import json, os, random, math from collections import defaultdict from copy import deepcopy import torch from torch.utils.data import Dataset import torchvision.transforms as transforms import numpy as np from PIL import Image, ImageOps from .base_dataset import BaseDataset, check_filenames_in_zipdata from io import BytesIO def clean_annotations(annotations): for anno in annotations: anno.pop("segmentation", None) anno.pop("area", None) anno.pop("iscrowd", None) anno.pop("id", None) def make_a_sentence(obj_names, clean=False): if clean: obj_names = [ name[:-6] if ("-other" in name) else name for name in obj_names] caption = "" tokens_positive = [] for obj_name in obj_names: start_len = len(caption) caption += obj_name end_len = len(caption) caption += ", " tokens_positive.append( [[start_len, end_len]] # in real caption, positive tokens can be disjoint, thus using list of list ) caption = caption[:-2] # remove last ", " return caption #, tokens_positive class LayoutDataset(BaseDataset): """ Note: this dataset can somehow be achieved in cd_dataset.CDDataset Since if you donot set prob_real_caption=0 in CDDataset, then that dataset will only use detection annotations. However, in that dataset, we do not remove images but remove boxes. However, in layout2img works, people will just resize raw image data into 256*256, thus they pre-calculate box size and apply min_box_size before min/max_boxes_per_image. And then they will remove images if does not follow the rule. These two different methods will lead to different number of training/val images. Thus this dataset here is only for layout2img. """ def __init__(self, image_root, instances_json_path, stuff_json_path, category_embedding_path, fake_caption_type = 'empty', image_size=256, max_samples=None, min_box_size=0.02, min_boxes_per_image=3, max_boxes_per_image=8, include_other=False, random_flip=True ): super().__init__(random_crop=None, random_flip=None, image_size=None) # we only use vis_getitem func in BaseDataset, donot use the others. assert fake_caption_type in ['empty', 'made'] self.image_root = image_root self.instances_json_path = instances_json_path self.stuff_json_path = stuff_json_path self.category_embedding_path = category_embedding_path self.fake_caption_type = fake_caption_type self.image_size = image_size self.max_samples = max_samples self.min_box_size = min_box_size self.min_boxes_per_image = min_boxes_per_image self.max_boxes_per_image = max_boxes_per_image self.include_other = include_other self.random_flip = random_flip self.transform = transforms.Compose([transforms.Resize( (image_size, image_size) ), transforms.ToTensor(), transforms.Lambda(lambda t: (t * 2) - 1) ]) # Load all jsons with open(instances_json_path, 'r') as f: instances_data = json.load(f) # keys: 'info', 'images', 'licenses', 'categories', 'annotations' clean_annotations(instances_data["annotations"]) self.instances_data = instances_data with open(stuff_json_path, 'r') as f: stuff_data = json.load(f) # keys: 'info', 'images', 'licenses', 'categories', 'annotations' clean_annotations(stuff_data["annotations"]) self.stuff_data = stuff_data # Load preprocessed name embedding self.category_embeddings = torch.load(category_embedding_path) self.embedding_len = list( self.category_embeddings.values() )[0].shape[0] # Misc self.image_ids = [] # main list for selecting images self.image_id_to_filename = {} # file names used to read image self.image_id_to_size = {} # original size of this image assert instances_data['images'] == stuff_data["images"] for image_data in instances_data['images']: image_id = image_data['id'] filename = image_data['file_name'] width = image_data['width'] height = image_data['height'] self.image_ids.append(image_id) self.image_id_to_filename[image_id] = filename self.image_id_to_size[image_id] = (width, height) # All category names (including things and stuff) self.things_id_list = [] self.stuff_id_list = [] self.object_idx_to_name = {} for category_data in instances_data['categories']: self.things_id_list.append( category_data['id'] ) self.object_idx_to_name[category_data['id']] = category_data['name'] for category_data in stuff_data['categories']: self.stuff_id_list.append( category_data['id'] ) self.object_idx_to_name[category_data['id']] = category_data['name'] self.all_categories = [ self.object_idx_to_name.get(k, None) for k in range(183+1) ] # Add object data from instances and stuff self.image_id_to_objects = defaultdict(list) self.select_objects( instances_data['annotations'] ) self.select_objects( stuff_data['annotations'] ) # Prune images that have too few or too many objects new_image_ids = [] for image_id in self.image_ids: num_objs = len(self.image_id_to_objects[image_id]) if self.min_boxes_per_image <= num_objs <= self.max_boxes_per_image: new_image_ids.append(image_id) self.image_ids = new_image_ids # Check if all filenames can be found in the zip file all_filenames = [self.image_id_to_filename[idx] for idx in self.image_ids] check_filenames_in_zipdata(all_filenames, image_root) def select_objects(self, annotations): for object_anno in annotations: image_id = object_anno['image_id'] _, _, w, h = object_anno['bbox'] W, H = self.image_id_to_size[image_id] box_area = (w * h) / (W * H) box_ok = box_area > self.min_box_size object_name = self.object_idx_to_name[object_anno['category_id']] other_ok = object_name != 'other' or self.include_other if box_ok and other_ok: self.image_id_to_objects[image_id].append(object_anno) def total_images(self): return len(self) def __getitem__(self, index): if self.max_boxes_per_image > 99: assert False, "Are you sure setting such large number of boxes?" out = {} image_id = self.image_ids[index] out['id'] = image_id flip = self.random_flip and random.random()<0.5 # Image filename = self.image_id_to_filename[image_id] zip_file = self.fetch_zipfile(self.image_root) image = Image.open(BytesIO(zip_file.read(filename))).convert('RGB') WW, HH = image.size if flip: image = ImageOps.mirror(image) out["image"] = self.transform(image) this_image_obj_annos = deepcopy(self.image_id_to_objects[image_id]) # Make a sentence obj_names = [] # used for make a sentence boxes = torch.zeros(self.max_boxes_per_image, 4) masks = torch.zeros(self.max_boxes_per_image) positive_embeddings = torch.zeros(self.max_boxes_per_image, self.embedding_len) for idx, object_anno in enumerate(this_image_obj_annos): obj_name = self.object_idx_to_name[ object_anno['category_id'] ] obj_names.append(obj_name) x, y, w, h = object_anno['bbox'] x0 = x / WW y0 = y / HH x1 = (x + w) / WW y1 = (y + h) / HH if flip: x0, x1 = 1-x1, 1-x0 boxes[idx] = torch.tensor([x0,y0,x1,y1]) masks[idx] = 1 positive_embeddings[idx] = self.category_embeddings[obj_name] if self.fake_caption_type == 'empty': caption = "" else: caption = make_a_sentence(obj_names, clean=True) out["caption"] = caption out["boxes"] = boxes out["masks"] = masks out["positive_embeddings"] = positive_embeddings return out def __len__(self): if self.max_samples is None: return len(self.image_ids) return min(len(self.image_ids), self.max_samples)