EVOLUTION-MANAGER

Edit File: transform.py

""" Copyright 2017-2018 Fizyr (https://fizyr.com) 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. """ import numpy as np DEFAULT_PRNG = np.random def colvec(*args): """ Create a numpy array representing a column vector. """ return np.array([args]).T def transform_aabb(transform, aabb): """ Apply a transformation to an axis aligned bounding box. The result is a new AABB in the same coordinate system as the original AABB. The new AABB contains all corner points of the original AABB after applying the given transformation. Args transform: The transformation to apply. x1: The minimum x value of the AABB. y1: The minimum y value of the AABB. x2: The maximum x value of the AABB. y2: The maximum y value of the AABB. Returns The new AABB as tuple (x1, y1, x2, y2) """ x1, y1, x2, y2 = aabb # Transform all 4 corners of the AABB. points = transform.dot([ [x1, x2, x1, x2], [y1, y2, y2, y1], [1, 1, 1, 1 ], ]) # Extract the min and max corners again. min_corner = points.min(axis=1) max_corner = points.max(axis=1) return [min_corner[0], min_corner[1], max_corner[0], max_corner[1]] def _random_vector(min, max, prng=DEFAULT_PRNG): """ Construct a random vector between min and max. Args min: the minimum value for each component max: the maximum value for each component """ min = np.array(min) max = np.array(max) assert min.shape == max.shape assert len(min.shape) == 1 return prng.uniform(min, max) def rotation(angle): """ Construct a homogeneous 2D rotation matrix. Args angle: the angle in radians Returns the rotation matrix as 3 by 3 numpy array """ return np.array([ [np.cos(angle), -np.sin(angle), 0], [np.sin(angle), np.cos(angle), 0], [0, 0, 1] ]) def random_rotation(min, max, prng=DEFAULT_PRNG): """ Construct a random rotation between -max and max. Args min: a scalar for the minimum absolute angle in radians max: a scalar for the maximum absolute angle in radians prng: the pseudo-random number generator to use. Returns a homogeneous 3 by 3 rotation matrix """ return rotation(prng.uniform(min, max)) def translation(translation): """ Construct a homogeneous 2D translation matrix. # Arguments translation: the translation 2D vector # Returns the translation matrix as 3 by 3 numpy array """ return np.array([ [1, 0, translation[0]], [0, 1, translation[1]], [0, 0, 1] ]) def random_translation(min, max, prng=DEFAULT_PRNG): """ Construct a random 2D translation between min and max. Args min: a 2D vector with the minimum translation for each dimension max: a 2D vector with the maximum translation for each dimension prng: the pseudo-random number generator to use. Returns a homogeneous 3 by 3 translation matrix """ return translation(_random_vector(min, max, prng)) def shear(angle): """ Construct a homogeneous 2D shear matrix. Args angle: the shear angle in radians Returns the shear matrix as 3 by 3 numpy array """ return np.array([ [1, -np.sin(angle), 0], [0, np.cos(angle), 0], [0, 0, 1] ]) def random_shear(min, max, prng=DEFAULT_PRNG): """ Construct a random 2D shear matrix with shear angle between -max and max. Args min: the minimum shear angle in radians. max: the maximum shear angle in radians. prng: the pseudo-random number generator to use. Returns a homogeneous 3 by 3 shear matrix """ return shear(prng.uniform(min, max)) def scaling(factor): """ Construct a homogeneous 2D scaling matrix. Args factor: a 2D vector for X and Y scaling Returns the zoom matrix as 3 by 3 numpy array """ return np.array([ [factor[0], 0, 0], [0, factor[1], 0], [0, 0, 1] ]) def random_scaling(min, max, prng=DEFAULT_PRNG): """ Construct a random 2D scale matrix between -max and max. Args min: a 2D vector containing the minimum scaling factor for X and Y. min: a 2D vector containing The maximum scaling factor for X and Y. prng: the pseudo-random number generator to use. Returns a homogeneous 3 by 3 scaling matrix """ return scaling(_random_vector(min, max, prng)) def random_flip(flip_x_chance, flip_y_chance, prng=DEFAULT_PRNG): """ Construct a transformation randomly containing X/Y flips (or not). Args flip_x_chance: The chance that the result will contain a flip along the X axis. flip_y_chance: The chance that the result will contain a flip along the Y axis. prng: The pseudo-random number generator to use. Returns a homogeneous 