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import os
import json
import torch
# import clip
from PIL import Image
# import sng_parser
from tqdm import tqdm
import codecs
import numpy as np
import csv
import sys
from io import BytesIO
import base64
import pickle
# uniq-id, image (base64 string), caption, question, answer,
#ground-truth objects (objects appearing in the caption or question),
#dataset name (source of the data) and task type (caption, qa or visual gronunding).
def remove_special(input_string):
final_string = ""
for character in input_string:
if character == " ":
final_string = final_string + character
else:
if(character.isalnum()):
final_string = final_string + character
return final_string
def convert_img_to_str(file_name):
img = Image.open(file_name) # path to file
img_buffer = BytesIO()
img.save(img_buffer, format=img.format)
byte_data = img_buffer.getvalue()
base64_str = base64.b64encode(byte_data) # bytes
base64_str = base64_str.decode("utf-8") # str
return base64_str
def get_tsv_caption_data_from_json(original_data, start_id, dataset_name, task_type, convert_images=True):
tsv_data = []
for i, d in tqdm(enumerate(original_data)):
caption = remove_special(d['caption'])
img_path = d['image']
if convert_images:
img = convert_img_to_str(img_path)
else:
img_path = img_path.replace('/data/mshukor/data/', '')
img = img_path
t = [start_id, img, caption, '','', '', dataset_name, task_type]
tsv_data.append(t)
start_id+=1
return tsv_data
def get_tsv_vqa_data_from_json(original_data, start_id, dataset_name, task_type, image_root=None, convert_images=True):
tsv_data = []
for i, d in tqdm(enumerate(original_data)):
question = remove_special(d['question'])
img_path = d['image']
if image_root is not None:
img_path = os.path.join(image_root, img_path)
if convert_images:
img = convert_img_to_str(img_path)
else:
img_path = img_path.replace('/data/mshukor/data/', '')
img = img_path
answers = set(d['answer'])
answer_weight = {}
for ans in answers:
if ans in answer_weight.keys():
answer_weight[ans] += 1/len(answers)
else:
answer_weight[ans] = 1/len(answers)
ans_ = ["{:.1f}".format(conf)+'|!+'+ans for ans, conf in answer_weight.items()]
ans_ = '&&'.join(ans_)
t = [start_id, img, '', question, ans_, '', dataset_name, task_type]
tsv_data.append(t)
start_id+=1
return tsv_data
def get_tsv_from_refcoco(ref_path, instances_path, start_id, dataset_name='refcoco_train', task_type='visual_grounding', convert_images=True, split='train'):
refs = pickle.load(open(ref_path, 'rb'))
instances = json.load(open(instances_path,'r'))
id_to_annot = {}
for annot in tqdm(instances['annotations']):
id_to_annot[annot['id']] = annot
id_to_images = {}
for annot in tqdm(instances['images']):
id_to_images[annot['id']] = annot
tsv_data = []
for ref in tqdm(refs):
ref_split = ref['split']
if ref_split == split:
image_id = ref['image_id']
file_name = id_to_images[ref['image_id']]['file_name']
if ref_split == 'train':
file_name = os.path.join('coco/train2014', file_name)
if convert_images:
img_path = os.path.join('/data/mshukor/data/', file_name)
img = convert_img_to_str(img_path)
else:
img_path = file_name.replace('/data/mshukor/data/', '')
img = img_path
ann_id = ref['ann_id']
annot = id_to_annot[ann_id]
bbox = annot['bbox'] # x,y,w,h bottom left
x1, y1, x2, y2 = bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3] # top left, bottom right
box = '{:.2f},{:.2f},{:.2f},{:.2f}'.format(x1, y1, x2, y2)
for sent in ref['sentences']:
sentence = remove_special(sent['sent'])
# [id, image, 'third book starting from left', '', '29.1,11.72,66.81,343.41', '', 'refcoco_train', 'visual_grounding']
t = [start_id, img, sentence, '', box, '', dataset_name, task_type]
tsv_data.append(t)
start_id+=1
return tsv_data
def get_tsv_data_from_jsons(datasets, start_id, task_types, image_root=None, convert_images=True):
tsvs = []
for (original_data_path, task_type) in zip(datasets, task_types):
print(task_type)
if task_type == 'caption':
dataset_name = original_data_path.split('/')[-1].split('.')[0]
print(dataset_name,'start_id:', start_id)
original_data = json.load(open(original_data_path,'r'))
tsvs += get_tsv_caption_data_from_json(original_data=original_data, start_id=start_id, dataset_name=dataset_name, task_type=task_type, convert_images=convert_images)
