Source code for mindarmour.natural_robustness.transform.image.transformation

# Copyright 2022 Huawei Technologies Co., Ltd
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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"""
Image transformation.
"""
import math
import numpy as np
import cv2

from mindarmour.natural_robustness.transform.image.natural_perturb import _NaturalPerturb
from mindarmour.utils._check_param import check_param_multi_types, check_param_type, check_value_non_negative
from mindarmour.utils.logger import LogUtil

LOGGER = LogUtil.get_instance()
TAG = 'Image Transformation'


[文档]class Translate(_NaturalPerturb): """ Translate an image. Args: x_bias (Union[int, float]): X-direction translation, x = x + x_bias*image_width. Suggested value range in [-0.1, 0.1]. y_bias (Union[int, float]): Y-direction translation, y = y + y_bias*image_length. Suggested value range in [-0.1, 0.1]. auto_param (bool): Auto selected parameters. Selected parameters will preserve semantics of image. Default: False. Examples: >>> img = cv2.imread('1.png') >>> img = np.array(img) >>> x_bias = 0.1 >>> y_bias = 0.1 >>> trans = Translate(x_bias, y_bias) >>> dst = trans(img) """ def __init__(self, x_bias=0, y_bias=0, auto_param=False): super(Translate, self).__init__() self.x_bias = check_param_multi_types('x_bias', x_bias, [int, float]) self.y_bias = check_param_multi_types('y_bias', y_bias, [int, float]) if auto_param: self.x_bias = np.random.uniform(-0.1, 0.1) self.y_bias = np.random.uniform(-0.1, 0.1) def __call__(self, image): """ Transform the image. Args: image (numpy.ndarray): Original image to be transformed. Returns: numpy.ndarray, transformed image. """ ori_dtype = image.dtype _, chw, normalized, gray3dim, image = self._check(image) h, w = image.shape[:2] matrix = np.array([[1, 0, self.x_bias * w], [0, 1, self.y_bias * h]], dtype=np.float) new_img = cv2.warpAffine(image, matrix, (w, h)) new_img = self._original_format(new_img, chw, normalized, gray3dim) return new_img.astype(ori_dtype)
[文档]class Scale(_NaturalPerturb): """ Scale an image in the middle. Args: factor_x (Union[float, int]): Rescale in X-direction, x=factor_x*x. Suggested value range in [0.5, 1] and abs(factor_y - factor_x) < 0.5. factor_y (Union[float, int]): Rescale in Y-direction, y=factor_y*y. Suggested value range in [0.5, 1] and abs(factor_y - factor_x) < 0.5. auto_param (bool): Auto selected parameters. Selected parameters will preserve semantics of image. Default: False. Examples: >>> img = cv2.imread('1.png') >>> img = np.array(img) >>> factor_x = 0.7 >>> factor_y = 0.6 >>> trans = Scale(factor_x, factor_y) >>> dst = trans(img) """ def __init__(self, factor_x=1, factor_y=1, auto_param=False): super(Scale, self).__init__() self.factor_x = check_param_multi_types('factor_x', factor_x, [int, float]) self.factor_y = check_param_multi_types('factor_y', factor_y, [int, float]) auto_param = check_param_type('auto_param', auto_param, bool) if auto_param: self.factor_x = np.random.uniform(0.5, 1) self.factor_y = np.random.uniform(0.5, 1) def __call__(self, image): """ Transform the image. Args: image (numpy.ndarray): Original image to be transformed. Returns: numpy.ndarray, transformed image. """ ori_dtype = image.dtype _, chw, normalized, gray3dim, image = self._check(image) h, w = image.shape[:2] matrix = np.array([[self.factor_x, 0, 0], [0, self.factor_y, 0]], dtype=np.float) new_img = cv2.warpAffine(image, matrix, (w, h)) new_img = self._original_format(new_img, chw, normalized, gray3dim) return new_img.astype(ori_dtype)
