├── localization.png ├── annotations ├── Procedure&Action_ID.xlsx ├── task1&3 │ ├── val.csv │ ├── test.csv │ └── train.csv └── task2 │ ├── procedure_val.csv │ └── action_val.csv ├── tools ├── preprocess_videos.py ├── clip.py ├── faceblur.py └── downloader.py ├── requirements.txt ├── feature_extraction ├── videotransforms.py ├── charades_dataset_full.py ├── charades_dataset.py ├── train_i3d.py ├── build_rawframes.py ├── extract_features.py └── pytorch_i3d.py ├── Disclaimer.txt ├── README.md └── CC-BY-4.0 /localization.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/minghu0830/NurViD-benchmark/HEAD/localization.png -------------------------------------------------------------------------------- /annotations/Procedure&Action_ID.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/minghu0830/NurViD-benchmark/HEAD/annotations/Procedure&Action_ID.xlsx -------------------------------------------------------------------------------- /tools/preprocess_videos.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | import os 3 | import glob 4 | from tqdm import tqdm 5 | 6 | def change_fps_and_resolution(src_path, dst_path, new_fps, new_width, new_height): 7 | cap = cv2.VideoCapture(src_path) 8 | 9 | width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) 10 | height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) 11 | fps = cap.get(cv2.CAP_PROP_FPS) 12 | 13 | frame_interval = round(fps / new_fps) 14 | 15 | fourcc = cv2.VideoWriter_fourcc(*'mp4v') 16 | 17 | out = cv2.VideoWriter(dst_path, fourcc, new_fps, (new_width, new_height)) 18 | 19 | frame_id = 0 20 | while True: 21 | ret, frame = cap.read() 22 | 23 | if not ret: 24 | break 25 | 26 | if frame_id % frame_interval == 0: 27 | frame = cv2.resize(frame, (new_width, new_height)) 28 | out.write(frame) 29 | 30 | frame_id += 1 31 | 32 | cap.release() 33 | out.release() 34 | 35 | source_path = "/source_path" 36 | destination_path = "/output_path" 37 | files = glob.glob(os.path.join(source_path, "*.mp4")) 38 | 39 | for file in tqdm(files, desc="Processing videos"): 40 | filename = os.path.basename(file) 41 | dst_path = os.path.join(destination_path, filename) 42 | change_fps_and_resolution(file, dst_path, 25, 256, 256) 43 | -------------------------------------------------------------------------------- /tools/clip.py: -------------------------------------------------------------------------------- 1 | import json 2 | import os 3 | import pandas as pd 4 | import subprocess 5 | 6 | # 读取json文件 7 | with open("NurViD_annotations.json", "r") as f: 8 | data = json.load(f) 9 | 10 | # 创建一个空的列表，用于存储剪辑视频的信息 11 | video_info = [] 12 | 13 | # 遍历json文件中的每个视频 14 | video_names = data.keys() 15 | for video_name in video_names: 16 | video_path = data[video_name]["url"] 17 | video_name = video_path.split("=")[-1] 18 | 19 | # 指定本地视频所在的目录 20 | video_dir = "/video_path" 21 | 22 | # 拼接本地视频的完整路径 23 | video_path = os.path.join(video_dir, video_name+'.mp4') 24 | 25 | operation_id = data[video_name]["operationID"] 26 | annotations = data[video_name]["annotations"] 27 | 28 | 29 | # 遍历每个视频的标注片段 30 | for i, annotation in enumerate(annotations): 31 | action_id = annotation["actionID"] 32 | start_time = annotation["segment"][0] 33 | end_time = annotation["segment"][1] 34 | duration = end_time - start_time 35 | 36 | # 指定导出视频所在的目录 37 | new_video_dir = "/segments_save_path" 38 | 39 | # 拼接导出视频的完整路径 40 | new_video_name = os.path.join(new_video_dir, video_name + "_" + str(i + 1) + ".mp4") 41 | 42 | # 用ffmpeg命令剪辑视频 43 | command = f"ffmpeg -ss {start_time} -i {video_path} -t {duration} -c copy {new_video_name}" 44 | #subprocess.call(command) 45 | os.system(command) 46 | 47 | # 将剪辑视频的信息作为一个字典添加到列表中 48 | video_info.append({"Video Name": new_video_name, "Operation ID": operation_id, "Action ID": action_id}) 49 | 50 | # 将列表转换为DataFrame对象 51 | df = pd.DataFrame(video_info) 52 | 53 | # 将DataFrame对象保存为excel文件，文件名为视频名字加.xlsx 54 | output_file = "excel_save_path" 55 | df.to_excel(output_file, index=False) 56 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | absl-py==1.4.0 2 | antlr4-python3-runtime==4.9.3 3 | astor==0.8.1 4 | av==9.0.0 5 | Babel==2.12.1 6 | bce-python-sdk==0.8.90 7 | black==23.3.0 8 | blinker==1.6.2 9 | blobfile==2.0.2 10 | Brotli==1.0.9 11 | cachetools==5.3.0 12 | certifi==2023.5.7 13 | charset-normalizer==3.1.0 14 | click==8.1.7 15 | cloudpickle==2.2.1 16 | contourpy==1.0.7 17 | cycler==0.11.0 18 | decorator==4.4.2 19 | detectron2 @ file:///home/huming/medicnet/SlowFast/detectron2_repo 20 | et-xmlfile==1.1.0 21 | fairscale==0.4.13 22 | filelock==3.12.2 23 | Flask==2.3.3 24 | flask-babel==3.1.0 25 | fonttools==4.39.4 26 | fvcore==0.1.5.post20221220 27 | gdown==4.7.1 28 | google-auth==2.18.0 29 | google-auth-oauthlib==1.0.0 30 | grpcio==1.54.0 31 | hydra-core==1.3.2 32 | idna==3.4 33 | imageio==2.28.1 34 | imageio-ffmpeg==0.4.8 35 | importlib-metadata==6.6.0 36 | importlib-resources==5.12.0 37 | iopath==0.1.9 38 | itsdangerous==2.1.2 39 | Jinja2==3.1.2 40 | joblib==1.2.0 41 | kiwisolver==1.4.4 42 | lxml==4.9.3 43 | Markdown==3.4.3 44 | MarkupSafe==2.1.3 45 | matplotlib==3.7.1 46 | moviepy==1.0.3 47 | mpi4py==3.1.4 48 | mutagen==1.46.0 49 | mypy-extensions==1.0.0 50 | networkx==3.1 51 | numpy==1.24.3 52 | oauthlib==3.2.2 53 | omegaconf==2.3.0 54 | opencv-python==4.5.5.64 55 | openpyxl==3.1.2 56 | opt-einsum==3.3.0 57 | packaging==23.1 58 | paddle-bfloat==0.1.7 59 | pandas==2.0.1 60 | parameterized==0.9.0 61 | pathspec==0.11.1 62 | Pillow==9.5.0 63 | platformdirs==3.5.1 64 | portalocker==2.7.0 65 | prettytable==3.9.0 66 | proglog==0.1.10 67 | protobuf==4.23.0 68 | psutil==5.9.5 69 | pyasn1==0.5.0 70 | pyasn1-modules==0.3.0 71 | pycocotools==2.0.6 72 | pycryptodome==3.19.0 73 | pycryptodomex==3.17 74 | pyparsing==3.0.9 75 | python-dateutil==2.8.2 76 | -e git+https://github.com/facebookresearch/pytorchvideo.git@1730313bb5eda307657f971365de7a1359e1a68e#egg=pytorchvideo 77 | pytz==2023.3.post1 78 | PyYAML==6.0 79 | rarfile==4.1 80 | requests==2.30.0 81 | requests-oauthlib==1.3.1 82 | rsa==4.9 83 | scikit-learn==1.2.2 84 | scipy==1.10.1 85 | simplejson==3.19.1 86 | six==1.16.0 87 | sklearn==0.0.post5 88 | -e git+https://github.com/facebookresearch/slowfast@7a6f09e2127188861d0a7393e3d43707aa0a7f93#egg=slowfast 89 | tabulate==0.9.0 90 | tensorboard==2.13.0 91 | tensorboard-data-server==0.7.0 92 | termcolor==2.3.0 93 | threadpoolctl==3.1.0 94 | tomli==2.0.1 95 | torch==1.13.0+cu117 96 | torchvision==0.14.0+cu117 97 | tqdm==4.66.1 98 | typing_extensions==4.5.0 99 | tzdata==2023.3 100 | urllib3==1.26.15 101 | visualdl==2.5.3 102 | websockets==10.4 103 | Werkzeug==2.3.7 104 | yacs==0.1.8 105 | yt-dlp==2023.3.4 106 | zipp==3.15.0 107 | -------------------------------------------------------------------------------- /tools/faceblur.py: -------------------------------------------------------------------------------- 1 | import os 2 | import cv2 3 | import concurrent.futures 4 | from tqdm import tqdm 5 | 6 | def blur_faces_in_video(video_path, output_path): 7 | # 加载人脸识别器（这里使用OpenCV的Haar级联分类器） 8 | face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml') 9 | 10 | # 打开视频文件 11 | video = cv2.VideoCapture(video_path) 12 | fps = video.get(cv2.CAP_PROP_FPS) 13 | width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH)) 14 | height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT)) 15 | 16 | # 创建视频编写器 17 | fourcc = cv2.VideoWriter_fourcc(*'mp4v') 18 | output_video = cv2.VideoWriter(output_path, fourcc, fps, (width, height)) 19 | 20 | while True: 21 | # 读取视频帧 22 | ret, frame = video.read() 23 | 24 | if not ret: 25 | break 26 | 27 | # 将帧转换为灰度图像 28 | gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) 29 | 30 | # 检测人脸 31 | faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30)) 32 | 33 | # 对每个检测到的人脸进行模糊处理 34 | for (x, y, w, h) in faces: 35 | # 提取人脸区域 36 | face_roi = frame[y:y+h, x:x+w] 37 | 38 | # 应用模糊处理 39 | blurred_face = cv2.GaussianBlur(face_roi, (99, 99), 30) 40 | 41 | # 将模糊后的人脸区域放回原始帧 42 | frame[y:y+h, x:x+w] = blurred_face 43 | 44 | # 将处理后的帧写入输出视频 45 | output_video.write(frame) 46 | 47 | # 释放资源 48 | video.release() 49 | output_video.release() 50 | 51 | def process_video(video): 52 | input_path, output_path = video 53 | blur_faces_in_video(input_path, output_path) 54 | 55 | def blur_faces_in_videos(input_folder, output_folder): 56 | # 创建输出文件夹 57 | os.makedirs(output_folder, exist_ok=True) 58 | 59 | # 构建视频文件路径列表 60 | videos = [] 61 | for file_name in os.listdir(input_folder): 62 | if file_name.endswith('.mp4'): 63 | input_path = os.path.join(input_folder, file_name) 64 | output_path = os.path.join(output_folder, file_name) 65 | videos.append((input_path, output_path)) 66 | 67 | # 使用多线程处理视频 68 | with concurrent.futures.ThreadPoolExecutor() as executor: 69 | futures = [executor.submit(process_video, video) for video in videos] 70 | 71 | # 显示进度条 72 | for _ in tqdm(concurrent.futures.as_completed(futures), total=len(futures), desc='Processing Videos'): 73 | pass 74 | 75 | print("批量处理完成！") 76 | 77 | # 使用示例 78 | input_folder_path = "input_folder" 79 | output_folder_path = "output_folder" 80 | 81 | blur_faces_in_videos(input_folder_path, output_folder_path) -------------------------------------------------------------------------------- /feature_extraction/videotransforms.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import numbers 3 | import random 4 | 5 | class RandomCrop(object): 6 | """Crop the given video sequences (t x h x w) at a random location. 7 | Args: 8 | size (sequence or int): Desired output size of the crop. If size is an 9 | int instead of sequence like (h, w), a square crop (size, size) is 10 | made. 11 | """ 12 | 13 | def __init__(self, size): 14 | if isinstance(size, numbers.Number): 15 | self.size = (int(size), int(size)) 16 | else: 17 | self.size = size 18 | 19 | @staticmethod 20 | def get_params(img, output_size): 21 | """Get parameters for ``crop`` for a random crop. 22 | Args: 23 | img (PIL Image): Image to be cropped. 24 | output_size (tuple): Expected output size of the crop. 25 | Returns: 26 | tuple: params (i, j, h, w) to be passed to ``crop`` for random crop. 27 | """ 28 | t, h, w, c = img.shape 29 | th, tw = output_size 30 | if w == tw and h == th: 31 | return 0, 0, h, w 32 | 33 | i = random.randint(0, h - th) if h!=th else 0 34 | j = random.randint(0, w - tw) if w!=tw else 0 35 | return i, j, th, tw 36 | 37 | def __call__(self, imgs): 38 | 39 | i, j, h, w = self.get_params(imgs, self.size) 40 | 41 | imgs = imgs[:, i:i+h, j:j+w, :] 42 | return imgs 43 | 44 | def __repr__(self): 45 | return self.__class__.__name__ + '(size={0})'.format(self.size) 46 | 47 | class CenterCrop(object): 48 | """Crops the given seq Images at the center. 