run_on_video/dataset.py

from torch.utils.data import Dataset
import csv
import os
import numpy as np


class HighlightDataset(Dataset):
    """Face Landmarks dataset."""

    def __init__(self, root_dir, transform=None):
        """
        Arguments:
            csv_file (string): Path to the csv file with annotations.
            root_dir (string): Directory with all datas including videos and annotations.
        """
        self.root_dir = root_dir
        self.video_dir = os.path.join(root_dir, "videos")
        self.anno_path = os.path.join(root_dir, "ydata-tvsum50-anno.tsv")

        #read annotations
        with open(self.anno_path, newline='') as f:
            reader = csv.reader(f, delimiter='\t')
            raw_annotations = list(reader)

        self.num_annotator = 20
        self.annotations = self.parse_annotations(raw_annotations)  # {video_id: [importance scores]}

        #get list of videos
        self.video_list = os.listdir(self.video_dir)

    def parse_annotations(self, annotations):
        '''
        format of annotations:
            [[video_id, video_category, importance score], ...]
        '''
        #separate annotations into chunks of length 20
        parsed_annotations = {}
        annotations_per_video = [annotations[i:i + self.num_annotator] for i in range(0, len(annotations), self.num_annotator)]
        for anno_video in annotations_per_video:
            video_id = anno_video[0][0]
            video_category = anno_video[0][1]
            #get importance score
            #anno[2] is a string of scores separated by commas
            importance_score = []
            for anno in anno_video:
                anno[2] = anno[2].split(',')
                anno[2] = [float(score) for score in anno[2]]
                importance_score.append(anno[2])
            importance_score = np.array(importance_score)

            #get average importance score
            parsed_annotations[video_id] = np.mean(importance_score, axis=0)

        return parsed_annotations
        
    
    def __len__(self):
        return len(self.video_list)

    def __getitem__(self, idx):
        if torch.is_tensor(idx):
            idx = idx.tolist()

        #should return frames and scores
        video_name = self.video_list[idx]
        video_id = video_name.split('.')[0]
        video_path = os.path.join(self.video_dir, video_name)

        #get annotations
        annotations = self.annotations[video_id]

        return video_path, annotations