[文档]class Shear(_NaturalPerturb): """ Shear an image, the mapping between sheared image and origin image is (new_x, new_y) = (x+factor_x*y, factor_y*x+y). Then the sheared image will be rescaled to fit original size. Args: factor (Union[float, int]): Shear rate in shear direction. Suggested value range in [0.05, 0.5]. direction (str): Direction of deformation. Optional value is 'vertical' or 'horizontal'. auto_param (bool): Auto selected parameters. Selected parameters will preserve semantics of image. Default: False. Examples: >>> img = cv2.imread('1.png') >>> img = np.array(img) >>> factor = 0.2 >>> trans = Shear(factor, direction='horizontal') >>> dst = trans(img) """ def __init__(self, factor=0.2, direction='horizontal', auto_param=False): super(Shear, self).__init__() self.factor = check_param_multi_types('factor', factor, [int, float]) if direction not in ['horizontal', 'vertical']: msg = "'direction must be in ['horizontal', 'vertical'], but got {}".format(direction) raise ValueError(msg) self.direction = direction auto_param = check_param_type('auto_params', auto_param, bool) if auto_param: self.factor = np.random.uniform(0.05, 0.5) self.direction = np.random.choice(['horizontal', 'vertical']) def __call__(self, image): """ Transform the image. Args: image (numpy.ndarray): Original image to be transformed. Returns: numpy.ndarray, transformed image. """ ori_dtype = image.dtype _, chw, normalized, gray3dim, image = self._check(image) h, w = image.shape[:2] if self.direction == 'horizontal': matrix = np.array([[1, self.factor, 0], [0, 1, 0]], dtype=np.float) nw = int(w + self.factor * h) nh = h else: matrix = np.array([[1, 0, 0], [self.factor, 1, 0]], dtype=np.float) nw = w nh = int(h + self.factor * w) new_img = cv2.warpAffine(image, matrix, (nw, nh)) new_img = cv2.resize(new_img, (w, h)) new_img = self._original_format(new_img, chw, normalized, gray3dim) return new_img.astype(ori_dtype)
[文档]class Rotate(_NaturalPerturb): """ Rotate an image of counter clockwise around its center. Args: angle (Union[float, int]): Degrees of counter clockwise. Suggested value range in [-60, 60]. auto_param (bool): Auto selected parameters. Selected parameters will preserve semantics of image. Default: False. Examples: >>> img = cv2.imread('1.png') >>> img = np.array(img) >>> angle = 20 >>> trans = Rotate(angle) >>> dst = trans(img) """ def __init__(self, angle=20, auto_param=False): super(Rotate, self).__init__() self.angle = check_param_multi_types('angle', angle, [int, float]) auto_param = check_param_type('auto_param', auto_param, bool) if auto_param: self.angle = np.random.uniform(0, 360) def __call__(self, image): """ Transform the image. Args: image (numpy.ndarray): Original image to be transformed. Returns: numpy.ndarray, rotated image. """ ori_dtype = image.dtype _, chw, normalized, gray3dim, image = self._check(image) h, w = image.shape[:2] center = (w // 2, h // 2) matrix = cv2.getRotationMatrix2D(center, -self.angle, 1.0) cos = np.abs(matrix[0, 0]) sin = np.abs(matrix[0, 1]) # Calculate new edge after rotated nw = int((h * sin) + (w * cos)) nh = int((h * cos) + (w * sin)) # Adjust move distance of rotate matrix. matrix[0, 2] += (nw / 2) - center[0] matrix[1, 2] += (nh / 2) - center[1] rotate = cv2.warpAffine(image, matrix, (nw, nh)) rotate = cv2.resize(rotate, (w, h)) new_img = self._original_format(rotate, chw, normalized, gray3dim) return new_img.astype(ori_dtype)
