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import os
import json
import torch
from torch.utils.data import Dataset
from torchvision.datasets.utils import download_url
from PIL import Image, ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
from glob import glob
from data.utils import pre_caption
class facecaption_train(Dataset):
def __init__(self, transform, image_root, ann_root, max_words=65, prompt=''):
'''
image_root (string): Root directory of images (e.g. coco/images/)
ann_root (string): directory to store the annotation file
'''
all_json = sorted(glob(os.path.join(ann_root, '*.json')))
self.annotation = []
# for json_path in all_json[:-1]:
for json_path in all_json[0:1]:
print("loading " + json_path)
with open(json_path, 'r') as json_file:
data = json.load(json_file)
self.annotation.extend(data)
self.transform = transform
self.image_root = image_root
self.max_words = max_words
self.prompt = prompt
self.img_ids = {}
n = 0
for ann in self.annotation:
img_id = ann['image_id']#[7:]
if img_id not in self.img_ids.keys():
self.img_ids[img_id] = n
n += 1
def __len__(self):
return len(self.annotation)
def __getitem__(self, index):
ann = self.annotation[index]
image_path = os.path.join(self.image_root, ann['image']) # for face image
# image_path = os.path.join(self.image_root, ann['image'][:21]+'.jpg') # for laion image
image = Image.open(image_path).convert('RGB')
image = self.transform(image)
caption = self.prompt + pre_caption(*ann['caption'], self.max_words) # for face caption in captionV3
# laion_caption = ann['laion_caption'][0] if ann['laion_caption'][0] is not None else ""
# caption = self.prompt + pre_caption(laion_caption, self.max_words) # for laion caption in captionV3
image_id = self.img_ids[ann['image_id']]
return image, caption, image_id
class facecaption_test(Dataset):
def __init__(self, transform, image_root, ann_root, max_words=65):
'''
image_root (string): Root directory of images (e.g. coco/images/)
ann_root (string): directory to store the annotation file
'''
all_json = sorted(glob(os.path.join(ann_root, '*.json')))
self.annotation = []
for json_path in all_json[-1:]:
with open(json_path, 'r') as json_file:
data = json.load(json_file)
self.annotation.extend(data)
self.annotation = self.annotation[:5000]
self.transform = transform
self.image_root = image_root
self.text = []
self.image = []
self.txt2img = {}
self.img2txt = {}
txt_id = 0
for img_id, ann in enumerate(self.annotation):
self.image.append(ann['image']) # for face image
# self.image.append(ann['image'][:21]+'.jpg') # for laion image
self.img2txt[img_id] = []
# for i, caption in enumerate(ann['laion_caption']): # for laion caption in captionV3
for i, caption in enumerate(ann['caption']): # for face caption in captionV3
self.text.append(pre_caption(caption, max_words))
self.img2txt[img_id].append(txt_id)
self.txt2img[txt_id] = img_id
txt_id += 1
def __len__(self):
return len(self.annotation)
def __getitem__(self, index):
ann = self.annotation[index]
image_path = os.path.join(self.image_root, ann['image']) # for face image
# image_path = os.path.join(self.image_root, ann['image'][:21]+'.jpg') # for laion image
image = Image.open(image_path).convert('RGB')
image = self.transform(image)
return image, index |