_to_image_ratio=0.1, random_seed=None, preprocess_vars_cache=None): """Randomly adds some black patches to the image. This op adds up to max_black_patches square black patches of a fixed size to the image where size is specified via the size_to_image_ratio parameter. Args: image: rank 3 float32 tensor containing 1 image -> [height, width, channels] with pixel values varying between [0, 1]. max_black_patches: number of times that the function tries to add a black box to the image. probability: at each try, what is the chance of adding a box. size_to_image_ratio: Determines the ratio of the size of the black patches to the size of the image. box_size = size_to_image_ratio * min(image_width, image_height) random_seed: random seed. preprocess_vars_cache: PreprocessorCache object that records previously performed augmentations. Updated in-place. If this function is called multiple times with the same non-null cache, it will perform deterministically. Returns: image """ def add_black_patch_to_image(image, idx): """Function for adding one patch to the image. Args: image: image idx: counter for number of patches that could have been added Returns: image with a randomly added black box """ image_shape = tf.shape(image) image_height = image_shape[0] image_width = image_shape[1] box_size = tf.to_int32( tf.multiply( tf.minimum(tf.to_float(image_height), tf.to_float(image_width)), size_to_image_ratio)) generator_func = functools.partial(tf.random_uniform, [], minval=0.0, maxval=(1.0 - size_to_image_ratio), seed=random_seed) normalized_y_min = _get_or_create_preprocess_rand_vars( generator_func, preprocessor_cache.PreprocessorCache.ADD_BLACK_PATCH, preprocess_vars_cache, key=str(idx) + 'y') normalized_x_min = _get_or_create_preprocess_rand_vars( generator_func, preprocessor_cache.PreprocessorCache.ADD_BLACK_PATCH, preprocess_vars_cache, key=str(idx) + 'x') y_min = tf.to_int32(normalized_y_min * tf.to_float(image_height)) x_min = tf.to_int32(normalized_x_min * tf.to_float(image_width)) black_box = tf.ones([box_size, box_size, 3], dtype=tf.float32) mask = 1.0 - tf.image.pad_to_bounding_box(black_box, y_min, x_min, image_height, image_width) image = tf.multiply(image, mask) return image with tf.name_scope('RandomBlackPatchInImage', values=[image]): for idx in range(max_black_patches): generator_func = functools.partial(tf.random_uniform, [], minval=0.0, maxval=1.0, dtype=tf.float32, seed=random_seed) random_prob = _get_or_create_preprocess_rand_vars( generator_func, preprocessor_cache.PreprocessorCache.BLACK_PATCHES, preprocess_vars_cache, key=idx) image = tf.cond( tf.greater(random_prob, probability), lambda: image, functools.partial(add_black_patch_to_image, image=image, idx=idx)) return image def image_to_float(image): """Used in Faster R-CNN. Casts image pixel values to float. Args: image: input image which might be in tf.uint8 or sth else format Returns: image: image in tf.float32 format. """ with tf.name_scope('ImageToFloat', values=[image]): image = tf.to_float(image) return image def random_resize_method(image, target_size, preprocess_vars_cache=None): """Uses a random resize method to resize the image to target size. Args: image: a rank 3 tensor. target_size: a list of [target_height, target_width] preprocess_vars_cache: PreprocessorCache object that records previously performed augmentations. Updated in-place. If this function is called multiple times with the same non-null cache, it will perform deterministically. Returns: resized image. """ resized_image = _apply_with_random_selector( image, lambda x, method: tf.image.resize_images(x, target_size, method), num_cases=4, preprocess_vars_cache=preprocess_vars_cache, key=preprocessor_cache.PreprocessorCache.RESIZE_METHOD) return resized_image def resize_to_range(image, masks=None, min_dimension=None, max_dimension=None, method=tf.image.ResizeMethod.BILINEAR, align_corners=False, pad_to_max_dimension=False, per_channel_pad_value=(0, 0, 0)): """Resizes an image so its dimensions are within the provided value. The output size can be described by two cases: 1. If the image can be rescaled so its minimum dimension is equal to the provided value without the other dimension exceeding max_dimension, then do so. 2. Otherwise, resize so the largest dimension is equal to max_dimension. Args: image: A 3D tensor of shape [height, width, channels] masks: (optional) rank 3 float32 tensor with shape [num_instances, height, width] containing instance masks. min_dimension: (optional) (scalar) desired size of the smaller image dimension. max_dimension: (optional) (scalar) maximum allowed size of the larger image dimension. method: (optional) interpolation method used in resizing. Defaults to BILINEAR. align_corners: bool. If true, exactly align all 4 corners of the input and output. Defaults to False. pad_to_max_dimension: Whether to resize the image and pad it with zeros so the resulting image is of the spatial size [max_dimension, max_dimension]. If masks are included they are padded similarly. per_channel_pad_value: A tuple of per-channel scalar value to use for padding. By default pads zeros. Returns: Note that the position of the resized_image_shape changes based on whether masks are present. resized_image: A 3D tensor of shape [new_height, new_width, channels], where the image has been resized (with bilinear interpolation) so that min(new_height, new_width) == min_dimension or max(new_height, new_width) == max_dimension. resized_masks: If masks is not None, also outputs masks. A 3D tensor of shape [num_instances, new_height, new_width]. resized_image_shape: A 1D tensor of shape [3] containing shape of the resized image. Raises: ValueError: if the image is not a 3D tensor. """ if len(image.get_shape()) != 3: raise ValueError('Image should be 3D tensor') def _resize_landscape_image(image): # resize a landscape image return tf.image.resize_images( image, tf.stack([min_dimension, max_dimension]), method=method, align_corners=align_corners, preserve_aspect_ratio=True) def _resize_portrait_image(image): # resize a portrait image return tf.image.resize_images( image, tf.stack([max_dimension, min_dimension]), method=method, align_corners=align_corners, preserve_aspect_ratio=True) with tf.name_scope('ResizeToRange', values=[image, min_dimension]): if image.get_shape().is_fully_defined(): if image.get_shape()[0] < image.get_shape()[1]: new_image = _resize_landscape_image(image) else: new_image = _resize_portrait_image(image) new_size = tf.constant(new_image.get_shape().as_list()) else: new_image = tf.cond( tf.less(tf.shape(image)[0], tf.shape(image)[1]), lambda: _resize_landscape_image(image), lambda: _resize_portrait_image(image)) new_size = tf.shape(new_image) if pad_to_max_dimension: channels = tf.unstack(new_image, axis=2) if len(channels) != len(per_channel_pad_value): raise ValueError('Number of channels must be equal to the length of ' 'per-channel pad value.') new_image = tf.stack( [ tf.pad( channels[i], [[0, max_dimension - new_size[0]], [0, max_dimension - new_size[1]]], constant_values=per_channel_pad_value[i]) for i in range(len(channels)) ], axis=2) new_image.set_shape([max_dimension, max_dimension, 3]) result = [new_image] if masks is not None: new_masks = tf.expand_dims(masks, 3) new_masks = tf.image.resize_images( new_masks, new_size[:-1], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR, align_corners=align_corners) if pad_to_max_dimension: new_masks = tf.image.pad_to_bounding_box( new_masks, 0, 0, max_dimension, max_dimension) new_masks = tf.squeeze(new_masks, 3) result.append(new_masks) result.append(new_size) return result # TODO(alirezafathi): Make sure the static shapes are preserved. def resize_to_min_dimension(image, masks=None, min_dimension=600): """Resizes image and masks given the min size maintaining the aspect ratio. If one of the image dimensions is smaller that min_dimension, it will scale the image such that its smallest dimension is equal to min_dimension. Otherwise, will keep the image size as is. Args: image: a tensor of size [height, width, channels]. masks: (optional) a tensors of size [num_instances, height, width]. min_dimension: minimum image dimension. Returns: Note that the position of the resized_image_shape changes based on whether masks are present. resized_image: A tensor of size [new_height, new_width, channels]. resized_masks: If masks is not None, also outputs masks. A 3D tensor of shape [num_instances, new_height, new_width] resized_image_shape: A 1D tensor of shape [3] containing the shape of the resized image. Raises: ValueError: if the image is not a 3D tensor. """ if len(image.get_shape()) != 3: raise ValueError('Image should be 3D tensor') with tf.name_scope('ResizeGivenMinDimension', values=[image, min_dimension]): image_height = tf.shape(image)[0] image_width = tf.shape(image)[1] num_channels = tf.shape(image)[2] min_image_dimension = tf.minimum(image_height, image_width) min_target_dimension = tf.maximum(min_image_dimension, min_dimension) target_ratio = tf.to_float(min_target_dimension) / tf.to_float( min_image_dimension) target_height = tf.to_int32(tf.to_float(image_height) * target_ratio) target_width = tf.to_int32(tf.to_float(image_width) * target_ratio) image = tf.image.resize_bilinear( tf.expand_dims(image, axis=0), size=[target_height, target_width], align_corners=True) result = [tf.squeeze(image, axis=0)] if masks is not None: masks = tf.image.resize_nearest_neighbor( tf.expand_dims(masks, axis=3), size=[target_height, target_width], align_corners=True) result.append(tf.squeeze(masks, axis=3)) result.append(tf.stack([target_height, target_width, num_channels])) return result def scale_boxes_to_pixel_coordinates(image, boxes, keypoints=None): """Scales boxes from normalized to pixel coordinates. Args: image: A 3D float32 tensor of shape [height, width, channels]. boxes: A 2D float32 tensor of shape [num_boxes, 4] containing the bounding boxes in normalized coordinates. Each row is of the form [ymin, xmin, ymax, xmax]. keypoints: (optional) rank 3 float32 tensor with shape [num_instances, num_keypoints, 2]. The keypoints are in y-x normalized coordinates. Returns: image: unchanged input image. scaled_boxes: a 2D float32 tensor of shape [num_boxes, 4] containing the bounding boxes in pixel coordinates. scaled_keypoints: a 3D float32 tensor with shape [num_instances, num_keypoints, 2] containing the keypoints in pixel coordinates. """ boxlist = box_list.BoxList(boxes) image_height = tf.shape(image)[0] image_width = tf.shape(image)[1] scaled_boxes = box_list_ops.scale(boxlist, image_height, image_width).get() result = [image, scaled_boxes] if keypoints is not None: scaled_keypoints = keypoint_ops.scale(keypoints, image_height, image_width) result.append(scaled_keypoints) return tuple(result) # TODO(alirezafathi): Investigate if instead the function should return None if # masks is None. # pylint: disable=g-doc-return-or-yield def resize_image(image, masks=None, new_height=600, new_width=1024, method=tf.image.ResizeMethod.BILINEAR, align_corners=False): """Resizes images to the given height and width. Args: image: A 3D tensor of shape [height, width, channels] masks: (optional) rank 3 float32 tensor with shape [num_instances, height, width] containing instance masks. new_height: (optional) (scalar) desired height of the image. new_width: (optional) (scalar) desired width of the image. method: (optional) interpolation method used in resizing. Defaults to BILINEAR. align_corners: bool. If true, exactly align all 4 corners of the input and output. Defaults to False. Returns: Note that the position of the resized_image_shape changes based on whether masks are present. resized_image: A tensor of size [new_height, new_width, channels]. resized_masks: If masks is not None, also outputs masks. A 3D tensor of shape [num_instances, new_height, new_width] resized_image_shape: A 1D tensor of shape [3] containing