# Copyright (c) 2019 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. # function: # operators to process sample, # eg: decode/resize/crop image from __future__ import absolute_import from __future__ import print_function from __future__ import division import uuid import logging import random import math import numpy as np import cv2 from PIL import Image, ImageEnhance from ppdet.core.workspace import serializable from .op_helper import (satisfy_sample_constraint, filter_and_process, generate_sample_bbox, clip_bbox, data_anchor_sampling, satisfy_sample_constraint_coverage, crop_image_sampling, generate_sample_bbox_square, bbox_area_sampling) logger = logging.getLogger(__name__) registered_ops = [] def register_op(cls): registered_ops.append(cls.__name__) if not hasattr(BaseOperator, cls.__name__): setattr(BaseOperator, cls.__name__, cls) else: raise KeyError("The {} class has been registered.".format(cls.__name__)) return serializable(cls) class BboxError(ValueError): pass class ImageError(ValueError): pass class BaseOperator(object): def __init__(self, name=None): if name is None: name = self.__class__.__name__ self._id = name + '_' + str(uuid.uuid4())[-6:] def __call__(self, sample, context=None): """ Process a sample. Args: sample (dict): a dict of sample, eg: {'image':xx, 'label': xxx} context (dict): info about this sample processing Returns: result (dict): a processed sample """ return sample def __str__(self): return str(self._id) @register_op class DecodeImage(BaseOperator): def __init__(self, to_rgb=True, with_mixup=False): """ Transform the image data to numpy format. Args: to_rgb (bool): whether to convert BGR to RGB with_mixup (bool): whether or not to mixup image and gt_bbbox/gt_score """ super(DecodeImage, self).__init__() self.to_rgb = to_rgb self.with_mixup = with_mixup if not isinstance(self.to_rgb, bool): raise TypeError("{}: input type is invalid.".format(self)) if not isinstance(self.with_mixup, bool): raise TypeError("{}: input type is invalid.".format(self)) def __call__(self, sample, context=None): """ load image if 'im_file' field is not empty but 'image' is""" if 'image' not in sample: with open(sample['im_file'], 'rb') as f: sample['image'] = f.read() im = sample['image'] data = np.frombuffer(im, dtype='uint8') im = cv2.imdecode(data, 1) # BGR mode, but need RGB mode if self.to_rgb: im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB) sample['image'] = im if 'h' not in sample: sample['h'] = im.shape[0] if 'w' not in sample: sample['w'] = im.shape[1] # make default im_info with [h, w, 1] sample['im_info'] = np.array( [im.shape[0], im.shape[1], 1.], dtype=np.float32) # decode mixup image if self.with_mixup and 'mixup' in sample: self.__call__(sample['mixup'], context) return sample @register_op class ResizeImage(BaseOperator): def __init__(self, target_size=0, max_size=0, interp=cv2.INTER_LINEAR, use_cv2=True): """ Rescale image to the specified target size, and capped at max_size if max_size != 0. If target_size is list, selected a scale randomly as the specified target size. Args: target_size (int|list): the target size of image's short side, multi-scale training is adopted when type is list. max_size (int): the max size of image interp (int): the interpolation method use_cv2 (bool): use the cv2 interpolation method or use PIL interpolation method """ super(ResizeImage, self).__init__() self.max_size = int(max_size) self.interp = int(interp) self.use_cv2 = use_cv2 if not (isinstance(target_size, int) or isinstance(target_size, list)): raise TypeError( "Type of target_size is invalid. Must be Integer or List, now is {}". format(type(target_size))) self.target_size = target_size if not (isinstance(self.max_size, int) and isinstance(self.interp, int)): raise TypeError("{}: input type is invalid.".format(self)) def __call__(self, sample, context=None): """ Resize the image numpy. """ im = sample['image'] if not isinstance(im, np.ndarray): raise TypeError("{}: image type is not numpy.".format(self)) if len(im.shape) != 3: raise ImageError('{}: image is not 3-dimensional.'