elif task_type == 'qa':
dataset_name = original_data_path.split('/')[-1].split('.')[0]
print(dataset_name,'start_id:', start_id)
original_data = json.load(open(original_data_path,'r'))
tsvs += get_tsv_vqa_data_from_json(original_data=original_data, start_id=start_id, dataset_name=dataset_name, task_type=task_type, image_root=image_root, convert_images=convert_images)
elif task_type == 'visual_grounding':
dataset_name = original_data_path[0].split('/')[-2].replace('+', '')+'_train'
print(dataset_name,'start_id:', start_id)
if dataset_name == 'refcoco_train':
tsvs += get_tsv_from_refcoco(original_data_path[0], original_data_path[1], start_id, dataset_name=dataset_name, task_type=task_type, convert_images=convert_images, split='train')
elif task_type == 'detection':
dataset_name = original_data_path[0]
if dataset_name == 'vg':
tsvs+= get_tsv_from_vg_detection(original_data_path[1], original_data_path[2], start_id, convert_images=convert_images, split='train')
elif dataset_name == 'coco':
tsvs+= get_tsv_from_coco_detection(original_data_path[1], start_id, convert_images=convert_images, split='train')
else:
raise
start_id = tsvs[-1][0] + 1
return tsvs
def create_imagenet_txt_files(path_data, output_path, dataset='imagenet'):
data = []
# start_id = 0
for root, dirs, files, in os.walk(path_data):
for d in tqdm(dirs):
dir_path = os.path.join(root, d)
for _, _, dir_files in os.walk(dir_path):
for f in dir_files:
file_path = os.path.join(dir_path, f)
if dataset == 'imagenet21k':
file_path = '/'.join(file_path.split('/')[-3:])
elif dataset == 'openimages':
file_path = '/'.join(file_path.split('/')[-4:])
elif dataset == 'yfcc':
file_path = '/'.join(file_path.split('/')[-5:])
elif dataset == 'imagenet':
file_path = '/'.join(file_path.split('/')[-5:])
else:
file_path = '/'.join(file_path.split('/')[-4:])
image_id = f.split('.')[0]
tmp = [image_id, file_path]
data.append(tmp)
# start_id+=1
with open(output_path, 'w', newline='') as f_output:
csv_output = csv.writer(f_output, delimiter='\t')
for t in tqdm(data):
csv_output.writerow(t)
def get_tsv_from_vg_detection(instances_path, path_images, start_id, convert_images=True, split='train'):
print('start id:', start_id)
instances = json.load(open(instances_path,'r'))
id_to_objects = {}
for d in instances:
id_to_objects[d['id']] = d
id_to_image_path = {}
for root, dirs, files, in os.walk(path_images):
for d in dirs:
dir_path = os.path.join(root, d)
for _, _, dir_files in os.walk(dir_path):
for f in dir_files:
file_path = os.path.join(dir_path, f)
file_path = '/'.join(file_path.split('/')[-4:])
image_id = f.split('.')[0]
id_to_image_path[image_id] = file_path
tsv_data = []
missied = []
for ref in tqdm(id_to_image_path.keys()):
ref_split = split
image_id = ref
file_name = id_to_image_path[image_id]
if convert_images:
img_path = os.path.join('/data/mshukor/data/', file_name)
img = convert_img_to_str(img_path)
else:
img_path = file_name.replace('/data/mshukor/data/', '')
img = img_path
if int(image_id) in id_to_objects:
objects = id_to_objects[int(image_id)]['objects']
else:
missied.append(image_id)
continue
if len(objects) == 0:
missied.append(image_id)
continue
areas = []
detections = []
for annot in objects:
x,y,w,h = annot['x'], annot['y'], annot['w'], annot['h'] # x,y,w,h bottom left
area = w*h
x1, y1, x2, y2 = x, y, x + w, y + h # top left, bottom right
x1 = max(0, x1)
x2 = max(0, x2)
category = ','.join(remove_special(annot['names'])).replace('\x00','')
object_id = annot['id']
tmp = '{:.3f},{:.3f},{:.3f},{:.3f},{},{}'.format(x1, y1, x2, y2, object_id, category)
detections.append(tmp)
areas.append(area)
sorted_indices = sorted(range(len(areas)), key=lambda k: areas[k], reverse=True)
detections = [detections[k] for k in sorted_indices]
detections = '&&'.join(detections)
t = [start_id, img, detections]
tsv_data.append(t)
start_id+=1
print('missed images:', len(missied))
return tsv_data
def get_tsv_from_coco_detection(instances_path, start_id, convert_images=True, split='train'):
print('start id:', start_id)
instances = json.load(open(instances_path,'r'))
imgid_to_annot = {}
for annot in tqdm(instances['annotations']):
if annot['image_id'] not in imgid_to_annot:
imgid_to_annot[annot['image_id']] = [annot]
else:
