""" # Copyright (c) 2020 PaddlePaddle 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. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import six import math import random import cv2 import numpy as np from .autoaugment import ImageNetPolicy class OperatorParamError(ValueError): """ OperatorParamError """ pass class DecodeImage(object): """ decode image """ def __init__(self, to_rgb=True, to_np=False, channel_first=False): self.to_rgb = to_rgb self.to_np = to_np # to numpy self.channel_first = channel_first # only enabled when to_np is True def __call__(self, img): if six.PY2: assert type(img) is str and len( img) > 0, "invalid input 'img' in DecodeImage" else: assert type(img) is bytes and len( img) > 0, "invalid input 'img' in DecodeImage" data = np.frombuffer(img, dtype='uint8') img = cv2.imdecode(data, 1) if self.to_rgb: assert img.shape[2] == 3, 'invalid shape of image[%s]' % ( img.shape) img = img[:, :, ::-1] if self.channel_first: img = img.transpose((2, 0, 1)) return img class ResizeImage(object): """ resize image """ def __init__(self, size=None, resize_short=None, interpolation=-1): self.interpolation = interpolation if interpolation >= 0 else None if resize_short is not None and resize_short > 0: self.resize_short = resize_short self.w = None self.h = None elif size is not None: self.resize_short = None self.w = size if type(size) is int else size[0] self.h = size if type(size) is int else size[1] else: raise OperatorParamError("invalid params for ReisizeImage for '\ 'both 'size' and 'resize_short' are None") def __call__(self, img): img_h, img_w = img.shape[:2] if self.resize_short is not None: percent = float(self.resize_short) / min(img_w, img_h) w = int(round(img_w * percent)) h = int(round(img_h * percent)) else: w = self.w h = self.h if self.interpolation is None: return cv2.resize(img, (w, h)) else: return cv2.resize(img, (w, h), interpolation=self.interpolation) class CropImage(object): """ crop image """ def __init__(self, size): if type(size) is int: self.size = (size, size) else: self.size = size # (h, w) def __call__(self, img): w, h = self.size img_h, img_w = img.shape[:2] w_start = (img_w - w) // 2 h_start = (img_h - h) // 2 w_end = w_start + w h_end = h_start + h return img[h_start:h_end, w_start:w_end, :] class RandCropImage(object): """ random crop image """ def __init__(self, size, scale=None, ratio=None, interpolation=-1): self.interpolation = interpolation if interpolation >= 0 else None if type(size) is int: self.size = (size, size) # (h, w) else: self.size = size self.scale = [0.08, 1.0] if scale is None else scale self.ratio = [3. / 4., 4. / 3.] if ratio is None else ratio def __call__(self, img): size = self.size scale = self.scale ratio = self.ratio aspect_ratio = math.sqrt(random.uniform(*ratio)) w = 1. * aspect_ratio h = 1. / aspect_ratio img_h, img_w = img.shape[:2] bound = min((float(img_w) / img_h) / (w**2), (float(img_h) / img_w) / (h**2)) scale_max = min(scale[1], bound) scale_min = min(scale[0], bound) target_area = img_w * img_h * random.uniform(scale_min, scale_max) target_size = math.sqrt(target_area) w = int(target_size * w) h = int(target_size * h) i = random.randint(0, img_w - w) j = random.randint(0, img_h - h) img = img[j:j + h, i:i + w, :] if self.interpolation is None: return cv2.resize(img, size) else: return cv2.resize(img, size, interpolation=self.interpolation) class RandFlipImage(object): """ random flip image flip_code: 1: Flipped Horizontally 0: Flipped Vertically -1: Flipped Horizontally & Vertically """ def __init__(self, flip_code=1): assert flip_code in [-1, 0, 1 ], "flip_code should be a value in [-1, 0, 1]" self.flip_code = flip_code def __call__(self, img): if random.randint(0, 1) == 1: return cv2.flip(img, self.flip_code) else: return img class AutoAugment(object): def __init__(self): self.policy = ImageNetPolicy() def __call__(self, img): from PIL import Image img = np.ascontiguousarray(img) img = Image.fromarray(img) img = self.policy(img) img = np.asarray(img) class NormalizeImage(object): """ normalize image such as substract mean, divide std """ def __init__(self, scale=None, mean=None, std=None, order='chw'): if isinstance(scale, str): scale = eval(scale) self.scale = np.float32(scale if scale is not None else 1.0 / 255.0) mean = mean if mean is not None else [0.485, 0.456, 0.406] std = std if std is not None else [0.229, 0.224, 0.225] shape = (3, 1, 1) if order == 'chw' else (1, 1, 3) self.mean = np.array(mean).reshape(shape).astype('float32') self.std = np.array(std).reshape(shape).astype('float32') def __call__(self, img): from PIL import Image if isinstance(img, Image.Image): img = np.array(img) assert isinstance(img, np.ndarray), "invalid input 'img' in NormalizeImage" return (img.astype('float32') * self.scale - self.mean) / self.std class ToCHWImage(object): """ convert hwc image to chw image """ def __init__(self): pass def __call__(self, img): from PIL import Image if isinstance(img, Image.Image): img = np.array(img) return img.transpose((2, 0, 1))