python+opencv实现文字颜色识别与标定功能 python+opencv实现文字颜色识别与

最近接了一个比较简单的图像处理的单子，花了一点时间随便写了一下：
【python+opencv实现文字颜色识别与标定功能】 数据集客户没有是自己随便创建的：

文章图片

程序如下：

"""Code creation time:September 11, 2021Author:PanBoRealize function:It mainly realizes the recognition and calibration of fonts with different colors"""import numpy as npimport cv2 as cv font = cv.FONT_HERSHEY_SIMPLEXlower_red = np.array([0, 120, 120])hight_red = np.array([10, 255, 255])#lower_black = np.array([0, 0, 0])height_black = np.array([144, 144, 144]) lower_yellow = np.array([10, 230, 230])height_yellow = np.array([35, 255, 255]) frame = cv.imread("test.png")cv.namedWindow("test_image", cv.WINDOW_AUTOSIZE)cv.imshow('test_image', frame) img_hsv = cv.cvtColor(frame, cv.COLOR_BGR2HSV)mask_red = cv.inRange(img_hsv, lower_red, hight_red)mask_black = cv.inRange(img_hsv, lower_black, height_black)mask_yellow = cv.inRange(img_hsv, lower_yellow, height_yellow) cv.namedWindow("mask_red", cv.WINDOW_AUTOSIZE)cv.imshow("mask_red", mask_red)cv.namedWindow("mask_black", cv.WINDOW_AUTOSIZE)cv.imshow("mask_black", mask_black)cv.namedWindow("mask_yellow", cv.WINDOW_AUTOSIZE)cv.imshow("mask_yellow", mask_yellow) kernel = cv.getStructuringElement(cv.MORPH_RECT, (3, 3))mask_yellow = cv.morphologyEx(mask_yellow, cv.MORPH_OPEN, kernel)cv.namedWindow("mask_yellow_open", cv.WINDOW_AUTOSIZE)cv.imshow("mask_yellow_open", mask_yellow) mask_yellow = cv.morphologyEx(mask_yellow, cv.MORPH_CLOSE, kernel)cv.namedWindow("mask_yellow_close", cv.WINDOW_AUTOSIZE)cv.imshow("mask_yellow_close", mask_yellow) mask_black = cv.medianBlur(mask_black, 3)mask_red = cv.medianBlur(mask_red, 3)mask_yellow = cv.medianBlur(mask_yellow, 3)# cv.imshow(" ", mask_green) cnts1, hierarchy1 = cv.findContours(mask_black, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_NONE)cnts2, hierarchy2 = cv.findContours(mask_red, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_NONE)cnts3, hierarchy3 = cv.findContours(mask_yellow, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_NONE) for cnt in cnts1:(x, y, w, h) = cv.boundingRect(cnt)cv.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 0), 2)cv.putText(frame, 'black', (x, y-5), font, 0.7, (0, 0, 25）,2）for cnt in cnts2:(x, y, w, h) = cv.boundingRect(cnt)cv.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 255), 2)cv.putText(frame, 'red', (x, y - 5), font, 0.7, (0, 0, 255), 2) for cnt in cnts3:(x, y, w, h) = cv.boundingRect(cnt)cv.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 255), 2)cv.putText(frame, 'yellow', (x, y - 5), font, 0.7, (0, 0, 255), 2) cv.namedWindow("output", cv.WINDOW_AUTOSIZE)cv.imshow("output", frame)cv.waitKey(0)

首先给定字体颜色的上下阈值，然后提取出红色、黑色以及黄色的字体如下所示：
检测出原始图像中存在的红色字体的为四和五