3 by 3 transformation matrix """ flip_x = prng.uniform(0, 1) < flip_x_chance flip_y = prng.uniform(0, 1) < flip_y_chance # 1 - 2 * bool gives 1 for False and -1 for True. return scaling((1 - 2 * flip_x, 1 - 2 * flip_y)) def change_transform_origin(transform, center): """ Create a new transform representing the same transformation, only with the origin of the linear part changed. Args transform: the transformation matrix center: the new origin of the transformation Returns translate(center) * transform * translate(-center) """ center = np.array(center) return np.linalg.multi_dot([translation(center), transform, translation(-center)]) def random_transform( min_rotation=0, max_rotation=0, min_translation=(0, 0), max_translation=(0, 0), min_shear=0, max_shear=0, min_scaling=(1, 1), max_scaling=(1, 1), flip_x_chance=0, flip_y_chance=0, prng=DEFAULT_PRNG ): """ Create a random transformation. The transformation consists of the following operations in this order (from left to right): * rotation * translation * shear * scaling * flip x (if applied) * flip y (if applied) Note that by default, the data generators in `keras_retinanet.preprocessing.generators` interpret the translation as factor of the image size. So an X translation of 0.1 would translate the image by 10% of it's width. Set `relative_translation` to `False` in the `TransformParameters` of a data generator to have it interpret the translation directly as pixel distances instead. Args min_rotation: The minimum rotation in radians for the transform as scalar. max_rotation: The maximum rotation in radians for the transform as scalar. min_translation: The minimum translation for the transform as 2D column vector. max_translation: The maximum translation for the transform as 2D column vector. min_shear: The minimum shear angle for the transform in radians. max_shear: The maximum shear angle for the transform in radians. min_scaling: The minimum scaling for the transform as 2D column vector. max_scaling: The maximum scaling for the transform as 2D column vector. flip_x_chance: The chance (0 to 1) that a transform will contain a flip along X direction. flip_y_chance: The chance (0 to 1) that a transform will contain a flip along Y direction. prng: The pseudo-random number generator to use. """ return np.linalg.multi_dot([ random_rotation(min_rotation, max_rotation, prng), random_translation(min_translation, max_translation, prng), random_shear(min_shear, max_shear, prng), random_scaling(min_scaling, max_scaling, prng), random_flip(flip_x_chance, flip_y_chance, prng) ]) def random_transform_generator(prng=None, **kwargs): """ Create a random transform generator. Uses a dedicated, newly created, properly seeded PRNG by default instead of the global DEFAULT_PRNG. The transformation consists of the following operations in this order (from left to right): * rotation * translation * shear * scaling * flip x (if applied) * flip y (if applied) Note that by default, the data generators in `keras_retinanet.preprocessing.generators` interpret the translation as factor of the image size. So an X translation of 0.1 would translate the image by 10% of it's width. Set `relative_translation` to `False` in the `TransformParameters` of a data generator to have it interpret the translation directly as pixel distances instead. Args min_rotation: The minimum rotation in radians for the transform as scalar. max_rotation: The maximum rotation in radians for the transform as scalar. min_translation: The minimum translation for the transform as 2D column vector. max_translation: The maximum translation for the transform as 2D column vector. min_shear: The minimum shear angle for the transform in radians. max_shear: The maximum shear angle for the transform in radians. min_scaling: The minimum scaling for the transform as 2D column vector. max_scaling: The maximum scaling for the transform as 2D column vector. flip_x_chance: The chance (0 to 1) that a transform will contain a flip along X direction. flip_y_chance: The chance (0 to 1) that a transform will contain a flip along Y direction. prng: The pseudo-random number generator to use. """ if prng is None: # RandomState automatically seeds using the best available method. prng = np.random.RandomState() while True: yield random_transform(prng=prng, **kwargs)