dataset.py

import torch.utils.data as data
import torch

from PIL import Image
import os
import os.path
import numpy as np
from numpy.random import randint

class VideoRecord(object):
    def __init__(self, row):
        self._data = row

    @property
    def path(self):
        return self._data[0]

    @property
    def num_frames(self):
        return int(self._data[1])

    @property
    def label(self):
        return [int(item) for item in self._data[2]]


class TSNDataSet(data.Dataset):
    def __init__(self, root_path, list_file, num_file, num_class,
                 num_segments=3, new_length=1, modality='RGB',
                 image_tmpl='img_{:05d}.jpg', transform=None,
                 force_grayscale=False, random_shift=True, test_mode=False):

        self.root_path = root_path
        self.list_file = list_file
        self.num_file = num_file
        self.num_segments = num_segments
        self.new_length = new_length
        self.modality = modality
        self.image_tmpl = image_tmpl
        self.transform = transform
        self.random_shift = random_shift
        self.test_mode = test_mode
        self.num_class = num_class

        if self.modality == 'RGBDiff':
            self.new_length += 1# Diff needs one more image to calculate diff

        self._parse_list()

    def _load_image(self, directory, idx):
        if self.modality == 'RGB' or self.modality == 'RGBDiff':
            try:
                return [Image.open(os.path.join(self.root_path, directory, self.image_tmpl.format(directory,idx))).convert('RGB')]
            except Exception:
                print('error loading image:', os.path.join(self.root_path, directory, self.image_tmpl.format(directory,idx)))
                return [Image.open(os.path.join(self.root_path, directory, self.image_tmpl.format(directory,1))).convert('RGB')]
        elif self.modality == 'Flow':
            try:
                idx_skip = 1 + (idx-1)*5
                flow = Image.open(os.path.join(self.root_path, directory, self.image_tmpl.format(directory,idx_skip))).convert('RGB')
            except Exception:
                print('error loading flow file:', os.path.join(self.root_path, directory, self.image_tmpl.format(directory,idx_skip)))
                flow = Image.open(os.path.join(self.root_path, directory, self.image_tmpl.format(directory,1))).convert('RGB')
            flow_x, flow_y, _ = flow.split()
            x_img = flow_x.convert('L')
            y_img = flow_y.convert('L')

            return [x_img, y_img]

    def _parse_list(self):
        with open(self.list_file, 'r') as point:
            tmp_class = [x.strip().split(' ') for x in point]
        tmp_class_dict = {}
        for item in tmp_class:
            if item[0] in tmp_class_dict:
                tmp_class_dict[item[0]].append(item[-1])
            else:
                tmp_class_dict[item[0]] = [item[-1]]
                    
        with open(self.num_file, 'r') as point:
            tmp_num = [x.strip().split(' ') for x in point]
            tmp_num_dict = {name: int(num) for name, num in tmp_num}
        tmp = [[name, tmp_num_dict[name], tmp_class_dict[name]] for name in tmp_class_dict]
        tmp.sort()

        tmp = [item for item in tmp if item[1] >= 3]
        self.video_list = [VideoRecord(item) for item in tmp]
        print('video number:%d'%(len(self.video_list)))

    def _sample_indices(self, record):
        """

        :param record: VideoRecord
        :return: list
        """

        average_duration = (record.num_frames - self.new_length + 1) // self.num_segments
        if average_duration > 0:
            offsets = np.multiply(list(range(self.num_segments)), average_duration) + randint(average_duration, size=self.num_segments)
        elif record.num_frames > self.num_segments:
            offsets = np.sort(randint(record.num_frames - self.new_length + 1, size=self.num_segments))
        else:
            offsets = np.zeros((self.num_segments,))
        return offsets + 1

    def _get_val_indices(self, record):
        if record.num_frames > self.num_segments + self.new_length - 1:
            tick = (record.num_frames - self.new_length + 1) / float(self.num_segments)
            offsets = np.array([int(tick / 2.0 + tick * x) for x in range(self.num_segments)])
        else:
            offsets = np.zeros((self.num_segments,))
        return offsets + 1

    def _get_test_indices(self, record):

        tick = (record.num_frames - self.new_length + 1) / float(self.num_segments)

        offsets = np.array([int(tick / 2.0 + tick * x) for x in range(self.num_segments)])

        return offsets + 1

    def __getitem__(self, index):
        record = self.video_list[index]
        # check this is a legit video folder
        while not os.path.exists(os.path.join(self.root_path, record.path, self.image_tmpl.format(record.path,1))):
            print(os.path.join(self.root_path, record.path, self.image_tmpl.format(record.path,1)))
            index = np.random.randint(len(self.video_list))
            record = self.video_list[index]

        if not self.test_mode:
            segment_indices = self._sample_indices(record) if self.random_shift else self._get_val_indices(record)
        else:
            segment_indices = self._get_test_indices(record)
            
        return self.get(record, segment_indices)

    def get(self, record, indices):

        images = list()
        for seg_ind in indices:
            p = int(seg_ind)
            for _ in range(self.new_length):
                seg_imgs = self._load_image(record.path, p)
                images.extend(seg_imgs)
                if p < record.num_frames:
                    p += 1

        process_data = self.transform(images)
        torch_label = torch.zeros(self.num_class)
        torch_label[record.label] = 1
        return process_data, torch_label

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