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import os
import orjson
import json
import webdataset as wds
from tqdm import tqdm, trange
import h5py
import numpy as np
from utils import MAXCOUNT, NAMING, check_sample
OUT_DIR = "/gpfs/u/home/LMCG/LMCGljnn/scratch-shared/junyan/raw/instruct/vg_relation"
BOX_SCALE = 512
def load_image_filenames(image_file, image_dir):
"""
Loads the image filenames from visual genome from the JSON file that contains them.
This matches the preprocessing in scene-graph-TF-release/data_tools/vg_to_imdb.py.
:param image_file: JSON file. Elements contain the param "image_id".
:param image_dir: directory where the VisualGenome images are located
:return: List of filenames corresponding to the good images
"""
with open(image_file, 'r') as f:
im_data = json.load(f)
corrupted_ims = ['1592.jpg', '1722.jpg', '4616.jpg', '4617.jpg']
fns = []
for i, img in enumerate(tqdm(im_data)):
basename = '{}.jpg'.format(img['image_id'])
height = int(img['height'])
width = int(img['width'])
if basename in corrupted_ims:
continue
filename = os.path.join(image_dir, basename)
if os.path.exists(filename):
fns.append([filename, height, width])
assert len(fns) == 108073
return fns
def load_graphs(graphs_file, mode='train', num_im=-1, num_val_im=0, filter_empty_rels=True,
filter_non_overlap=False):
"""
Load the file containing the GT boxes and relations, as well as the dataset split
:param graphs_file: HDF5
:param mode: (train, val, or test)
:param num_im: Number of images we want
:param num_val_im: Number of validation images
:param filter_empty_rels: (will be filtered otherwise.)
:param filter_non_overlap: If training, filter images that dont overlap.
:return: image_index: numpy array corresponding to the index of images we're using
boxes: List where each element is a [num_gt, 4] array of ground
truth boxes (x1, y1, x2, y2)
gt_classes: List where each element is a [num_gt] array of classes
relationships: List where each element is a [num_r, 3] array of
(box_ind_1, box_ind_2, predicate) relationships
"""
if mode not in ('train', 'val', 'test'):
raise ValueError('{} invalid'.format(mode))
roi_h5 = h5py.File(graphs_file, 'r')
data_split = roi_h5['split'][:]
split = 2 if mode == 'test' else 0
split_mask = data_split == split
# Filter out images without bounding boxes
split_mask &= roi_h5['img_to_first_box'][:] >= 0
if filter_empty_rels:
split_mask &= roi_h5['img_to_first_rel'][:] >= 0
image_index = np.where(split_mask)[0]
if num_im > -1:
image_index = image_index[:num_im]
if num_val_im > 0:
if mode == 'val':
image_index = image_index[:num_val_im]
elif mode == 'train':
image_index = image_index[num_val_im:]
split_mask = np.zeros_like(data_split).astype(bool)
split_mask[image_index] = True
# Get box information
all_labels = roi_h5['labels'][:, 0]
all_boxes = roi_h5['boxes_{}'.format(BOX_SCALE)][:] # will index later
assert np.all(all_boxes[:, :2] >= 0) # sanity check
assert np.all(all_boxes[:, 2:] > 0) # no empty box
# convert from xc, yc, w, h to x1, y1, x2, y2
all_boxes[:, :2] = all_boxes[:, :2] - all_boxes[:, 2:] / 2
all_boxes[:, 2:] = all_boxes[:, :2] + all_boxes[:, 2:]
im_to_first_box = roi_h5['img_to_first_box'][:][split_mask]
im_to_last_box = roi_h5['img_to_last_box'][:][split_mask]
im_to_first_rel = roi_h5['img_to_first_rel'][:][split_mask]
im_to_last_rel = roi_h5['img_to_last_rel'][:][split_mask]
# load relation labels
_relations = roi_h5['relationships'][:]
_relation_predicates = roi_h5['predicates'][:, 0]
assert (im_to_first_rel.shape[0] == im_to_last_rel.shape[0])