.format(self)) im_shape = im.shape im_size_min = np.min(im_shape[0:2]) im_size_max = np.max(im_shape[0:2]) if isinstance(self.target_size, list): # Case for multi-scale training selected_size = random.choice(self.target_size) else: selected_size = self.target_size if float(im_size_min) == 0: raise ZeroDivisionError('{}: min size of image is 0'.format(self)) if self.max_size != 0: im_scale = float(selected_size) / float(im_size_min) # Prevent the biggest axis from being more than max_size if np.round(im_scale * im_size_max) > self.max_size: im_scale = float(self.max_size) / float(im_size_max) im_scale_x = im_scale im_scale_y = im_scale resize_w = np.round(im_scale_x * float(im_shape[1])) resize_h = np.round(im_scale_y * float(im_shape[0])) sample['im_info'] = np.array( [resize_h, resize_w, im_scale], dtype=np.float32) else: im_scale_x = float(selected_size) / float(im_shape[1]) im_scale_y = float(selected_size) / float(im_shape[0]) resize_w = selected_size resize_h = selected_size if self.use_cv2: im = cv2.resize( im, None, None, fx=im_scale_x, fy=im_scale_y, interpolation=self.interp) else: im = Image.fromarray(im) im = im.resize((resize_w, resize_h), self.interp) im = np.array(im) sample['image'] = im return sample @register_op class RandomFlipImage(BaseOperator): def __init__(self, prob=0.5, is_normalized=False, is_mask_flip=False): """ Args: prob (float): the probability of flipping image is_normalized (bool): whether the bbox scale to [0,1] is_mask_flip (bool): whether flip the segmentation """ super(RandomFlipImage, self).__init__() self.prob = prob self.is_normalized = is_normalized self.is_mask_flip = is_mask_flip if not (isinstance(self.prob, float) and isinstance(self.is_normalized, bool) and isinstance(self.is_mask_flip, bool)): raise TypeError("{}: input type is invalid.".format(self)) def flip_segms(self, segms, height, width): def _flip_poly(poly, width): flipped_poly = np.array(poly) flipped_poly[0::2] = width - np.array(poly[0::2]) - 1 return flipped_poly.tolist() def _flip_rle(rle, height, width): if 'counts' in rle and type(rle['counts']) == list: rle = mask_util.frPyObjects([rle], height, width) mask = mask_util.decode(rle) mask = mask[:, ::-1, :] rle = mask_util.encode(np.array(mask, order='F', dtype=np.uint8)) return rle def is_poly(segm): assert isinstance(segm, (list, dict)), \ "Invalid segm type: {}".format(type(segm)) return isinstance(segm, list) flipped_segms = [] for segm in segms: if is_poly(segm): # Polygon format flipped_segms.append([_flip_poly(poly, width) for poly in segm]) else: # RLE format import pycocotools.mask as mask_util flipped_segms.append(_flip_rle(segm, height, width)) return flipped_segms def __call__(self, sample, context=None): """Filp the image and bounding box. Operators: 1. Flip the image numpy. 2. Transform the bboxes' x coordinates. (Must judge whether the coordinates are normalized!) 3. Transform the segmentations' x coordinates. (Must judge whether the coordinates are normalized!) Output: sample: the image, bounding box and segmentation part in sample are flipped. """ gt_bbox = sample['gt_bbox'] im = sample['image'] if not isinstance(im, np.ndarray): raise TypeError("{}: image is not a numpy array.".format(self)) if len(im.shape) != 3: raise ImageError("{}: image is not 3-dimensional.".format(self)) height, width, _ = im.shape if np.random.uniform(0, 1) < self.prob: im = im[:, ::-1, :] if gt_bbox.shape[0] == 0: return sample oldx1 = gt_bbox[:, 0].copy() oldx2 = gt_bbox[:, 2].copy() if self.is_normalized: gt_bbox[:, 0] = 1 - oldx2 gt_bbox[:, 2] = 1 - oldx1 else: gt_bbox[:, 0] = width - oldx2 - 1 gt_bbox[:, 2] = width - oldx1 - 1 if gt_bbox.shape[0] != 0 and (gt_bbox[:, 2] < gt_bbox[:, 0]).all(): m = "{}: invalid box, x2 should be greater than x1".format(self) raise BboxError(m) sample['gt_bbox'] = gt_bbox if self.is_mask_flip and len(sample['gt_poly']) != 0: sample['gt_poly'] = self.flip_segms(sample['gt_poly'], height, width) sample['flipped'] = True sample['image'] = im return sample @register_op class NormalizeImage(BaseOperator): def __init__(self, mean=[0.485, 0.456, 0.406], std=[1, 1, 1], is_scale=True, is_channel_first=True): """ Args: mean (list): the pixel mean std (list): the pixel variance """ super(NormalizeImage, self).