data/datasets/action_recognition/common_dataset.py

import os
from pathlib import Path

import random

import numpy as np
import pickle as pk
import cv2
from tqdm import tqdm
from PIL import Image

import torchvision.transforms as transforms
import torch

# from prefetch_generator import BackgroundGenerator
from torch.utils.data import DataLoader, Dataset


class VideoDataset(Dataset):

    def __init__(self, directory_list, local_rank=0, enable_GPUs_num=0, distributed_load=False, resize_shape=[224, 224] , mode='train', clip_len=32, crop_size = 168):
        
        self.clip_len, self.crop_size, self.resize_shape = clip_len, crop_size, resize_shape
        self.mode = mode

        self.fnames, labels = [],[]
        # get the directory of the specified split
        for directory in directory_list:
            folder = Path(directory)
            print("Load dataset from folder : ", folder)
            for label in sorted(os.listdir(folder)):
                for fname in os.listdir(os.path.join(folder, label)) if mode=="train" else os.listdir(os.path.join(folder, label))[:10]:
                    self.fnames.append(os.path.join(folder, label, fname))
                    labels.append(label)
        # print(labels)
        random_list = list(zip(self.fnames, labels))
        random.shuffle(random_list)
        self.fnames[:], labels[:] = zip(*random_list)
        self.labels = labels

        # self.fnames = self.fnames[:240]

        if mode == 'train' and distributed_load:
            single_num_ = len(self.fnames)//enable_GPUs_num
            self.fnames = self.fnames[local_rank*single_num_:((local_rank+1)*single_num_)]
            labels = labels[local_rank*single_num_:((local_rank+1)*single_num_)]

        # prepare a mapping between the label names (strings) and indices (ints)
        self.label2index = {label:index for index, label in enumerate(sorted(set(labels)))} 
        # convert the list of label names into an array of label indices
        self.label_array = np.array([self.label2index[label] for label in labels], dtype=int)
                
    def __getitem__(self, index):
        # loading and preprocessing. TODO move them to transform classess
        buffer = self.loadvideo(self.fnames[index])
        
        height_index = np.random.randint(buffer.shape[2] - self.crop_size)
        width_index = np.random.randint(buffer.shape[3] - self.crop_size)

        return buffer[:,:,height_index:height_index + self.crop_size, width_index:width_index + self.crop_size], self.label_array[index]


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


    def loadvideo(self, fname):
        # initialize a VideoCapture object to read video data into a numpy array
        self.transform = transforms.Compose([
                transforms.Resize([self.resize_shape[0], self.resize_shape[1]]),
                transforms.ToTensor(),
                transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
                ])

        flip, flipCode = 1, random.choice([-1,0,1]) if np.random.random() < 0.5 and self.mode=="train" else 0

        try:
            video_stream = cv2.VideoCapture(fname)
            frame_count = int(video_stream.get(cv2.CAP_PROP_FRAME_COUNT))
        except RuntimeError:
            index = np.random.randint(self.__len__())
            video_stream = cv2.VideoCapture(self.fnames[index])
            frame_count = int(video_stream.get(cv2.CAP_PROP_FRAME_COUNT))

        while frame_count<self.clip_len+2:
            index = np.random.randint(self.__len__())
            video_stream = cv2.VideoCapture(self.fnames[index])
            frame_count = int(video_stream.get(cv2.CAP_PROP_FRAME_COUNT))

        speed_rate = np.random.randint(1, 3) if frame_count > self.clip_len*2+2 else 1
        time_index = np.random.randint(frame_count - self.clip_len * speed_rate)

        start_idx, end_idx, final_idx = time_index, time_index+(self.clip_len*speed_rate), frame_count-1
        count, sample_count, retaining = 0, 0, True

        # create a buffer. Must have dtype float, so it gets converted to a FloatTensor by Pytorch later
        buffer = np.empty((self.clip_len, 3, self.resize_shape[0], self.resize_shape[1]), np.dtype('float32'))
        
        while (count <= end_idx and retaining):
            retaining, frame = video_stream.read()
            if count < start_idx:
                count += 1
                continue
            if count % speed_rate == speed_rate-1 and count >= start_idx and sample_count < self.clip_len:
                if flip:
                    frame = cv2.flip(frame, flipCode=flipCode)
                try:
                    buffer[sample_count] = self.transform(Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)))
                except cv2.error as err:
                    continue
                sample_count += 1
            count += 1
        video_stream.release()

        return buffer.transpose((1, 0, 2, 3))


if __name__ == '__main__':

    datapath = ['/data/datasets/ucf101/videos']
    
    dataset = VideoDataset(datapath, 
                            resize_shape=[224, 224],
                            mode='validation')
    x, y = dataset[0]
    # x: (3, num_frames, w, h)
    print(x.shape, y.shape, y)
    
    # dataloader = DataLoader(dataset, batch_size=8, shuffle=True, num_workers=24, pin_memory=True)

    # bar = tqdm(total=len(dataloader), ncols=80)

    # prefetcher = DataPrefetcher(BackgroundGenerator(dataloader), 0)
    # batch = prefetcher.next()
    # iter_id = 0
    # while batch is not None:
    #     iter_id += 1
    #     bar.update(1)
    #     if iter_id >= len(dataloader):
    #         break

    #     batch = prefetcher.next()
    #     print(batch[0].shape)
    #     print("label: ", batch[1])

    # '''
    # for step, (buffer, labels) in enumerate(BackgroundGenerator(dataloader)):
    #     print(buffer.shape)
    #     print("label: ", labels)
    #     bar.update(1)
    # '''