Video reading benchmarks | OpenCV and Torchvision

benchmark.py

"""Benchmarks for the video reader: OpenCV vs Torchvision.

OpenCV is faster than Torchvision when just reading the frames, but it's slower when
    adding the conversions to RGB, tensor, float, and stacking the frames.

Results:
    Benchmarking OpenCV
            Read 300 frames in 0.84 seconds
            Read 300 frames in 0.81 seconds
            Read 300 frames in 0.98 seconds
            Read 300 frames in 1.02 seconds
            Read 300 frames in 0.92 seconds
            Read 300 frames in 1.02 seconds
            Read 300 frames in 0.92 seconds
            Read 300 frames in 0.97 seconds
            Read 300 frames in 0.87 seconds
            Read 300 frames in 1.01 seconds
            Read 300 frames in 0.88 seconds
            Read 300 frames in 0.91 seconds
    100%|███████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:11<00:00,  1.01it/s]
    Average time for 12 runs: 0.9286 (+/- 0.0678)
    Benchmarking Torchvision
            Read 300 frames in 0.69 seconds
            Read 300 frames in 0.49 seconds
            Read 300 frames in 0.49 seconds
            Read 300 frames in 0.50 seconds
            Read 300 frames in 0.49 seconds
            Read 300 frames in 0.50 seconds
            Read 300 frames in 0.49 seconds
            Read 300 frames in 0.50 seconds
            Read 300 frames in 0.51 seconds
            Read 300 frames in 0.49 seconds
            Read 300 frames in 0.50 seconds
            Read 300 frames in 0.49 seconds
    100%|███████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:06<00:00,  1.93it/s]
    Average time for 12 runs: 0.5116 (+/- 0.0537)
"""

import os
import time
from typing import Callable

import cv2
import numpy as np
import torch
from tqdm.auto import tqdm

import torchvision


def get_frames_opencv(video_file: str, frame_start: int = 0, frame_end: int = 300):
    """Returns the video tensor using OpenCV."""
    frames = list()
    cap = cv2.VideoCapture(video_file)
    try:
        if frame_start > 0:
            cap.set(propId=cv2.CAP_PROP_POS_FRAMES, value=frame_start - 1)
        for frame_idx in range(frame_end - frame_start + 1):
            ret, frame = cap.read()
            if ret:
                frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
                frame = torch.from_numpy(frame).permute(2, 0, 1).float().div(255.0)
                frames.append(frame)
            else:
                tqdm.write("Failure reading frame {}".format(frame_idx))
                break
    finally:
        cap.release()
    frames = torch.stack(frames, dim=0)
    return frames


def get_frames_torch(
    video_file: str, frame_start: int = 0, frame_end: int = 300
) -> torch.Tensor:
    """Returns the video tensor using PyTorch."""
    frame_rate = 25
    vid_tensor, _, info = torchvision.io.read_video(
        video_file,
        pts_unit="sec",
        start_pts=frame_start / frame_rate,
        end_pts=frame_end / frame_rate,
    )
    return vid_tensor


def benchmark_fn(fn: Callable, **kwargs):
    """Benchmarks a function."""
    start = time.time()
    results = fn(**kwargs)
    return time.time() - start, results


def main():

    vid_name = os.path.join("argument-short.mp4")
    n_runs = 12

    experiments = [
        {
            "name": "OpenCV",
            "fn": get_frames_opencv,
            "kwargs": {"video_file": vid_name, "frame_start": 0, "frame_end": 299},
        },
        {
            "name": "Torchvision",
            "fn": get_frames_torch,
            "kwargs": {"video_file": vid_name, "frame_start": 0, "frame_end": 299},
        },
    ]

    for experiment in experiments:
        tqdm.write(f"Benchmarking {experiment['name']}")
        times = list()
        for _ in tqdm(range(n_runs)):
            time, results = benchmark_fn(experiment["fn"], **experiment["kwargs"])
            tqdm.write("\tRead {} frames in {:.2f} seconds".format(len(results), time))
            times.append(time)
        tqdm.write(
            "Average time for {} runs: {:.4f} (+/- {:.4f})".format(
                n_runs, np.mean(times), np.std(times)
            )
        )


if __name__ == "__main__":
    main()

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pyproject.toml

[tool.poetry]
name = "torchvision-demo"
version = "0.1.0"
description = ""
authors = ["Lucas Parzianello <lucaspar@users.noreply.github.com>"]

[tool.poetry.dependencies]
python = "^3.10"
torch = "^1.12.1"
opencv-python = "^4.6.0"
torchvision = "^0.13.1"
av = "^9.2.0"
tqdm = "^4.64.0"

[tool.poetry.dev-dependencies]

[build-system]
requires = ["poetry-core>=1.0.0"]
build-backend = "poetry.core.masonry.api"