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opencv做颜色识别（python）

花匠小妙招
2024-12-15 00:12

使用Raspberry摄像头抓图然后进行颜色识别。

#!/usr/bin/python

# -*- coding: UTF-8 -*-

import sys
import time

from picamera.array import PiRGBArray
from picamera import PiCamera
import numpy as np
import cv2

#颜色HSV范围
red_min = np.array([0, 128, 46])
red_max = np.array([5, 255, 255])
red2_min = np.array([156, 128, 46])
red2_max = np.array([180, 255, 255])

green_min = np.array([35, 128, 46])
green_max = np.array([77, 255, 255])

blue_min = np.array([100, 128, 46])
blue_max = np.array([124, 255, 255])

yellow_min = np.array([15, 128, 46])
yellow_max = np.array([34, 255, 255])

black_min = np.array([0, 0, 0])
black_max = np.array([180, 255, 10])

white_min = np.array([0, 0, 70])
white_max = np.array([180, 30, 255])

COLOR_ARRAY = [ [ red_min, red_max, 'red'], [ red2_min, red2_max, 'red'], [ green_min, green_max, 'green'], [ blue_min, blue_max, 'blue'],
[yellow_min, yellow_max, 'yellow'] ] #, [ black_min, black_max, 'black'], [ white_min, white_max, 'white'] ]

camera = PiCamera()
camera.resolution = (640, 480)
camera.framerate = 25
rawCapture = PiRGBArray(camera, size=(640, 480))
time.sleep(0.1)

#while True:
for frame in camera.capture_continuous(rawCapture, format="bgr", use_video_port=True):
frame = frame.array
cv2.imwrite("frame.jpg", frame)
#frame = cv2.imread("frame.jpg")
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
cv2.imwrite("hsv.jpg", hsv)

for (color_min, color_max, name) in COLOR_ARRAY:
mask=cv2.inRange(hsv, color_min, color_max)
res=cv2.bitwise_and(frame, frame, mask=mask)
#cv2.imshow("res",res)
cv2.imwrite("2.jpg", res)

#前面是为了得到一张二值图
img = cv2.imread("2.jpg")
h, w = img.shape[:2]

blured = cv2.blur(img,(5,5))
cv2.imwrite("blured.jpg", blured)
ret, bright = cv2.threshold(blured,10,255,cv2.THRESH_BINARY)

gray = cv2.cvtColor(bright,cv2.COLOR_BGR2GRAY)
cv2.imwrite("gray.jpg", gray)

kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (50, 50))
opened = cv2.morphologyEx(gray, cv2.MORPH_OPEN, kernel)
cv2.imwrite("opened.jpg", opened)
closed = cv2.morphologyEx(opened, cv2.MORPH_CLOSE, kernel)
#cv2.imshow("closed", closed)
cv2.imwrite("closed.jpg", closed)

contours, hierarchy = cv2.findContours(closed,cv2.RETR_LIST,cv2.CHAIN_APPROX_NONE)
cv2.drawContours(img,contours,-1,(0,0,255),3)
cv2.imwrite("result.jpg", img )

#输出轮廓个数
number = len(contours)
print('Total:', number)
if number >=1:
total = 0
for i in range(0, number):
total = total + len(contours[i])
print 'NO:',i,' size:', len(contours[i])
if total > 400:
print 'Currrent color is ', name
cv2.destroyAllWindows()
sys.exit()

rawCapture.truncate(0)

难点是颜色范围的选择，可以使用取色工具对图片进行HSV采样，然后转换成opencv中的hsv（H/2, S*255/100, V*255/100）。

目前识别饱和度比较高的颜色（如：红、蓝、绿、黄）效果好，但对于黑白色不大容易检测，需要针对实际场景进行调试。