一、说明

让我们谈谈在Python中使用网络摄像头。我有一个简单的任务，从相机读取帧，并在每一帧上运行神经网络。对于一个特定的网络摄像头，我在设置目标 fps 时遇到了问题（正如我现在所理解的——因为相机可以用 mjpeg 格式运行 30 fps，但不能运行原始），所以我决定深入研究 FFmpeg 看看它是否有帮助。

二、OpenCV和FFmpeg两个选项

我最终让OpenCV和FFmpeg都工作了，但我发现了一件非常有趣的事情：FFmpeg性能优于OpenCV是我的主要用例。事实上，使用 FFmpeg，我读取帧的速度提高了 15 倍，整个管道的加速提高了 32%。我简直不敢相信结果，并多次重新检查了所有内容，但它们是一致的。

注意：当我只是一帧一帧地读取时，性能完全相同，但是当我在读取帧后运行某些内容时，FFmpeg 速度更快（这需要时间）。我将在下面确切地说明我的意思。

2.1 openCV的代码实现

现在，让我们看一下代码。首先 — 使用 OpenCV 读取网络摄像头帧的类：

class VideoStreamCV:
    def __init__(self, src: int, fps: int, resolution: Tuple[int, int]):
        self.src = src
        self.fps = fps
        self.resolution = resolution
        self.cap = self._open_camera()
        self.wait_for_cam()

    def _open_camera(self):
        cap = cv2.VideoCapture(self.src)
        cap.set(cv2.CAP_PROP_FRAME_WIDTH, self.resolution[0])
        cap.set(cv2.CAP_PROP_FRAME_HEIGHT, self.resolution[1])
        fourcc = cv2.VideoWriter_fourcc(*"MJPG")
        cap.set(cv2.CAP_PROP_FOURCC, fourcc)
        cap.set(cv2.CAP_PROP_FPS, self.fps)
        return cap

    def read(self):
        ret, frame = self.cap.read()
        if not ret:
            return None
        return frame

    def release(self):
        self.cap.release()

    def wait_for_cam(self):
        for _ in range(30):
            frame = self.read()
        if frame is not None:
            return True
        return False

2.2 使用FFmpeg

我使用功能，因为相机通常需要时间“热身”。FFmpeg 类使用相同的预热：wait_for_cam

class VideoStreamFFmpeg:
    def __init__(self, src: int, fps: int, resolution: Tuple[int, int]):
        self.src = src
        self.fps = fps
        self.resolution = resolution
        self.pipe = self._open_ffmpeg()
        self.frame_shape = (self.resolution[1], self.resolution[0], 3)
        self.frame_size = np.prod(self.frame_shape)
        self.wait_for_cam()

    def _open_ffmpeg(self):
        os_name = platform.system()
        if os_name == "Darwin":  # macOS
            input_format = "avfoundation"
            video_device = f"{self.src}:none"
        elif os_name == "Linux":
            input_format = "v4l2"
            video_device = f"{self.src}"
        elif os_name == "Windows":
            input_format = "dshow"
            video_device = f"video={self.src}"
        else:
            raise ValueError("Unsupported OS")

        command = [
            'ffmpeg',
            '-f', input_format,
            '-r', str(self.fps),
            '-video_size', f'{self.resolution[0]}x{self.resolution[1]}',
            '-i', video_device,
            '-vcodec', 'mjpeg',  # Input codec set to mjpeg
            '-an', '-vcodec', 'rawvideo',  # Decode the MJPEG stream to raw video
            '-pix_fmt', 'bgr24',
            '-vsync', '2',
            '-f', 'image2pipe', '-'
        ]

        if os_name == "Linux":
            command.insert(2, "-input_format")
            command.insert(3, "mjpeg")

        return subprocess.Popen(
            command, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL, bufsize=10**8
        )

    def read(self):
        raw_image = self.pipe.stdout.read(self.frame_size)
        if len(raw_image) != self.frame_size:
            return None
        image = np.frombuffer(raw_image, dtype=np.uint8).reshape(self.frame_shape)
        return image

    def release(self):
        self.pipe.terminate()

    def wait_for_cam(self):
        for _ in range(30):
            frame = self.read()
        if frame is not None:
            return True
        return False

For timing function, I used decorator:run

def timeit(func):
    def wrapper(*args, **kwargs):
        t0 = time.perf_counter()
        result = func(*args, **kwargs)
        t1 = time.perf_counter()
        print(f"Main function time: {round(t1-t0, 4)}s")
        return result

    return wrapper

作为一个繁重的合成任务，我使用了这个简单的函数来代替神经网络（它也可以只是）。这是一个非常重要的部分，因为没有任何任务，OpenCV和FFmpeg的读取速度是相同的：time.sleep

def computation_task():
    for _ in range(5000000):
        9999 * 9999

现在功能与我读取框架的循环，它的时间，运行：computation_task

@timeit
def run(cam: VideoStreamCV | VideoStreamFFmpeg, run_task: bool):
    timer = []
    for _ in range(100):
        t0 = time.perf_counter()
        cam.read()
        timer.append(time.perf_counter() - t0)

        if run_task:
            computation_task()

    cam.release()
    return round(np.mean(timer), 4)

最后，我设置了几个参数，使用 OpenCV 和 FFmpeg 初始化 2 个视频流，并在没有和使用它的情况下运行它们。maincomputation_task

def main():
    fsp = 30
    resolution = (1920, 1080)

    for run_task in [False, True]:
        ff_cam = VideoStreamFFmpeg(src=0, fps=fsp, resolution=resolution)
        cv_cam = VideoStreamCV(src=0, fps=fsp, resolution=resolution)

        print(f"FFMPEG, task {run_task}:")
        print(f"Mean frame read time: {run(cam=ff_cam, run_task=run_task)}s\n")
        print(f"CV2, task {run_task}:")
        print(f"Mean frame read time: {run(cam=cv_cam, run_task=run_task)}s\n")