the shape of the resized image. """ with tf.name_scope( 'ResizeImage', values=[image, new_height, new_width, method, align_corners]): new_image = tf.image.resize_images( image, tf.stack([new_height, new_width]), method=method, align_corners=align_corners) image_shape = shape_utils.combined_static_and_dynamic_shape(image) result = [new_image] if masks is not None: num_instances = tf.shape(masks)[0] new_size = tf.stack([new_height, new_width]) def resize_masks_branch(): new_masks = tf.expand_dims(masks, 3) new_masks = tf.image.resize_nearest_neighbor( new_masks, new_size, align_corners=align_corners) new_masks = tf.squeeze(new_masks, axis=3) return new_masks def reshape_masks_branch(): # The shape function will be computed for both branches of the # condition, regardless of which branch is actually taken. Make sure # that we don't trigger an assertion in the shape function when trying # to reshape a non empty tensor into an empty one. new_masks = tf.reshape(masks, [-1, new_size[0], new_size[1]]) return new_masks masks = tf.cond(num_instances > 0, resize_masks_branch, reshape_masks_branch) result.append(masks) result.append(tf.stack([new_height, new_width, image_shape[2]])) return result def subtract_channel_mean(image, means=None): """Normalizes an image by subtracting a mean from each channel. Args: image: A 3D tensor of shape [height, width, channels] means: float list containing a mean for each channel Returns: normalized_images: a tensor of shape [height, width, channels] Raises: ValueError: if images is not a 4D tensor or if the number of means is not equal to the number of channels. """ with tf.name_scope('SubtractChannelMean', values=[image, means]): if len(image.get_shape()) != 3: raise ValueError('Input must be of size [height, width, channels]') if len(means) != image.get_shape()[-1]: raise ValueError('len(means) must match the number of channels') return image - [[means]] def one_hot_encoding(labels, num_classes=None): """One-hot encodes the multiclass labels. Example usage: labels = tf.constant([1, 4], dtype=tf.int32) one_hot = OneHotEncoding(labels, num_classes=5) one_hot.eval() # evaluates to [0, 1, 0, 0, 1] Args: labels: A tensor of shape [None] corresponding to the labels. num_classes: Number of classes in the dataset. Returns: onehot_labels: a tensor of shape [num_classes] corresponding to the one hot encoding of the labels. Raises: ValueError: if num_classes is not specified. """ with tf.name_scope('OneHotEncoding', values=[labels]): if num_classes is None: raise ValueError('num_classes must be specified') labels = tf.one_hot(labels, num_classes, 1, 0) return tf.reduce_max(labels, 0) def rgb_to_gray(image): """Converts a 3 channel RGB image to a 1 channel grayscale image. Args: image: Rank 3 float32 tensor containing 1 image -> [height, width, 3] with pixel values varying between [0, 1]. Returns: image: A single channel grayscale image -> [image, height, 1]. """ return _rgb_to_grayscale(image) def random_self_concat_image( image, boxes, labels, label_weights, label_confidences=None, multiclass_scores=None, concat_vertical_probability=0.1, concat_horizontal_probability=0.1, seed=None, preprocess_vars_cache=None): """Randomly concatenates the image with itself. This function randomly concatenates the image with itself; the random variables for vertical and horizontal concatenation are independent. Afterwards, we adjust the old bounding boxes, and add new bounding boxes for the new objects. Args: image: rank 3 float32 tensor containing 1 image -> [height, width, channels] with pixel values varying between [0, 1]. boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4]. Boxes are in normalized form meaning their coordinates vary between [0, 1]. Each row is in the form of [ymin, xmin, ymax, xmax]. labels: rank 1 int32 tensor containing the object classes. label_weights: rank 1 float32 containing the label weights. label_confidences: (optional) rank 1 float32 containing the label confidences. multiclass_scores: (optional) float32 tensor of shape [num_instances, num_classes] representing the score for each box for each class. concat_vertical_probability: (optional) a tf.float32 scalar denoting the probability of a vertical concatenation. concat_horizontal_probability: (optional) a tf.float32 scalar denoting the probability of a horizontal concatenation. seed: random seed. preprocess_vars_cache: PreprocessorCache object that records previously performed augmentations. Updated in-place. If this function is called multiple times with the same non-null cache, it will perform deterministically. Returns: image: Image shape will be [new_height, new_width, channels]. boxes: boxes which is the same rank as input boxes. Boxes are in normalized form. if label_confidences is not None also returns: maybe_concat_label_confidences: cropped label weights. if multiclass_scores is not None also returns: maybe_concat_multiclass_scores: cropped_multiclass_scores. """ concat_vertical = (tf.random_uniform([], seed=seed) < concat_vertical_probability) # Note the seed + 1 so we get some semblance of independence even with # fixed seeds. concat_horizontal = (tf.random_uniform([], seed=seed + 1 if seed else None) < concat_horizontal_probability) gen_func = lambda: (concat_vertical, concat_horizontal) params = _get_or_create_preprocess_rand_vars( gen_func, preprocessor_cache.PreprocessorCache.SELF_CONCAT_IMAGE, preprocess_vars_cache) concat_vertical, concat_horizontal = params def _concat_image(image, boxes, labels, label_weights, axis): """Concats the image to itself on `axis`.""" output_images = tf.concat([image, image], axis=axis) if axis == 0: # Concat vertically, so need to reduce the y coordinates. old_scaling = tf.to_float([0.5, 1.0, 0.5, 1.0]) new_translation = tf.to_float([0.5, 0.0, 0.5, 0.0]) elif axis == 1: old_scaling = tf.to_float([1.0, 0.5, 1.0, 0.5]) new_translation = tf.to_float([0.0, 0.5, 0.0, 0.5]) old_boxes = old_scaling * boxes