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            <h1 class="title-article" id="articleContentId">opencv读取摄像头不是实时的处理方法</h1>
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                <pre data-index="0" class="set-code-show prettyprint"><code class="prism language-python has-numbering" onclick="mdcp.copyCode(event)" style="position: unset;">cap <span class="token operator">=</span> cv2<span class="token punctuation">.</span>VideoCapture<span class="token punctuation">(</span><span class="token number">0</span><span class="token punctuation">)</span>

cap.read()

读取的不是实时的画面，存在延迟怎么处理

如果你使用的是OpenCV库，你可以通过将缓冲区大小设置为更小的值来减小延迟。默认情况下，缓冲区大小是16，但可以通过在cv2.VideoCapture()中传递参数来更改。例如：

cap = cv2.VideoCapture(0)
cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)

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这将把缓冲区大小减小到1，从而减小延迟。但是，如果你将缓冲区大小设置得太小，你可能会遇到画面卡顿或丢帧的问题。

另一种方式是使用多线程或异步处理来实现更快的画面更新。你可以在一个线程中读取摄像头的帧，并将它们传递到另一个线程或进程中进行处理和显示。这将使你能够更快地处理帧并减少延迟。但是，这需要更多的编程和计算资源，并可能会增加代码的复杂性。

下面是一个基本的多线程处理OpenCV视频帧的例子：

import cv2
import threading
class VideoCapture:
    def __init__(self, src=0):
        self.cap = cv2.VideoCapture(src)
        self.cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)
        self.grabbed, self.frame = self.cap.read()
        self.started = False
        self.read_lock = threading.Lock()

<span class="token keyword">def</span> <span class="token function">start</span><span class="token punctuation">(</span>self<span class="token punctuation">)</span><span class="token punctuation">:</span>
    <span class="token keyword">if</span> self<span class="token punctuation">.</span>started<span class="token punctuation">:</span>
        <span class="token keyword">print</span><span class="token punctuation">(</span><span class="token string">'VideoCapture already started'</span><span class="token punctuation">)</span>
        <span class="token keyword">return</span> <span class="token boolean">None</span>
    self<span class="token punctuation">.</span>started <span class="token operator">=</span> <span class="token boolean">True</span>
    self<span class="token punctuation">.</span>thread <span class="token operator">=</span> threading<span class="token punctuation">.</span>Thread<span class="token punctuation">(</span>target<span class="token operator">=</span>self<span class="token punctuation">.</span>update<span class="token punctuation">,</span> args<span class="token operator">=</span><span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">)</span>
    self<span class="token punctuation">.</span>thread<span class="token punctuation">.</span>start<span class="token punctuation">(</span><span class="token punctuation">)</span>
    <span class="token keyword">return</span> self

<span class="token keyword">def</span> <span class="token function">update</span><span class="token punctuation">(</span>self<span class="token punctuation">)</span><span class="token punctuation">:</span>
    <span class="token keyword">while</span> self<span class="token punctuation">.</span>started<span class="token punctuation">:</span>
        grabbed<span class="token punctuation">,</span> frame <span class="token operator">=</span> self<span class="token punctuation">.</span>cap<span class="token punctuation">.</span>read<span class="token punctuation">(</span><span class="token punctuation">)</span>
        <span class="token keyword">with</span> self<span class="token punctuation">.</span>read_lock<span class="token punctuation">:</span>
            self<span class="token punctuation">.</span>grabbed <span class="token operator">=</span> grabbed
            self<span class="token punctuation">.</span>frame <span class="token operator">=</span> frame

<span class="token keyword">def</span> <span class="token function">read</span><span class="token punctuation">(</span>self<span class="token punctuation">)</span><span class="token punctuation">:</span>
    <span class="token keyword">with</span> self<span class="token punctuation">.</span>read_lock<span class="token punctuation">:</span>
        grabbed <span class="token operator">=</span> self<span class="token punctuation">.</span>grabbed
        frame <span class="token operator">=</span> self<span class="token punctuation">.</span>frame
    <span class="token keyword">return</span> grabbed<span class="token punctuation">,</span> frame

<span class="token keyword">def</span> <span class="token function">stop</span><span class="token punctuation">(</span>self<span class="token punctuation">)</span><span class="token punctuation">:</span>
    self<span class="token punctuation">.</span>started <span class="token operator">=</span> <span class="token boolean">False</span>
    self<span class="token punctuation">.</span>thread<span class="token punctuation">.</span>join<span class="token punctuation">(</span><span class="token punctuation">)</span>

<span class="token keyword">def</span> <span class="token function">__exit__</span><span class="token punctuation">(</span>self<span class="token punctuation">,</span> exc_type<span class="token punctuation">,</span> exc_value<span class="token punctuation">,</span> traceback<span class="token punctuation">)</span><span class="token punctuation">:</span>
    self<span class="token punctuation">.</span>cap<span class="token punctuation">.</span>release<span class="token punctuation">(</span><span class="token punctuation">)</span>

if name == ‘main‘:
    cap = VideoCapture(0).start()
    while True:
        grabbed, frame = cap.read()
        if not grabbed:
            break
        cv2.imshow(‘frame’, frame)
        if cv2.waitKey(1) & 0xFF == ord(‘q’):
            break
    cap.stop()
    cv2.destroyAllWindows()
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这个例子使用了一个VideoCapture类，该类在一个线程中不断读取摄像头帧，并将它们保存在一个共享的grabbed和frame变量中。在主程序中，我们通过调用start()方法来启动线程，然后不断读取帧并在窗口中显示它们，直到用户按下’q’键退出。最后，我们调用stop()方法来停止线程并释放资源。

需要注意的是，这个例子仅仅是一个基本的框架，你可能需要对其进行修改和扩展以适应你的具体应用场景。例如，你可以添加一个另一个线程来处理帧，然后显示处理后的结果，以此来减少延迟。

读取的不是实时的画面 ，存在延迟 怎么处理

下面是一个基本的多线程处理OpenCV视频帧的例子：

你的赏识是我前进的动力

读取的不是实时的画面，存在延迟怎么处理