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gen_patches.py
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import os
from PIL import Image
import time
import argparse
import torch
import pickle
import numpy as np
from torchvision.ops import roi_align
from torchvision import transforms
from torch.utils.data import Dataset
from tqdm import tqdm
class VideoAnomalyDataset(Dataset):
"""Video Anomaly Dataset."""
def __init__(self,
data_dir=None,
dataset='shanghaitech',
detect_dir=None,
filter_ratio=0.9,
frame_num=7):
assert os.path.exists(data_dir), "{} does not exist.".format(data_dir)
assert dataset in ['shanghaitech', 'ped2', 'avenue'], 'dataset missing'
self.dataset = dataset
self.data_dir = data_dir
self.filter_ratio = filter_ratio
file_list = os.listdir(data_dir)
file_list.sort()
self.videos = 0
self.frame_num = frame_num
assert self.frame_num % 2 == 1, 'odd number is preferred'
self.half_frame_num = self.frame_num // 2
self.videos_list = []
# speed up image loading in the case of multiple objects within the same frame
self.cache_clip = None
self.cache_video = None
self.cache_frame = None
if 'train' in data_dir:
self.test_stage = False
elif 'test' in data_dir:
self.test_stage = True
else:
raise ValueError("data dir: {} is error, not train or test.".format(data_dir))
self.phase = 'testing' if self.test_stage else 'training'
# only use 20% samples for shanghaitech
self.sample_step = 1 if self.test_stage else 5
if self.dataset != 'shanghaitech':
self.sample_step = 1
with open(detect_dir, 'rb') as f:
self.detect = pickle.load(f)
self.objects_list = []
self._load_data(file_list)
self.save_objects()
def _load_data(self, file_list):
t0 = time.time()
total_frames = 0
start_ind = self.half_frame_num if self.test_stage else self.frame_num - 1
for video_file in file_list:
if video_file not in self.videos_list:
self.videos_list.append(video_file)
l = os.listdir(self.data_dir + '/' + video_file)
self.videos += 1
length = len(l)
total_frames += length
for frame in range(start_ind, length - start_ind, self.sample_step):
detect_result = self.detect[video_file][frame]
detect_result = detect_result[detect_result[:, 4] > self.filter_ratio, :]
object_num = detect_result.shape[0]
for i in range(object_num):
self.objects_list.append({"video_name":video_file, "frame":frame, "object": i})
print("Load {} videos {} frames, {} objects, in {} s.".format(self.videos, total_frames, len(self.objects_list), time.time() - t0))
def save_objects(self):
if not os.path.exists(self.dataset):
os.makedirs(self.dataset)
static_obj = []
for i in tqdm(range(len(self.objects_list))):
record = self.objects_list[i]
obj = self.get_object(record["video_name"], record["frame"], record["object"])
video_dir = os.path.join(self.dataset, self.phase, record["video_name"])
if not os.path.exists(video_dir):
os.makedirs(video_dir)
obj = obj.numpy()
np.save(os.path.join(video_dir, str(record['frame']) + '_' + str(record['object']) + '.npy'), obj)
def __len__(self):
return len(self.objects_list)
def __video_list__(self):
return self.videos_list
def get_object(self, video_name, frame, obj_id):
detect_result = self.detect[video_name][frame]
detect_result = detect_result[detect_result[:, 4] > self.filter_ratio, :]
frame = frame - self.half_frame_num
if video_name == self.cache_video and self.cache_frame == frame:
img = self.cache_clip
else:
img = self.get_frame(video_name, frame)
self.cache_frame = frame
self.cache_video = video_name
self.cache_clip = img
obj = self.crop_object(img, detect_result, obj_id)
return obj
def get_frame(self, video_name, frame):
video_dir = self.data_dir + '/' + video_name + '/'
frame_list = os.listdir(video_dir)
img = self.read_frame_data(video_dir, frame, frame_list)
return img
def read_single_frame(self, video_dir, frame, frame_list):
transform = transforms.ToTensor()
img = None
if self.dataset == 'ped2':
frame_ = "{:03d}.jpg".format(frame)
elif self.dataset == 'avenue':
frame_ = "{:04d}.jpg".format(frame)
else:
if(self.test_stage):
frame_ = "{:03d}.jpg".format(frame)
else:
frame_ = "{:06d}.jpg".format(frame)
assert (frame_ in frame_list),\
"The frame {} is out of the range:{}.".format(int(frame_), len(frame_list))
jpg_dir = '{}/{}'.format(video_dir, frame_)
assert os.path.exists(jpg_dir), "{} isn\'t exists.".format(jpg_dir)
img = Image.open(jpg_dir)
img = transform(img).unsqueeze(dim=0)
img = img.permute([1, 0, 2, 3])
return img
def read_frame_data(self, video_dir, frame, frame_list):
img = None
for f in range(self.frame_num):
_img = self.read_single_frame(video_dir, frame + f, frame_list)
if f == 0:
img = _img
else:
img = torch.cat((img, _img), dim=1)
return img
# TO-DO: compare the RoiAlign with Crop+Resize
def crop_object(self, frame_img, bbox, i, size=(64, 64)):
# frame_img : C * D * H * W
shape = frame_img.shape
bbox = torch.from_numpy(bbox[i, :4]).float()
frame_img = frame_img.reshape(1, -1, shape[2], shape[3])
frame_img = roi_align(frame_img, [bbox.unsqueeze(dim=0)], output_size=size)
frame_img = frame_img.reshape(-1, shape[1], size[0], size[1])
return frame_img
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="patch generation")
parser.add_argument("--dataset", type=str, default='shanghaitech')
parser.add_argument("--phase", type=str, default='test', choices=['train', 'test'])
parser.add_argument("--filter_ratio", type=float, default=0.8)
parser.add_argument("--sample_num", type=int, default=9)
args = parser.parse_args()
data_dir = "/irip/wangguodong_2020/projects/datasets/vad/" # directory for raw frames
shanghai_dataset = VideoAnomalyDataset(data_dir=data_dir + args.dataset + '/' + args.phase + 'ing/',
detect_dir='detect/' + args.dataset + '_' + args.phase + '_detect_result_yolov3.pkl',
dataset=args.dataset,
filter_ratio=args.filter_ratio,
frame_num=args.sample_num)