[文档]class Perspective(_NaturalPerturb): """ Perform perspective transformation on a given picture. Args: ori_pos (list): Four points in original image. dst_pos (list): The point coordinates of the 4 points in ori_pos after perspective transformation. auto_param (bool): Auto selected parameters. Selected parameters will preserve semantics of image. Default: False. Examples: >>> img = cv2.imread('1.png') >>> img = np.array(img) >>> ori_pos = [[0, 0], [0, 800], [800, 0], [800, 800]] >>> dst_pos = [[50, 0], [0, 800], [780, 0], [800, 800]] >>> trans = Perspective(ori_pos, dst_pos) >>> dst = trans(img) """ def __init__(self, ori_pos, dst_pos, auto_param=False): super(Perspective, self).__init__() ori_pos = check_param_type('ori_pos', ori_pos, list) dst_pos = check_param_type('dst_pos', dst_pos, list) self.ori_pos = np.float32(ori_pos) self.dst_pos = np.float32(dst_pos) self.auto_param = check_param_type('auto_param', auto_param, bool) def _set_auto_param(self, w, h): self.ori_pos = [[h * 0.25, w * 0.25], [h * 0.25, w * 0.75], [h * 0.75, w * 0.25], [h * 0.75, w * 0.75]] self.dst_pos = [[np.random.uniform(0, h * 0.5), np.random.uniform(0, w * 0.5)], [np.random.uniform(0, h * 0.5), np.random.uniform(w * 0.5, w)], [np.random.uniform(h * 0.5, h), np.random.uniform(0, w * 0.5)], [np.random.uniform(h * 0.5, h), np.random.uniform(w * 0.5, w)]] self.ori_pos = np.float32(self.ori_pos) self.dst_pos = np.float32(self.dst_pos) def __call__(self, image): """ Transform the image. Args: image (numpy.ndarray): Original image to be transformed. Returns: numpy.ndarray, transformed image. """ ori_dtype = image.dtype _, chw, normalized, gray3dim, image = self._check(image) h, w = image.shape[:2] if self.auto_param: self._set_auto_param(w, h) matrix = cv2.getPerspectiveTransform(self.ori_pos, self.dst_pos) new_img = cv2.warpPerspective(image, matrix, (w, h)) new_img = self._original_format(new_img, chw, normalized, gray3dim) return new_img.astype(ori_dtype)
[文档]class Curve(_NaturalPerturb): """ Curve picture using sin method. Args: curves (union[float, int]): Number of curve cycles. Suggested value range in [0.1, 5]. depth (union[float, int]): Amplitude of sin method. Suggested value not exceed 1/10 of the length of the picture. mode (str): Direction of deformation. Optional value is 'vertical' or 'horizontal'. auto_param (bool): Auto selected parameters. Selected parameters will preserve semantics of image. Default: False. Examples: >>> img = cv2.imread('x.png') >>> curves =1 >>> depth = 10 >>> trans = Curve(curves, depth, mode='vertical') >>> img_new = trans(img) """ def __init__(self, curves=3, depth=10, mode='vertical', auto_param=False): super(Curve).__init__() self.curves = check_value_non_negative('curves', curves) self.depth = check_value_non_negative('depth', depth) if mode in ['vertical', 'horizontal']: self.mode = mode else: msg = "Value of param mode must be in ['vertical', 'horizontal']" LOGGER.error(TAG, msg) raise ValueError(msg) self.auto_param = check_param_type('auto_param', auto_param, bool) def _set_auto_params(self, height, width): if self.auto_param: self.curves = np.random.uniform(1, 5) self.mode = np.random.choice(['vertical', 'horizontal']) if self.mode == 'vertical': self.depth = np.random.uniform(1, 0.1 * width) else: self.depth = np.random.uniform(1, 0.1 * height) def __call__(self, image): """ Curve picture using sin method. Args: image (numpy.ndarray): Original image. Returns: numpy.ndarray, curved image. """ ori_dtype = image.dtype _, chw, normalized, gray3dim, image = self._check(image) shape = image.shape height, width = shape[:2] if self.mode == 'vertical': if len(shape) == 3: image = np.transpose(image, [1, 0, 2]) else: image = np.transpose(image, [1, 0]) src_x = np.zeros((height, width), np.float32) src_y = np.zeros((height, width), np.float32) for y in range(height): for x in range(width): src_x[y, x] = x src_y[y, x] = y + self.depth * math.sin(x / (width / self.curves / 2 / math.pi)) img_new = cv2.remap(image, src_x, src_y, cv2.INTER_LINEAR) if self.mode == 'vertical': if len(shape) == 3: img_new = np.transpose(img_new, [1, 0, 2]) else: img_new = np.transpose(image, [1, 0]) new_img = self._original_format(img_new, chw, normalized, gray3dim) return new_img.astype(ori_dtype)