__init__() self.mean = mean self.std = std self.is_scale = is_scale self.is_channel_first = is_channel_first if not (isinstance(self.mean, list) and isinstance(self.std, list) and isinstance(self.is_scale, bool)): raise TypeError("{}: input type is invalid.".format(self)) from functools import reduce if reduce(lambda x, y: x * y, self.std) == 0: raise ValueError('{}: std is invalid!'.format(self)) def __call__(self, sample, context=None): """Normalize the image. Operators: 1.(optional) Scale the image to [0,1] 2. Each pixel minus mean and is divided by std """ im = sample['image'] im = im.astype(np.float32, copy=False) if self.is_channel_first: mean = np.array(self.mean)[:, np.newaxis, np.newaxis] std = np.array(self.std)[:, np.newaxis, np.newaxis] else: mean = np.array(self.mean)[np.newaxis, np.newaxis, :] std = np.array(self.std)[np.newaxis, np.newaxis, :] if self.is_scale: im = im / 255.0 im -= mean im /= std sample['image'] = im return sample @register_op class RandomDistort(BaseOperator): def __init__(self, brightness_lower=0.5, brightness_upper=1.5, contrast_lower=0.5, contrast_upper=1.5, saturation_lower=0.5, saturation_upper=1.5, hue_lower=-18, hue_upper=18, brightness_prob=0.5, contrast_prob=0.5, saturation_prob=0.5, hue_prob=0.5, count=4, is_order=False): """ Args: brightness_lower/ brightness_upper (float): the brightness between brightness_lower and brightness_upper contrast_lower/ contrast_upper (float): the contrast between contrast_lower and contrast_lower saturation_lower/ saturation_upper (float): the saturation between saturation_lower and saturation_upper hue_lower/ hue_upper (float): the hue between hue_lower and hue_upper brightness_prob (float): the probability of changing brightness contrast_prob (float): the probability of changing contrast saturation_prob (float): the probability of changing saturation hue_prob (float): the probability of changing hue count (int): the kinds of doing distrot is_order (bool): whether determine the order of distortion """ super(RandomDistort, self).__init__() self.brightness_lower = brightness_lower self.brightness_upper = brightness_upper self.contrast_lower = contrast_lower self.contrast_upper = contrast_upper self.saturation_lower = saturation_lower self.saturation_upper = saturation_upper self.hue_lower = hue_lower self.hue_upper = hue_upper self.brightness_prob = brightness_prob self.contrast_prob = contrast_prob self.saturation_prob = saturation_prob self.hue_prob = hue_prob self.count = count self.is_order = is_order def random_brightness(self, img): brightness_delta = np.random.uniform(self.brightness_lower, self.brightness_upper) prob = np.random.uniform(0, 1) if prob < self.brightness_prob: img = ImageEnhance.Brightness(img).enhance(brightness_delta) return img def random_contrast(self, img): contrast_delta = np.random.uniform(self.contrast_lower, self.contrast_upper) prob = np.random.uniform(0, 1) if prob < self.contrast_prob: img = ImageEnhance.Contrast(img).enhance(contrast_delta) return img def random_saturation(self, img): saturation_delta = np.random.uniform(self.saturation_lower, self.saturation_upper) prob = np.random.uniform(0, 1) if prob < self.saturation_prob: img = ImageEnhance.Color(img).enhance(saturation_delta) return img def random_hue(self, img): hue_delta = np.random.uniform(self.hue_lower, self.hue_upper) prob = np.random.uniform(0, 1) if prob < self.hue_prob: img = np.array(img.convert('HSV')) img[:, :, 0] = img[:, :, 0] + hue_delta img = Image.fromarray(img, mode='HSV').convert('RGB') return img def __call__(self, sample, context): """random distort the image""" ops = [ self.random_brightness, self.random_contrast, self.random_saturation, self.random_hue ] if self.is_order: prob = np.random.uniform(0, 1) if prob < 0.5: ops = [ self.random_brightness, self.random_saturation, self.random_hue, self.random_contrast, ] else: ops = random.sample(ops, self.count) assert 'image' in sample, "image data not found" im = sample['image'] im = Image.fromarray(im) for id in range(self.count): im = ops[id](im) im = np.asarray(im) sample['image'] = im return sample @register_op class ExpandImage(BaseOperator): def __init__(self, max_ratio, prob, mean=[127.5, 127.5, 127.5]): """ Args: max_ratio (float): the ratio of expanding prob (float): the probability of expanding image mean (list): the pixel mean """ super(ExpandImage, self).