49 | Args: 50 | size (sequence or int): Desired output size of the crop. If size is an 51 | int instead of sequence like (h, w), a square crop (size, size) is 52 | made. 53 | """ 54 | 55 | def __init__(self, size): 56 | if isinstance(size, numbers.Number): 57 | self.size = (int(size), int(size)) 58 | else: 59 | self.size = size 60 | 61 | def __call__(self, imgs): 62 | """ 63 | Args: 64 | img (PIL Image): Image to be cropped. 65 | Returns: 66 | PIL Image: Cropped image. 67 | """ 68 | t, h, w, c = imgs.shape 69 | th, tw = self.size 70 | i = int(np.round((h - th) / 2.)) 71 | j = int(np.round((w - tw) / 2.)) 72 | 73 | return imgs[:, i:i+th, j:j+tw, :] 74 | 75 | 76 | def __repr__(self): 77 | return self.__class__.__name__ + '(size={0})'.format(self.size) 78 | 79 | 80 | class RandomHorizontalFlip(object): 81 | """Horizontally flip the given seq Images randomly with a given probability. 82 | Args: 83 | p (float): probability of the image being flipped. Default value is 0.5 84 | """ 85 | 86 | def __init__(self, p=0.5): 87 | self.p = p 88 | 89 | def __call__(self, imgs): 90 | """ 91 | Args: 92 | img (seq Images): seq Images to be flipped. 93 | Returns: 94 | seq Images: Randomly flipped seq images. 95 | """ 96 | if random.random() < self.p: 97 | # t x h x w 98 | return np.flip(imgs, axis=2).copy() 99 | return imgs 100 | 101 | def __repr__(self): 102 | return self.__class__.__name__ + '(p={})'.format(self.p) 103 | -------------------------------------------------------------------------------- /Disclaimer.txt: -------------------------------------------------------------------------------- 1 | The software project, data, and models provided by our GitHub project are provided "as is," without warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose, and non-infringement. 2 | 3 | In no event shall the project owners or contributors be liable for any direct, indirect, incidental, special, exemplary, or consequential damages (including, but not limited to, procurement of substitute goods or services; loss of use, data, or profits; or business interruption) however caused and on any theory of liability, whether in contract, strict liability, or tort (including negligence or otherwise) arising in any way out of the use of this software project, data, or models, even if advised of the possibility of such damage. 4 | 5 | Users of this software project, data, and models are solely responsible for any consequences of their use. The project owners and contributors shall not be held responsible for any subsequent or potential harm caused by the use of this software project, data, or models. 6 | 7 | By using this software project, data, or models, users accept and agree to this disclaimer. If users do not agree to the terms of this disclaimer, they should not use this software project, data, or models. 8 | 9 | It is important to note that this software project, data, and models are still in the research phase and are provided for experimental purposes only. As such, the project owners and contributors do not guarantee the accuracy, completeness, or usefulness of the software project, data, or models. 10 | 11 | Furthermore, due to the experimental nature of this software project, data, and models, it is possible that they may contain or generate inappropriate responses, errors, or inconsistencies. Users should exercise caution when using this software project, data, or models, and should not rely solely on them for any critical or sensitive tasks. 12 | 13 | The project owners and contributors shall not be held responsible for any damages, losses, or liabilities arising from the use of this software project, data, or models, including but not limited to, any inappropriate responses generated by the software project, data, or models. 14 | 15 | By using this software project, data, or models, users acknowledge and accept the experimental nature of the software project, data, and models, and understand the potential risks and limitations associated with their use. If users do not agree to the terms of this disclaimer, they should not use this software project, data, or models. 16 | 17 | The software project, data, and models provided by our GitHub project are intended for research purposes only. They should not be used for any commercial, business, or legal purposes, and should not be relied upon as a substitute for professional advice or judgment. 18 | 19 | Users of this software project, data, and models are strictly prohibited from using them for any commercial purposes, including but not limited to, selling, licensing, or distributing the software project, data, or models to third parties. 20 | 21 | The project owners and contributors shall not be held responsible for any damages, losses, or liabilities arising from the use of this software project, data, or models for any commercial or business purposes. 22 | 23 | By using this software project, data, or models, users agree to use them for research purposes only, and not for any commercial or business purposes. If users do not agree to the terms of this disclaimer, they should not use this software project, data, or models. -------------------------------------------------------------------------------- /feature_extraction/charades_dataset_full.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.utils.data as data_utl 3 | from torch.utils.data.dataloader import default_collate 4 | 5 | import numpy as np 6 | import json 7 | import csv 8 | import h5py 9 | 10 | import os 11 | import os.path 12 | 13 | import cv2 14 | 15 | def video_to_tensor(pic): 16 | """Convert a ``numpy.ndarray`` to tensor. 17 | Converts a numpy.ndarray (T x H x W x C) 18 | to a torch.FloatTensor of shape (C x T x H x W) 19 | 20 | Args: 21 | pic (numpy.ndarray): Video to be converted to tensor. 22 | Returns: 23 | Tensor: Converted video. 24 | """ 25 | return torch.from_numpy(pic.transpose([3,0,1,2])) 26 | 27 | 28 | def load_rgb_frames(image_dir, vid, start, num): 29 | frames = [] 30 | for i in range(start, start+num): 31 | img = cv2.imread(os.path.join(image_dir, vid, vid+'-'+str(i).zfill(6)+'.jpg'))[:, :, [2, 1, 0]] 32 | w,h,c = img.shape 33 | if w < 226 or h < 226: 34 | d = 226.-min(w,h) 35 | sc = 1+d/min(w,h) 36 | img = cv2.resize(img,dsize=(0,0),fx=sc,fy=sc) 37 | img = (img/255.)*2 - 1 38 | frames.append(img) 39 | return np.asarray(frames, dtype=np.float32) 40 | 41 | def load_flow_frames(image_dir, vid, start, num): 42 | frames = [] 43 | for i in range(start, start+num): 44 | imgx = cv2.imread(os.path.join(image_dir, vid, vid+'-'+str(i).zfill(6)+'x.jpg'), cv2.IMREAD_GRAYSCALE) 45 | imgy = cv2.imread(os.path.join(image_dir, vid, vid+'-'+str(i).zfill(6)+'y.jpg'), cv2.IMREAD_GRAYSCALE) 46 | 47 | w,h = imgx.shape 48 | if w < 224 or h < 224: 49 | d = 224.-min(w,h) 50 | sc = 1+d/min(w,h) 51 | imgx = cv2.resize(imgx,dsize=(0,0),fx=sc,fy=sc) 52 | imgy = cv2.resize(imgy,dsize=(0,0),fx=sc,fy=sc) 53 | 54 | imgx = (imgx/255.)*2 - 1 55 | imgy = (imgy/255.)*2 - 1 56 | img = np.asarray([imgx, imgy]).transpose([1,2,0]) 57 | frames.append(img) 58 | return np.asarray(frames, dtype=np.float32) 59 | 60 | 61 | def make_dataset(split_file, split, root, mode, num_classes=157): 62 | dataset = [] 63 | with open(split_file, 'r') as f: 64 | data = json.load(f) 65 | 66 | i = 0 67 | for vid in data.keys(): 68 | if data[vid]['subset'] != split: 69 | continue 70 | 71 | if not os.path.exists(os.path.join(root, vid)): 72 | continue 73 | num_frames = len(os.listdir(os.path.join(root, vid))) 74 | if mode == 'flow': 75 | num_frames = num_frames//2 76 | 77 | label = np.zeros((num_classes,num_frames), np.float32) 78 | 79 | fps = num_frames/data[vid]['duration'] 80 | for ann in data[vid]['actions']: 81 | for fr in range(0,num_frames,1): 82 | if fr/fps > ann[1] and fr/fps < ann[2]: 83 | label[ann[0], fr] = 1 # binary classification 84 | dataset.append((vid, label, data[vid]['duration'], num_frames)) 85 | i += 1 86 | 87 | return dataset 88 | 89 | 90 | class Charades(data_utl.Dataset): 91 | 92 | def __init__(self, split_file, split, root, mode, transforms=None, save_dir='', num=0): 93 | 94 | self.data = make_dataset(split_file, split, root, mode) 95 | self.split_file = split_file 96 | self.transforms = transforms 97 | self.mode = mode 98 | self.root = root 99 | self.save_dir = save_dir 100 | 101 | def __getitem__(self, index): 102 | """ 103 | Args: 104 | index (int): Index 105 | 106 | Returns: 107 | tuple: (image, target) where target is class_index of the target class. 108 | """ 109 | vid, label, dur, nf = self.data[index] 110 | if os.path.exists(os.path.join(self.save_dir, vid+'.npy')): 111 | return 0, 0, vid 112 | 113 | if self.mode == 'rgb': 114 | imgs = load_rgb_frames(self.root, vid, 1, nf) 115 | else: 116 | imgs = load_flow_frames(self.root, vid, 1, nf) 117 | 118 | imgs = self.transforms(imgs) 119 | 120 | return video_to_tensor(imgs), torch.from_numpy(label), vid 121 | 122 | def __len__(self): 123 | return len(self.data) 124 | -------------------------------------------------------------------------------- /feature_extraction/charades_dataset.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.utils.data as data_utl 3 | from torch.utils.data.dataloader import default_collate 4 | 5 | import numpy as np 6 | import json 7 | import csv 8 | import h5py 9 | import random 10 | import os 11 | import os.path 12 | 13 | import cv2 14 | 15 | def video_to_tensor(pic): 16 | """Convert a ``numpy.ndarray`` to tensor. 17 | Converts a numpy.ndarray (T x H x W x C) 18 | to a torch.FloatTensor of shape (C x T x H x W) 19 | 20 | Args: 21 | pic (numpy.ndarray): Video to be converted to tensor. 22 | Returns: 23 | Tensor: Converted video. 24 | """ 25 | return torch.from_numpy(pic.transpose([3,0,1,2])) 26 | 27 | 28 | def load_rgb_frames(image_dir, vid, start, num): 29 | frames = [] 30 | for i in range(start, start+num): 31 | img = cv2.imread(os.path.join(image_dir, vid, vid+'-'+str(i).zfill(6)+'.jpg'))[:, :, [2, 1, 0]] 32 | w,h,c = img.shape 33 | if w < 226 or h < 226: 34 | d = 226.-min(w,h) 35 | sc = 1+d/min(w,h) 36 | img = cv2.resize(img,dsize=(0,0),fx=sc,fy=sc) 37 | img = (img/255.)*2 - 1 38 | frames.append(img) 39 | return np.asarray(frames, dtype=np.float32) 40 | 41 | def load_flow_frames(image_dir, vid, start, num): 42 | frames = [] 43 | for i in range(start, start+num): 44 | imgx = cv2.imread(os.path.join(image_dir, vid, vid+'-'+str(i).zfill(6)+'x.jpg'), cv2.IMREAD_GRAYSCALE) 45 | imgy = cv2.imread(os.path.join(image_dir, vid, vid+'-'+str(i).zfill(6)+'y.jpg'), cv2.IMREAD_GRAYSCALE) 46 | 47 | w,h = imgx.shape 48 | if w < 224 or h < 224: 49 | d = 224.-min(w,h) 50 | sc = 1+d/min(w,h) 51 | imgx = cv2.resize(imgx,dsize=(0,0),fx=sc,fy=sc) 52 | imgy = cv2.resize(imgy,dsize=(0,0),fx=sc,fy=sc) 53 | 54 | imgx = (imgx/255.)*2 - 1 55 | imgy = (imgy/255.)