首先参考以下博文将图片读取为RGB矩阵
/u012421101/article/details/116798509
之后将RGB转为HSV
def rgb2hsv(r, g, b):r, g, b = r/255.0, g/255.0, b/255.0mx = max(r, g, b)mn = min(r, g, b)m = mx-mnif mx == mn:h = 0elif mx == r:if g >= b:h = ((g-b)/m)*60else:h = ((g-b)/m)*60 + 360elif mx == g:h = ((b-r)/m)*60 + 120elif mx == b:h = ((r-g)/m)*60 + 240if mx == 0:s = 0else:s = m/mxv = mxH = h / 2S = s * 255.0V = v * 255.0return H, S, V
查看判断表格
设定颜色判断公式（此处仅判断黄色和绿色）
def yellow(H, S, V):if(H>=26 and H<=34 and S>=43 and S<=255 and V>=46 and V<=255):return Truedef green(H, S, V):if(H>=35 and H<=77 and S>=43 and S<=255 and V>=46 and V<=255):return True
判断实例
提取像素点代码
from typing import Sizedfrom PIL import Imageimport numpy as npimport matplotlib.pyplot as pltimport mathimport randomimport timeplt.rcParams['font.sans-serif']=['SimHei'] #显示中文标签plt.rcParams['axes.unicode_minus']=False #这两行需要手动设置'''首先读取图片的RGB特征值，读取结果为三维矩阵形式'''L_path='train/B/Rock/4-2.jpg'L_image=Image.open(L_path)out = L_image.convert("RGB")dataset=np.array(out)'''绘制原始图片'''fig = plt.figure()ax = fig.add_subplot(1,2,1)plt.imshow(dataset)ax.set_title("原始图")size = dataset.shapeprint(size)count = 0'''黄色与绿色荧光点将会设置为黑色其它颜色设置白色'''for i in range(size[0]):for j in range(size[1]):temp0,temp1,temp2 = rgb2hsv(dataset[i,j,0],dataset[i,j,1],dataset[i,j,2])#颜色转化if(green(temp0,temp1,temp2)):#设置为黑色count = count + 1 dataset[i,j,0] = 0dataset[i,j,1] = 0dataset[i,j,2] = 0elif (yellow(temp0,temp1,temp2)):#判定为黄色count = count + 1 dataset[i,j,0] = 0dataset[i,j,1] = 0dataset[i,j,2] = 0else:dataset[i,j,0] = 255dataset[i,j,1] = 255dataset[i,j,2] = 255print(count)'''绘制提取图'''ax = fig.add_subplot(1,2,2)plt.imshow(dataset)string = str(count/(size[0]*size[1]))ax.set_title("荧光点提取图\n"+string)plt.show()
结果下如图：
