#!/usr/bin/env python # -*- encoding: utf-8 -*- ''' @File : dataset.py @Time : 2023/04/06 22:39:31 @Author : BQH @Version : 1.0 @Contact : raogx.vip@hotmail.com @License : (C)Copyright 2017-2018, Liugroup-NLPR-CASIA @Desc : None ''' # here put the import lib import os import json import torch import numpy as np import random from PIL import Image from PIL import ImageOps from copy import deepcopy from .aug_strategy import imgaug_mask from .aug_strategy import pipe_sequential_rotate from .aug_strategy import pipe_sequential_translate from .aug_strategy import pipe_sequential_scale from .aug_strategy import pipe_someof_flip from .aug_strategy import pipe_someof_blur from .aug_strategy import pipe_sometimes_mpshear from .aug_strategy import pipe_someone_contrast from .NuImages.nuimages import NuImages def imresize(im, size, interp='bilinear'): if interp == 'nearest': resample = Image.NEAREST elif interp == 'bilinear': resample = Image.BILINEAR elif interp == 'bicubic': resample = Image.BICUBIC else: raise Exception('resample method undefined!') return im.resize(size, resample) class BaseDataset(torch.utils.data.Dataset): def __init__(self, odgt, opt, **kwargs): # parse options self.imgSizes = opt.INPUT.CROP.SIZE self.imgMaxSize = opt.INPUT.CROP.MAX_SIZE # max down sampling rate of network to avoid rounding during conv or pooling self.padding_constant = 2**5 # resnet 总共下采样5次 # parse the input list if odgt is not None: self.parse_input_list(odgt, **kwargs) self.pixel_mean = np.array(opt.DATASETS.PIXEL_MEAN) self.pixel_std = np.array(opt.DATASETS.PIXEL_STD) def parse_input_list(self, odgt, max_sample=-1, start_idx=-1, end_idx=-1): if isinstance(odgt, list): self.list_sample = odgt elif isinstance(odgt, str): self.list_sample = [json.loads(x.rstrip()) for x in open(odgt, 'r')] if max_sample > 0: self.list_sample = self.list_sample[0:max_sample] if start_idx >= 0 and end_idx >= 0: # divide file list self.list_sample = self.list_sample[start_idx:end_idx] self.num_sample = len(self.list_sample) assert self.num_sample > 0 print('# samples: {}'.format(self.num_sample)) def img_transform(self, img): # 0-255 to 0-1 img = np.float32(np.array(img)) / 255. img = (img - self.pixel_mean) / self.pixel_std img = img.transpose((2, 0, 1)) # [c, h, w] return img def segm_transform(self, segm: np.ndarray): # to tensor, -1 to 149 segm = torch.from_numpy(np.array(segm)).long() return segm # Round x to the nearest multiple of p and x' >= x def round2nearest_multiple(self, x, p): return ((x - 1) // p + 1) * p def get_img_ratio(self, img_size, target_size): img_rate = np.max(img_size) / np.min(img_size) target_rate = np.max(target_size) / np.min(target_size) if img_rate > target_rate: # 按长边缩放 ratio = max(target_size) / max(img_size) else: ratio = min(target_size) / min(img_size) return ratio def resize_padding(self, img, outsize, Interpolation=Image.BILINEAR): w, h = img.size target_w, target_h = outsize[0], outsize[1] ratio = self.get_img_ratio([w, h], outsize) ow, oh = round(w * ratio), round(h * ratio) img = img.resize((ow, oh), Interpolation) dh, dw = target_h - oh, target_w - ow top, bottom = dh // 2, dh - (dh // 2) left, right = dw // 2, dw - (dw // 2) img = ImageOps.expand(img, border=(left, top, right, bottom), fill=0) # 左 顶 右 底 顺时针 return img class ADE200kDataset(BaseDataset): def __init__(self, odgt, opt, dynamic_batchHW=False, **kwargs): super(ADE200kDataset, self).__init__(odgt, opt, **kwargs) self.root_dataset = opt.DATASETS.ROOT_DIR # down sampling rate of segm labe self.segm_downsampling_rate = opt.MODEL.SEM_SEG_HEAD.COMMON_STRIDE # 网络输出相对于输入缩小的倍数 self.dynamic_batchHW = dynamic_batchHW # 是否动态调整batchHW, cswin_transformer需要使用固定image size self.num_querys = opt.MODEL.MASK_FORMER.NUM_OBJECT_QUERIES # self.visualize = ADEVisualize() self.aug_pipe = self.get_data_aug_pipe() def get_data_aug_pipe(self): pipe_aug = [] if random.random() > 0.5: aug_list = [pipe_sequential_rotate, pipe_sequential_scale, pipe_sequential_translate, pipe_someof_blur, pipe_someof_flip, pipe_sometimes_mpshear, pipe_someone_contrast] index = np.random.choice(a=[0, 1, 2, 3, 4, 5, 6], p=[0.05, 0.25, 0.20, 0.25, 0.15, 0.05, 0.05]) if (index == 0 or index == 4 or index == 5) and random.random() < 0.5: # 会稍微削弱旋转 但是会极大增强其他泛化能力 index2 = np.random.choice(a=[1, 