*2 - 1 56 | img = np.asarray([imgx, imgy]).transpose([1,2,0]) 57 | frames.append(img) 58 | return np.asarray(frames, dtype=np.float32) 59 | 60 | 61 | def make_dataset(split_file, split, root, mode, num_classes=157): 62 | dataset = [] 63 | with open(split_file, 'r') as f: 64 | data = json.load(f) 65 | 66 | i = 0 67 | for vid in data.keys(): 68 | if data[vid]['subset'] != split: 69 | continue 70 | 71 | if not os.path.exists(os.path.join(root, vid)): 72 | continue 73 | num_frames = len(os.listdir(os.path.join(root, vid))) 74 | if mode == 'flow': 75 | num_frames = num_frames//2 76 | 77 | if num_frames < 66: 78 | continue 79 | 80 | label = np.zeros((num_classes,num_frames), np.float32) 81 | 82 | fps = num_frames/data[vid]['duration'] 83 | for ann in data[vid]['actions']: 84 | for fr in range(0,num_frames,1): 85 | if fr/fps > ann[1] and fr/fps < ann[2]: 86 | label[ann[0], fr] = 1 # binary classification 87 | dataset.append((vid, label, data[vid]['duration'], num_frames)) 88 | i += 1 89 | 90 | return dataset 91 | 92 | 93 | class Charades(data_utl.Dataset): 94 | 95 | def __init__(self, split_file, split, root, mode, transforms=None): 96 | 97 | self.data = make_dataset(split_file, split, root, mode) 98 | self.split_file = split_file 99 | self.transforms = transforms 100 | self.mode = mode 101 | self.root = root 102 | 103 | def __getitem__(self, index): 104 | """ 105 | Args: 106 | index (int): Index 107 | 108 | Returns: 109 | tuple: (image, target) where target is class_index of the target class. 110 | """ 111 | vid, label, dur, nf = self.data[index] 112 | start_f = random.randint(1,nf-65) 113 | 114 | if self.mode == 'rgb': 115 | imgs = load_rgb_frames(self.root, vid, start_f, 64) 116 | else: 117 | imgs = load_flow_frames(self.root, vid, start_f, 64) 118 | label = label[:, start_f:start_f+64] 119 | 120 | imgs = self.transforms(imgs) 121 | 122 | return video_to_tensor(imgs), torch.from_numpy(label) 123 | 124 | def __len__(self): 125 | return len(self.data) 126 | -------------------------------------------------------------------------------- /feature_extraction/train_i3d.py: -------------------------------------------------------------------------------- 1 | import os 2 | os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID" 3 | #os.environ["CUDA_VISIBLE_DEVICES"]='0,1,2,3' 4 | import sys 5 | import argparse 6 | 7 | parser = argparse.ArgumentParser() 8 | parser.add_argument('-mode', type=str, help='rgb or flow') 9 | parser.add_argument('-save_model', type=str) 10 | parser.add_argument('-root', type=str) 11 | 12 | args = parser.parse_args() 13 | 14 | 15 | import torch 16 | import torch.nn as nn 17 | import torch.nn.functional as F 18 | import torch.optim as optim 19 | from torch.optim import lr_scheduler 20 | from torch.autograd import Variable 21 | 22 | import torchvision 23 | from torchvision import datasets, transforms 24 | import videotransforms 25 | 26 | 27 | import numpy as np 28 | 29 | from pytorch_i3d import InceptionI3d 30 | 31 | from charades_dataset import Charades as Dataset 32 | 33 | 34 | def run(init_lr=0.1, max_steps=64e3, mode='rgb', root='/ssd/Charades_v1_rgb', train_split='charades/charades.json', batch_size=8*5, save_model=''): 35 | # setup dataset 36 | train_transforms = transforms.Compose([videotransforms.RandomCrop(224), 37 | videotransforms.RandomHorizontalFlip(), 38 | ]) 39 | test_transforms = transforms.Compose([videotransforms.CenterCrop(224)]) 40 | 41 | dataset = Dataset(train_split, 'training', root, mode, train_transforms) 42 | dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=36, pin_memory=True) 43 | 44 | val_dataset = Dataset(train_split, 'testing', root, mode, test_transforms) 45 | val_dataloader = torch.utils.data.DataLoader(val_dataset, batch_size=batch_size, shuffle=True, num_workers=36, pin_memory=True) 46 | 47 | dataloaders = {'train': dataloader, 'val': val_dataloader} 48 | datasets = {'train': dataset, 'val': val_dataset} 49 | 50 | 51 | # setup the model 52 | if mode == 'flow': 53 | i3d = InceptionI3d(400, in_channels=2) 54 | i3d.load_state_dict(torch.load('models/flow_imagenet.pt')) 55 | else: 56 | i3d = InceptionI3d(400, in_channels=3) 57 | i3d.load_state_dict(torch.load('models/rgb_imagenet.pt')) 58 | i3d.replace_logits(157) 59 | #i3d.load_state_dict(torch.load('/ssd/models/000920.pt')) 60 | i3d.cuda() 61 | i3d = nn.DataParallel(i3d) 62 | 63 | lr = init_lr 64 | optimizer = optim.SGD(i3d.parameters(), lr=lr, momentum=0.9, weight_decay=0.0000001) 65 | lr_sched = optim.lr_scheduler.MultiStepLR(optimizer, [300, 1000]) 66 | 67 | 68 | num_steps_per_update = 4 # accum gradient 69 | steps = 0 70 | # train it 71 | while steps < max_steps:#for epoch in range(num_epochs): 72 | print 'Step {}/{}'.format(steps, max_steps) 73 | print '-' * 10 74 | 75 | # Each epoch has a training and validation phase 76 | for phase in ['train', 'val']: 77 | if phase == 'train': 78 | i3d.train(True) 79 | else: 80 | i3d.train(False) # Set model to evaluate mode 81 | 82 | tot_loss = 0.0 83 | tot_loc_loss = 0.0 84 | tot_cls_loss = 0.0 85 | num_iter = 0 86 | optimizer.zero_grad() 87 | 88 | # Iterate over data. 89 | for data in dataloaders[phase]: 90 | num_iter += 1 91 | # get the inputs 92 | inputs, labels = data 93 | 94 | # wrap them in Variable 95 | inputs = Variable(inputs.cuda()) 96 | t = inputs.size(2) 97 | labels = Variable(labels.cuda()) 98 | 99 | per_frame_logits = i3d(inputs) 100 | # upsample to input size 101 | per_frame_logits = F.upsample(per_frame_logits, t, mode='linear') 102 | 103 | # compute localization loss 104 | loc_loss = F.binary_cross_entropy_with_logits(per_frame_logits, labels) 105 | tot_loc_loss += loc_loss.data[0] 106 | 107 | # compute classification loss (with max-pooling along time B x C x T) 108 | cls_loss = F.binary_cross_entropy_with_logits(torch.max(per_frame_logits, dim=2)[0], torch.max(labels, dim=2)[0]) 109 | tot_cls_loss += cls_loss.data[0] 110 | 111 | loss = (0.5*loc_loss + 0.5*cls_loss)/num_steps_per_update 112 | tot_loss += loss.data[0] 113 | loss.backward() 114 | 115 | if num_iter == num_steps_per_update and phase == 'train': 116 | steps += 1 117 | num_iter = 0 118 | optimizer.step() 119 | optimizer.zero_grad() 120 | lr_sched.step() 121 | if steps % 10 == 0: 122 | print '{} Loc Loss: {:.4f} Cls Loss: {:.4f} Tot Loss: {:.4f}'.format(phase, tot_loc_loss/(10*num_steps_per_update), tot_cls_loss/(10*num_steps_per_update), tot_loss/10) 123 | # save model 124 | torch.save(i3d.module.state_dict(), save_model+str(steps).zfill(6)+'.pt') 125 | tot_loss = tot_loc_loss = tot_cls_loss = 0. 126 | if phase == 'val': 127 | print '{} Loc Loss: {:.4f} Cls Loss: {:.4f} Tot Loss: {:.4f}'.format(phase, tot_loc_loss/num_iter, tot_cls_loss/num_iter, (tot_loss*num_steps_per_update)/num_iter) 128 | 129 | 130 | 131 | if __name__ == '__main__': 132 | # need to add argparse 133 | run(mode=args.mode, root=args.root, save_model=args.save_model) 134 | -------------------------------------------------------------------------------- /annotations/task1&3/val.csv: 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/video_path/tbdGw97gVMA.mp4 42 138 | /video_path/uIvdELL9its.mp4 42 139 | /video_path/qeZDy8O_cn4.mp4 28 140 | /video_path/R1usywaxSdU.mp4 28 141 | /video_path/RJQ5Q5jQZmg.mp4 28 142 | /video_path/UpY_lyC2DKg.mp4 24 143 | /video_path/V44OI-FjBCk.mp4 24 144 | /video_path/Vbz3eG9WmEE.mp4 24 145 | /video_path/te0qNl30VdM.mp4 29 146 | /video_path/uT9vOC6lm_w.mp4 29 147 | /video_path/WZgh0hgEJxE.mp4 29 148 | /video_path/rPTLcEBI5Fc.mp4 6 149 | /video_path/ShRI1L0Q0gw.mp4 6 150 | /video_path/SjrIit0fYgo.mp4 6 151 | /video_path/s_20-g15v54.mp4 6 152 | /video_path/T4NWm7mqbHI.mp4 6 153 | /video_path/UH1yTqt1sK8.mp4 6 154 | /video_path/StTuvR_l-OY.mp4 49 155 | /video_path/rOH2qF3EtKA.mp4 33 156 | /video_path/sr9KfphbgQE.mp4 33 157 | /video_path/rh02673jFEI.mp4 30 158 | /video_path/S4jQzg8GrXY.mp4 30 159 | /video_path/uRmLT6KXMas.mp4 30 160 | /video_path/udxHIUUzrRk.mp4 46 161 | /video_path/PxB8kSbydPc.mp4 43 162 | /video_path/p85kCsduF8M.mp4 36 163 | /video_path/pae-MY3-1xw.mp4 36 164 | /video_path/Tcqb74Vz7B8.mp4 18 165 | /video_path/u59Zly1rr1Y.mp4 18 166 | /video_path/v6-3Fw1t4H8.mp4 18 167 | /video_path/vhVQk4GeMrI.mp4 18 168 | /video_path/kF3_Y3LOM0o.mp4 47 169 | /video_path/KQNaB37x8-A.mp4 35 170 | /video_path/LstlIyTlT3k.mp4 35 171 | /video_path/kGlSXbnNYvI.mp4 44 172 | /video_path/zls5pP7OgOM.mp4 48 173 | /video_path/RFFBfBdScxg.mp4 45 174 | /video_path/Qs6P8vv0R_c.mp4 50 175 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # NurViD: A Large Expert-Level Video Database for Nursing Procedure Activity Understanding 2 | [Ming Hu](https://minghu0830.github.io/), [Lin Wang](https://github.com/wangsssky), [Siyuan Yan](https://siyuanyan1.github.io/), [Don Ma](), [Qingli Ren](), [Peng Xia](https://peng-xia.site/), [Wei Feng](https://fengweie.github.io/), [Peibo Duan](https://scholar.google.com/citations?user=wdIMVqsAAAAJ&hl=zh-CN), [Lie Ju](https://mmai.group/peoples/julie/), [Zongyuan Ge](https://zongyuange.github.io/). 3 | 4 | 5 | 6 | ## :sparkles: Introduction 7 | NurViD is a large video dataset with expert-level annotation for nursing procedure activity understanding. NurViD consists of over 1.5k videos totaling 144 hours. Notably, it encompasses 51 distinct nursing procedures and 177 action steps. 8 | 9 | ![demo](./localization.png) 10 | 11 | ## :partying_face: News 12 | * **[2023.09.22]** NurViD was accepted at NeurIPS 2023 Track Datasets and Benchmarks! 13 | 14 | ## :cowboy_hat_face:Installation 15 | 16 | This package has the following requirements: 17 | * `GCC >= 4.9` 18 | * `python >= 3.8` 19 | * `PyTorch >= 1.8` 20 | * `Denseflow` 21 | * `MMAction2` 22 | * `PySlowFast` 23 | ### 1.Create a virtual environment 24 | ``` 25 | conda create --name nurvid python=3.9 -y 26 | conda activate nurvid 27 | pip install -r requirements.txt 28 | ``` 29 | ### 2.MMAction2、PySlowFast、Denseflow 30 | Please refer to the official websites of [MMAction2](https://github.com/open-mmlab/mmaction2), [PySlowFast](https://github.com/facebookresearch/SlowFast), [Denseflow](https://github.com/yjxiong/dense_flow)(Optional: A GPU-accelerated library designed for efficient extraction of optical flow features) for detailed instructions. 31 | 32 | ## :hand_over_mouth: Directory Structure 33 | In the context of the whole project, the folder structure will look like: 34 | 35 | ``` 36 | NurViD-benchmark 37 | ├── annotations 38 | │ ├── task1&3 39 | │ │ ├── train.csv 40 | │ │ ├── val.csv 41 | │ │ ├── test.csv.csv 42 | │ ├── task2 43 | │ │ ├── procedure_train.csv 44 | │ │ ├── procedure_val.csv 45 | │ │ ├── procedure_testcsv 46 | │ │ ├── action_train.csv 47 | │ │ ├── action_val.csv 48 | │ │ ├── action_test.csv 49 | │ ├── NurViD_annotations.json 50 | │ ├── Procedure&Action_ID.xlsx 51 | ├── feature_extraction 52 | │ ├── feature 53 | │ │ ├── --Ly-qjodoIs.npz 54 | │ │ ├── -0z1P7sw2qs.npz 55 | │ │ ├── .. 56 | │ ├── build_rawframes.py 57 | │ ├── extract_features.py 58 | │ ├── .. 