（2） 采用OpenCV计算机视觉库实现了对微流控芯片显色图像的图像处

(2) The OpenCV computer vision library is used to realize the image processing of the color image of the microfluidic chip. The median filtering method eliminates the noise interference in the image; according to the corresponding relationship between the nitrite nitrogen concentration and the chromaticity after biochemical color development, the image is converted from the RGB color space to the HSV space, and the difference between the target and the background is obvious S-component map: By comparing the segmentation effects of different algorithms, the dynamic threshold segmentation algorithm is selected to segment the S-component map of the chip image, which is convenient for extracting and analyzing the feature value of the target area, so as to obtain the RGB of the image that characterizes the nitrite nitrogen concentration. value

(2) The image processing of the microflow control chip color rendering image is realized by using OpenCV computer vision library. The noise interference in the image is eliminated by the median filtering method, according to the correspondence between nitrite nitrogen concentration and bio-chroma after color rendering, the image is converted from RGB color space to HSV space and the S-part graph with a more obvious difference between target and background is obtained

(2) Opencv computer vision library is used to process the color image of microfluidic chip. The noise interference in the image is eliminated by median filtering method; according to the corresponding relationship between nitrite nitrogen concentration and chroma after biochemical color development, the image is converted from RGB color space to HSV space, and the s-component image with obvious difference between target and background is obtained; by comparing the segmentation results of different algorithms, the s-component image of chip image is segmented by dynamic threshold segmentation algorithm It is easy to extract and analyze the characteristic value of the target area by segmenting the component image, so as to obtain the RGB value of the image which represents the concentration of nitrite nitrogen<br>