assert (_relations.shape[0] == _relation_predicates.shape[0]) # sanity check
# Get everything by image.
boxes = []
gt_classes = []
relationships = []
for i in trange(len(image_index)):
boxes_i = all_boxes[im_to_first_box[i]:im_to_last_box[i] + 1, :]
gt_classes_i = all_labels[im_to_first_box[i]:im_to_last_box[i] + 1]
if im_to_first_rel[i] >= 0:
predicates = _relation_predicates[im_to_first_rel[i]:im_to_last_rel[i] + 1]
obj_idx = _relations[im_to_first_rel[i]:im_to_last_rel[i] + 1] - im_to_first_box[i]
assert np.all(obj_idx >= 0)
assert np.all(obj_idx < boxes_i.shape[0])
rels = np.column_stack((obj_idx, predicates))
else:
assert not filter_empty_rels
rels = np.zeros((0, 3), dtype=np.int32)
if filter_non_overlap:
raise NotImplementedError
assert mode == 'train'
inters = bbox_overlaps(boxes_i, boxes_i)
rel_overs = inters[rels[:, 0], rels[:, 1]]
inc = np.where(rel_overs > 0.0)[0]
if inc.size > 0:
rels = rels[inc]
else:
split_mask[image_index[i]] = 0
continue
boxes.append(boxes_i)
gt_classes.append(gt_classes_i)
relationships.append(rels)
return split_mask, boxes, gt_classes, relationships
def load_info(info_file):
"""
Loads the file containing the visual genome label meanings
:param info_file: JSON
:return: ind_to_classes: sorted list of classes
ind_to_predicates: sorted list of predicates
"""
info = json.load(open(info_file, 'r'))
info['label_to_idx']['__background__'] = 0
info['predicate_to_idx']['__background__'] = 0
class_to_ind = info['label_to_idx']
predicate_to_ind = info['predicate_to_idx']
ind_to_classes = sorted(class_to_ind, key=lambda k: class_to_ind[k])
ind_to_predicates = sorted(predicate_to_ind, key=lambda k: predicate_to_ind[k])
return ind_to_classes, ind_to_predicates
if __name__ == "__main__":
root = "/gpfs/u/home/LMCG/LMCGljnn/scratch/datasets/raw/vg"
filenames = load_image_filenames(os.path.join(root, "image_data.json"), os.path.join(root, "VG_100K"))
split_mask, boxes, gt_classes, relationships = load_graphs(
graphs_file=os.path.join(root, "VG-SGG.h5"),
mode="train",
)
split_filenames = []
for i, mask in enumerate(split_mask):
if mask:
split_filenames.append(filenames[i])
filenames = split_filenames
ind_to_classes, ind_to_predicates = load_info(os.path.join(root, "VG-SGG-dicts.json"))
assert len(filenames) == len(boxes)
assert len(filenames) == len(gt_classes)
assert len(filenames) == len(relationships)
uuid = 0
os.makedirs(OUT_DIR, exist_ok=True)
pbar = tqdm()
with wds.ShardWriter(os.path.join(OUT_DIR, NAMING), maxcount=MAXCOUNT) as sink:
for box, box_class, relationship, (filename, height, width) in zip(boxes, gt_classes, relationships, filenames):
size = float(BOX_SCALE) / max(height, width)
size = np.array([width, height, width, height]) * size
box = (box.astype(float) / size).clip(0, 1)
for relation in relationship:
box1_id = relation[0]
box2_id = relation[1]
predicate = ind_to_predicates[relation[2]]
box1 = [box[box1_id], ind_to_classes[box_class[box1_id]]]
box2 = [box[box2_id], ind_to_classes[box_class[box2_id]]]
data = [box1, box2, predicate]
dataset = "vg_relation"
image_path = filename
key = f"{dataset}_{uuid}"
uuid += 1
pbar.update()
sample = {
"__key__": key,
"image_path.txt": image_path,
"dataset.txt": dataset,
"data.pyd": data,
}
check_sample(sample)
sink.write(sample)
# if __name__ == "__main__":
# root = "/gpfs/u/home/LMCG/LMCGljnn/scratch/datasets/raw/vg"
# relationships = orjson.loads(open("/gpfs/u/home/LMCG/LMCGljnn/scratch/datasets/raw/vg/relationships.json").read())
# image_data = orjson.loads(open("/gpfs/u/home/LMCG/LMCGljnn/scratch/datasets/raw/vg/image_data.json").read())
# image_id_to_filename = {}
# image_id_to_wh = {}
# for image in tqdm(image_data):
# image_id = image["image_id"]
# subfolder, filename = image['url'].split("/")[-2:]
# image_id_to_filename[image_id] = os.path.join(root, subfolder, filename)
# image_id_to_wh[image_id] = (image["width"], image["height"])
# unique_predicates = []
# # with wds.ShardWriter(os.path.join(OUT_DIR, "%05d.tar"), maxcount=500) as sink:
# for relation_per_image in tqdm(relationships):
# image_id = relation_per_image["image_id"]
# for relation in relation_per_image["relationships"]:
# predicate = relation["predicate"]
# unique_predicates.append(predicate)
# object = {
# "name": relation["object"]["name"],
# "x": relation["object"]["x"],
# "y": relation["object"]["y"],
# "w": relation["object"]["w"],
# "h": relation["object"]["h"],
# }
# subject = {
# "name": relation["subject"]["name"],
# "x": relation["subject"]["x"],
# "y": relation["subject"]["y"],
# "w": relation["subject"]["w"],
# "h": relation["subject"]["h"],
# }
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