59 | ├── tools 60 | │ ├── downloader.py 61 | │ ├── preprocess_videos.py 62 | │ ├── clip.py 63 | ├── model (Baseline models.) 64 | │ ├── SlowFast 65 | │ ├── C3D 66 | │ ├── I3D 67 | ├── dataset 68 | │ ├── Original_videos 69 | │ │ ├── --Ly-qjodoI.mp4 70 | │ │ ├── -0z1P7sw2qs.mp4 71 | │ │ ├── .. 72 | │ ├── Preprocessed videos 73 | │ │ ├── --Ly-qjodoI.mp4 74 | │ │ ├── -0z1P7sw2qs.mp4 75 | │ │ ├── .. 76 | │ ├── Segments 77 | │ │ ├── --Ly-qjodoI_1.mp4 78 | │ │ ├── --Ly-qjodoI_2.mp4 79 | │ │ ├── --Ly-qjodoI_3.mp4 80 | │ │ ├── .. 81 | ``` 82 | 83 | ## :sunglasses: Dataset Preparation 84 | 85 | ### 1.Download Videos 86 | Download videos automatically from the source YouTube by running the script below： 87 | ``` 88 | python /tools/downloader.py 89 | ``` 90 | ### 2.Preprocess Videos 91 | By running the script below, the video will be resized to the short edge size of 256 and a frame rate of 25 FPS: 92 | ``` 93 | python /tools/preprocess_videos.py 94 | ``` 95 | ### 3.Create Trimmed Segments 96 | We clip the video into segments according to the order specified in the JSON annotation file and add a sequential number as a label. 97 | ``` 98 | python /tools/clip.py 99 | ``` 100 | ### 4.Extract RGB and Flow Features 101 | We start by extracting frames from each video at 25 frames per second and optical flow using the TV-L1 algorithm.: 102 | ``` 103 | python /feature_extraction/build_rawframes.py /video_path /rgb&flow_frmaes_save_path --level 1 --flow-type tvl1 --ext mp4 --task both 104 | ``` 105 | Next, we utilize a pre-trained I3D model on the ImageNet dataset to generate features for each RGB and optical flow frame: 106 | ``` 107 | python /feature_extraction/extract_features.py --mode rgb --load_model models/rgb_imagenet.pt --input_dir /rgb&flow_frmaes_save_path --output_dir /rgb_feature_save_path --batch_size 100 --sample_mode resize --no-usezip 108 | python /feature_extraction/extract_features.py --mode flow --load_model models/flow_imagenet.pt --input_dir /rgb&flow_frmaes_save_path --output_dir /rgb_feature_save_path --batch_size 100 --sample_mode resize --no-usezip 109 | ``` 110 | To handle varying video durations, we perform uniform interpolation to generate 100 fixed-length features for each video. Lastly, we combine the RGB and optical flow features into a 2048-dimensional embedding as the model input. 111 | 112 | ### 5.Our Source 113 | We also provide a method to directly access our data, but it requires you to sign the [data agreement form](https://form.jotform.com/232158342596158). Once you have completed the form, you will receive an email from our team with Google Drive and Baidu Netdisk download links. 114 | 115 | ## :teddy_bear: Acknowledgement 116 | 117 | Part of our code is borrowed from the following repositories: 118 | 119 | - [yt-dlp](https://github.com/yt-dlp/yt-dlp) 120 | - [MMAction2](https://github.com/open-mmlab/mmaction2) 121 | - [I3D Feature Extraction](https://github.com/Finspire13/pytorch-i3d-feature-extraction) 122 | - [PySlowFast](https://github.com/facebookresearch/SlowFast/tree/main) 123 | 124 | ## :partying_face: Citation 125 | If you find this work useful, please cite: 126 | ```bibtex 127 | @inproceedings{ming2023nurvid, 128 | title={NurViD: A Large Expert-Level Video Database for Nursing Procedure Activity Understanding}, 129 | author={Ming, Hu and Lin, Wang and Siyuan, Yan and Don, Ma and Qingli, Ren and Peng, Xia and Wei, Feng and Peibo, Duan and Lie, Ju and Zongyuan, Ge}, 130 | booktitle={Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track}, 131 | year={2023} 132 | } 133 | ``` 134 | 135 | ## :white_check_mark: License and Disclaimer 136 | The CC-BY-4.0 license and disclaimer statement for the project can be found in the following file description： 137 | ``` 138 | CC BY 4.0 139 | Disclaimer.txt 140 | ``` 141 | 142 | ## :hospital: Contributors 143 | This research was supported by a team from Shanxi Medical University. We are grateful for their dedication in the data annotation process. 144 | | Name | Name | Name | 145 | | ------------ | ----------- | ----------- | 146 | | Qingli Ren | Peizhe Zhang| Hao Guo | 147 | | Yidi Liu | Yaokai Xing | Jiaqi Li | 148 | | Rujie Gao | Zhen Lv | Jun Wang | 149 | | Jiayu Tian | Guangyan Niu| Ruixin Wang | 150 | | Huikang Huang| Yuxin Zhao | Jing Li | 151 | | Yijin Wang | Yajing Hao | Wenxua Wu | 152 | | Ziyi Wang | Xu Guo | Yuhua Cai | 153 | | Xinrong Guo | Xueying Ma | Yingjuan Zhang | 154 | | Yuqi Zhang | Liru Ma | Sinan Li | 155 | -------------------------------------------------------------------------------- /annotations/task1&3/test.csv: -------------------------------------------------------------------------------- 1 | path-label 2 | /video_path/wMbegc0txx4.mp4 3 3 | /video_path/xdi9mCRZnY4.mp4 3 4 | /video_path/xsqaL7c22q8.mp4 3 5 | /video_path/xwJOXomT1jk.mp4 3 6 | /video_path/Y3qj40IrPvo.mp4 3 7 | /video_path/yNAPdZ-SiOU.mp4 3 8 | 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/video_path/wN3Bmhhohjk.mp4 33 282 | /video_path/YeXollGBQ8Y.mp4 33 283 | /video_path/XztBUNsMQ2c.mp4 30 284 | /video_path/YRrJqyh7OfQ.mp4 30 285 | /video_path/yYgIkj2ebP8.mp4 30 286 | /video_path/_1507-Lo6Eg.mp4 30 287 | /video_path/_bXiwiVKfGs.mp4 30 288 | /video_path/umcmHk789Ag.mp4 46 289 | /video_path/SCF6bmk8KWc.mp4 43 290 | /video_path/Z6Sr3Rk1k-8.mp4 43 291 | /video_path/v6zAy9krPjM.mp4 36 292 | /video_path/wP1_CqcEV5Y.mp4 36 293 | /video_path/ZeWGuUbdnTU.mp4 36 294 | /video_path/_7MBer1SeCM.mp4 36 295 | /video_path/x4vRQ_jSJJ0.mp4 18 296 | /video_path/xofnnPS2KyY.mp4 18 297 | /video_path/Y-r9hjRY1DM.mp4 18 298 | /video_path/Z8rkp1cEyTE.mp4 18 299 | /video_path/zqZ1n_xSJfE.mp4 18 300 | /video_path/zSU8dvbdKnU.mp4 18 301 | /video_path/_c4dKyR3enc.mp4 18 302 | /video_path/p0l9ONq9Rsk.mp4 47 303 | /video_path/n6gD9nl5_vk.mp4 35 304 | /video_path/Pp96EhX__kE.mp4 35 305 | /video_path/QANmjYtiA0Q.mp4 35 306 | /video_path/V2p6Kr-ru1M.mp4 35 307 | /video_path/qfSFx93S5cY.mp4 44 308 | /video_path/U52oraqTGWU.mp4 44 309 | /video_path/zp4adGsyopE.mp4 48 310 | /video_path/To-KPRqE0_w.mp4 45 311 | /video_path/uRlAUI2l3dA.mp4 45 312 | /video_path/SX_bbPvbKMM.mp4 50 313 | -------------------------------------------------------------------------------- /feature_extraction/build_rawframes.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import argparse 3 | import glob 4 | import os 5 | import os.path as osp 6 | import sys 7 | import warnings 8 | from multiprocessing import Lock, Pool 9 | 10 | import mmcv 11 | import numpy as np 12 | 13 | 14 | def extract_frame(vid_item): 15 | """Generate optical flow using dense flow. 16 | 17 | Args: 18 | vid_item (list): Video item containing video full path, 19 | video (short) path, video id. 20 | 21 | Returns: 22 | bool: Whether generate optical flow successfully. 23 | """ 24 | full_path, vid_path, vid_id, method, task, report_file = vid_item 25 | if '/' in vid_path: 26 | act_name = osp.basename(osp.dirname(vid_path)) 27 | out_full_path = osp.join(args.out_dir, act_name) 28 | else: 29 | out_full_path = args.out_dir 30 | 31 | run_success = -1 32 | 33 | if task == 'rgb': 34 | if args.use_opencv: 35 | # Not like using denseflow, 36 | # Use OpenCV will not make a sub directory with the video name 37 | try: 38 | video_name = osp.splitext(osp.basename(vid_path))[0] 39 | out_full_path = osp.join(out_full_path, video_name) 40 | 41 | vr = mmcv.VideoReader(full_path) 42 | for i, vr_frame in enumerate(vr): 43 | if vr_frame is not None: 44 | w, h, _ = np.shape(vr_frame) 45 | if args.new_short == 0: 46 | if args.new_width == 0 or args.new_height == 0: 47 | # Keep original shape 48 | out_img = vr_frame 49 | else: 50 | out_img = mmcv.imresize( 51 | vr_frame, 52 | (args.new_width, args.new_height)) 53 | else: 54 | if min(h, w) == h: 55 | new_h = args.new_short 56 | new_w = int((new_h / h) * w) 57 | else: 58 | new_w = args.new_short 59 | new_h = int((new_w / w) * h) 60 | out_img = mmcv.imresize(vr_frame, (new_h, new_w)) 61 | mmcv.imwrite(out_img, 62 | f'{out_full_path}/img_{i + 1:05d}.jpg') 63 | else: 64 | warnings.warn( 65 | 'Length inconsistent!' 66 | f'Early stop with {i + 1} out of {len(vr)} frames.' 67 | ) 68 | break 69 | run_success = 0 70 | except Exception: 71 | run_success = -1 72 | else: 73 | if args.new_short == 0: 74 | cmd = osp.join( 75 | f"denseflow '{full_path}' -b=20 -s=0 -o='{out_full_path}'" 76 | f' -nw={args.new_width} -nh={args.new_height} -v') 77 | else: 78 | cmd = osp.join( 79 | f"denseflow '{full_path}' -b=20 -s=0 -o='{out_full_path}'" 80 | f' -ns={args.new_short} -v') 81 | run_success = os.system(cmd) 82 | elif task == 'flow': 83 | if args.input_frames: 84 | if args.new_short == 0: 85 | cmd = osp.join( 86 | f"denseflow '{full_path}' -a={method} -b=20 -s=1 -o='{out_full_path}'" # noqa: E501 87 | f' -nw={args.new_width} --nh={args.new_height} -v --if') 88 | else: 89 | cmd = osp.join( 90 | f"denseflow '{full_path}' -a={method} -b=20 -s=1 -o='{out_full_path}'" # noqa: E501 91 | f' -ns={args.new_short} -v --if') 92 | else: 93 | if args.new_short == 0: 94 | cmd = osp.join( 95 | f"denseflow '{full_path}' -a={method} -b=20 -s=1 -o='{out_full_path}'" # noqa: E501 96 | f' -nw={args.new_width} --nh={args.new_height} -v') 97 | else: 98 | cmd = osp.join( 99 | f"denseflow '{full_path}' -a={method} -b=20 -s=1 -o='{out_full_path}'" # noqa: E501 100 | f' -ns={args.new_short} -v') 101 | run_success = os.system(cmd) 102 | else: 103 | if args.new_short == 0: 104 | cmd_rgb = osp.join( 105 | f"denseflow '{full_path}' -b=20 -s=0 -o='{out_full_path}'" 106 | f' -nw={args.new_width} -nh={args.new_height} -v') 107 | cmd_flow = osp.join( 108 | f"denseflow '{full_path}' -a={method} -b=20 -s=1 -o='{out_full_path}'" # noqa: E501 109 | f' -nw={args.new_width} -nh={args.new_height} -v') 110 | else: 111 | cmd_rgb = osp.join( 112 | f"denseflow '{full_path}' -b=20 -s=0 -o='{out_full_path}'" 113 | f' -ns={args.new_short} -v') 114 | cmd_flow = osp.join( 115 | f"denseflow '{full_path}' -a={method} -b=20 -s=1 -o='{out_full_path}'" # noqa: E501 116 | f' -ns={args.new_short} -v') 117 | run_success_rgb = os.system(cmd_rgb) 118 | run_success_flow = os.system(cmd_flow) 119 | if run_success_flow == 0 and run_success_rgb == 0: 120 | run_success = 0 121 | 122 | if run_success == 0: 123 | print(f'{task} {vid_id} {vid_path} {method} done') 124 | sys.stdout.flush() 125 | 126 | lock.acquire() 127 | with open(report_file, 'a') as f: 128 | line = full_path + '\n' 129 | f.write(line) 130 | lock.release() 131 | else: 132 | print(f'{task} {vid_id} {vid_path} {method} got something wrong') 133 | sys.stdout.flush() 134 | 135 | return True 136 | 137 | 138 | def parse_args(): 139 | parser = argparse.ArgumentParser(description='extract optical flows') 140 | parser.add_argument('src_dir', type=str, help='source video directory') 141 | parser.add_argument('out_dir', type=str, help='output