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 from .base_dataset import BaseDataset, check_filenames_in_zipdata, recalculate_box_and_verify_if_valid from io import BytesIO def not_in_at_all(list1, list2): for a in list1: if a in list2: return False return True 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 def check_all_have_same_images(instances_data, stuff_data, caption_data): if stuff_data is not None: assert instances_data["images"] == stuff_data["images"] if caption_data is not None: assert instances_data["images"] == caption_data["images"] class CDDataset(BaseDataset): "CD: Caption Detection" def __init__(self, image_root, category_embedding_path, instances_json_path = None, stuff_json_path = None, caption_json_path = None, prob_real_caption = 0, fake_caption_type = 'empty', image_size=256, max_images=None, min_box_size=0.01, max_boxes_per_image=8, include_other=False, random_crop = False, random_flip = True, ): super().__init__(random_crop, random_flip, image_size) self.image_root = image_root self.category_embedding_path = category_embedding_path self.instances_json_path = instances_json_path self.stuff_json_path = stuff_json_path self.caption_json_path = caption_json_path self.prob_real_caption = prob_real_caption self.fake_caption_type = fake_caption_type self.max_images = max_images self.min_box_size = min_box_size self.max_boxes_per_image = max_boxes_per_image self.include_other = include_other assert fake_caption_type in ["empty", "made"] if prob_real_caption > 0: assert caption_json_path is not None, "caption json must be given" # 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 self.stuff_data = None if stuff_json_path is not None: 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 self.captions_data = None if caption_json_path is not None: with open(caption_json_path, 'r') as f: captions_data = json.load(f) # keys: 'info', 'images', 'licenses', 'categories', 'annotations' clean_annotations(captions_data["annotations"]) self.captions_data = captions_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 check_all_have_same_images(self.instances_data, self.stuff_data, self.captions_data) for image_data in self.instances_data['images']: image_id = image_data['id'] filename = image_data['file_name'] self.image_ids.append(image_id) self.image_id_to_filename[image_id] = filename # All category names (including things and stuff) self.object_idx_to_name = {} for category_data in self.instances_data['categories']: self.object_idx_to_name[category_data['id']] = category_data['name'] if self.stuff_data is not None: for category_data in self.stuff_data['categories']: self.object_idx_to_name[category_data['id']] = category_data['name'] # Add object data from instances and stuff self.image_id_to_objects = defaultdict(list) self.select_objects( self.instances_data['annotations'] ) if self.stuff_data is not None: self.select_objects( self.stuff_data['annotations'] ) # Add caption data if self.captions_data is not None: self.image_id_to_captions = defaultdict(list) self.select_captions( self.captions_data['annotations'] ) # 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'] object_name = self.object_idx_to_name[object_anno['category_id']] other_ok = object_name != 'other' or self.include_other if other_ok: self.image_id_to_objects[image_id].append(object_anno) def select_captions(self, annotations): for caption_data in annotations: image_id = caption_data['image_id'] self.image_id_to_captions[image_id].append(caption_data) 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 # Image filename = self.image_id_to_filename[image_id] image = self.fetch_image(filename) #WW, HH = image.size image_tensor, trans_info = self.transform_image(image) out["image"] = image_tensor # Select valid boxes after cropping (center or random) this_image_obj_annos = deepcopy(self.image_id_to_objects[image_id]) areas = [] all_obj_names = [] all_boxes = [] all_masks = [] all_positive_embeddings = [] for object_anno in this_image_obj_annos: x, y, w, h = object_anno['bbox'] valid, (x0, y0, x1, y1) = recalculate_box_and_verify_if_valid(x, y, w, h, trans_info, self.image_size, self.min_box_size) if valid: areas.append( (x1-x0)*(y1-y0) ) obj_name = self.object_idx_to_name[ object_anno['category_id'] ] all_obj_names.append(obj_name) all_boxes.append( torch.tensor([x0,y0,x1,y1]) / self.image_size ) # scale to 0-1 all_masks.append(1) all_positive_embeddings.append( self.category_embeddings[obj_name] ) wanted_idxs = torch.tensor(areas).sort(descending=True)[1] wanted_idxs = wanted_idxs[0:self.max_boxes_per_image] obj_names = [] # used for making 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 i, idx in enumerate(wanted_idxs): obj_names.append( all_obj_names[idx] ) boxes[i] = all_boxes[idx] masks[i] = all_masks[idx] positive_embeddings[i] = all_positive_embeddings[idx] # Caption if random.uniform(0, 1) < self.prob_real_caption: caption_data = self.image_id_to_captions[image_id] idx = random.randint(0, len(caption_data)-1 ) caption = caption_data[idx]["caption"] else: 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_images is None: return len(self.image_ids) return min(len(self.image_ids), self.max_images)