__init__() self.max_ratio = max_ratio self.mean = mean self.prob = prob def __call__(self, sample, context): """ Expand the image and modify bounding box. Operators: 1. Scale the image weight and height. 2. Construct new images with new height and width. 3. Fill the new image with the mean. 4. Put original imge into new image. 5. Rescale the bounding box. 6. Determine if the new bbox is satisfied in the new image. Returns: sample: the image, bounding box are replaced. """ prob = np.random.uniform(0, 1) assert 'image' in sample, 'not found image data' im = sample['image'] gt_bbox = sample['gt_bbox'] gt_class = sample['gt_class'] im_width = sample['w'] im_height = sample['h'] if prob < self.prob: if self.max_ratio - 1 >= 0.01: expand_ratio = np.random.uniform(1, self.max_ratio) height = int(im_height * expand_ratio) width = int(im_width * expand_ratio) h_off = math.floor(np.random.uniform(0, height - im_height)) w_off = math.floor(np.random.uniform(0, width - im_width)) expand_bbox = [ -w_off / im_width, -h_off / im_height, (width - w_off) / im_width, (height - h_off) / im_height ] expand_im = np.ones((height, width, 3)) expand_im = np.uint8(expand_im * np.squeeze(self.mean)) expand_im = Image.fromarray(expand_im) im = Image.fromarray(im) expand_im.paste(im, (int(w_off), int(h_off))) expand_im = np.asarray(expand_im) gt_bbox, gt_class, _ = filter_and_process(expand_bbox, gt_bbox, gt_class) sample['image'] = expand_im sample['gt_bbox'] = gt_bbox sample['gt_class'] = gt_class sample['w'] = width sample['h'] = height return sample @register_op class CropImage(BaseOperator): def __init__(self, batch_sampler, satisfy_all=False, avoid_no_bbox=True): """ Args: batch_sampler (list): Multiple sets of different parameters for cropping. satisfy_all (bool): whether all boxes must satisfy. e.g.[[1, 1, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0], [1, 50, 0.3, 1.0, 0.5, 2.0, 0.1, 1.0], [1, 50, 0.3, 1.0, 0.5, 2.0, 0.3, 1.0], [1, 50, 0.3, 1.0, 0.5, 2.0, 0.5, 1.0], [1, 50, 0.3, 1.0, 0.5, 2.0, 0.7, 1.0], [1, 50, 0.3, 1.0, 0.5, 2.0, 0.9, 1.0], [1, 50, 0.3, 1.0, 0.5, 2.0, 0.0, 1.0]] [max sample, max trial, min scale, max scale, min aspect ratio, max aspect ratio, min overlap, max overlap] avoid_no_bbox (bool): whether to to avoid the situation where the box does not appear. """ super(CropImage, self).__init__() self.batch_sampler = batch_sampler self.satisfy_all = satisfy_all self.avoid_no_bbox = avoid_no_bbox def __call__(self, sample, context): """ Crop the image and modify bounding box. Operators: 1. Scale the image weight and height. 2. Crop the image according to a radom sample. 3. Rescale the bounding box. 4. Determine if the new bbox is satisfied in the new image. Returns: sample: the image, bounding box are replaced. """ assert 'image' in sample, "image data not found" im = sample['image'] gt_bbox = sample['gt_bbox'] gt_class = sample['gt_class'] im_width = sample['w'] im_height = sample['h'] gt_score = None if 'gt_score' in sample: gt_score = sample['gt_score'] sampled_bbox = [] gt_bbox = gt_bbox.tolist() for sampler in self.batch_sampler: found = 0 for i in range(sampler[1]): if found >= sampler[0]: break sample_bbox = generate_sample_bbox(sampler) if satisfy_sample_constraint(sampler, sample_bbox, gt_bbox, self.satisfy_all): sampled_bbox.append(sample_bbox) found = found + 1 im = np.array(im) while sampled_bbox: idx = int(np.random.uniform(0, len(sampled_bbox))) sample_bbox = sampled_bbox.pop(idx) sample_bbox = clip_bbox(sample_bbox) crop_bbox, crop_class, crop_score = \ filter_and_process(sample_bbox, gt_bbox, gt_class, gt_score) if self.avoid_no_bbox: if len(crop_bbox) < 1: continue xmin = int(sample_bbox[0] * im_width) xmax = int(sample_bbox[2] * im_width) ymin = int(sample_bbox[1] * im_height) ymax = int(sample_bbox[3] * im_height) im = im[ymin:ymax, xmin:xmax] sample['image'] = im sample['gt_bbox'] = crop_bbox sample['gt_class'] = crop_class sample['gt_score'] = crop_score return sample return sample @register_op class CropImageWithDataAchorSampling(BaseOperator): def __init__(self, batch_sampler, anchor_sampler=None, target_size=None, das_anchor_scales=[16, 32, 64, 128], sampling_prob=0.5, min_size=8., avoid_no_bbox=True): """ Args: anchor_sampler (list): anchor_sampling sets of different parameters for cropping. batch_sampler (list): Multiple sets of different parameters for cropping. e.g.