rawframe directory') 142 | parser.add_argument( 143 | '--task', 144 | type=str, 145 | default='flow', 146 | choices=['rgb', 'flow', 'both'], 147 | help='which type of frames to be extracted') 148 | parser.add_argument( 149 | '--level', 150 | type=int, 151 | choices=[1, 2], 152 | default=2, 153 | help='directory level of data') 154 | parser.add_argument( 155 | '--num-worker', 156 | type=int, 157 | default=8, 158 | help='number of workers to build rawframes') 159 | parser.add_argument( 160 | '--flow-type', 161 | type=str, 162 | default=None, 163 | choices=[None, 'tvl1', 'warp_tvl1', 'farn', 'brox'], 164 | help='flow type to be generated') 165 | parser.add_argument( 166 | '--out-format', 167 | type=str, 168 | default='jpg', 169 | choices=['jpg', 'h5', 'png'], 170 | help='output format') 171 | parser.add_argument( 172 | '--ext', 173 | type=str, 174 | default='avi', 175 | choices=['avi', 'mp4', 'webm'], 176 | help='video file extensions') 177 | parser.add_argument( 178 | '--mixed-ext', 179 | action='store_true', 180 | help='process video files with mixed extensions') 181 | parser.add_argument( 182 | '--new-width', type=int, default=0, help='resize image width') 183 | parser.add_argument( 184 | '--new-height', type=int, default=0, help='resize image height') 185 | parser.add_argument( 186 | '--new-short', 187 | type=int, 188 | default=0, 189 | help='resize image short side length keeping ratio') 190 | parser.add_argument('--num-gpu', type=int, default=8, help='number of GPU') 191 | parser.add_argument( 192 | '--resume', 193 | action='store_true', 194 | default=False, 195 | help='resume optical flow extraction instead of overwriting') 196 | parser.add_argument( 197 | '--use-opencv', 198 | action='store_true', 199 | help='Whether to use opencv to extract rgb frames') 200 | parser.add_argument( 201 | '--input-frames', 202 | action='store_true', 203 | help='Whether to extract flow frames based on rgb frames') 204 | parser.add_argument( 205 | '--report-file', 206 | type=str, 207 | default='build_report.txt', 208 | help='report to record files which have been successfully processed') 209 | args = parser.parse_args() 210 | 211 | return args 212 | 213 | 214 | def init(lock_): 215 | global lock 216 | lock = lock_ 217 | 218 | 219 | if __name__ == '__main__': 220 | args = parse_args() 221 | 222 | if not osp.isdir(args.out_dir): 223 | print(f'Creating folder: {args.out_dir}') 224 | os.makedirs(args.out_dir) 225 | 226 | if args.level == 2: 227 | classes = os.listdir(args.src_dir) 228 | for classname in classes: 229 | new_dir = osp.join(args.out_dir, classname) 230 | if not osp.isdir(new_dir): 231 | print(f'Creating folder: {new_dir}') 232 | os.makedirs(new_dir) 233 | 234 | if args.input_frames: 235 | print('Reading rgb frames from folder: ', args.src_dir) 236 | fullpath_list = glob.glob(args.src_dir + '/*' * args.level) 237 | print('Total number of rgb frame folders found: ', len(fullpath_list)) 238 | else: 239 | print('Reading videos from folder: ', args.src_dir) 240 | if args.mixed_ext: 241 | print('Extension of videos is mixed') 242 | fullpath_list = glob.glob(args.src_dir + '/*' * args.level) 243 | else: 244 | print('Extension of videos: ', args.ext) 245 | fullpath_list = glob.glob(args.src_dir + '/*' * args.level + '.' + 246 | args.ext) 247 | print('Total number of videos found: ', len(fullpath_list)) 248 | 249 | if args.resume: 250 | done_fullpath_list = [] 251 | with open(args.report_file) as f: 252 | for line in f: 253 | if line == '\n': 254 | continue 255 | done_full_path = line.strip().split()[0] 256 | done_fullpath_list.append(done_full_path) 257 | done_fullpath_list = set(done_fullpath_list) 258 | fullpath_list = list(set(fullpath_list).difference(done_fullpath_list)) 259 | 260 | if args.level == 2: 261 | vid_list = list( 262 | map( 263 | lambda p: osp.join( 264 | osp.basename(osp.dirname(p)), osp.basename(p)), 265 | fullpath_list)) 266 | elif args.level == 1: 267 | vid_list = list(map(osp.basename, fullpath_list)) 268 | 269 | lock = Lock() 270 | pool = Pool(args.num_worker, initializer=init, initargs=(lock, )) 271 | pool.map( 272 | extract_frame, 273 | zip(fullpath_list, vid_list, range(len(vid_list)), 274 | len(vid_list) * [args.flow_type], 275 | len(vid_list) * [args.task], 276 | len(vid_list) * [args.report_file])) 277 | pool.close() 278 | pool.join() 279 | -------------------------------------------------------------------------------- /feature_extraction/extract_features.py: -------------------------------------------------------------------------------- 1 | import os 2 | os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID" 3 | import sys 4 | import io 5 | import zipfile 6 | import torch 7 | import torch.nn as nn 8 | import torch.nn.functional as F 9 | import torch.optim as optim 10 | from torch.optim import lr_scheduler 11 | from torch.autograd import Variable 12 | import argparse 13 | import torchvision 14 | from PIL import Image 15 | 16 | import numpy as np 17 | 18 | from pytorch_i3d import InceptionI3d 19 | 20 | import pdb 21 | 22 | 23 | def load_frame(frame_file, resize=False): 24 | 25 | data = Image.open(frame_file) 26 | 27 | assert(data.size[1] == 256) 28 | assert(data.size[0] == 340) 29 | 30 | if resize: 31 | data = data.resize((224, 224), Image.ANTIALIAS) 32 | 33 | data = np.array(data) 34 | data = data.astype(float) 35 | data = (data * 2 / 255) - 1 36 | 37 | assert(data.max()<=1.0) 38 | assert(data.min()>=-1.0) 39 | 40 | return data 41 | 42 | 43 | def load_zipframe(zipdata, name, resize=False): 44 | 45 | stream = zipdata.read(name) 46 | data = Image.open(io.BytesIO(stream)) 47 | 48 | assert(data.size[1] == 256) 49 | assert(data.size[0] == 340) 50 | 51 | if resize: 52 | data = data.resize((224, 224), Image.ANTIALIAS) 53 | 54 | data = np.array(data) 55 | data = data.astype(float) 56 | data = (data * 2 / 255) - 1 57 | 58 | assert(data.max()<=1.0) 59 | assert(data.min()>=-1.0) 60 | 61 | return data 62 | 63 | 64 | 65 | 66 | def oversample_data(data): # (39, 16, 224, 224, 2) # Check twice 67 | 68 | data_flip = np.array(data[:,:,:,::-1,:]) 69 | 70 | data_1 = np.array(data[:, :, :224, :224, :]) 71 | data_2 = np.array(data[:, :, :224, -224:, :]) 72 | data_3 = np.array(data[:, :, 16:240, 58:282, :]) # ,:,16:240,58:282,: 73 | data_4 = np.array(data[:, :, -224:, :224, :]) 74 | data_5 = np.array(data[:, :, -224:, -224:, :]) 75 | 76 | data_f_1 = np.array(data_flip[:, :, :224, :224, :]) 77 | data_f_2 = np.array(data_flip[:, :, :224, -224:, :]) 78 | data_f_3 = np.array(data_flip[:, :, 16:240, 58:282, :]) 79 | data_f_4 = np.array(data_flip[:, :, -224:, :224, :]) 80 | data_f_5 = np.array(data_flip[:, :, -224:, -224:, :]) 81 | 82 | return [data_1, data_2, data_3, data_4, data_5, 83 | data_f_1, data_f_2, data_f_3, data_f_4, data_f_5] 84 | 85 | 86 | 87 | 88 | def load_rgb_batch(frames_dir, rgb_files, 89 | frame_indices, resize=False): 90 | 91 | if resize: 92 | batch_data = np.zeros(frame_indices.shape + (224,224,3)) 93 | else: 94 | batch_data = np.zeros(frame_indices.shape + (256,340,3)) 95 | 96 | for i in range(frame_indices.shape[0]): 97 | for j in range(frame_indices.shape[1]): 98 | 99 | batch_data[i,j,:,:,:] = load_frame(os.path.join(frames_dir, 100 | rgb_files[frame_indices[i][j]]), resize) 101 | 102 | return batch_data 103 | 104 | 105 | def load_ziprgb_batch(rgb_zipdata, rgb_files, 106 | frame_indices, resize=False): 107 | 108 | if resize: 109 | batch_data = np.zeros(frame_indices.shape + (224,224,3)) 110 | else: 111 | batch_data = np.zeros(frame_indices.shape + (256,340,3)) 112 | 113 | for i in range(frame_indices.shape[0]): 114 | for j in range(frame_indices.shape[1]): 115 | 116 | batch_data[i,j,:,:,:] = load_zipframe(rgb_zipdata, 117 | rgb_files[frame_indices[i][j]], resize) 118 | 119 | return batch_data 120 | 121 | 122 | def load_flow_batch(frames_dir, flow_x_files, flow_y_files, 123 | frame_indices, resize=False): 124 | 125 | if resize: 126 | batch_data = np.zeros(frame_indices.shape + (224,224,2)) 127 | else: 128 | batch_data = np.zeros(frame_indices.shape + (256,340,2)) 129 | 130 | for i in range(frame_indices.shape[0]): 131 | for j in range(frame_indices.shape[1]): 132 | 133 | batch_data[i,j,:,:,0] = load_frame(os.path.join(frames_dir, 134 | flow_x_files[frame_indices[i][j]]), resize) 135 | 136 | batch_data[i,j,:,:,1] = load_frame(os.path.join(frames_dir, 137 | flow_y_files[frame_indices[i][j]]), resize) 138 | 139 | return batch_data 140 | 141 | 142 | def load_zipflow_batch(flow_x_zipdata, flow_y_zipdata, 143 | flow_x_files, flow_y_files, 144 | frame_indices, resize=False): 145 | 146 | if resize: 147 | batch_data = np.zeros(frame_indices.shape + (224,224,2)) 148 | else: 149 | batch_data = np.zeros(frame_indices.shape + (256,340,2)) 150 | 151 | for i in range(frame_indices.shape[0]): 152 | for j in range(frame_indices.shape[1]): 153 | 154 | batch_data[i,j,:,:,0] = load_zipframe(flow_x_zipdata, 155 | flow_x_files[frame_indices[i][j]], resize) 156 | 157 | batch_data[i,j,:,:,1] = load_zipframe(flow_y_zipdata, 158 | flow_y_files[frame_indices[i][j]], resize) 159 | 160 | return batch_data 161 | 162 | 163 | 164 | def run(mode='rgb', load_model='', sample_mode='oversample', frequency=16, 165 | input_dir='', output_dir='', batch_size=40, usezip=False): 166 | 167 | chunk_size = 16 168 | 169 | assert(mode in ['rgb', 'flow']) 170 | assert(sample_mode in ['oversample', 'center_crop', 'resize']) 171 | 172 | # setup the model 173 | if mode == 'flow': 174 | i3d = InceptionI3d(400, in_channels=2) 175 | else: 176 | i3d = InceptionI3d(400, in_channels=3) 177 | 178 | #i3d.replace_logits(157) 179 | i3d.load_state_dict(torch.load(load_model)) 180 | i3d.cuda() 181 | 182 | i3d.train(False) # Set model to evaluate mode 183 | 184 | def forward_batch(b_data): 185 | b_data = b_data.transpose([0, 4, 1, 2, 3]) 186 | b_data = torch.from_numpy(b_data) # b,c,t,h,w # 40x3x16x224x224 187 | 188 | b_data = Variable(b_data.cuda(), volatile=True).float() 189 | b_features = i3d.extract_features(b_data) 190 | 191 | b_features = b_features.data.cpu().numpy()[:,:,0,0,0] 192 | return b_features 193 | 194 | 195 | video_names = [i for i in os.listdir(input_dir) if i[0] == 'v'] 196 | 197 | for video_name in video_names: 198 | 199 | save_file = '{}-{}.npz'.format(video_name, mode) 200 | if save_file in os.listdir(output_dir): 201 | continue 202 | 203 | frames_dir = os.path.join(input_dir, video_name) 204 | 205 | 206 | if mode == 'rgb': 207 | if usezip: 208 | rgb_zipdata = zipfile.ZipFile(os.path.join(frames_dir, 'img.zip'), 'r') 209 | rgb_files = [i for i in rgb_zipdata.namelist() if i.startswith('img')] 210 | else: 211 | rgb_files = [i for i in os.listdir(frames_dir) if i.startswith('img')] 212 | 213 | rgb_files.sort() 214 | frame_cnt = len(rgb_files) 215 | 216 | else: 217 | if usezip: 218 | flow_x_zipdata = zipfile.ZipFile(os.path.join(frames_dir, 