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from tkinter.messagebox import NO
import torch
import json
from collections import defaultdict
from PIL import Image, ImageDraw
from copy import deepcopy
import os
import torchvision.transforms as transforms
import torchvision
from .base_dataset import BaseDataset, check_filenames_in_zipdata, recalculate_box_and_verify_if_valid
from io import BytesIO
import random
def check_unique(images, fields):
for field in fields:
temp_list = []
for img_info in images:
temp_list.append(img_info[field])
assert len(set(temp_list)) == len(temp_list), field
def clean_data(data):
for data_info in data:
data_info.pop("original_img_id", None)
data_info.pop("original_id", None)
data_info.pop("sentence_id", None) # sentence id for each image (multiple sentences for one image)
data_info.pop("dataset_name", None)
data_info.pop("data_source", None)
data_info["data_id"] = data_info.pop("id")
def clean_annotations(annotations):
for anno_info in annotations:
anno_info.pop("iscrowd", None) # I have checked that all 0 for flickr, vg, coco
anno_info.pop("category_id", None) # I have checked that all 1 for flickr vg. This is not always 1 for coco, but I do not think we need this annotation
anno_info.pop("area", None)
# anno_info.pop("id", None)
anno_info["data_id"] = anno_info.pop("image_id")
def draw_box(img, boxes):
draw = ImageDraw.Draw(img)
for box in boxes:
draw.rectangle([box[0], box[1], box[2], box[3]], outline ="red", width=2) # x0 y0 x1 y1
return img
def xyhw2xyxy(box):
x0, y0, w, h = box
return [ x0, y0, x0+w, y0+h ]
class GroundingDataset(BaseDataset):
def __init__(self,
image_root,
json_path,
annotation_embedding_path,
prob_real_caption=1,
image_size=256,
min_box_size=0.01,
max_boxes_per_data=8,
max_images=None, # set as 30K used to eval
random_crop = False,
random_flip = True,
):
super().__init__(image_root, random_crop, random_flip, image_size)
self.image_root = image_root
self.json_path = json_path
self.annotation_embedding_path = annotation_embedding_path
self.prob_real_caption = prob_real_caption
self.min_box_size = min_box_size
self.max_boxes_per_data = max_boxes_per_data
self.max_images = max_images
# Load raw data
with open(json_path, 'r') as f:
json_raw = json.load(f) # keys: 'info', 'images', 'licenses', 'categories', 'annotations'
self.data = json_raw["images"] # donot name it images, which is misleading
self.annotations = json_raw["annotations"]
# Load preprocessed name embedding
if 'bert' in annotation_embedding_path:
self.embedding_len = 1280
elif 'clip' in annotation_embedding_path:
self.embedding_len = 768
else:
assert False
# clean data and annotation
check_unique( self.data, ['id'] )
check_unique( self.annotations, ['id'] )
clean_data(self.data)
clean_annotations(self.annotations)
self.data_id_list = [ datum['data_id'] for datum in self.data ]
self.data = { datum['data_id']:datum for datum in self.data } # map self.data from a list into a dict
# data point to its annotation mapping
self.data_id_to_annos = defaultdict(list)
for anno in self.annotations:
self.data_id_to_annos[ anno["data_id"] ].append(anno)
# These are not used that offen, but are useful in some cases
self.file_names = [] # all training images
self.file_name_to_data_ids = defaultdict(list) # for each image, there are multiple data points (captions)
for data_id in self.data_id_list:
fine_name = self.data[data_id]["file_name"]
self.file_names.append(fine_name)
self.file_name_to_data_ids[fine_name].append(data_id)
self.file_names = list(set(self.file_names))
if self.max_images is not None:
"This is only used as COCO2017P evulation, when we set max_images as 30k"
assert False, 'I have commented out the following code to save cpu memory'
# new_data_id_list = []
# new_file_name_to_data_ids = defaultdict(list)
# self.file_names = self.file_names[0:self.max_images]
# for file_name in self.file_names:
# data_id = self.file_name_to_data_ids[file_name][0]
# new_data_id_list.append(data_id)
# new_file_name_to_data_ids[file_name].append(data_id)
# self.data_id_list = new_data_id_list
# self.file_name_to_data_ids = new_file_name_to_data_ids
# Check if all filenames can be found in the zip file
# all_filenames = [self.data[idx]['file_name'] for idx in self.data_id_list ]
# check_filenames_in_zipdata(all_filenames, image_root)
def total_images(self):
return len(self.file_names)
def __getitem__(self, index):
if self.max_boxes_per_data > 99:
assert False, "Are you sure setting such large number of boxes?"
out = {}
data_id = self.data_id_list[index]
out['id'] = data_id
# Image and caption
file_name = self.data[data_id]['file_name']
image = self.fetch_image(file_name)
image_tensor, trans_info = self.transform_image(image)
out["image"] = image_tensor
if random.uniform(0, 1) < self.prob_real_caption:
out["caption"] = self.data[data_id]["caption"]
else:
out["caption"] = ""
annos = deepcopy(self.data_id_to_annos[data_id])
areas = []
all_boxes = []
all_masks = []
all_positive_embeddings = []
for anno in annos:
x, y, w, h = 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) )
all_boxes.append( torch.tensor([x0,y0,x1,y1]) / self.image_size ) # scale to 0-1
all_masks.append(1)
all_positive_embeddings.append( torch.load(os.path.join(self.annotation_embedding_path,str(anno["id"])), map_location='cpu' ) )
wanted_idxs = torch.tensor(areas).sort(descending=True)[1]
wanted_idxs = wanted_idxs[0:self.max_boxes_per_data]
boxes = torch.zeros(self.max_boxes_per_data, 4)
masks = torch.zeros(self.max_boxes_per_data)
positive_embeddings = torch.zeros(self.max_boxes_per_data, self.embedding_len)
for i, idx in enumerate(wanted_idxs):
boxes[i] = all_boxes[idx]
masks[i] = all_masks[idx]
positive_embeddings[i] = all_positive_embeddings[idx]
out["boxes"] = boxes
out["masks"] = masks
out["positive_embeddings"] = positive_embeddings
return out
def __len__(self):
return len(self.data_id_list)
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