[[1, 10, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.2, 0.0]] [[1, 50, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0], [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0], [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0], [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0], [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0]] [max sample, max trial, min scale, max scale, min aspect ratio, max aspect ratio, min overlap, max overlap, min coverage, max coverage] target_size (bool): target image size. das_anchor_scales (list[float]): a list of anchor scales in data anchor smapling. min_size (float): minimum size of sampled bbox. avoid_no_bbox (bool): whether to to avoid the situation where the box does not appear. """ super(CropImageWithDataAchorSampling, self).__init__() self.anchor_sampler = anchor_sampler self.batch_sampler = batch_sampler self.target_size = target_size self.sampling_prob = sampling_prob self.min_size = min_size self.avoid_no_bbox = avoid_no_bbox self.scale_array = np.array(das_anchor_scales) def __call__(self, sample, context): """ Crop the image and modify bounding box. Operators: 1. Scale the image weight and height. 2. Crop the image according to a radom sample. 3. Rescale the bounding box. 4. Determine if the new bbox is satisfied in the new image. Returns: sample: the image, bounding box are replaced. """ assert 'image' in sample, "image data not found" im = sample['image'] gt_bbox = sample['gt_bbox'] gt_class = sample['gt_class'] image_width = sample['w'] image_height = sample['h'] gt_score = None if 'gt_score' in sample: gt_score = sample['gt_score'] sampled_bbox = [] gt_bbox = gt_bbox.tolist() prob = np.random.uniform(0., 1.) if prob > self.sampling_prob: # anchor sampling assert self.anchor_sampler for sampler in self.anchor_sampler: found = 0 for i in range(sampler[1]): if found >= sampler[0]: break sample_bbox = data_anchor_sampling( gt_bbox, image_width, image_height, self.scale_array, self.target_size) if sample_bbox == 0: break if satisfy_sample_constraint_coverage(sampler, sample_bbox, gt_bbox): sampled_bbox.append(sample_bbox) found = found + 1 im = np.array(im) while sampled_bbox: idx = int(np.random.uniform(0, len(sampled_bbox))) sample_bbox = sampled_bbox.pop(idx) crop_bbox, crop_class, crop_score = filter_and_process( sample_bbox, gt_bbox, gt_class, gt_score) crop_bbox, crop_class, crop_score = bbox_area_sampling( crop_bbox, crop_class, crop_score, self.target_size, self.min_size) if self.avoid_no_bbox: if len(crop_bbox) < 1: continue im = crop_image_sampling(im, sample_bbox, image_width, image_height, self.target_size) sample['image'] = im sample['gt_bbox'] = crop_bbox sample['gt_class'] = crop_class sample['gt_score'] = crop_score return sample return sample else: for sampler in self.batch_sampler: found = 0 for i in range(sampler[1]): if found >= sampler[0]: break sample_bbox = generate_sample_bbox_square( sampler, image_width, image_height) if satisfy_sample_constraint_coverage(sampler, sample_bbox, gt_bbox): sampled_bbox.append(sample_bbox) found = found + 1 im = np.array(im) while sampled_bbox: idx = int(np.random.uniform(0, len(sampled_bbox))) sample_bbox = sampled_bbox.pop(idx) sample_bbox = clip_bbox(sample_bbox) crop_bbox, crop_class, crop_score = filter_and_process( sample_bbox, gt_bbox, gt_class, gt_score) # sampling bbox according the bbox area crop_bbox, crop_class, crop_score = bbox_area_sampling( crop_bbox, crop_class, crop_score, self.target_size, self.min_size) if self.avoid_no_bbox: if len(crop_bbox) < 1: continue xmin = int(sample_bbox[0] * image_width) xmax = int(sample_bbox[2] * image_width) ymin = int(sample_bbox[1] * image_height) ymax = int(sample_bbox[3] * image_height) im = im[ymin:ymax, xmin:xmax] sample['image'] = im sample['gt_bbox'] = crop_bbox sample['gt_class'] = crop_class sample['gt_score'] = crop_score return sample return sample @register_op class NormalizeBox(BaseOperator): """Transform the bounding box's coornidates to [0,1].""" def __init__(self): super(NormalizeBox, self).