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video_preprocess.py
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video_preprocess.py
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import os
import torch
import numpy as np
import json
import pickle
import argparse
from torch import nn
from tqdm import tqdm
from mmdet.apis import init_detector
from mmdet.datasets.pipelines import Compose
from mmcv.parallel import collate, scatter
def parse_arguments():
parser = argparse.ArgumentParser()
parser.add_argument('--detection_checkpoint_path', type=str, default='mmdetection/checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth', help="The directory of the detection checkpoint path.")
parser.add_argument('--detection_config_path', type=str, default='mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py', help="The directory of the detection configuration path.")
parser.add_argument('--video_data_path', type=str, default='MIA/datasets/video_data', help="The directory of the video data path.")
parser.add_argument('--video_feats_path', type=str, default='video_feats_test.pkl', help="The directory of the video features path.")
parser.add_argument('--frames_path', type=str, default='MIA/datasets/human_annotations/screenshots', help="The directory of human-annotated frames with bbox.")
parser.add_argument('--speaker_annotation_path', type=str, default='MIA/datasets/human_annotations/speaker_annotations.json', help="The original file of annotated speaker ids.")
parser.add_argument('--TalkNet_speaker_path', type=str, default='MIA/datasets/speaker_annotation/Talknet', help="The output directory of TalkNet model.")
parser.add_argument("--use_TalkNet", action="store_true", help="whether using the annotations from TalkNet to get video features.")
parser.add_argument("--roi_feat_size", type = int, default=7, help="The size of Faster R-CNN region of interest.")
args = parser.parse_args()
return args
class VideoFeature:
def __init__(self, args):
self.model, self.device = self._init_detection_model(args)
self.avg_pool = nn.AvgPool2d(args.roi_feat_size)
def _get_feats(self, args):
if args.use_TalkNet:
self.bbox_feats = self._get_TalkNet_features(args)
else:
self.bbox_feats = self._get_Annotated_features(args)
def _save_feats(self, args):
video_feats_path = os.path.join(args.video_data_path, args.video_feats_path)
with open(video_feats_path, 'wb') as f:
pickle.dump(self.bbox_feats, f)
def _init_detection_model(self, args):
model = init_detector(args.detection_config_path, args.detection_checkpoint_path, device='cuda:0')
device = next(model.parameters()).device # model device
return model, device
def _get_TalkNet_features(self, args):
'''
Input:
args.TalkNet_speaker_path
Output:
The format of video features
{
'video_clip_id_a':[frame_a_feat, frame_b_feat, ..., frame_N_feat],
'video_clip_id_b':[xxx]
}
'''
video_feats = {}
error_cnt = 0
error_path = 0
for video_clip_name in tqdm(os.listdir(args.TalkNet_speaker_path), desc = 'Video'):
frames_path = os.path.join(args.TalkNet_speaker_path, video_clip_name, 'pyframes')
bestperson_path = os.path.join(args.TalkNet_speaker_path, video_clip_name, 'pywork', 'best_persons.npy')
if not os.path.exists(bestperson_path):
error_path += 1
continue
bestpersons = np.load(bestperson_path)
for frame, bbox in tqdm(enumerate(bestpersons), desc = 'Frame'):
if (bbox[0] == 0) and (bbox[1] == 0) and (bbox[2] == 0) and (bbox[3] == 0):
error_cnt += 1
continue
frame_name = str('%06d' % frame)
frame_path = os.path.join(frames_path, frame_name + '.jpg')
"""
img = cv2.imread(img_ath)
height, width, channel = img.shape
roi = [0, 0, width, height]
"""
roi = bbox.tolist()
roi.insert(0, 0.)
bbox_feat = self._extract_roi_feats(self.model, self.device, frame_path, roi)
bbox_feat = self._average_pooling(bbox_feat)
bbox_feat = bbox_feat.detach().cpu().numpy()
if video_clip_name not in video_feats.keys():
video_feats[video_clip_name] = [bbox_feat]
else:
video_feats[video_clip_name].append(bbox_feat)
print('The number of error annotations is {}'.format(error_cnt))
print('The number of error paths is {}'.format(error_path))
return video_feats
def _get_Annotated_features(self, args):
'''
Input:
args.video_data_path
args.speaker_annotation_path
args.frames_path
Output:
The format of video features
{
'video_clip_id_a':[frame_a_feat, frame_b_feat, ..., frame_N_feat],
'video_clip_id_b':[xxx]
}
'''
speaker_annotation_path = os.path.join(args.video_data_path, args.speaker_annotation_path)
speaker_annotations = json.load(open(speaker_annotation_path, 'r'))
video_feats = {}
error_cnt = 0
try:
for key in tqdm(speaker_annotations.keys(), desc = 'Frame'):
if 'bbox' not in speaker_annotations[key].keys():
error_cnt += 1
continue
roi = speaker_annotations[key]['bbox'][:4]
roi.insert(0, 0.)
frame_name = '_'.join(key.strip('.jpg').split('_')[:-1])
frame_path = os.path.join(args.frames_path, frame_name + '.jpg')
bbox_feat = self._extract_roi_feats(self.model, self.device, frame_path, roi)
bbox_feat = self._average_pooling(bbox_feat)
bbox_feat = bbox_feat.detach().cpu().numpy()
video_clip_name = '_'.join(key.strip('.jpg').split('_')[:-2])
if video_clip_name not in video_feats.keys():
video_feats[video_clip_name] = [bbox_feat]
else:
video_feats[video_clip_name].append(bbox_feat)
except Exception as e:
print(e)
print('The number of error annotations is {}'.format(error_cnt))
return video_feats
def _extract_roi_feats(self, model, device, file_path, roi):
roi = torch.tensor([roi]).to(device)
cfg = model.cfg
# prepare data
data = dict(img_info=dict(filename = file_path), img_prefix=None)
# build the data pipeline
test_pipeline = Compose(cfg.data.test.pipeline)
data = test_pipeline(data)
data = collate([data], samples_per_gpu=1)
data = scatter(data, [device])[0]
img = data['img'][0]
x = model.extract_feat(img)
bbox_feat = model.roi_head.bbox_roi_extractor(
x[:model.roi_head.bbox_roi_extractor.num_inputs], roi)
return bbox_feat
def _average_pooling(self, x):
"""
Args:
x: dtype: numpy.ndarray
"""
x = self.avg_pool(x)
x = x.flatten(1)
return x
if __name__ == '__main__':
args = parse_arguments()
args.use_TalkNet = True
video_data = VideoFeature(args)
video_data._get_feats(args)
video_data._save_feats(args)