imgid_to_annot[annot['image_id']].append(annot)
id_to_category = {}
for annot in tqdm(instances['categories']):
id_to_category[annot['id']] = annot['name']
tsv_data = []
missied = []
for ref in tqdm(instances['images']):
ref_split = split
image_id = ref['id']
file_name = ref['file_name']
if ref_split == 'train':
file_name = os.path.join('coco/train2014', file_name)
if convert_images:
img_path = os.path.join('/data/mshukor/data/', file_name)
img = convert_img_to_str(img_path)
else:
img_path = file_name.replace('/data/mshukor/data/', '')
img = img_path
# ann_id = ref['id']
# annot = id_to_annot[ann_id]
if image_id not in imgid_to_annot:
missied.append(image_id)
continue
annots = imgid_to_annot[image_id]
detections = []
areas = []
for annot in annots:
bbox = annot['bbox'] # x,y,w,h bottom left
area = bbox[2]*bbox[3]
x1, y1, x2, y2 = bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3] # top left, bottom right
# box = '{:.3f},{:.3f},{:.3f},{:.3f}'.format(x1, y1, x2, y2)
object_id = annot['category_id']
category = remove_special(id_to_category[object_id])
tmp = '{:.3f},{:.3f},{:.3f},{:.3f},{},{}'.format(x1, y1, x2, y2, object_id, category)
areas.append(area)
detections.append(tmp)
sorted_indices = sorted(range(len(areas)), key=lambda k: areas[k], reverse=True)
detections = [detections[k] for k in sorted_indices]
detections = '&&'.join(detections)
t = [start_id, img, detections]
tsv_data.append(t)
start_id+=1
return tsv_data
def get_tsv_from_openimages_detection(instances_path, path_images, class_path,
start_id, convert_images=False, split='train', image_root='/gpfsdswork/dataset'):
id_to_image_path = {}
for root, dirs, files, in os.walk(path_images):
for d in dirs:
dir_path = os.path.join(root, d)
for _, _, dir_files in os.walk(dir_path):
for f in dir_files:
file_path = os.path.join(dir_path, f)
file_path = '/'.join(file_path.split('/')[-4:])
image_id = f.split('.')[0]
id_to_image_path[image_id] = file_path
def imagepath_to_image_size(img_path):
w, h = Image.open(img_path).size
return w, h
id_to_annot = {}
with open(instances_path) as file:
tsv_file = csv.reader(file, delimiter='\t')
for i, line in tqdm(enumerate(tsv_file)):
if i == 0:
continue # skip header
img_id = line[0].split(',')[0]
if img_id in id_to_annot:
id_to_annot[img_id].append(line)
else:
id_to_annot[img_id] = [line]
classid_to_class = {}
with open(class_path) as file:
tsv_file = csv.reader(file, delimiter=',')
for i, line in tqdm(enumerate(tsv_file)):
classid_to_class[line[0]] = line[1]
tsv_data = []
for i, img_id in tqdm(enumerate(id_to_annot.keys())):
annots = id_to_annot[img_id]
img_path = id_to_image_path[img_id]
orig_img_path = os.path.join(image_root, img_path)
w, h = imagepath_to_image_size(orig_img_path)
if convert_images:
img = convert_img_to_str(orig_img_path)
else:
img = img_path
areas = []
detections = []
for d in annots:
d = d[0].split(',')
x1, x2, y1, y2 = d[4:8]
x1, x2, y1, y2 = float(x1), float(x2), float(y1), float(y2)
x1, x2, y1, y2 = x1*w, x2*w, y1*h, y2*h
box_w, box_h = x2 - x1, y2 - y1
area = box_w*box_h
areas.append(area)
object_id = d[2]
category = remove_special(classid_to_class[object_id])
tmp = '{:.3f},{:.3f},{:.3f},{:.3f},{},{}'.format(x1, y1, x2, y2, object_id, category)
detections.append(tmp)
sorted_indices = sorted(range(len(areas)), key=lambda k: areas[k], reverse=True)
detections = [detections[k] for k in sorted_indices]
detections = '&&'.join(detections)
t = [start_id, img, detections]
tsv_data.append(t)
start_id+=1
return tsv_data
def replace_image_id_by_path(input_tsv, output_tsv, mapping_file):
selected_cols='0,1,2'
data = []
selected_col_ids = [int(col_id) for col_id in selected_cols.split(",")]
with open(input_tsv) as file:
tsv_file = csv.reader(file, delimiter='\t')
for line in tqdm(tsv_file):
d = [line[i] for i in selected_col_ids]
data.append(d)
im_id_to_path = {}
with open(mapping_file) as file:
tsv_file = csv.reader(file, delimiter='\t')
for line in tqdm(tsv_file):
d = [line[i] for i in [0, 1]]
im_id_to_path[d[0]] = d[1]
for d in tqdm(data):
im_id = d[1].split('/')[-1].split('.')[0]
im_path = im_id_to_path[im_id]
d[1] = im_path
with open(output_tsv, 'w', newline='') as f_output:
csv_output = csv.writer(f_output, delimiter='\t')
for t in tqdm(data):
csv_output.writerow(t)
return data |