2, 3], p=[0.4, 0.3, 0.3]) pipe_aug = [aug_list[index], aug_list[index2]] else: pipe_aug = [aug_list[index]] return pipe_aug def get_batch_size(self, batch_records): batch_width, batch_height = self.imgMaxSize[0], self.imgMaxSize[1] if self.dynamic_batchHW: if isinstance(self.imgSizes, list) or isinstance(self.imgSizes, tuple): this_short_size = np.random.choice(self.imgSizes) else: this_short_size = self.imgSizes batch_widths = np.zeros(len(batch_records), np.int32) batch_heights = np.zeros(len(batch_records), np.int32) for i, item in enumerate(batch_records): img_height, img_width = item['image'].shape[0], item['image'].shape[1] this_scale = min( this_short_size / min(img_height, img_width), \ self.imgMaxSize / max(img_height, img_width)) batch_widths[i] = img_width * this_scale batch_heights[i] = img_height * this_scale batch_width = np.max(batch_widths) batch_height = np.max(batch_heights) batch_width = int(self.round2nearest_multiple(batch_width, self.padding_constant)) batch_height = int(self.round2nearest_multiple(batch_height, self.padding_constant)) return batch_width, batch_height def __getitem__(self, index): this_record = self.list_sample[index] # load image and label image_path = os.path.join(self.root_dataset, this_record['fpath_img']) segm_path = os.path.join(self.root_dataset, this_record['fpath_segm']) img = Image.open(image_path).convert('RGB') segm = Image.open(segm_path).convert('L') # data augmentation img = np.array(img) segm = np.array(segm) for seq in self.aug_pipe: img, segm = imgaug_mask(img, segm, seq) output = dict() output['image'] = img output['mask'] = segm return output def collate_fn(self, batch): batch_width, batch_height = self.get_batch_size(batch) out = {} images = [] masks = [] raw_images = [] for item in batch: img = deepcopy(item['image']) segm = item['mask'] img = Image.fromarray(img) segm = Image.fromarray(segm) img = self.resize_padding(img, (batch_width, batch_height)) img = self.img_transform(img) segm = self.resize_padding(segm, (batch_width, batch_height), Image.NEAREST) segm = segm.resize((batch_width // self.segm_downsampling_rate, batch_height // self.segm_downsampling_rate), Image.NEAREST) images.append(torch.from_numpy(img).float()) masks.append(torch.from_numpy(np.array(segm)).long()) raw_images.append(item['image']) out['images'] = torch.stack(images) out['masks'] = torch.stack(masks) out['raw_img'] = raw_images return out def __len__(self): return self.num_sample class LaneDetec(ADE200kDataset): def __init__(self, odgt, opt, dynamic_batchHW=False, **kwargs): super(LaneDetec, self).__init__(odgt, opt, dynamic_batchHW, **kwargs) def __getitem__(self, index): this_record = self.list_sample[index] # load image and label image_path = os.path.join(self.root_dataset, this_record['fpath_img']) segm_path = os.path.join(self.root_dataset, this_record['fpath_segm']) img = Image.open(image_path).convert('RGB') segm = Image.open(segm_path).convert('L') # data augmentation img = np.array(img)[800:, :, :] # 移除图片上方的天空部分 segm = np.array(segm)[800:, :] for seq in self.aug_pipe: img, segm = imgaug_mask(img, segm, seq) output = dict() output['image'] = img output['mask'] = segm return output # 用于nuImages数据集的Dataset类 class NuImagesDataset(ADE200kDataset): def __init__(self, data_root, opt, version='v1.0-train', **kwargs): super(NuImagesDataset, self).__init__(None, opt, **kwargs) self.nuim = NuImages(dataroot=data_root, version=version, lazy=False) self.num_sample = len(self.nuim.sample) print(f'Load {self.num_sample} samples from {version}') def __getitem__(self, index): sample = self.nuim.sample[index] sd_token = sample['key_camera_token'] sample_data = self.nuim.get('sample_data', sd_token) im_path = os.path.join(self.nuim.dataroot, sample_data['filename']) img = Image.open(im_path).convert('RGB') img = np.array(img) semseg_mask, instanceseg_mask = self.nuim.get_segmentation(sd_token) semseg_mask[semseg_mask==31] = 0 # 31是vehicle.ego, 不做预测 output = dict() output['image'] = img output['mask'] = semseg_mask output['ins_mask'] = instanceseg_mask # self.nuim.render_image(sd_token, annotation_type='all', with_category=True, with_attributes=True, out_path='/home/dataset/nuImages/ImageData/out_test.png') return output def __len__(self): return self.num_sample