new_boxes = old_boxes + new_translation all_boxes = tf.concat([old_boxes, new_boxes], axis=0) return [output_images, all_boxes, tf.tile(labels, [2]), tf.tile( label_weights, [2])] image, boxes, labels, label_weights = tf.cond( concat_vertical, lambda: _concat_image(image, boxes, labels, label_weights, axis=0), lambda: [image, boxes, labels, label_weights], strict=True) outputs = tf.cond( concat_horizontal, lambda: _concat_image(image, boxes, labels, label_weights, axis=1), lambda: [image, boxes, labels, label_weights], strict=True) if label_confidences is not None: label_confidences = tf.cond(concat_vertical, lambda: tf.tile(label_confidences, [2]), lambda: label_confidences) outputs.append(tf.cond(concat_horizontal, lambda: tf.tile(label_confidences, [2]), lambda: label_confidences)) if multiclass_scores is not None: multiclass_scores = tf.cond(concat_vertical, lambda: tf.tile(multiclass_scores, [2, 1]), lambda: multiclass_scores) outputs.append(tf.cond(concat_horizontal, lambda: tf.tile(multiclass_scores, [2, 1]), lambda: multiclass_scores)) return outputs def ssd_random_crop(image, boxes, labels, label_weights, label_confidences=None, multiclass_scores=None, masks=None, keypoints=None, min_object_covered=(0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0), aspect_ratio_range=((0.5, 2.0),) * 7, area_range=((0.1, 1.0),) * 7, overlap_thresh=(0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0), clip_boxes=(True,) * 7, random_coef=(0.15,) * 7, seed=None, preprocess_vars_cache=None): """Random crop preprocessing with default parameters as in SSD paper. Liu et al., SSD: Single shot multibox detector. For further information on random crop preprocessing refer to RandomCrop function above. Args: image: rank 3 float32 tensor contains 1 image -> [height, width, channels] with pixel values varying between [0, 1]. boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4]. Boxes are in normalized form meaning their coordinates vary between [0, 1]. Each row is in the form of [ymin, xmin, ymax, xmax]. labels: rank 1 int32 tensor containing the object classes. label_weights: rank 1 float32 tensor containing the weights. label_confidences: rank 1 float32 tensor containing the confidences. multiclass_scores: (optional) float32 tensor of shape [num_instances, num_classes] representing the score for each box for each class. masks: (optional) rank 3 float32 tensor with shape [num_instances, height, width] containing instance masks. The masks are of the same height, width as the input `image`. keypoints: (optional) rank 3 float32 tensor with shape [num_instances, num_keypoints, 2]. The keypoints are in y-x normalized coordinates. min_object_covered: the cropped image must cover at least this fraction of at least one of the input bounding boxes. aspect_ratio_range: allowed range for aspect ratio of cropped image. area_range: allowed range for area ratio between cropped image and the original image. overlap_thresh: minimum overlap thresh with new cropped image to keep the box. clip_boxes: whether to clip the boxes to the cropped image. random_coef: a random coefficient that defines the chance of getting the original image. If random_coef is 0, we will always get the cropped image, and if it is 1.0, we will always get the original image. seed: random seed. preprocess_vars_cache: PreprocessorCache object that records previously performed augmentations. Updated in-place. If this function is called multiple times with the same non-null cache, it will perform deterministically. Returns: image: image which is the same rank as input image. boxes: boxes which is the same rank as input boxes. Boxes are in normalized form. labels: new labels. If label_weights, multiclass_scores, masks, or keypoints is not None, the function also returns: label_weights: rank 1 float32 tensor with shape [num_instances]. multiclass_scores: rank 2 float32 tensor with shape [num_instances, num_classes] masks: rank 3 float32 tensor with shape [num_instances, height, width] containing instance masks. keypoints: rank 3 float32 tensor with shape [num_instances, num_keypoints, 2] """ def random_crop_selector(selected_result, index): """Applies random_crop_image to selected result. Args: selected_result: A tuple containing image, boxes, labels, keypoints (if not None), and masks (if not None). index: The index that was randomly selected. Returns: A tuple containing image, boxes, labels, keypoints (if not None), and masks (if not None). """ i = 3 image, boxes, labels = selected_result[:i] selected_label_weights = None selected_label_confidences = None selected_multiclass_scores = None selected_masks = None selected_keypoints = None if label_weights is not None: selected_label_weights = selected_result[i] i += 1 if label_confidences is not None: selected_label_confidences = selected_result[i] i += 1 if multiclass_scores is not None: selected_multiclass_scores = selected_result[i] i += 1 if masks is not None: selected_masks = selected_result[i] i += 1 if keypoints is not None: selected_keypoints = selected_result[i] return random_crop_image( image=image, boxes=boxes, labels=labels, label_weights=selected_label_weights, label_confidences=selected_label_confidences, multiclass_scores=selected_multiclass_scores, masks=selected_masks, keypoints=selected_keypoints, min_object_covered=min_object_covered[index], aspect_ratio_range=aspect_ratio_range[index], area_range=area_range[index], overlap_thresh=overlap_thresh[index], clip_boxes=clip_boxes[index], random_coef=random_coef[index], seed=seed, preprocess_vars_cache=preprocess_vars_cache) result = _apply_with_random_selector_tuples( tuple( t for t in (image, boxes, labels, label_weights, label_confidences, multiclass_scores, masks, keypoints) if t is not None), random_crop_selector, num_cases=len(min_object_covered), preprocess_vars_cache=preprocess_vars_cache, key=preprocessor_cache.PreprocessorCache.SSD_CROP_SELECTOR_ID) return result def