如何使用此 Python 屏幕录像机代码实现 30 FPS 的帧速率？

Question

我想要一个屏幕录像机。 我想自己做。

我检查了 inte.net 并发现： https://www.thepythoncode.com/code/make-screen-recorder-python

代码：

import cv2
import numpy as np
import pyautogui

# Display screen resolution, get it from your OS settings
SCREEN_SIZE = (1366, 768)
# Define the codec
fourcc = cv2.VideoWriter_fourcc(*"XVID")
# Create the video write object
out = cv2.VideoWriter("output.avi", fourcc, 30.0, (SCREEN_SIZE))

while True:
    # make a screenshot
    img = pyautogui.screenshot()
    # img = pyautogui.screenshot(region=(0, 0, 300, 400))
    # convert these pixels to a proper numpy array to work with OpenCV
    frame = np.array(img)
    # convert colors from BGR to RGB
    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    # write the frame
    out.write(frame)
    # show the frame
    cv2.imshow("screenshot", frame)
    # if the user clicks q, it exits
    if cv2.waitKey(1) == ord("q"):
        break

# Make sure everything is closed when exited
cv2.destroyAllWindows()
out.release()

问题：

当我运行它时，效果很好。 但是它在output之后有一个随机速度。fps是30但是当我录制1分钟时，视频是5秒或10分钟（随机）。

我如何让这个记录器以正确的速度以 30 fps 的速度提供 output？

Answer 1

基本上，如果您想继续使用相同的代码，您将不得不在分辨率或帧速率上做出妥协。

我的建议是尝试cv2.VideoCapture()功能。

我附上网页链接，其中有详细的分步过程，作者实现了 30.75 的 FPS 速率。

这是链接： https://www.pyimagesearch.com/2017/02/06/faster-video-file-fps-with-cv2-videocapture-and-opencv/

链接中内容的后半部分有 The faster, threaded method to reading video frames with OpenCV。

# import the necessary packages
from imutils.video import FileVideoStream
from imutils.video import FPS
import numpy as np
import argparse
import imutils
import time
import cv2
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-v", "--video", required=True,
    help="path to input video file")
args = vars(ap.parse_args())
# start the file video stream thread and allow the buffer to
# start to fill
print("[INFO] starting video file thread...")
fvs = FileVideoStream(args["video"]).start()
time.sleep(1.0)
# start the FPS timer
fps = FPS().start()

# loop over frames from the video file stream
while fvs.more():
    # grab the frame from the threaded video file stream, resize
    # it, and convert it to grayscale (while still retaining 3
    # channels)
    frame = fvs.read()
    frame = imutils.resize(frame, width=450)
    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    frame = np.dstack([frame, frame, frame])
    # display the size of the queue on the frame
    cv2.putText(frame, "Queue Size: {}".format(fvs.Q.qsize()),
        (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)    
    # show the frame and update the FPS counter
    cv2.imshow("Frame", frame)
    cv2.waitKey(1)
    fps.update()

# stop the timer and display FPS information
fps.stop()
print("[INFO] elasped time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))
# do a bit of cleanup
cv2.destroyAllWindows()
fvs.stop()