这是我得到的：

FFMPEG, task False:
Main function time: 3.2334s
Mean frame read time: 0.0323s

CV2, task False:
Main function time: 3.3934s
Mean frame read time: 0.0332s

FFMPEG, task True:
Main function time: 4.461s
Mean frame read time: 0.0014s

CV2, task True:
Main function time: 6.6833s
Mean frame read time: 0.023s

因此，如果没有合成任务，我可以获得相同的阅读时间：，。但是对于合成任务：和，所以FFmpeg要快得多。美妙之处在于，我的神经网络应用程序得到了真正的加速，而不仅仅是综合测试，所以我决定分享结果。0.03230.03320.00140.023

下图显示了 1 次迭代所需的时间：读取帧，使用 yolov8s 模型（在 CPU 上）处理它，并使用检测到的对象保存帧：

三完整脚本

以下是包含综合测试的完整脚本：

import platform
import subprocess
import time
from typing import Tuple
import cv2
import numpy as np


class VideoStreamFFmpeg:
    def __init__(self, src: int, fps: int, resolution: Tuple[int, int]):
        self.src = src
        self.fps = fps
        self.resolution = resolution
        self.pipe = self._open_ffmpeg()
        self.frame_shape = (self.resolution[1], self.resolution[0], 3)
        self.frame_size = np.prod(self.frame_shape)
        self.wait_for_cam()

    def _open_ffmpeg(self):
        os_name = platform.system()
        if os_name == "Darwin":  # macOS
            input_format = "avfoundation"
            video_device = f"{self.src}:none"
        elif os_name == "Linux":
            input_format = "v4l2"
            video_device = f"{self.src}"
        elif os_name == "Windows":
            input_format = "dshow"
            video_device = f"video={self.src}"
        else:
            raise ValueError("Unsupported OS")

        command = [
            'ffmpeg',
            '-f', input_format,
            '-r', str(self.fps),
            '-video_size', f'{self.resolution[0]}x{self.resolution[1]}',
            '-i', video_device,
            '-vcodec', 'mjpeg',  # Input codec set to mjpeg
            '-an', '-vcodec', 'rawvideo',  # Decode the MJPEG stream to raw video
            '-pix_fmt', 'bgr24',
            '-vsync', '2',
            '-f', 'image2pipe', '-'
        ]

        if os_name == "Linux":
            command.insert(2, "-input_format")
            command.insert(3, "mjpeg")

        return subprocess.Popen(
            command, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL, bufsize=10**8
        )

    def read(self):
        raw_image = self.pipe.stdout.read(self.frame_size)
        if len(raw_image) != self.frame_size:
            return None
        image = np.frombuffer(raw_image, dtype=np.uint8).reshape(self.frame_shape)
        return image

    def release(self):
        self.pipe.terminate()

    def wait_for_cam(self):
        for _ in range(30):
            frame = self.read()
        if frame is not None:
            return True
        return False


class VideoStreamCV:
    def __init__(self, src: int, fps: int, resolution: Tuple[int, int]):
        self.src = src
        self.fps = fps
        self.resolution = resolution
        self.cap = self._open_camera()
        self.wait_for_cam()

    def _open_camera(self):
        cap = cv2.VideoCapture(self.src)
        cap.set(cv2.CAP_PROP_FRAME_WIDTH, self.resolution[0])
        cap.set(cv2.CAP_PROP_FRAME_HEIGHT, self.resolution[1])
        fourcc = cv2.VideoWriter_fourcc(*"MJPG")
        cap.set(cv2.CAP_PROP_FOURCC, fourcc)
        cap.set(cv2.CAP_PROP_FPS, self.fps)
        return cap

    def read(self):
        ret, frame = self.cap.read()
        if not ret:
            return None
        return frame

    def release(self):
        self.cap.release()

    def wait_for_cam(self):
        for _ in range(30):
            frame = self.read()
        if frame is not None:
            return True
        return False


def timeit(func):
    def wrapper(*args, **kwargs):
        t0 = time.perf_counter()
        result = func(*args, **kwargs)
        t1 = time.perf_counter()
        print(f"Main function time: {round(t1-t0, 4)}s")
        return result

    return wrapper


def computation_task():
    for _ in range(5000000):
        9999 * 9999


@timeit
def run(cam: VideoStreamCV | VideoStreamFFmpeg, run_task: bool):
    timer = []
    for _ in range(100):
        t0 = time.perf_counter()
        cam.read()
        timer.append(time.perf_counter() - t0)

        if run_task:
            computation_task()

    cam.release()
    return round(np.mean(timer), 4)


def main():
    fsp = 30
    resolution = (1920, 1080)

    for run_task in [False, True]:
        ff_cam = VideoStreamFFmpeg(src=0, fps=fsp, resolution=resolution)
        cv_cam = VideoStreamCV(src=0, fps=fsp, resolution=resolution)

        print(f"FFMPEG, task {run_task}:")
        print(f"Mean frame read time: {run(cam=ff_cam, run_task=run_task)}s\n")
        print(f"CV2, task {run_task}:")
        print(f"Mean frame read time: {run(cam=cv_cam, run_task=run_task)}s\n")


if __name__ == "__main__":
    main()

注意：此脚本已在Apple的M1 Pro芯片上进行了测试。希望这是有帮助的！阿尔戈·萨基扬

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使用 Python 的高效相机流

一、说明

二、OpenCV和FFmpeg两个选项

2.1 openCV的代码实现

2.2 使用FFmpeg

三 完整脚本

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三完整脚本