ssd_random_crop_pad(image, boxes, labels, label_weights, label_confidences=None, multiclass_scores=None, min_object_covered=(0.1, 0.3, 0.5, 0.7, 0.9, 1.0), aspect_ratio_range=((0.5, 2.0),) * 6, area_range=((0.1, 1.0),) * 6, overlap_thresh=(0.1, 0.3, 0.5, 0.7, 0.9, 1.0), clip_boxes=(True,) * 6, random_coef=(0.15,) * 6, min_padded_size_ratio=((1.0, 1.0),) * 6, max_padded_size_ratio=((2.0, 2.0),) * 6, pad_color=(None,) * 6, seed=None, preprocess_vars_cache=None): """Random crop preprocessing with default parameters as in SSD paper. Liu et al., SSD: Single shot multibox detector. For further information on random crop preprocessing refer to RandomCrop function above. Args: image: rank 3 float32 tensor containing 1 image -> [height, width, channels] with pixel values varying between [0, 1]. boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4]. Boxes are in normalized form meaning their coordinates vary between [0, 1]. Each row is in the form of [ymin, xmin, ymax, xmax]. labels: rank 1 int32 tensor containing the object classes. label_weights: float32 tensor of shape [num_instances] representing the weight for each box. label_confidences: float32 tensor of shape [num_instances] representing the confidences for each box. multiclass_scores: (optional) float32 tensor of shape [num_instances, num_classes] representing the score for each box for each class. min_object_covered: the cropped image must cover at least this fraction of at least one of the input bounding boxes. aspect_ratio_range: allowed range for aspect ratio of cropped image. area_range: allowed range for area ratio between cropped image and the original image. overlap_thresh: minimum overlap thresh with new cropped image to keep the box. clip_boxes: whether to clip the boxes to the cropped image. random_coef: a random coefficient that defines the chance of getting the original image. If random_coef is 0, we will always get the cropped image, and if it is 1.0, we will always get the original image. min_padded_size_ratio: min ratio of padded image height and width to the input image's height and width. max_padded_size_ratio: max ratio of padded image height and width to the input image's height and width. pad_color: padding color. A rank 1 tensor of [3] with dtype=tf.float32. if set as None, it will be set to average color of the randomly cropped image. seed: random seed. preprocess_vars_cache: PreprocessorCache object that records previously performed augmentations. Updated in-place. If this function is called multiple times with the same non-null cache, it will perform deterministically. Returns: image: Image shape will be [new_height, new_width, channels]. boxes: boxes which is the same rank as input boxes. Boxes are in normalized form. new_labels: new labels. new_label_weights: new label weights. """ def random_crop_pad_selector(image_boxes_labels, index): """Random crop preprocessing helper.""" i = 3 image, boxes, labels = image_boxes_labels[:i] selected_label_weights = None selected_label_confidences = None selected_multiclass_scores = None if label_weights is not None: selected_label_weights = image_boxes_labels[i] i += 1 if label_confidences is not None: selected_label_confidences = image_boxes_labels[i] i += 1 if multiclass_scores is not None: selected_multiclass_scores = image_boxes_labels[i] return random_crop_pad_image( image, boxes, labels, label_weights=selected_label_weights, label_confidences=selected_label_confidences, multiclass_scores=selected_multiclass_scores, min_object_covered=min_object_covered[index], aspect_ratio_range=aspect_ratio_range[index], area_range=area_range[index], overlap_thresh=overlap_thresh[index], clip_boxes=clip_boxes[index], random_coef=random_coef[index], min_padded_size_ratio=min_padded_size_ratio[index], max_padded_size_ratio=max_padded_size_ratio[index], pad_color=pad_color[index], seed=seed, preprocess_vars_cache=preprocess_vars_cache) return _apply_with_random_selector_tuples( tuple(t for t in (image, boxes, labels, label_weights, label_confidences, multiclass_scores) if t is not None), random_crop_pad_selector, num_cases=len(min_object_covered), preprocess_vars_cache=preprocess_vars_cache, key=preprocessor_cache.PreprocessorCache.SSD_CROP_PAD_SELECTOR_ID) def ssd_random_crop_fixed_aspect_ratio( image, boxes, labels, label_weights, label_confidences=None, multiclass_scores=None, masks=None, keypoints=None, min_object_covered=(0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0), aspect_ratio=1.0, area_range=((0.1, 1.0),) * 7, overlap_thresh=(0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0), clip_boxes=(True,) * 7, random_coef=(0.15,) * 7, seed=None, preprocess_vars_cache=None): """Random crop preprocessing with default parameters as in SSD paper. Liu et al., SSD: Single shot multibox detector. For further information on random crop preprocessing refer to RandomCrop function above. The only difference is that the aspect ratio of the crops are fixed. Args: image: rank 3 float32 tensor contains 1 image -> [height, width, channels] with pixel values varying between [0, 1]. boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4]. Boxes are in normalized form meaning their coordinates vary between [0, 1]. Each row is in the form of [ymin, xmin, ymax, xmax]. labels: rank 1 int32 tensor containing the object classes. label_weights: float32 tensor of shape [num_instances] representing the weight for each box. label_confidences: (optional) float32 tensor of shape [num_instances] representing the confidences for each box. multiclass_scores: (optional) float32 tensor of shape [num_instances, num_classes] representing the score for each box for each class. masks: (optional) rank 3 float32 tensor with shape [num_instances, height, width] containing instance masks. The masks are of the same height, width as the input `image`. keypoints: (optional) rank 3 float32 tensor with shape [num_instances, num_keypoints, 2]. The keypoints are in y-x normalized