HSV色彩空间筛选 2021-10-06

运行效果：（筛选蓝色）
白色为符合颜色空间的区域
运行代码：

# -*- coding: utf-8 -*-import cv2
import numpy as npcamera = cv2.VideoCapture(0)def nothing(x):pass# 创建一副黑色图像
img = np.zeros((300, 512, 3), np.uint8)# 设置滑动条组件
cv2.namedWindow('HSV_Window')
cv2.createTrackbar('H_L', 'HSV_Window', 0, 255, nothing)
cv2.createTrackbar('H_H', 'HSV_Window', 0, 255, nothing)
cv2.createTrackbar('S_L', 'HSV_Window', 0, 255, nothing)
cv2.createTrackbar('S_H', 'HSV_Window', 0, 255, nothing)
cv2.createTrackbar('V_L', 'HSV_Window', 0, 255, nothing)
cv2.createTrackbar('V_H', 'HSV_Window', 0, 255, nothing)while (1):success, frame = camera.read()cv2.imshow("HSV_Window", frame)hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)  # 将读取的BGR转换为HSVH_L = cv2.getTrackbarPos('H_L', 'HSV_Window')H_H = cv2.getTrackbarPos('H_H', 'HSV_Window')S_L = cv2.getTrackbarPos('S_L', 'HSV_Window')S_H = cv2.getTrackbarPos('S_H', 'HSV_Window')V_L = cv2.getTrackbarPos('V_L', 'HSV_Window')V_H = cv2.getTrackbarPos('V_H', 'HSV_Window')lower = np.array([H_L, S_L, V_L])  # 所要检测的像素范围upper = np.array([H_H, S_H, V_H])  # 此处检测绿色区域mask = cv2.inRange(hsv, lowerb=lower, upperb=upper)cv2.imshow("mask", mask)k = cv2.waitKey(1) & 0xFFif k == 27:break# sw = cv2.getTrackbarPos(switch, 'HSV_Window')# if s == 0:#     img[:] = 0# else:#     img[:] = [b, g, r]# 销毁窗口
cv2.destroyAllWindows()

intel.py

## License: Apache 2.0. See LICENSE file in root directory.
## Copyright(c) 2015-2017 Intel Corporation. All Rights Reserved.###############################################
##      Open CV and Numpy integration        ##
###############################################import pyrealsense2 as rs
import numpy as np
import cv2# Configure depth and color streams
pipeline = rs.pipeline()
config = rs.config()# Get device product line for setting a supporting resolution
pipeline_wrapper = rs.pipeline_wrapper(pipeline)
pipeline_profile = config.resolve(pipeline_wrapper)
device = pipeline_profile.get_device()
device_product_line = str(device.get_info(rs.camera_info.product_line))found_rgb = False
for s in device.sensors:if s.get_info(rs.camera_info.name) == 'RGB Camera':found_rgb = Truebreak
if not found_rgb:print("The demo requires Depth camera with Color sensor")exit(0)config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)if device_product_line == 'L500':config.enable_stream(rs.stream.color, 960, 540, rs.format.bgr8, 30)
else:config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)# Start streaming
pipeline.start(config)try:while True:# Wait for a coherent pair of frames: depth and colorframes = pipeline.wait_for_frames()# depth_frame = frames.get_depth_frame()color_frame = frames.get_color_frame()# if not depth_frame or not color_frame:#     continue# Convert images to numpy arrays# depth_image = np.asanyarray(depth_frame.get_data())color_image = np.asanyarray(color_frame.get_data())# Apply colormap on depth image (image must be converted to 8-bit per pixel first)# depth_colormap = cv2.applyColorMap(cv2.convertScaleAbs(depth_image, alpha=0.03), cv2.COLORMAP_JET)# depth_colormap_dim = depth_colormap.shapecolor_colormap_dim = color_image.shape# If depth and color resolutions are different, resize color image to match depth image for display# if depth_colormap_dim != color_colormap_dim:#     resized_color_image = cv2.resize(color_image, dsize=(depth_colormap_dim[1], depth_colormap_dim[0]), interpolation=cv2.INTER_AREA)#     images = np.hstack((resized_color_image, depth_colormap))# else:#     images = np.hstack((color_image, depth_colormap))# Show imagescv2.namedWindow('RealSense', cv2.WINDOW_AUTOSIZE)cv2.imshow('RealSense', color_colormap_dim)cv2.waitKey(1)finally:# Stop streamingpipeline.stop()

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