'flow_x.zip'), 'r') 219 | flow_x_files = [i for i in flow_x_zipdata.namelist() if i.startswith('x_')] 220 | 221 | flow_y_zipdata = zipfile.ZipFile(os.path.join(frames_dir, 'flow_y.zip'), 'r') 222 | flow_y_files = [i for i in flow_y_zipdata.namelist() if i.startswith('y_')] 223 | else: 224 | flow_x_files = [i for i in os.listdir(frames_dir) if i.startswith('flow_x')] 225 | flow_y_files = [i for i in os.listdir(frames_dir) if i.startswith('flow_y')] 226 | 227 | flow_x_files.sort() 228 | flow_y_files.sort() 229 | assert(len(flow_y_files) == len(flow_x_files)) 230 | frame_cnt = len(flow_y_files) 231 | 232 | 233 | 234 | # clipped_length = (frame_cnt // chunk_size) * chunk_size # Cut frames 235 | 236 | # Cut frames 237 | assert(frame_cnt > chunk_size) 238 | clipped_length = frame_cnt - chunk_size 239 | clipped_length = (clipped_length // frequency) * frequency # The start of last chunk 240 | 241 | frame_indices = [] # Frames to chunks 242 | for i in range(clipped_length // frequency + 1): 243 | frame_indices.append( 244 | [j for j in range(i * frequency, i * frequency + chunk_size)]) 245 | 246 | frame_indices = np.array(frame_indices) 247 | 248 | #frame_indices = np.reshape(frame_indices, (-1, 16)) # Frames to chunks 249 | chunk_num = frame_indices.shape[0] 250 | 251 | batch_num = int(np.ceil(chunk_num / batch_size)) # Chunks to batches 252 | frame_indices = np.array_split(frame_indices, batch_num, axis=0) 253 | 254 | if sample_mode == 'oversample': 255 | full_features = [[] for i in range(10)] 256 | else: 257 | full_features = [[]] 258 | 259 | for batch_id in range(batch_num): 260 | 261 | require_resize = sample_mode == 'resize' 262 | 263 | if mode == 'rgb': 264 | if usezip: 265 | batch_data = load_ziprgb_batch(rgb_zipdata, rgb_files, 266 | frame_indices[batch_id], require_resize) 267 | else: 268 | batch_data = load_rgb_batch(frames_dir, rgb_files, 269 | frame_indices[batch_id], require_resize) 270 | else: 271 | if usezip: 272 | batch_data = load_zipflow_batch( 273 | flow_x_zipdata, flow_y_zipdata, 274 | flow_x_files, flow_y_files, 275 | frame_indices[batch_id], require_resize) 276 | else: 277 | batch_data = load_flow_batch(frames_dir, 278 | flow_x_files, flow_y_files, 279 | frame_indices[batch_id], require_resize) 280 | 281 | if sample_mode == 'oversample': 282 | batch_data_ten_crop = oversample_data(batch_data) 283 | 284 | for i in range(10): 285 | pdb.set_trace() 286 | assert(batch_data_ten_crop[i].shape[-2]==224) 287 | assert(batch_data_ten_crop[i].shape[-3]==224) 288 | full_features[i].append(forward_batch(batch_data_ten_crop[i])) 289 | 290 | else: 291 | if sample_mode == 'center_crop': 292 | batch_data = batch_data[:,:,16:240,58:282,:] # Centrer Crop (39, 16, 224, 224, 2) 293 | 294 | assert(batch_data.shape[-2]==224) 295 | assert(batch_data.shape[-3]==224) 296 | full_features[0].append(forward_batch(batch_data)) 297 | 298 | 299 | 300 | full_features = [np.concatenate(i, axis=0) for i in full_features] 301 | full_features = [np.expand_dims(i, axis=0) for i in full_features] 302 | full_features = np.concatenate(full_features, axis=0) 303 | 304 | np.savez(os.path.join(output_dir, save_file), 305 | feature=full_features, 306 | frame_cnt=frame_cnt, 307 | video_name=video_name) 308 | 309 | print('{} done: {} / {}, {}'.format( 310 | video_name, frame_cnt, clipped_length, full_features.shape)) 311 | 312 | 313 | 314 | if __name__ == '__main__': 315 | 316 | parser = argparse.ArgumentParser() 317 | parser.add_argument('--mode', type=str) 318 | parser.add_argument('--load_model', type=str) 319 | parser.add_argument('--input_dir', type=str) 320 | parser.add_argument('--output_dir', type=str) 321 | parser.add_argument('--batch_size', type=int, default=40) 322 | parser.add_argument('--sample_mode', type=str) 323 | parser.add_argument('--frequency', type=int, default=16) 324 | 325 | parser.add_argument('--usezip', dest='usezip', action='store_true') 326 | parser.add_argument('--no-usezip', dest='usezip', action='store_false') 327 | parser.set_defaults(usezip=True) 328 | 329 | args = parser.parse_args() 330 | 331 | run(mode=args.mode, 332 | load_model=args.load_model, 333 | sample_mode=args.sample_mode, 334 | input_dir=args.input_dir, 335 | output_dir=args.output_dir, 336 | batch_size=args.batch_size, 337 | frequency=args.frequency, 338 | usezip=args.usezip) 339 | -------------------------------------------------------------------------------- /feature_extraction/pytorch_i3d.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch.nn.functional as F 4 | from torch.autograd import Variable 5 | 6 | import numpy as np 7 | 8 | import os 9 | import sys 10 | from collections import OrderedDict 11 | 12 | 13 | class MaxPool3dSamePadding(nn.MaxPool3d): 14 | 15 | def compute_pad(self, dim, s): 16 | if s % self.stride[dim] == 0: 17 | return max(self.kernel_size[dim] - self.stride[dim], 0) 18 | else: 19 | return max(self.kernel_size[dim] - (s % self.stride[dim]), 0) 20 | 21 | def forward(self, x): 22 | # compute 'same' padding 23 | (batch, channel, t, h, w) = x.size() 24 | #print t,h,w 25 | out_t = np.ceil(float(t) / float(self.stride[0])) 26 | out_h = np.ceil(float(h) / float(self.stride[1])) 27 | out_w = np.ceil(float(w) / float(self.stride[2])) 28 | #print out_t, out_h, out_w 29 | pad_t = self.compute_pad(0, t) 30 | pad_h = self.compute_pad(1, h) 31 | pad_w = self.compute_pad(2, w) 32 | #print pad_t, pad_h, pad_w 33 | 34 | pad_t_f = pad_t // 2 35 | pad_t_b = pad_t - pad_t_f 36 | pad_h_f = pad_h // 2 37 | pad_h_b = pad_h - pad_h_f 38 | pad_w_f = pad_w // 2 39 | pad_w_b = pad_w - pad_w_f 40 | 41 | pad = (pad_w_f, pad_w_b, pad_h_f, pad_h_b, pad_t_f, pad_t_b) 42 | #print x.size() 43 | #print pad 44 | x = F.pad(x, pad) 45 | return super(MaxPool3dSamePadding, self).forward(x) 46 | 47 | 48 | class Unit3D(nn.Module): 49 | 50 | def __init__(self, in_channels, 51 | output_channels, 52 | kernel_shape=(1, 1, 1), 53 | stride=(1, 1, 1), 54 | padding=0, 55 | activation_fn=F.relu, 56 | use_batch_norm=True, 57 | use_bias=False, 58 | name='unit_3d'): 59 | 60 | """Initializes Unit3D module.""" 61 | super(Unit3D, self).__init__() 62 | 63 | self._output_channels = output_channels 64 | self._kernel_shape = kernel_shape 65 | self._stride = stride 66 | self._use_batch_norm = use_batch_norm 67 | self._activation_fn = activation_fn 68 | self._use_bias = use_bias 69 | self.name = name 70 | self.padding = padding 71 | 72 | self.conv3d = nn.Conv3d(in_channels=in_channels, 73 | out_channels=self._output_channels, 74 | kernel_size=self._kernel_shape, 75 | stride=self._stride, 76 | padding=0, # we always want padding to be 0 here. We will dynamically pad based on input size in forward function 77 | bias=self._use_bias) 78 | 79 | if self._use_batch_norm: 80 | self.bn = nn.BatchNorm3d(self._output_channels, eps=0.001, momentum=0.01) 81 | 82 | def compute_pad(self, dim, s): 83 | if s % self._stride[dim] == 0: 84 | return max(self._kernel_shape[dim] - self._stride[dim], 0) 85 | else: 86 | return max(self._kernel_shape[dim] - (s % self._stride[dim]), 0) 87 | 88 | 89 | def forward(self, x): 90 | # compute 'same' padding 91 | (batch, channel, t, h, w) = x.size() 92 | #print t,h,w 93 | out_t = np.ceil(float(t) / float(self._stride[0])) 94 | out_h = np.ceil(float(h) / float(self._stride[1])) 95 | out_w = np.ceil(float(w) / float(self._stride[2])) 96 | #print out_t, out_h, out_w 97 | pad_t = self.compute_pad(0, t) 98 | pad_h = self.compute_pad(1, h) 99 | pad_w = self.compute_pad(2, w) 100 | #print pad_t, pad_h, pad_w 101 | 102 | pad_t_f = pad_t // 2 103 | pad_t_b = pad_t - pad_t_f 104 | pad_h_f = pad_h // 2 105 | pad_h_b = pad_h - pad_h_f 106 | pad_w_f = pad_w // 2 107 | pad_w_b = pad_w - pad_w_f 108 | 109 | pad = (pad_w_f, pad_w_b, pad_h_f, pad_h_b, pad_t_f, pad_t_b) 110 | #print x.size() 111 | #print pad 112 | x = F.pad(x, pad) 113 | #print x.size() 114 | 115 | x = self.conv3d(x) 116 | if self._use_batch_norm: 117 | x = self.bn(x) 118 | if self._activation_fn is not None: 119 | x = self._activation_fn(x) 120 | return x 121 | 122 | 123 | 124 | class InceptionModule(nn.Module): 125 | def __init__(self, in_channels, out_channels, name): 126 | super(InceptionModule, self).__init__() 127 | 128 | self.b0 = Unit3D(in_channels=in_channels, output_channels=out_channels[0], kernel_shape=[1, 1, 1], padding=0, 129 | name=name+'/Branch_0/Conv3d_0a_1x1') 130 | self.b1a = Unit3D(in_channels=in_channels, output_channels=out_channels[1], kernel_shape=[1, 1, 1], padding=0, 131 | name=name+'/Branch_1/Conv3d_0a_1x1') 132 | self.b1b = Unit3D(in_channels=out_channels[1], output_channels=out_channels[2], kernel_shape=[3, 3, 3], 133 | name=name+'/Branch_1/Conv3d_0b_3x3') 134 | self.b2a = Unit3D(in_channels=in_channels, output_channels=out_channels[3], kernel_shape=[1, 1, 1], padding=0, 135 | name=name+'/Branch_2/Conv3d_0a_1x1') 136 | self.b2b = Unit3D(in_channels=out_channels[3], output_channels=out_channels[4], kernel_shape=[3, 3, 3], 137 | name=name+'/Branch_2/Conv3d_0b_3x3') 138 | self.b3a = MaxPool3dSamePadding(kernel_size=[3, 3, 3], 139 | stride=(1, 1, 1), padding=0) 140 | self.b3b = Unit3D(in_channels=in_channels, output_channels=out_channels[5], kernel_shape=[1, 1, 1], padding=0, 141 | name=name+'/Branch_3/Conv3d_0b_1x1') 142 | self.name = name 143 | 144 | def forward(self, x): 145 | b0 = self.b0(x) 146 | b1 = self.b1b(self.b1a(x)) 147 | b2 = self.b2b(self.b2a(x)) 148 | b3 = self.b3b(self.b3a(x)) 149 | return torch.cat([b0,b1,b2,b3], dim=1) 150 | 151 | 152 | class InceptionI3d(nn.Module): 153 | """Inception-v1 I3D architecture. 154 | The model is introduced in: 155 | Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset 156 | Joao Carreira, Andrew Zisserman 157 | https://arxiv.org/pdf/1705.07750v1.pdf. 158 | See also the Inception architecture, introduced in: 159 | Going deeper with convolutions 160 | Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, 161 | Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich. 162 | http://arxiv.org/pdf/1409.4842v1.pdf. 163 | """ 164 | 165 | # Endpoints of the model in order. During construction, all the endpoints up 166 | # to a designated `final_endpoint` are returned in a dictionary as the 167 | # second return value. 168 | VALID_ENDPOINTS = ( 169 | 'Conv3d_1a_7x7', 170 | 'MaxPool3d_2a_3x3', 171 | 'Conv3d_2b_1x1', 172 | 'Conv3d_2c_3x3', 173 | 'MaxPool3d_3a_3x3', 174 | 'Mixed_3b', 175 | 'Mixed_3c', 176 | 'MaxPool3d_4a_3x3', 177 | 'Mixed_4b', 178 | 'Mixed_4c', 179 | 'Mixed_4d', 180 | 'Mixed_4e', 181 | 'Mixed_4f', 182 | 'MaxPool3d_5a_2x2', 183 | 'Mixed_5b', 184 | 'Mixed_5c', 185 | 'Logits', 186 | 'Predictions', 187 | ) 188 | 189 | def __init__(self, num_classes=400, spatial_squeeze=True, 190 | final_endpoint='Logits', name='inception_i3d', in_channels=3, dropout_keep_prob=0.5): 191 | """Initializes I3D model instance. 