coordinates. min_object_covered: the cropped image must cover at least this fraction of at least one of the input bounding boxes. aspect_ratio: aspect ratio of the cropped image. area_range: allowed range for area ratio between cropped image and the original image. overlap_thresh: minimum overlap thresh with new cropped image to keep the box. clip_boxes: whether to clip the boxes to the cropped image. random_coef: a random coefficient that defines the chance of getting the original image. If random_coef is 0, we will always get the cropped image, and if it is 1.0, we will always get the original image. seed: random seed. preprocess_vars_cache: PreprocessorCache object that records previously performed augmentations. Updated in-place. If this function is called multiple times with the same non-null cache, it will perform deterministically. Returns: image: image which is the same rank as input image. boxes: boxes which is the same rank as input boxes. Boxes are in normalized form. labels: new labels. If multiclass_scores, masks, or keypoints is not None, the function also returns: multiclass_scores: rank 2 float32 tensor with shape [num_instances, num_classes] masks: rank 3 float32 tensor with shape [num_instances, height, width] containing instance masks. keypoints: rank 3 float32 tensor with shape [num_instances, num_keypoints, 2] """ aspect_ratio_range = ((aspect_ratio, aspect_ratio),) * len(area_range) crop_result = ssd_random_crop( image, boxes, labels, label_weights=label_weights, label_confidences=label_confidences, multiclass_scores=multiclass_scores, masks=masks, keypoints=keypoints, min_object_covered=min_object_covered, aspect_ratio_range=aspect_ratio_range, area_range=area_range, overlap_thresh=overlap_thresh, clip_boxes=clip_boxes, random_coef=random_coef, seed=seed, preprocess_vars_cache=preprocess_vars_cache) i = 3 new_image, new_boxes, new_labels = crop_result[:i] new_label_weights = None new_label_confidences = None new_multiclass_scores = None new_masks = None new_keypoints = None if label_weights is not None: new_label_weights = crop_result[i] i += 1 if label_confidences is not None: new_label_confidences = crop_result[i] i += 1 if multiclass_scores is not None: new_multiclass_scores = crop_result[i] i += 1 if masks is not None: new_masks = crop_result[i] i += 1 if keypoints is not None: new_keypoints = crop_result[i] result = random_crop_to_aspect_ratio( new_image, new_boxes, new_labels, label_weights=new_label_weights, label_confidences=new_label_confidences, multiclass_scores=new_multiclass_scores, masks=new_masks, keypoints=new_keypoints, aspect_ratio=aspect_ratio, clip_boxes=clip_boxes, seed=seed, preprocess_vars_cache=preprocess_vars_cache) return result def ssd_random_crop_pad_fixed_aspect_ratio( image, boxes, labels, label_weights, label_confidences=None, multiclass_scores=None, masks=None, keypoints=None, min_object_covered=(0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0), aspect_ratio=1.0, aspect_ratio_range=((0.5, 2.0),) * 7, area_range=((0.1, 1.0),) * 7, overlap_thresh=(0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0), clip_boxes=(True,) * 7, random_coef=(0.15,) * 7, min_padded_size_ratio=(1.0, 1.0), max_padded_size_ratio=(2.0, 2.0), seed=None, preprocess_vars_cache=None): """Random crop and pad preprocessing with default parameters as in SSD paper. Liu et al., SSD: Single shot multibox detector. For further information on random crop preprocessing refer to RandomCrop function above. The only difference is that after the initial crop, images are zero-padded to a fixed aspect ratio instead of being resized to that aspect ratio. Args: image: rank 3 float32 tensor contains 1 image -> [height, width, channels] with pixel values varying between [0, 1]. boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4]. Boxes are in normalized form meaning their coordinates vary between [0, 1]. Each row is in the form of [ymin, xmin, ymax, xmax]. labels: rank 1 int32 tensor containing the object classes. label_weights: float32 tensor of shape [num_instances] representing the weight for each box. label_confidences: (optional) float32 tensor of shape [num_instances] representing the confidence for each box. multiclass_scores: (optional) float32 tensor of shape [num_instances, num_classes] representing the score for each box for each class. masks: (optional) rank 3 float32 tensor with shape [num_instances, height, width] containing instance masks. The masks are of the same height, width as the input `image`. keypoints: (optional) rank 3 float32 tensor with shape [num_instances, num_keypoints, 2]. The keypoints are in y-x normalized coordinates. min_object_covered: the cropped image must cover at least this fraction of at least one of the input bounding boxes. aspect_ratio: the final aspect ratio to pad to. aspect_ratio_range: allowed range for aspect ratio of cropped image. area_range: allowed range for area ratio between cropped image and the original image. overlap_thresh: minimum overlap thresh with new cropped image to keep the box. clip_boxes: whether to clip the boxes to the cropped image. random_coef: a random coefficient that defines the chance of getting the original image. If random_coef is 0, we will always get the cropped image, and if it is 1.0, we will always get the original image. min_padded_size_ratio: min ratio of padded image height and width to the input image's height and width. max_padded_size_ratio: max ratio of padded image height and width to the input image's height and width. seed: random seed. preprocess_vars_cache: PreprocessorCache object that records previously performed augmentations. Updated in-place. If this function is called multiple times with the same non-null cache, it will perform deterministically. Returns: image: image which is the same rank as input image. boxes: boxes which is the same rank as input boxes. Boxes are in normalized form. labels: new labels. If