I'm attempting to automate a game using opencv and yolov5. My final problem is that I can't get a script to capture the screen fast enough to follow the objects correctly. I found a module called DXCam that can capture images at astounding speeds, though I can't use them in my algorithm as they are ndarrays and not normal arrays. Is there a way to convert the ndarrays into normal arrays (this process does not need to be fast, I can simply buffer them and alter them after they've been captured)? This is the script that produces the ndarrays:

def record():
    size = (pyautogui.size())
    box = pyautogui.locateOnScreen('game_screen.png', region=(0, 0, size[0], size[1]), grayscale=True, confidence=0.7)

    record_time = 3

    target_fps = 120
    writer = cv2.VideoWriter(
        "output.mp4", cv2.VideoWriter_fourcc(*"mp4v"), target_fps, (box.width, box.height)
    )
    camera = dxcam.create(output_color="RGB")
    camera.start(target_fps=120, video_mode=True, region=(box.left, box.top, box.left + box.width, box.top + box.height))
    cur_time = time.time()
    while time.time() < cur_time + record_time:
        writer.write(camera.get_latest_frame())
    camera.stop()
    writer.release()

Alternatively, are there any faster methods than this? This one for example is not fast enough for my needs (Merely 12-13 frames a second):

def record_bad():
    height = GetSystemMetrics(1)
    width = GetSystemMetrics(0)
    file_name = 'output.mp4v'
    fourcc = cv2.VideoWriter_fourcc(*'MP4V')
    captured_video = cv2.VideoWriter(file_name, fourcc, 120.0, (width, height))
    time_before = time.time()
    frames = []
    while time.time() > time_before + 3:
        img = ImageGrab.grab(bbox=(0, 0, width, height))  # bbox specifies specific region (bbox= x,y,width,height)
        img_final = cv2.cvtColor(np.array(img), cv2.COLOR_BGR2GRGB)
        frames.append(img_final)
        cv2.waitKey(0)
    for frame in frames:
        captured_video.write(frame)
    captured_video.release()
    cv2.destroyAllWindows()