__init__() def __call__(self, sample, context): gt_bbox = sample['gt_bbox'] width = sample['w'] height = sample['h'] for i in range(gt_bbox.shape[0]): gt_bbox[i][0] = gt_bbox[i][0] / width gt_bbox[i][1] = gt_bbox[i][1] / height gt_bbox[i][2] = gt_bbox[i][2] / width gt_bbox[i][3] = gt_bbox[i][3] / height sample['gt_bbox'] = gt_bbox return sample @register_op class Permute(BaseOperator): def __init__(self, to_bgr=True, channel_first=True): """ Change the channel. Args: to_bgr (bool): confirm whether to convert RGB to BGR channel_first (bool): confirm whether to change channel """ super(Permute, self).__init__() self.to_bgr = to_bgr self.channel_first = channel_first if not (isinstance(self.to_bgr, bool) and isinstance(self.channel_first, bool)): raise TypeError("{}: input type is invalid.".format(self)) def __call__(self, sample, context=None): assert 'image' in sample, "image data not found" im = sample['image'] if self.channel_first: im = np.swapaxes(im, 1, 2) im = np.swapaxes(im, 1, 0) if self.to_bgr: im = im[[2, 1, 0], :, :] sample['image'] = im return sample @register_op class MixupImage(BaseOperator): def __init__(self, alpha=1.5, beta=1.5): """ Mixup image and gt_bbbox/gt_score Args: alpha (float): alpha parameter of beta distribute beta (float): beta parameter of beta distribute """ super(MixupImage, self).__init__() self.alpha = alpha self.beta = beta if self.alpha <= 0.0: raise ValueError("alpha shold be positive in {}".format(self)) if self.beta <= 0.0: raise ValueError("beta shold be positive in {}".format(self)) def _mixup_img(self, img1, img2, factor): h = max(img1.shape[0], img2.shape[0]) w = max(img1.shape[1], img2.shape[1]) img = np.zeros((h, w, img1.shape[2]), 'float32') img[:img1.shape[0], :img1.shape[1], :] = \ img1.astype('float32') * factor img[:img2.shape[0], :img2.shape[1], :] += \ img2.astype('float32') * (1.0 - factor) return img.astype('uint8') def __call__(self, sample, context=None): if 'mixup' not in sample: return sample factor = np.random.beta(self.alpha, self.beta) factor = max(0.0, min(1.0, factor)) if factor >= 1.0: sample.pop('mixup') return sample if factor <= 0.0: return sample['mixup'] im = self._mixup_img(sample['image'], sample['mixup']['image'], factor) gt_bbox1 = sample['gt_bbox'] gt_bbox2 = sample['mixup']['gt_bbox'] gt_bbox = np.concatenate((gt_bbox1, gt_bbox2), axis=0) gt_class1 = sample['gt_class'] gt_class2 = sample['mixup']['gt_class'] gt_class = np.concatenate((gt_class1, gt_class2), axis=0) gt_score1 = sample['gt_score'] gt_score2 = sample['mixup']['gt_score'] gt_score = np.concatenate( (gt_score1 * factor, gt_score2 * (1. - factor)), axis=0) sample['image'] = im sample['gt_bbox'] = gt_bbox sample['gt_score'] = gt_score sample['gt_class'] = gt_class sample['h'] = im.shape[0] sample['w'] = im.shape[1] sample.pop('mixup') return sample @register_op class RandomInterpImage(BaseOperator): def __init__(self, target_size=0, max_size=0): """ Random reisze image by multiply interpolate method. Args: target_size (int): the taregt size of image's short side max_size (int): the max size of image """ super(RandomInterpImage, self).__init__() self.target_size = target_size self.max_size = max_size if not (isinstance(self.target_size, int) and isinstance(self.max_size, int)): raise TypeError('{}: input type is invalid.'.format(self)) interps = [ cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_AREA, cv2.INTER_CUBIC, cv2.INTER_LANCZOS4, ] self.resizers = [] for interp in interps: self.resizers.append(ResizeImage(target_size, max_size, interp)) def __call__(self, sample, context=None): """Resise the image numpy by random resizer.""" resizer = random.choice(self.resizers) return resizer(sample, context)