multiclass_scores, masks, or keypoints is not None, the function also returns: multiclass_scores: rank 2 with shape [num_instances, num_classes] masks: rank 3 float32 tensor with shape [num_instances, height, width] containing instance masks. keypoints: rank 3 float32 tensor with shape [num_instances, num_keypoints, 2] """ crop_result = ssd_random_crop( image, boxes, labels, label_weights=label_weights, label_confidences=label_confidences, multiclass_scores=multiclass_scores, masks=masks, keypoints=keypoints, min_object_covered=min_object_covered, aspect_ratio_range=aspect_ratio_range, area_range=area_range, overlap_thresh=overlap_thresh, clip_boxes=clip_boxes, random_coef=random_coef, seed=seed, preprocess_vars_cache=preprocess_vars_cache) i = 3 new_image, new_boxes, new_labels = crop_result[:i] new_label_weights = None new_label_confidences = None new_multiclass_scores = None new_masks = None new_keypoints = None if label_weights is not None: new_label_weights = crop_result[i] i += 1 if label_confidences is not None: new_label_confidences = crop_result[i] i += 1 if multiclass_scores is not None: new_multiclass_scores = crop_result[i] i += 1 if masks is not None: new_masks = crop_result[i] i += 1 if keypoints is not None: new_keypoints = crop_result[i] result = random_pad_to_aspect_ratio( new_image, new_boxes, masks=new_masks, keypoints=new_keypoints, aspect_ratio=aspect_ratio, min_padded_size_ratio=min_padded_size_ratio, max_padded_size_ratio=max_padded_size_ratio, seed=seed, preprocess_vars_cache=preprocess_vars_cache) result = list(result) i = 3 result.insert(2, new_labels) if new_label_weights is not None: result.insert(i, new_label_weights) i += 1 if new_label_confidences is not None: result.insert(i, new_label_confidences) i += 1 if multiclass_scores is not None: result.insert(i, new_multiclass_scores) result = tuple(result) return result def convert_class_logits_to_softmax(multiclass_scores, temperature=1.0): """Converts multiclass logits to softmax scores after applying temperature. Args: multiclass_scores: float32 tensor of shape [num_instances, num_classes] representing the score for each box for each class. temperature: Scale factor to use prior to applying softmax. Larger temperatures give more uniform distruibutions after softmax. Returns: multiclass_scores: float32 tensor of shape [num_instances, num_classes] with scaling and softmax applied. """ # Multiclass scores must be stored as logits. Apply temp and softmax. multiclass_scores_scaled = tf.divide( multiclass_scores, temperature, name='scale_logits') multiclass_scores = tf.nn.softmax(multiclass_scores_scaled, name='softmax') return multiclass_scores def get_default_func_arg_map(include_label_weights=True, include_label_confidences=False, include_multiclass_scores=False, include_instance_masks=False, include_keypoints=False): """Returns the default mapping from a preprocessor function to its args. Args: include_label_weights: If True, preprocessing functions will modify the label weights, too. include_label_confidences: If True, preprocessing functions will modify the label confidences, too. include_multiclass_scores: If True, preprocessing functions will modify the multiclass scores, too. include_instance_masks: If True, preprocessing functions will modify the instance masks, too. include_keypoints: If True, preprocessing functions will modify the keypoints, too. Returns: A map from preprocessing functions to the arguments they receive. """ groundtruth_label_weights = None if include_label_weights: groundtruth_label_weights = ( fields.InputDataFields.groundtruth_weights) groundtruth_label_confidences = None if include_label_confidences: groundtruth_label_confidences = ( fields.InputDataFields.groundtruth_confidences) multiclass_scores = None if include_multiclass_scores: multiclass_scores = (fields.InputDataFields.multiclass_scores) groundtruth_instance_masks = None if include_instance_masks: groundtruth_instance_masks = ( fields.InputDataFields.groundtruth_instance_masks) groundtruth_keypoints = None if include_keypoints: groundtruth_keypoints = fields.InputDataFields.groundtruth_keypoints prep_func_arg_map = { normalize_image: (fields.InputDataFields.image,), random_horizontal_flip: ( fields.InputDataFields.image, fields.InputDataFields.groundtruth_boxes, groundtruth_instance_masks, groundtruth_keypoints, ), random_vertical_flip: ( fields.InputDataFields.image, fields.InputDataFields.groundtruth_boxes, groundtruth_instance_masks, groundtruth_keypoints, ), random_rotation90: ( fields.InputDataFields.image, fields.InputDataFields.groundtruth_boxes, groundtruth_instance_masks, groundtruth_keypoints, ), random_pixel_value_scale: (fields.InputDataFields.image,), random_image_scale: ( fields.InputDataFields.image, groundtruth_instance_masks, ), random_rgb_to_gray: (fields.InputDataFields.image,), random_adjust_brightness: (fields.InputDataFields.image,), random_adjust_contrast: (fields.InputDataFields.image,), random_adjust_hue: (fields.InputDataFields.image,), random_adjust_saturation: (fields.InputDataFields.image,), random_distort_color: (fields.InputDataFields.image,), random_jitter_boxes: (fields.InputDataFields.groundtruth_boxes,), random_crop_image: (fields.InputDataFields.image, fields.InputDataFields.groundtruth_boxes, fields.InputDataFields.groundtruth_classes, groundtruth_label_weights, groundtruth_label_confidences, multiclass_scores, groundtruth_instance_masks, groundtruth_keypoints), random_pad_image: (fields.InputDataFields.image, fields.InputDataFields.groundtruth_boxes, groundtruth_keypoints), random_absolute_pad_image: (fields.InputDataFields.image, fields.InputDataFields.groundtruth_boxes), random_crop_pad_image: (fields.InputDataFields.image, fields.InputDataFields.groundtruth_boxes, fields.InputDataFields.groundtruth_classes, groundtruth_label_weights, groundtruth_label_confidences, multiclass_scores), random_crop_to_aspect_ratio: ( fields.InputDataFields.image, fields.InputDataFields.groundtruth_boxes, fields.InputDataFields.groundtruth_classes, groundtruth_label_weights, groundtruth_label_confidences, multiclass_scores, groundtruth_instance_masks, groundtruth_keypoints, ), random_pad_to_aspect_ratio: ( fields.InputDataFields.image, fields.InputDataFields.groundtruth_boxes, groundtruth_instance_masks, groundtruth_keypoints, ), random_black_patches: (fields.InputDataFields.image,), retain_boxes_above_threshold: ( fields.InputDataFields.groundtruth_boxes, fields.InputDataFields.groundtruth_classes, groundtruth_label_weights, groundtruth_label_confidences, multiclass_scores, groundtruth_instance_masks, groundtruth_keypoints, ), image_to_float: (fields.InputDataFields.image,), random_resize_method: (fields.InputDataFields.image,), resize_to_range: ( fields.InputDataFields.image, groundtruth_instance_masks, ), resize_to_min_dimension: ( fields.InputDataFields.image, groundtruth_instance_masks, ), scale_boxes_to_pixel_coordinates: ( fields.InputDataFields.image, fields.InputDataFields.groundtruth_boxes, groundtruth_keypoints, ), resize_image: ( fields.InputDataFields.image, groundtruth_instance_masks, ), subtract_channel_mean: (fields.InputDataFields.image,), one_hot_encoding: (fields.InputDataFields.groundtruth_image_classes,), rgb_to_gray: (fields.InputDataFields.image,), random_self_concat_image: (fields.InputDataFields.image, fields.InputDataFields.groundtruth_boxes, fields.InputDataFields.groundtruth_classes, groundtruth_label_weights, groundtruth_label_confidences, multiclass_scores), ssd_random_crop: (fields.InputDataFields.image, fields.InputDataFields.groundtruth_boxes, fields.InputDataFields.groundtruth_classes, groundtruth_label_weights, groundtruth_label_confidences, multiclass_scores, groundtruth_instance_masks, groundtruth_keypoints), ssd_random_crop_pad: (fields.InputDataFields.image, fields.InputDataFields.groundtruth_boxes, fields.InputDataFields.groundtruth_classes, groundtruth_label_weights, groundtruth_label_confidences, multiclass_scores), ssd_random_crop_fixed_aspect_ratio: ( fields.InputDataFields.image, fields.InputDataFields.groundtruth_boxes, fields.InputDataFields.groundtruth_classes, groundtruth_label_weights, groundtruth_label_confidences, multiclass_scores, groundtruth_instance_masks, groundtruth_keypoints), ssd_random_crop_pad_fixed_aspect_ratio: ( fields.InputDataFields.image, fields.InputDataFields.groundtruth_boxes, fields.InputDataFields.groundtruth_classes, groundtruth_label_weights, groundtruth_label_confidences, multiclass_scores, groundtruth_instance_masks, groundtruth_keypoints, ), convert_class_logits_to_softmax: (multiclass_scores,), } return prep_func_arg_map def preprocess(tensor_dict, preprocess_options, func_arg_map=None, preprocess_vars_cache=None): """Preprocess images and bounding boxes. Various types of preprocessing (to be implemented) based on the preprocess_options dictionary e.g. "crop image" (affects image and possibly boxes), "white balance image" (affects only image), etc. If self._options is None, no preprocessing is done. Args: tensor_dict: dictionary that contains images, boxes, and can contain other things as well. images-> rank 4 float32 tensor contains 1 image -> [1, height, width, 3]. with pixel values varying between [0, 1] boxes-> rank 2 float32 tensor containing the bounding boxes -> [N, 4]. Boxes are in normalized form meaning their coordinates vary between [0, 1]. Each row is in the form of [ymin, xmin, ymax, xmax]. preprocess_options: It is a list of tuples, where each tuple contains a function and a dictionary that contains arguments and their values. func_arg_map: mapping from preprocessing functions to arguments that they expect to receive and return. preprocess_vars_cache: PreprocessorCache object that records previously performed augmentations. Updated in-place. If this function is called multiple times with the same non-null cache, it will perform deterministically. Returns: tensor_dict: which contains the preprocessed images, bounding boxes, etc. Raises: ValueError: (a) If the functions passed to Preprocess are not in func_arg_map. (b) If the arguments that a function needs do not exist in tensor_dict. (c) If image in tensor_dict is not rank 4 """ if func_arg_map is None: func_arg_map = get_default_func_arg_map() # changes the images to image (rank 4 to rank 3) since the functions # receive rank 3 tensor for image if fields.InputDataFields.image in tensor_dict: images = tensor_dict[fields.InputDataFields.image] if len(images.get_shape()) != 4: raise ValueError('images in tensor_dict should be rank 4') image = tf.squeeze(images, axis=0) tensor_dict[fields.InputDataFields.image] = image # Preprocess inputs based on preprocess_options for option in preprocess_options: func, params = option if func not in func_arg_map: raise ValueError('The function %s does not exist in func_arg_map' % (func.__name__)) arg_names = func_arg_map[func] for a in arg_names: if a is not None and a not in tensor_dict: raise ValueError('The function %s requires argument %s' % (func.__name__, a)) def get_arg(key): return tensor_dict[key] if key is not None else None args = [get_arg(a) for a in arg_names] if (preprocess_vars_cache is not None and 'preprocess_vars_cache' in inspect.getargspec(func).args): params['preprocess_vars_cache'] = preprocess_vars_cache results = func(*args, **params) if not isinstance(results, (list, tuple)): results = (results,) # Removes None args since the return values will not contain those. arg_names = [arg_name for arg_name in arg_names if arg_name is not None] for res, arg_name in zip(results, arg_names): tensor_dict[arg_name] = res # changes the image to images (rank 3 to rank 4) to be compatible to what # we received in the first place if fields.InputDataFields.image in tensor_dict: image = tensor_dict[fields.InputDataFields.image] images = tf.expand_dims(image, 0) tensor_dict[fields.InputDataFields.image] = images return tensor_dict