# 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. import random import traceback import numpy as np from PIL import Image import logging logger = logging.getLogger(__name__) __all__ = [ 'GroupScale', 'GroupMultiScaleCrop', 'GroupRandomCrop', 'GroupRandomFlip', 'GroupCenterCrop', 'NormalizeImage' ] class GroupScale(object): """ Group scale image Args: target_size (int): image resize target size """ def __init__(self, target_size=224): self.target_size = target_size def __call__(self, imgs, label): resized_imgs = [] for i in range(len(imgs)): img = imgs[i] w, h = img.size if (w <= h and w == self.target_size) or \ (h <= w and h == self.target_size): resized_imgs.append(img) continue if w < h: ow = self.target_size oh = int(self.target_size * 4.0 / 3.0) resized_imgs.append(img.resize((ow, oh), Image.BILINEAR)) else: oh = self.target_size ow = int(self.target_size * 4.0 / 3.0) resized_imgs.append(img.resize((ow, oh), Image.BILINEAR)) return resized_imgs, label class GroupMultiScaleCrop(object): """ FIXME: add comments """ def __init__(self, short_size=256, scales=None, max_distort=1, fix_crop=True, more_fix_crop=True): self.short_size = short_size self.scales = scales if scales is not None \ else [1, .875, .75, .66] self.max_distort = max_distort self.fix_crop = fix_crop self.more_fix_crop = more_fix_crop def __call__(self, imgs, label): input_size = [self.short_size, self.short_size] im_size = imgs[0].size # get random crop offset def _sample_crop_size(im_size): image_w, image_h = im_size[0], im_size[1] base_size = min(image_w, image_h) crop_sizes = [int(base_size * x) for x in self.scales] crop_h = [ input_size[1] if abs(x - input_size[1]) < 3 else x for x in crop_sizes ] crop_w = [ input_size[0] if abs(x - input_size[0]) < 3 else x for x in crop_sizes ] pairs = [] for i, h in enumerate(crop_h): for j, w in enumerate(crop_w): if abs(i - j) <= self.max_distort: pairs.append((w, h)) crop_pair = random.choice(pairs) if not self.fix_crop: w_offset = np.random.randint(0, image_w - crop_pair[0]) h_offset = np.random.randint(0, image_h - crop_pair[1]) else: w_step = (image_w - crop_pair[0]) / 4 h_step = (image_h - crop_pair[1]) / 4 ret = list() ret.append((0, 0)) # upper left if w_step != 0: ret.append((4 * w_step, 0)) # upper right if h_step != 0: ret.append((0, 4 * h_step)) # lower left if h_step != 0 and w_step != 0: ret.append((4 * w_step, 4 * h_step)) # lower right if h_step != 0 or w_step != 0: ret.append((2 * w_step, 2 * h_step)) # center if self.more_fix_crop: ret.append((0, 2 * h_step)) # center left ret.append((4 * w_step, 2 * h_step)) # center right ret.append((2 * w_step, 4 * h_step)) # lower center ret.append((2 * w_step, 0 * h_step)) # upper center ret.append((1 * w_step, 1 * h_step)) # upper left quarter ret.append((3 * w_step, 1 * h_step)) # upper right quarter ret.append((1 * w_step, 3 * h_step)) # lower left quarter ret.append((3 * w_step, 3 * h_step)) # lower righ quarter w_offset, h_offset = random.choice(ret) return crop_pair[0], crop_pair[1], w_offset, h_offset crop_w, crop_h, offset_w, offset_h = _sample_crop_size(im_size) crop_imgs = [ img.crop( (offset_w, offset_h, offset_w + crop_w, offset_h + crop_h)) for img in imgs ] ret_imgs = [ img.resize((input_size[0], input_size[1]), Image.BILINEAR) for img in crop_imgs ] return ret_imgs, label class GroupRandomCrop(object): def __init__(self, target_size=224): self.target_size = target_size def __call__(self, imgs, label): w, h = imgs[0].size th, tw = self.target_size, self.target_size assert (w >= self.target_size) and (h >= self.target_size), \ "image width({}) and height({}) should be larger than " \ "crop size".format(w, h, self.target_size) out_images = [] x1 = np.random.randint(0, w - tw) y1 = np.random.randint(0, h - th) for img in imgs: if w == tw and h == th: out_images.append(img) else: out_images.append(img.crop((x1, y1, x1 + tw, y1 + th))) return out_images, label class GroupRandomFlip(object): def __call__(self, imgs, label): v = np.random.random() if v < 0.5: ret = [img.transpose(Image.FLIP_LEFT_RIGHT) for img in imgs] return ret, label else: return imgs, label class GroupCenterCrop(object): def __init__(self, target_size=224): self.target_size = target_size def __call__(self, imgs, label): crop_imgs = [] for img in imgs: w, h = img.size th, tw = self.target_size, self.target_size assert (w >= self.target_size) and (h >= self.target_size), \ "image width({}) and height({}) should be larger " \ "than crop size".format(w, h, self.target_size) x1 = int(round((w - tw) / 2.)) y1 = int(round((h - th) / 2.)) crop_imgs.append(img.crop((x1, y1, x1 + tw, y1 + th))) return crop_imgs, label class NormalizeImage(object): def __init__(self, target_size=224, img_mean=[0.485, 0.456, 0.406], img_std=[0.229, 0.224, 0.225], seg_num=8, seg_len=1): self.target_size = target_size self.img_mean = np.array(img_mean).reshape((3, 1, 1)).astype('float32') self.img_std = np.array(img_std).reshape((3, 1, 1)).astype('float32') self.seg_num = seg_num self.seg_len = seg_len def __call__(self, imgs, label): np_imgs = (np.array(imgs[0]).astype('float32').transpose( (2, 0, 1))).reshape(1, 3, self.target_size, self.target_size) / 255 for i in range(len(imgs) - 1): img = (np.array(imgs[i + 1]).astype('float32').transpose( (2, 0, 1))).reshape(1, 3, self.target_size, self.target_size) / 255 np_imgs = np.concatenate((np_imgs, img)) np_imgs -= self.img_mean np_imgs /= self.img_std np_imgs = np.reshape(np_imgs, (self.seg_num, self.seg_len * 3, self.target_size, self.target_size)) return np_imgs, label