【发布时间】:2021-03-26 18:07:31
【问题描述】:
我想将网络摄像头捕获的视频叠加到另一个直播视频上。所以我尝试了上面的代码,但是速度太慢了。
我应该切换到另一种语言或 lib 吗?
任何建议或帮助将不胜感激。
import numpy as np
from cv2 import cv2
def image_resize(image, width = None, height = None, inter = cv2.INTER_AREA):
# initialize the dimensions of the image to be resized and
# grab the image size
dim = None
(h, w) = image.shape[:2]
# if both the width and height are None, then return the
# original image
if width is None and height is None:
return image
# check to see if the width is None
if width is None:
# calculate the ratio of the height and construct the
# dimensions
r = height / float(h)
dim = (int(w * r), height)
# otherwise, the height is None
else:
# calculate the ratio of the width and construct the
# dimensions
r = width / float(w)
dim = (width, int(h * r))
# resize the image
resized = cv2.resize(image, dim, interpolation = inter)
# return the resized image
return resized
cap2 = cv2.VideoCapture('http://192.168.43.1:8080/video')
cap = cv2.VideoCapture('test.mp4')
# Define the codec and create VideoWriter object
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter('sample3.mp4',fourcc,30, (640,480))
# watermark = logo
# cv2.imshow("watermark",watermark)
while(cap.isOpened()):
ret, frame = cap.read()
frame = cv2.cvtColor(frame,cv2.COLOR_BGR2BGRA)
ret2 ,frame2 = cap2.read()
frame2 = cv2.cvtColor(frame2,cv2.COLOR_BGR2BGRA)
watermark = image_resize(frame2,height=177)
if ret==True:
frame_h, frame_w, frame_c = frame.shape
overlay = np.zeros((frame_h, frame_w, 4), dtype='uint8')
overlay[543:543+177,1044:1044+236] = watermark
cv2.addWeighted(frame, 0.25, overlay, 1.0, 0, frame)
frame = cv2.cvtColor(frame,cv2.COLOR_BGRA2BGR)
out.write(frame)
cv2.imshow('frame',frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
else:
break
# Release everything if job is finished
cap.release()
out.release()
cv2.destroyAllWindows()
要求是将网络摄像头实时视频平滑地覆盖在底部的另一个视频上。
谢谢。
【问题讨论】:
-
您发现管道中的瓶颈了吗?
-
@eldesgraciado 结果帧速率非常低(1 或 2 fps),但在不叠加的情况下运行速度高达 30 fps。
-
给定的答案解决了您最初的问题。从那以后,您已经删除了运行缓慢的代码并用答案替换了它。这使您的问题无法理解。此外,您当前版本的问题中的代码不再慢。
标签: python opencv video webcam video-processing