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create_dataset_ytbid.py
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create_dataset_ytbid.py
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from IPython import embed
import numpy as np
import pickle
import os
import cv2
import functools
import xml.etree.ElementTree as ET
import sys
import multiprocessing as mp
from multiprocessing import Pool
from fire import Fire
from tqdm import tqdm
from glob import glob
sys.path.append(os.getcwd())
from net.config import config
from lib.utils import get_instance_image, add_box_img
def worker(output_dir, video_dir):
instance_crop_size = 500
if 'YT-BB' in video_dir:
image_names = glob(os.path.join(video_dir, '*.jpg'))
image_names = sorted(image_names, key=lambda x: int(x.split('/')[-1].split('_')[1]))
video_name = '_'.join(os.path.basename(video_dir).split('_')[:-1])
with open('/dataset_ssd/std_xml_ytb/' + video_name + '.pkl', 'rb') as f:
std_xml_dict = pickle.load(f)
save_folder = os.path.join(output_dir, video_name)
if not os.path.exists(save_folder):
os.mkdir(save_folder)
trajs = {}
for image_name in image_names:
img = cv2.imread(image_name)
h_img, w_img, _ = img.shape
img_mean = tuple(map(int, img.mean(axis=(0, 1))))
frame = image_name.split('_')[-2]
if int(frame) == 0:
anno = std_xml_dict[str(int(frame))]
else:
anno = std_xml_dict[frame]
filename = '_'.join(image_name.split('/')[-1].split('_')[:-1])
for class_id in anno.keys():
for track_id in anno[class_id].keys():
class_name, present, xmin_scale, xmax_scale, ymin_scale, ymax_scale = anno[class_id][track_id]
new_track_id = class_id.zfill(3) + track_id.zfill(3)
bbox = np.array(list(map(float, [xmin_scale, xmax_scale, ymin_scale, ymax_scale]))) * np.array(
[w_img, w_img, h_img, h_img])
if present == 'present':
if new_track_id in trajs.keys():
trajs[new_track_id].append(filename)
else:
trajs[new_track_id] = [filename]
bbox = np.array(
[(bbox[1] + bbox[0]) / 2, (bbox[3] + bbox[2]) / 2, bbox[1] - bbox[0] + 1,
bbox[3] - bbox[2] + 1])
instance_img, w, h, _ = get_instance_image(img, bbox,
config.exemplar_size, instance_crop_size,
config.context_amount,
img_mean)
instance_img_name = os.path.join(save_folder,
filename + ".{}.x_{:.2f}_{:.2f}_{:.0f}_{:.0f}.jpg".format(
new_track_id,
w, h, w_img, h_img))
cv2.imwrite(instance_img_name, instance_img)
elif present == 'absent':
continue
else:
image_names = glob(os.path.join(video_dir, '*.JPEG'))
image_names = sorted(image_names, key=lambda x: int(x.split('/')[-1].split('.')[0]))
video_name = video_dir.split('/')[-1]
save_folder = os.path.join(output_dir, video_name)
if not os.path.exists(save_folder):
os.mkdir(save_folder)
trajs = {}
for image_name in image_names:
img = cv2.imread(image_name)
h_img, w_img, _ = img.shape
img_mean = tuple(map(int, img.mean(axis=(0, 1))))
anno_name = image_name.replace('Data', 'Annotations')
anno_name = anno_name.replace('JPEG', 'xml')
tree = ET.parse(anno_name)
root = tree.getroot()
bboxes = []
filename = root.find('filename').text
for obj in root.iter('object'):
bbox = obj.find('bndbox')
bbox = list(map(int, [bbox.find('xmin').text,
bbox.find('ymin').text,
bbox.find('xmax').text,
bbox.find('ymax').text]))
trkid = int(obj.find('trackid').text)
if trkid in trajs:
trajs[trkid].append(filename)
else:
trajs[trkid] = [filename]
bbox = np.array(
[(bbox[2] + bbox[0]) / 2, (bbox[3] + bbox[1]) / 2, bbox[2] - bbox[0] + 1,
bbox[3] - bbox[1] + 1])
instance_img, w, h, _ = get_instance_image(img, bbox,
config.exemplar_size, instance_crop_size,
config.context_amount,
img_mean)
instance_img_name = os.path.join(save_folder,
filename + ".{:02d}.x_{:.2f}_{:.2f}_{:.0f}_{:.0f}.jpg".format(trkid, w,
h, w_img,
h_img))
cv2.imwrite(instance_img_name, instance_img)
return video_name, trajs
def processing(vid_dir, ytb_dir, output_dir, num_threads=mp.cpu_count()):
# get all 4417 videos in vid and all video in ytbb
vid_video_dir = os.path.join(vid_dir, 'Data/VID')
ytb_video_dir = ytb_dir
# -------------------------------------------------------------------------------------
# all_videos = glob(os.path.join(ytb_video_dir, 'v*/youtube_dection_frame_temp/*'))
# all_videos = glob('/mnt/diska1/YT-BB/v1/youtube_dection_frame_temp/130dH0FNXio_*')
# -------------------------------------------------------------------------------------
all_videos = glob(os.path.join(vid_video_dir, 'train/ILSVRC2015_VID_train_0000/*')) + \
glob(os.path.join(vid_video_dir, 'train/ILSVRC2015_VID_train_0001/*')) + \
glob(os.path.join(vid_video_dir, 'train/ILSVRC2015_VID_train_0002/*')) + \
glob(os.path.join(vid_video_dir, 'train/ILSVRC2015_VID_train_0003/*')) + \
glob(os.path.join(vid_video_dir, 'val/*')) + \
glob(os.path.join(ytb_video_dir, 'v*/youtube_dection_frame_temp/*'))
meta_data = []
if not os.path.exists(output_dir):
os.makedirs(output_dir)
all_videos = [x for x in all_videos if 'imagelist' not in x]
# for video in tqdm(all_videos):
# functools.partial(worker, output_dir)(video)
# -------------------------------------------------------------------------------------
# load former meta_data
# with open('/dataset_ssd/ytb_vid_rpn/meta_data.pkl', 'rb') as f:
# former_pkl = pickle.load(f)
# former_dict = {x[0]: x[1] for x in former_pkl if 'ILSVRC2015' in x[0]}
# former_vid = []
# for k in former_dict.keys():
# former_vid.append((k, former_dict[k]))
# meta_data.extend(former_vid)
# -------------------------------------------------------------------------------------
with Pool(processes=num_threads) as pool:
for ret in tqdm(pool.imap_unordered(
functools.partial(worker, output_dir), all_videos), total=len(all_videos)):
meta_data.append(ret)
tqdm.write(ret[0])
# save meta data
pickle.dump(meta_data, open(os.path.join(output_dir, "meta_data.pkl"), 'wb'))
if __name__ == '__main__':
Fire(processing)