192 | Args: 193 | num_classes: The number of outputs in the logit layer (default 400, which 194 | matches the Kinetics dataset). 195 | spatial_squeeze: Whether to squeeze the spatial dimensions for the logits 196 | before returning (default True). 197 | final_endpoint: The model contains many possible endpoints. 198 | `final_endpoint` specifies the last endpoint for the model to be built 199 | up to. In addition to the output at `final_endpoint`, all the outputs 200 | at endpoints up to `final_endpoint` will also be returned, in a 201 | dictionary. `final_endpoint` must be one of 202 | InceptionI3d.VALID_ENDPOINTS (default 'Logits'). 203 | name: A string (optional). The name of this module. 204 | Raises: 205 | ValueError: if `final_endpoint` is not recognized. 206 | """ 207 | 208 | if final_endpoint not in self.VALID_ENDPOINTS: 209 | raise ValueError('Unknown final endpoint %s' % final_endpoint) 210 | 211 | super(InceptionI3d, self).__init__() 212 | self._num_classes = num_classes 213 | self._spatial_squeeze = spatial_squeeze 214 | self._final_endpoint = final_endpoint 215 | self.logits = None 216 | 217 | if self._final_endpoint not in self.VALID_ENDPOINTS: 218 | raise ValueError('Unknown final endpoint %s' % self._final_endpoint) 219 | 220 | self.end_points = {} 221 | end_point = 'Conv3d_1a_7x7' 222 | self.end_points[end_point] = Unit3D(in_channels=in_channels, output_channels=64, kernel_shape=[7, 7, 7], 223 | stride=(2, 2, 2), padding=(3,3,3), name=name+end_point) 224 | if self._final_endpoint == end_point: return 225 | 226 | end_point = 'MaxPool3d_2a_3x3' 227 | self.end_points[end_point] = MaxPool3dSamePadding(kernel_size=[1, 3, 3], stride=(1, 2, 2), 228 | padding=0) 229 | if self._final_endpoint == end_point: return 230 | 231 | end_point = 'Conv3d_2b_1x1' 232 | self.end_points[end_point] = Unit3D(in_channels=64, output_channels=64, kernel_shape=[1, 1, 1], padding=0, 233 | name=name+end_point) 234 | if self._final_endpoint == end_point: return 235 | 236 | end_point = 'Conv3d_2c_3x3' 237 | self.end_points[end_point] = Unit3D(in_channels=64, output_channels=192, kernel_shape=[3, 3, 3], padding=1, 238 | name=name+end_point) 239 | if self._final_endpoint == end_point: return 240 | 241 | end_point = 'MaxPool3d_3a_3x3' 242 | self.end_points[end_point] = MaxPool3dSamePadding(kernel_size=[1, 3, 3], stride=(1, 2, 2), 243 | padding=0) 244 | if self._final_endpoint == end_point: return 245 | 246 | end_point = 'Mixed_3b' 247 | self.end_points[end_point] = InceptionModule(192, [64,96,128,16,32,32], name+end_point) 248 | if self._final_endpoint == end_point: return 249 | 250 | end_point = 'Mixed_3c' 251 | self.end_points[end_point] = InceptionModule(256, [128,128,192,32,96,64], name+end_point) 252 | if self._final_endpoint == end_point: return 253 | 254 | end_point = 'MaxPool3d_4a_3x3' 255 | self.end_points[end_point] = MaxPool3dSamePadding(kernel_size=[3, 3, 3], stride=(2, 2, 2), 256 | padding=0) 257 | if self._final_endpoint == end_point: return 258 | 259 | end_point = 'Mixed_4b' 260 | self.end_points[end_point] = InceptionModule(128+192+96+64, [192,96,208,16,48,64], name+end_point) 261 | if self._final_endpoint == end_point: return 262 | 263 | end_point = 'Mixed_4c' 264 | self.end_points[end_point] = InceptionModule(192+208+48+64, [160,112,224,24,64,64], name+end_point) 265 | if self._final_endpoint == end_point: return 266 | 267 | end_point = 'Mixed_4d' 268 | self.end_points[end_point] = InceptionModule(160+224+64+64, [128,128,256,24,64,64], name+end_point) 269 | if self._final_endpoint == end_point: return 270 | 271 | end_point = 'Mixed_4e' 272 | self.end_points[end_point] = InceptionModule(128+256+64+64, [112,144,288,32,64,64], name+end_point) 273 | if self._final_endpoint == end_point: return 274 | 275 | end_point = 'Mixed_4f' 276 | self.end_points[end_point] = InceptionModule(112+288+64+64, [256,160,320,32,128,128], name+end_point) 277 | if self._final_endpoint == end_point: return 278 | 279 | end_point = 'MaxPool3d_5a_2x2' 280 | self.end_points[end_point] = MaxPool3dSamePadding(kernel_size=[2, 2, 2], stride=(2, 2, 2), 281 | padding=0) 282 | if self._final_endpoint == end_point: return 283 | 284 | end_point = 'Mixed_5b' 285 | self.end_points[end_point] = InceptionModule(256+320+128+128, [256,160,320,32,128,128], name+end_point) 286 | if self._final_endpoint == end_point: return 287 | 288 | end_point = 'Mixed_5c' 289 | self.end_points[end_point] = InceptionModule(256+320+128+128, [384,192,384,48,128,128], name+end_point) 290 | if self._final_endpoint == end_point: return 291 | 292 | end_point = 'Logits' 293 | self.avg_pool = nn.AvgPool3d(kernel_size=[2, 7, 7], 294 | stride=(1, 1, 1)) 295 | self.dropout = nn.Dropout(dropout_keep_prob) 296 | self.logits = Unit3D(in_channels=384+384+128+128, output_channels=self._num_classes, 297 | kernel_shape=[1, 1, 1], 298 | padding=0, 299 | activation_fn=None, 300 | use_batch_norm=False, 301 | use_bias=True, 302 | name='logits') 303 | 304 | self.build() 305 | 306 | 307 | def replace_logits(self, num_classes): 308 | self._num_classes = num_classes 309 | self.logits = Unit3D(in_channels=384+384+128+128, output_channels=self._num_classes, 310 | kernel_shape=[1, 1, 1], 311 | padding=0, 312 | activation_fn=None, 313 | use_batch_norm=False, 314 | use_bias=True, 315 | name='logits') 316 | 317 | 318 | def build(self): 319 | for k in self.end_points.keys(): 320 | self.add_module(k, self.end_points[k]) 321 | 322 | def forward(self, x): 323 | for end_point in self.VALID_ENDPOINTS: 324 | if end_point in self.end_points: 325 | x = self._modules[end_point](x) # use _modules to work with dataparallel 326 | 327 | x = self.logits(self.dropout(self.avg_pool(x))) 328 | if self._spatial_squeeze: 329 | logits = x.squeeze(3).squeeze(3) 330 | # logits is batch X time X classes, which is what we want to work with 331 | return logits 332 | 333 | 334 | def extract_features(self, x): 335 | for end_point in self.VALID_ENDPOINTS: 336 | if end_point in self.end_points: 337 | x = self._modules[end_point](x) 338 | return self.avg_pool(x) 339 | -------------------------------------------------------------------------------- /CC-BY-4.0: -------------------------------------------------------------------------------- 1 | Attribution 4.0 International 2 | 3 | ======================================================================= 4 | 5 | Creative Commons Corporation ("Creative Commons") is not a law firm and 6 | does not provide legal services or legal advice. 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For 392 | the avoidance of doubt, this paragraph does not form part of the 393 | public licenses. 394 | 395 | Creative Commons may be contacted at creativecommons.org. 396 | -------------------------------------------------------------------------------- /annotations/task2/procedure_val.csv: -------------------------------------------------------------------------------- 1 | path-label 2 | /segment_path/j4vCio2bdbo_8.mp4 2 3 | /segment_path/GE24mrcUkhg_7.mp4 2 4 | /segment_path/Nsm3HIH7QdU_9.mp4 2 5 | /segment_path/wlssXdkjCLs_11.mp4 2 6 | /segment_path/lNnvx6hIN78_11.mp4 2 7 | /segment_path/l2svlMYCBiQ_1.mp4 2 8 | /segment_path/GE24mrcUkhg_14.mp4 2 9 | /segment_path/l2svlMYCBiQ_4.mp4 2 10 | /segment_path/xwJOXomT1jk_1.mp4 2 11 | /segment_path/wlssXdkjCLs_6.mp4 2 12 | /segment_path/YTEniEh6uS0_10.mp4 2 13 | /segment_path/Qp78x7NxUho_8.mp4 2 14 | /segment_path/cKtB81VN_4Q_11.mp4 2 15 | /segment_path/Cl-Cn0qK5lg_14.mp4 2 16 | /segment_path/Q8lZR3Fo6SM_7.mp4 2 17 | 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| /segment_path/ZtmLdYTVszA_11.mp4 150 618 | /segment_path/dtL-2hf6RHk_1.mp4 151 619 | /segment_path/rNnEiYVU6lg_5.mp4 74 620 | /segment_path/svW_04e1Oi8_5.mp4 74 621 | /segment_path/TZKn9CcJ-Y4_7.mp4 152 622 | /segment_path/SX_bbPvbKMM_2.mp4 153 623 | /segment_path/RFFBfBdScxg_2.mp4 138 624 | /segment_path/Lta0GbIKCZA_4.mp4 144 625 | /segment_path/XeMwoMk8wSw_1.mp4 101 626 | /segment_path/svW_04e1Oi8_3.mp4 101 627 | /segment_path/TZ6xeS_GjSk_3.mp4 107 628 | /segment_path/_Bj0FbjFFJY_1.mp4 107 629 | /segment_path/Q9daM1wqPxs_5.mp4 145 630 | -------------------------------------------------------------------------------- /tools/downloader.py: -------------------------------------------------------------------------------- 1 | import yt_dlp 2 | import json 3 | from tqdm import tqdm 4 | from concurrent.futures import ThreadPoolExecutor 5 | import os 6 | 7 | 8 | def download_video(video_id, output_path): 9 | ydl_opts = { 10 | 'outtmpl': os.path.join(output_path, f'%(id)s.%(ext)s'), 11 | 'format': 'bestvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best', 12 | 'ignoreerrors': False, 13 | } 14 | with yt_dlp.YoutubeDL(ydl_opts) as ydl: 15 | try: 16 | ydl.download([video_id]) 17 | except: 18 | return video_id 19 | 20 | 21 | def download_videos(video_ids, output_path): 22 | os.makedirs(output_path, exist_ok=True) 23 | failed_videos = [] 24 | with ThreadPoolExecutor(max_workers=8) as executor: 25 | futures = [] 26 | for video_id in video_ids: 27 | futures.append(executor.submit(download_video, video_id, output_path)) 28 | for future in tqdm(futures, total=len(futures), desc='Downloading', unit='video'): 29 | result = future.result() 30 | if result: 31 | failed_videos.append(result) 32 | file_path = "failed_list.txt" 33 | write_list_to_file(file_path, failed_videos) 34 | return failed_videos 35 | 36 | 37 | def write_list_to_file(file_path, input_list): 38 | try: 39 | with open(file_path, 'w') as file: 40 | for item in input_list: 41 | file.write(str(item) + '\n') 42 | except Exception as e: 43 | print(f"Error occurred: {e}") 44 | 45 | 46 | if __name__ == '__main__': 47 | output_path = './dataset/original_video' 48 | video_ids = ['--Ly-qjodoI', '-0z1P7sw2qs', '-1mH9wYWd5w', '-2N0GEdJltw', '-aeuTQzxq6Y', '-BtEiJjukV4', '-C7K3i5Ea7s', '-DMEyIfWBV8', 49 | '-EPJquqxJ1c', '-FdD0-zF_K4', '-fKaTT9nnlg', '-fvRejnoN3M', '-ged3dOpfK8', '-hiZ75P9iyM', '-JSu9M6Re4w', '-k2pgWCPmsc', 50 | '-l635D0n-kk', '-MsywHWz1Z0', '-NoC1rkNgnU', '-qXC-IUT7Hk', '-QznLhQqdHI', '-SZbLkUKfAc', '-uyBJ0nv4oI', '-UZx2zSTfdo', 51 | '-XBAxsIcQs8', '-Y2wMe48BfI', '-Yst1qShEbo', '-zlNQfbrStg', '-zT0oDRiz6E', '-_OkMf_umyE', '0-90-ucVHFs', '00ZWd_WwJ3g', 52 | '01D17PnsGpA', '02PqJ8iPLxQ', '031fy7eHW4o', '06Eero7ok5A', '06wv8JmNyy8', '06XQuZLTENE', '07wJDVB95dI', '08eXvhJZprk', 53 | '0a3B_9RNCPQ', '0B4x_mHgVN4', '0CCSV5QFgGo', '0cGHykGfO-U', '0dUlnBGu4rY', '0F62OyVKOwk', '0fJPgUFJLcQ', '0h9Bj-OLSr0', 54 | '0IWzSE0ZSlE', '0LPkpY4KIkU', '0MIQLk4nwms', '0mSQn8SBmRE', '0Oj4AsZruZk', '0RcsKGEc-fw', '0t2t6Lh-k3U', '0tKSN4M_YZI', 55 | '0VGuYnlB2Yc', '0vjVJuPik9k', '0vtVEtVXVz4', '0WMoZ_sInmc', '0xT5mfWrW_I', '0z5Xtb8ipRo', '0_qk_CrqRM4', '0_XPg6jBVvY', 56 | '11hwCe3w_UU', '1bNc2CgwU-4', '1e7USvwCAxQ', '1f4VGwqUKYI', '1fV0Y5QLzLQ', '1jOmhNgJNuY', '1jw80-_vWtA', '1K7alb7Ne_A', 