PS:MJPG streamer为实时显示视频流提供了思路，并提供了丰富的API供调用，此处使用MJPG streamer提供的HTTP API对接Python OpenCV实现HSV调节
1. 启动MJPG streamer
- 此处未使用base auth验证，公网使用请注意安全
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./mjpg_streamer -i "/home/MetaNetworks/github/mjpg-streamer/mjpg-streamer-experimental/input_uvc.so -d /dev/video0 -n -f 30" -o "/home/MetaNetworks/github/mjpg-streamer/mjpg-streamer-experimental/output_http.so -l 0.0.0.0 -p 8080 -w /home/MetaNetworks/github/mjpg-streamer/mjpg-streamer-experimental/www"
- input_uvc.so表示使用uvc摄像头
- -d 指定device
- -f 表示30帧
- -o表示输出
- output_http.so表示启动http服务器
- -l 监听地址，
0.0.0.0表示内外网都监听 - -p 端口号
- -w HTTP服务器根目录
- -l 监听地址，
- output_http.so表示启动http服务器
2.Python HSV编辑器实现
整体分为两部：
- 请求API下载实时帧
- API地址：
http://localhost:8080/?action=snapshot
- API地址：
- OpenCV显示图片
整体代码实现：
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# -*- coding:utf-8 -*-
import cv2
import numpy as np
from urllib import request
"""
功能：读取一张图片，显示出来，转化为HSV色彩空间
并通过滑块调节HSV阈值，实时显示
"""
#cv2.imshow("BGR", image) # 显示图片
hsv_low = np.array([25, 75, 165])
hsv_high = np.array([40, 255, 255])
def h_low(value):
hsv_low[0] = value
pass
def h_high(value):
hsv_high[0] = value
pass
def s_low(value):
hsv_low[1] = value
pass
def s_high(value):
hsv_high[1] = value
pass
def v_low(value):
hsv_low[2] = value
pass
def v_high(value):
hsv_high[2] = value
pass
def binary_HSV(img):
hsv_low = np.array([25, 75, 165])
hsv_high = np.array([40, 255, 255])
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) # BGR转HSV
dst = cv2.inRange(hsv, hsv_low, hsv_high) # 通过HSV的高低阈值，提取图像部分区域
cv2.show("Binary HSV Outpt:",dst)
return dst
cv2.namedWindow('image',cv2.WINDOW_FREERATIO)
cv2.createTrackbar('H low', 'image', 0, 255, h_low)
cv2.createTrackbar('H high', 'image', 0, 255, h_high)
cv2.createTrackbar('S low', 'image', 0, 255, s_low)
cv2.createTrackbar('S high', 'image', 0, 255, s_high)
cv2.createTrackbar('V low', 'image', 0, 255, v_low)
cv2.createTrackbar('V high', 'image', 0, 255, v_high)
url = "http://localhost:8080/?action=snapshot"
def downloadImg():
global url
with request.urlopen(url) as f:
data = f.read()
img1 = np.frombuffer(data, np.uint8)
#print("img1 shape ", img1.shape) # (83653,)
img_cv = cv2.imdecode(img1, cv2.IMREAD_ANYCOLOR)
return img_cv
while True:
# image = downloadImg()
image = downloadImg() #cv2.imread('1.jpg') # 根据路径读取一张图片
dst = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) # BGR转HSV
dst = cv2.inRange(dst, hsv_low, hsv_high) # 通过HSV的高低阈值，提取图像部分区域
cv2.imshow('output', dst)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cv2.destroyAllWindows()
