制作更快的视频捕捉 opencv

Question

所以我创建了一个神经网络（CNN），可以使用 opencv 实时预测一个人的性别，一切正常，但是，当我运行代码 OpenCv 有这么多延迟时，我的网络摄像头还不错，这里是我的代码

    '''
Real-time Face Gender Recognition using Conv-Nueral Network (CNN) and Cv2

Here we predict the save model that it is train
'''
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.models import load_model
import numpy as np
import cv2
import os
import cvlib as cv
import imutils

# load the model
model = load_model('gender_detection.model')

# open webcams and initiate the camara
webcam = cv2.VideoCapture(0, cv2.CAP_DSHOW)

classes = ['hombre', 'mujer']

# loop through frames
while webcam.isOpened():
    # read frame from webcam
    status, frame = webcam.read()
    #webcam.set(cv2.CAP_PROP_FPS, 1000)
    frame = cv2.flip(frame, 1)

    # apply face detection
    face, confidence = cv.detect_face(frame) # this detects that there is a face in the camara, cvlib does, but not if it is a man that detects the neural network

    # loop through detected faces
    for idx, f in enumerate(face):
        # get corner points of face rectangle
        # this only will draw a rectangle when the cvlib detects the face with the vars giving up there
        startX, startY = f[0], f[1]
        endX, endY = f[2], f[3]

        # draw the rectangle over the face
        cv2.rectangle(frame, (startX, startY), (endX, endY), (0,255,0), 2)

        # crop the detected face region
        face_crop = np.copy(frame[startY:endY, startX:endX])

        if face_crop.shape[0] < 10 or face_crop.shape[1] < 10:
            continue

        # preprocessing for gender detection model
        face_crop = cv2.resize(face_crop, (96,96))
        face_crop = face_crop.astype("float") / 255.0
        face_crop = img_to_array(face_crop)
        face_crop = np.expand_dims(face_crop, axis=0)

        # apply gender detection face with the model
        conf = model.predict(face_crop)[0]

        # get label with max acc
        idx = np.argmax(conf)
        label = classes[idx]

        label = "{}: {:.2f}".format(label, conf[idx] * 100)

        Y = startY - 10 if startY - 10 > 10 else startY + 10

        # write label and confidence above the face rectangle
        cv2.putText(frame, label, (startX, Y), cv2.FONT_HERSHEY_SIMPLEX,
                    0.7, (0,255,0), 2)

    # display output
    cv2.imshow("Gender Detection", frame)

    # press "Q" to stop
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break


#realese resources
webcam.release()
cv2.destroyAllWindows()

而且我也尝试使用 cv2.CAP_PROB_FPS 但这只会有一点帮助，没有多大帮助。

Answer 1

我在使用带有文本检测的 openCV 视频捕获时遇到了同样的问题。 这不是网络摄像头的质量，而是 openCV 只能以您在性别检测中处理它们的速度向您显示帧。 对我有用的解决方案是使用多线程。

您可以为 OpenCV 视频捕获创建一个线程，然后为您的图像处理创建另一个线程。 警告：如果不对图像处理本身进行更改，就无法神奇地让图像处理更快地发生。 只要它需要，它就需要。 您可以做的是让 openCV 自行工作并将帧发送到交换 class，然后允许图像处理抓取帧并在 CV2 继续正常工作时以自己的速度工作。

这是用于 OCR 图像处理的 class 的（缩短）版本。 您可以在 start() 中看到我正在创建一个指向 ocr() 进程的线程。 这是您的性别识别过程可以 go 的地方。

class OCR:

    # def __init__(self, exchange: VideoStream, language=None):
    def __init__(self):
        self.exchange = None
        # init stuff for OCR not relevant to my example, but note that it 
        # takes a VideoStream class called exchange which is where this class 
        # grabs frames to process

    def start(self):
        Thread(target=self.ocr, args=()).start()
        return self

    def set_exchange(self, video_stream):
        self.exchange = video_stream

    def ocr(self):

        while not self.stopped:
            if self.exchange is not None:
                frame = self.exchange.frame

                # # # OCR stuff goes here

现在这里是 VideoStream class，它以自己的节奏在不同的线程中抓取帧。 然后，图像处理 class (OCR) 可以按照自己的节奏获取这些帧，并且两者不会影响彼此的性能。

class VideoStream:
    """Class for CV2 video capture. The start() method will create a new 
thread to read the video stream"""
    def __init__(self, src=0):
        self.stream = cv2.VideoCapture(src)
        (self.grabbed, self.frame) = self.stream.read()
        # self._boxes = None
        self.stopped = False

    def start(self):
        Thread(target=self.get, args=()).start()
        return self

    def get(self):
        while not self.stopped:
            (self.grabbed, self.frame) = self.stream.read()

    def get_video_dimensions(self):
        width = self.stream.get(cv2.CAP_PROP_FRAME_WIDTH)
        height = self.stream.get(cv2.CAP_PROP_FRAME_HEIGHT)
        return int(width), int(height)

    def stop_process(self):
        self.stopped = True

然后你可以像往常一样做你的 CV2 imshow 循环。

exchange = VideoStream(0).start()
ocr = OCR().start()
ocr.set_exchange(exchange)

 while True:  # Begins a loop for the real-time OCR display
        pressed_key = cv2.waitKey(1) & 0xFF
        if pressed_key == ord('q'):
            stop_stream_ocr(exchange, ocr)
            break

        frame = exchange.frame 

        cv2.imshow("Video Get Frame", frame)
        cps1.increment()

请注意，您无法真正控制 CV2 决定在其内部工作中使用的线程，但这种方法将允许您以自然 fps 显示您的网络摄像头，同时在后台进行图像处理。