57 | '1lsprM6MJ1A', '1mfjSry8WUQ', '1nDjNf8YXlE', '1OakmxZDa5c', '1OgR_bJM_Zw', '1Onm7fJ0zYE', '1PNlj_VEDtI', '1PqD-MYDV3g', 58 | '1rlxKsx_n-Y', '1sje0zq-F10', '1WLcyNiBt2s', '1WxoWefo7gQ', '1_2u8oNloBs', '21v_YbzDiJ8', '23Ioifx2zX0', '24Jlbd9bOr4', 59 | '25DJ1kvaN60', '25efi9K5eOQ', '29QOClzPHGQ', '29wOwkW7-ME', '2hAmWBEzFBU', '2i_rjQ9SGa0', '2KtmwJDhujU', '2M2gPCNunZw', 60 | '2oztxG2wEVU', '2paEgKKEK5o', '2R2CzkzVdL0', '2rZoIVf4pYY', '2sNV9dixX4o', '2SqBXs-Y3ts', '2tjFVGih71A', '2Ux_m8iCgcU', 61 | '2vt973I9_1Q', '2WsBzx82CnQ', '2ZvWaLst-E8', '2_-EBIujCQw', '3-SOKCuMujc', '31lF8VNCJ8k', '31qBW0Imj_0', '31rEOB9AwG4', 62 | '32zHF9Yvyp0', '34TOon0KiPQ', '396l_q4T1NA', '3cUkDXZzeyg', '3dTzfSRxRZc', '3fk_8uR_uhQ', '3gLhooOemZw', '3GO_A7O8ILg', 63 | '3iQ664gFHLw', '3kXnL0AQhYA', '3lorXZWgnDI', '3MeO7o0m7oU', '3Nw_fZ3B1sc', '3OjroDjmskM', '3RCUz_d9XQQ', '3Vlt_EZRGgc', 64 | '3yA1e4RFlfg', '3_UUq7p6-5M', '40ad0Iq23KA', '40bZthiD2o0', '41y1unGur-Q', '44mJ9VUsGHM', '45C8N8mRKcc', '47dMHoWKpWI', 65 | '47FjtSgpN4U', '47UgIBUoR_A', '48M_CwJE7Z0', '49jF6RZy3GE', '4A2I-sZHXvI', '4B_rg5uTRIo', '4c6bm_iT6pg', '4Du0_Sd_s2s', 66 | '4eHQ5KmOZaM', '4eJAxp-gqn4', '4fQUQ94fPYM', '4GpXy_srgRs', '4MVIhgbTOsk', '4NAJuycN4Co', '4ntqS_R1r70', '4ONAC6uE6xA', 67 | '4Qil5I1XLZ8', '4rifH8nB48o', '4rQkQj3gv3Q', '4UdYawfe1R0', '4UvjeoJGgV4', '4yQO__Khl5U', '4zxgX9IpmMY', '5-F_W5WYLKI', 68 | '51ecxm8oLP8', '57I72z9R1H0', '57zApLuswoo', '58Ku24EerKw', '58wAvY_aF-Y', '59B4LwCURJ0', '5Cid7q9_Uq0', '5DSG23B5XGQ', 69 | '5egAHIPiGlY', '5EVZHPaszAI', '5FfVjiou_YE', '5Fud4xTBgwk', '5FUnepktYxE', '5I1lclB_tPU', '5jci72lIJ-8', '5KPMeBTNOfA', 70 | '5nQYrFwC7G4', '5q4APWu8FDY', '5qd1EONfJi4', '5qfIRPxoHuw', '5SJxq-D6-Bk', '5W3o4HjyV2k', '5w6EUzNYLXk', '5wxGEfY9egw', 71 | '5Z-teZy3bCw', '6-33XEnm3to', '64-2Ly9aBn8', '660fOrTJnCc', '671uYTHpalI', '69VYrk0GeaU', '6Ayxz5p7GBs', '6BnxXcUxcBs', 72 | '6cSbLJnjzTA', '6DCKEQVAFR8', '6eGGq4fz8SE', '6Er52I7LxPs', '6eY9QGkbmlU', '6gc07xlcuWU', '6jbVcMgN_HI', '6n--CYH4Pw4', 73 | '6Ny4wSUdYNo', '6oHYb6_bFYo', '6s27NmJQi70', '6Xu2Sgk1y80', '6Y4DDmlWtwo', '6ydbP1_C8rk', '6zcsYUqaWCE', '6ZX6F1PUUe0', 74 | '71LlfdW548U', '724-cEL_Z6I', '74kVE4Ixn8g', '74ttY4-QlIE', '77XNYG1twd4', '7Bm_BwZrq6c', '7CTdDHBzj7Q', '7F4nYxJgoJY', 75 | '7jRryPp8C3A', '7kv3YZNpo8Q', '7l7hZVYkgto', '7OGnuOkTDsU', '7Ps9jswWgbw', '7q6HHVy-bjw', '7qYYDH4NUj0', '7s8W4dnsFQQ', 76 | '7XPQYY0B7Rk', '7XyfHY9iSWY', '7yDymtT809s', '86RzAgHu75U', '87mdfPOh_Rw', '8Ab1Z-c3EjU', '8BaeLRCb0B4', '8CGlrISSmnU', 77 | '8j0fucgtSGM', '8MPdw-0HbTg', '8OTKidNwLlo', '8oX4SQHxKBw', '8PKKTwzxG7k', '8pKp9dnRX7c', '8PZqdLjy5Tc', '8uXhDt79KNQ', 78 | '8vKssX6wvwY', '8w-3h50E17Q', '8WEjKytxNxg', '8WU3Q0_dYXY', '8z44ryOVEEM', '8_LRJ8vLMxI', '95lKeVet2qQ', '968cu9kFRPM', 79 | '98ftIyS59lw', '99TlXo46g0I', '9akxlH6BtyM', '9ba1LjgPHGg', '9c1Jq7Ekago', '9Dgjirf3Dss', '9FvUsjje8ic', '9i2oJR_1ZXs', 80 | '9jAc3y6Gv4k', '9JLVK138A4U', '9kOYDulwQv0', '9LYBiLy_a-k', '9mGUSQKOVw4', '9MMc5A0MMWg', '9msWAqfA5lg', '9OCVSsKJsdE', 81 | '9oeVA0nl0m0', '9r1ShyTIJ90', '9rDS5VK1Xqs', '9sFPahcX7z0', '9umaLJ6qdXo', '9vhOSbxGle8', '9VHtLRhwrBM', '9VKox-wy4fU', 82 | '9XkOssKciqU', 'a0RhbpehcQg', 'a1-dLLOeKUA', 'a1DFmgLJmMk', 'a4L_014Czao', 'abNuOlCTu-g', 'Adcba-WMbEE', 'AdkoFiFrz7o', 83 | 'AdnKbipn5es', 'ae1lL3GLVdM', 'Af4CT12J8GI', 'afFMH2lgH-U', 'ag5-goiMNsQ', 'AGnKuYiN1Bg', 'AgVZ3p8OJ0A', 'Ai3YQJbuS0A', 84 | 'AI8jESrzqx0', 'ajDGW_bOm5Y', 'AkeRnPUGdv0', 'aKPL2Rmj0hI', 'aKTGjx3afvs', 'aLoP1MG8aLA', 'AMO52zk3AXk', 'aN78RVfe0Js', 85 | 'aNc7TR80tVo', 'ANVFQV0PHIo', 'AnYWNPDKwjc', 'ap1qFh-lS-8', 'apJZq1vEjLQ', 'APQwu9Qx3As', 'ApSPE69IPNc', 'aq8OwFaIQ4I', 86 | 'AqBOMERKS_4', 'aQYQtmzkd28', 'aR73Ik_GthM', 'arqG_2ftfOY', 'ARRfrFwC87M', 'AuGRfsWTFnA', 'aUkEBsZGfA4', 'aV9JJSTU6Ak', 87 | 'AvbxofLe-MI', 'aw_2mvb69Ow', 'ayf3coj_zMQ', 'aZ3DJnCxujA', 'aZOYJigvPbI', 'azQ7tuTFmno', 'B-g_5WLffMM', 'B02i_pL7CnA', 88 | 'b1nxS_yjiEk', 'B1oCcX1BCrQ', 'b1uy8cwYlcM', 'b26uL9PtfKE', 'b2UCiQs0KMg', 'b6c4-U6Z1RI', 'B7SJ3QW5nik', 'B7SWLiDjMvI', 89 | 'b7WIr_JkgLQ', 'B9LzzcN3JmE', 'Ba55wzNxM2w', 'BADeHh0xNME', 'bctTKygOj6o', 'bE4MZ6NQhsA', 'BfP1yZevi1c', 'bFrFHy_JEDk', 90 | 'BfYvfy97D0Y', 'bGRpVOlkmSI', 'BH9mYVQGdm4', 'BHH_nHIhJO4', 'bj1LoKDgR4A', 'BJ76KUwrn3I', 'bJ7ilV9872k', 'BJXsXISIQhk', 91 | 'bL3PQ_3hrbc', 'BLm4fCyj1CY', 'BloOJGSZ2S8', 'BLoV1TTTe9Y', 'BLVruFeiJGA', 'BM8hJsBsRXI', 'bmI0TI0M9D4', 'bNLtwX2LDQI', 92 | 'bnMRJzhF2lI', 'bnvs8IzCQqk', 'Bpng0Ex9pho', 'BQHKdSKK7kk', 'bRPSi32Gcw4', 'BTBBzcdZ0kg', 'bVbBCtiFIPU', 'BwJL9FO6fMg', 93 | 'BWph-kU5VcM', 'BwzeCSd2bVc', 'BxhVnMPLR2I', 'BXWkN_YAMnA', 'bZWr4lidwaM', 'C-tFDOR--fQ', 'C4uRBT4IxWk', 'C5lVMsrp33E', 94 | 'C5PgxwxA9XU', 'C7r2LlGqrcE', 'Ca-nhIeZxuo', 'casR8s0oS6g', 'cbltQ7-EZJ0', 'cbT18UtpGJQ', 'CbwhqSnt54I', 'cCgdYZoybhY', 95 | 'cctNcVuGYfo', 'cCzwH7d4Ags', 'cDcuU3KVpuo', 'CEq6QWOHTlo', 'cEWXs-UmOuA', 'cF1Nu3o87Z0', 'CfEymG-VQNQ', 'CgNZLuHrV0o', 96 | 'Cgrn1Vr010E', 'CguNhVWqojs', 'cHPUPBFVHaU', 'CIBf9EewvU0', 'cIx7mOV8FM8', 'CJrrnLxe3lc', 'CJ_-t9AtkpA', 'cK5YxQFFlZE', 97 | 'cKtB81VN_4Q', 'Cl-Cn0qK5lg', 'ClWyk7-Idq4', 'cM1H92_5hqs', 'cmUXo4Crrm0', 'CNjDXnLcVfA', 'cnp4XfHaQgE', 'CosdRXJ9jAw', 98 | 'cpnRZgCWIXU', 'Cpoc5ht51NY', 'cqD-EvrqhKU', 'cQm4r5QFHBA', 'crCCyOlDY3w', 'crSd9cVn0qo', 'cTSUeeG12Sk', 'cTVnjY0bXlU', 99 | 'cTwCK3dRV90', 'CUT22MsW04I', 'CWEIWlqueBY', 'cWyPbYD9lGo', 'cx5G1VdC9UA', 'CyczQoe3MiQ', 'CYiHiRd6l1g', 'cYzF4GOE8Fo', 100 | 'CztDIt5nen8', 'cZ_eoeZx6kY', 'd-SGsAN22lU', 'D3s7heDD5rE', 'D47KVB-oT98', 'd4EOOvtqj6Y', 'd6JZXKg79yA', 'D7FnXaoPwR0', 101 | 'd7ymmR8WuYE', 'd97m4pNuBEI', 'dAF1K6DR8i0', 'DALragCYQr8', 'DbDBriTlmms', 'Dbl84rzEZWw', 'DBulVOm5LGk', 'DbUNhWmKw4M', 102 | 'dCyakRWH9a0', 'DFAHKL_l6KE', 'dFebX7PCg3w', 'dGV5XWzPF9Y', 'DhfyYXvszBU', 'DhGuOzl5Aks', 'DhlIQskLhKU', 'DJd02xCNNc0', 103 | 'djiMq2c_fCo', 'DKXMxzegPAE', 'dl-p2Kb44B8', 'DlENkC5Zwg4', 'DMh25N3Qa4Y', 'DN4E4VunBAo', 'DNAtW0EVR9w', 'Dngvez7MLiM', 104 | 'Doqwn0_xe8I', 'DPbxUe4qyyE', 'DQRPCEbGFSk', 'dS9H3EMxQJI', 'DscPAFMdo1w', 'dSPO1KBwUXo', 'dtL-2hf6RHk', 'DuO1tWIDEe8', 105 | 'dWE2jGv8-8E', 'Dwj4qZcN2Cg', 'DxA2Nng_KBo', 'Dxb-zo3lWPM', 'DyHsx_AOrzI', 'Dy_OnvkVG5Q', 'DzDDgK_PdR4', 'DZVoNuBsjN8', 106 | 'E3lxXZHSkdA', 'e49kAYjvMcQ', 'e595abxh9m4', 'e61ASYkJIRw', 'E844Aa1b5hg', 'EAjzt1M4ueU', 'eaSp-INcrwk', 'EAWVQWpgvUA', 107 | 'eckl2DvLZIQ', 'EcvnyTIs9JI', 'EDdzJnJyIdM', 'edjwF_XlNzE', 'eDLHAN6u2yQ', 'Edz9o56EgD0', 'eEyTzVbpG3w', 'EF84272YoPE', 108 | 'efIdLoYdxps', 'EgFSMM3ReE4', 'ehAp300c0e8', 'ehnhb5_IPdI', 'eHw1ICKt8sE', 'EIhCVahxZkA', 'eIqBFOX5YTo', 'EJ4_zTzzNbY', 109 | 'EJD6BT-zPHU', 'EkcmabUBzyc', 'EkKtFMKRIXM', 'ELWyxjy4jOo', 'enDCSfO4GRo', 'eORbJowLOFg', 'eOsY84oYqKg', 'eqX2nymZHWA', 110 | 'eQzsy3NCFEs', 'erabEK1fB_c', 'eR_xGQH1qYk', 'et4GjVERUR4', 'eUS8tJfDV6c', 'Euws_PLlpMo', 'Evp5EETgGhI', 'ex90Nui_S5k', 111 | 'eylbjkgzFVk', 'Ez-dyu4gJEg', 'E_IXck9171A', 'F-VPXPctDV0', 'F0863luTyfA', 'F0ve4k_sINQ', 'f2oyLPHXNT8', 'F5MRDLajCz0', 112 | 'f6HtqolhKqo', 'f6N38hfD4o4', 'F88btEbzdw0', 'F98AtFqq2q4', 'fb29LCjX4-E', 'fBIrnLxKB94', 'FbTEEN7p-p4', 'Fd8GDFzySQ8', 113 | 'feVH2esO1FI', 'fGsYDa3bzzI', 'FifaSTgtaBU', 'fIiMJ80KVy0', 'fJBcBEBcKKg', 'fjcQKVFvPlQ', 'FjHtL7gIQLk', 'Fk2Dj6wyQAE', 114 | 'fkBuY4iMK7E', 'Fkev7W8rr3I', 'fkIeURclwn0', 'FkyW7DoWHAM', 'FKZDX79-fxo', 'fM0rd3yFSjg', 'FMmoEUEiFVY', 'fnBA9l-3liw', 115 | 'fnIPcKKj7gk', 'fnXB1xyRFaA', 'fOeg8IofzV0', 'FplYx3s8uEg', 'FQ5N13TlXLQ', 'FQJmw7b-ylI', 'Fs5P2Ptz3Vg', 'FSTfAqQpsrI', 116 | 'fu6kGcDpPWU', 'fwvVdw3tzg0', 'fY7SAR5RXbY', 'fyDyN2PRNGU', 'FyHooK1u3pg', 'fZ3RyWrtjYM', 'G-RjMWCE3U4', 'g-z44AlcZJY', 117 | 'G1EOqEUyDEk', 'g5vdCoCp4IY', 'G69MHKMNzFY', 'g9F8cTZ4eDQ', 'GASkp3nW4ZI', 'gatJCfnz6bw', 'gbAYUh77P7k', 'GbeLVnVmxX4', 118 | 'gBEtcBmZsp8', 'gbI3qxMQ5GE', 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'hVwmifJdlfg', 'HWOadHxYUgA', 'HWrr0tJTlsE', 'HXI1V2VEkSA', 'HxT7E14euZ8', 'hxxXvsrIL18', 'hYXYcOHT6aE', 127 | 'HZEx6iNVDpw', 'I-y9hYJVHc0', 'I0TjUM8qYVs', 'i4qwUWVR0ik', 'I4U5qBSdUb0', 'I5qc5frLypE', 'i9InFg2vGf8', 'I9SR2wleAwk', 128 | 'i9VGEtH__zU', 'IcAIaBzQrkI', 'IccSohdKw5o', 'idL717ww3jE', 'ieTSyTRlyLY', 'If8ewVG8UZg', 'IFHriQpQB50', 'IfNLtevR64c', 129 | 'IhlG2J2PAbs', 'IiB3ZV3FHdQ', 'IIeq4I3pwHA', 'iIo5LtWfxrY', 'IisgnbMfKvI', 'IiueXfhu4D0', 'iivqpZsN76M', 'iiy9xPuVvYs', 130 | 'ijuoM54oCao', 'iJvKxxn40zE', 'IKjNOP0W76I', 'Il0iJUNnq7k', 'ilbYTM2tnpE', 'Inf44ssPIUA', 'InngTKk9zJg', 'iONubn3sC98', 131 | 'ipliu022a3k', 'iQCLPg5f3tE', 'irDLHivG1ro', 'IrhtHZFRBqU', 'IRwKri1rqpU', 'IwJlXwISDsI', 'iwvnA_b9Q8Y', 'Iw_sNY02eh4', 132 | 'Ixzc6RJv3GM', 'iyVphDMrJ9M', 'IZORhyAHhZs', 'J-8WcSqVwq0', 'J0KI-i6aEmc', 'j2iuJUSaUT0', 'j2VfzKjn4no', 'j4vCio2bdbo', 133 | 'j5dwnDyxh4E', 'j5GYMOvjjtk', 'j7bdt74NOcQ', 'J8dkqhWBWTA', 'j8s1B4ew7U8', 'J9L26AxMLEw', 'J9V8oVSIqXI', 'jaAehsIKB40', 134 | 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'ZVRp-8w3qhI', 'Zw0jo3SePtA', 'zWyQW8YIIf8', 'zXmO5_tiuM0', 'Zxn9X6XXOnU', 237 | 'ZxRlWfvn6Dk', 'zyr1F12AuAY', 'zz7x23rrwl8', '_0z11rWXLrg', '_1507-Lo6Eg', '_6DU0pPzzoY', '_7MBer1SeCM', '_AL1EoJWM5s', 238 | '_Bj0FbjFFJY', '_bu8Bb7XV5U', '_bXiwiVKfGs', '_c4dKyR3enc', '_dwL-FO2NLk', '_EctlaeJjuo', '_f46uY1hvC0', '_HbecwTgugc', 239 | '_htrPTOvX-s', '_K4phsNsy5U', '_KzjEAGue1U', '_nD1eYhp9NQ', '_rTq99Y8T_w', '_S8JfTbZbdk', '_yxBZ4qJNe8', '_Zj47bS5auo', 240 | '_Zocs3SxShk', '__Oe9dJ3ZZ0'] 241 | 242 | failed_videos = download_videos(video_ids, output_path) 243 | 244 | if failed_videos: 245 | print('Video IDs unable to download:') 246 | for video_id in failed_videos: 247 | print(video_id) 248 | else: 249 | print('All videos downloaded successfully!') 250 | -------------------------------------------------------------------------------- /annotations/task1&3/train.csv: -------------------------------------------------------------------------------- 1 | path-label 2 | /video_path/--Ly-qjodoI.mp4 3 3 | /video_path/-DMEyIfWBV8.mp4 3 4 | /video_path/0z5Xtb8ipRo.mp4 3 5 | /video_path/1PNlj_VEDtI.mp4 3 6 | /video_path/31lF8VNCJ8k.mp4 3 7 | /video_path/3gLhooOemZw.mp4 3 8 | /video_path/4yQO__Khl5U.mp4 3 9 | /video_path/58wAvY_aF-Y.mp4 3 10 | /video_path/5W3o4HjyV2k.mp4 3 11 | /video_path/6eGGq4fz8SE.mp4 3 12 | /video_path/6Ny4wSUdYNo.mp4 3 13 | /video_path/6Xu2Sgk1y80.mp4 3 14 | /video_path/8OTKidNwLlo.mp4 3 15 | /video_path/9VHtLRhwrBM.mp4 3 16 | /video_path/b2UCiQs0KMg.mp4 3 17 | /video_path/cKtB81VN_4Q.mp4 3 18 | /video_path/Cl-Cn0qK5lg.mp4 3 19 | /video_path/Edz9o56EgD0.mp4 3 20 | /video_path/eR_xGQH1qYk.mp4 3 21 | /video_path/FKZDX79-fxo.mp4 3 22 | /video_path/GE24mrcUkhg.mp4 3 23 | /video_path/gmWjVoc3mM8.mp4 3 24 | /video_path/j2VfzKjn4no.mp4 3 25 | /video_path/j4vCio2bdbo.mp4 3 26 | /video_path/j7bdt74NOcQ.mp4 3 27 | /video_path/JGgzTJtzaqc.mp4 3 28 | /video_path/jGJNtSF2RLA.mp4 3 29 | /video_path/KMJCl5PU7yo.mp4 3 30 | /video_path/Ko-oArJLfFc.mp4 3 31 | 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