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python+opencv實(shí)現(xiàn)文字顏色識別與標(biāo)定功能

更新時間：2021年09月13日 10:47:39 作者：AI炮灰

最近小編接了一個比較簡單的圖像處理的單子，今天小編給大家分享python+opencv實(shí)現(xiàn)文字顏色識別與標(biāo)定功能的完整思路及代碼，感興趣的朋友一起看看吧

最近接了一個比較簡單的圖像處理的單子，花了一點(diǎn)時間隨便寫了一下：

數(shù)據(jù)集客戶沒有是自己隨便創(chuàng)建的：

程序如下：

"""
    Code creation time:September 11, 2021
    Author:PanBo
    Realize function:It mainly realizes the recognition and calibration of fonts with different colors
"""
import numpy as np
import cv2 as cv
 
font = cv.FONT_HERSHEY_SIMPLEX
lower_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)

首先給定字體顏色的上下閾值，然后提取出紅色、黑色以及黃色的字體如下所示：

檢測出原始圖像中存在的紅色字體的為四和五