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Python学习笔记十一之一个简单的opencv阈值分割实例，求取圆度、轮廓椭圆拟合

opencv-python阈值分割应用实例

简介：
1.给定数据是.txt的数据文件，首先读取文本数据转换为图像进行可视化
2.然后运用阈值分割、形态学、位运算分割出脚掌
3.对脚掌进行三等分，分别求出每个部分的特征参数：面积、等效椭圆长短轴、圆度

结果预览：

1.包含库

#脚掌分割与特征分析
#pip install opencv-python matplotlib

import numpy as np
import matplotlib.pyplot as plt
import cv2

2.读取txt文件数据转为矩阵并显示

#读取txt文件输出矩阵
def rdnumpy(txtname):
    f = open(txtname)
    line = f.readlines()
    lines = len(line)  # 行数
    for l in line:
        le = l.strip('\n').split('\t')
        columns = len(le) - 1  # 列
    print(lines,columns)
    A = np.zeros((lines, columns,1), dtype= np.float64)
    print(A.shape)
    row=0
    for lin in line:
        list = lin.strip('\n').split('\t')
        if(len(list)>8):
            #print(list[len(list)-1])
            for col in range(0,len(list)-1): #因为最后有个空格
                #print(col)
                if(row==3):
                    A[row, col] = 0
                else:
                    A[row,col]=list[col]
            row+=1

    return A

3.获取单个连通域外接矩形

def get_ROI(bw):
    [rows,cols]=bw.shape[0:2]
    x_min=cols*2
    x_max=-10
    y_min=rows*2
    y_max=-10
    for r in range(rows):
        for c in range(cols):
            if(bw[r,c]==255):
                x_min=x_min if x_min<c else c
                x_max=x_max if x_max>c else c
                y_min=y_min if y_min<r else r
                y_max=y_max if y_max>r else r
    w=x_max-x_min
    h=y_max-y_min
    rect=[x_min,y_min,w,h]
    return rect

4.单个连通域三等分

#分割
#输入：脚掌二值图
#输出：三个区域
def get_seg(img,rect):
    mask_0 = np.zeros(img.shape, np.uint8)
    mask_1 = np.zeros(img.shape, np.uint8)
    mask_2 = np.zeros(img.shape, np.uint8)

    p1 = rect[1] + int(rect[3] / 3)
    p2 = rect[1] + int(rect[3] * 2 / 3)
    m, n = img.shape
    for row in range(m):
        for col in range(n):
            if (img[row, col] == 255):
                if (row < p1):
                    mask_0[row, col] = 255
                elif (row >= p1 and row < p2):
                    mask_1[row, col] = 255
                else:
                    mask_2[row, col] = 255

    masks = [mask_0, mask_1, mask_2]
    return masks

获取每个连通域轮廓

def get_contours(masks):
    m0=cv2.cvtColor(masks[0],cv2.COLOR_GRAY2BGR)
    m1 = cv2.cvtColor(masks[1], cv2.COLOR_GRAY2BGR)
    m2 = cv2.cvtColor(masks[2], cv2.COLOR_GRAY2BGR)
    ms=[m0,m1,m2]
    colors=[(255,0,0),(0,255,0),(0,0,255)]
    for i in range(3):
        cnts, _ = cv2.findContours(masks[i], cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        cv2.drawContours(ms[i], cnts, 0, colors[i], 1)
    #conts_=np.array(conts,dtype=int32)
    #res = np.hstack((ms[0],ms[1],ms[2]))
    res=cv2.bitwise_or(ms[0],ms[1])
    res_=cv2.bitwise_or(res,ms[2])
    return res_

5.特征评价

#特征评价
#输入：二值图像
#输出：双层列表，存放每个区域的评价指标，依次是[面积，长轴，短轴，圆度]
def get_canshu(masks):
    # mask_0 = np.zeros(img.shape, np.uint8)
    # mask_1 = np.zeros(img.shape, np.uint8)
    # mask_2 = np.zeros(img.shape, np.uint8)
    #
    # p1 = rect[1] + int(rect[3] / 3)
    # p2 = rect[1] + int(rect[3] * 2 / 3)
    # m, n = img.shape
    # for row in range(m):
    #     for col in range(n):
    #         if (thresh0[row, col] == 255):
    #             if (row < p1):
    #                 mask_0[row, col] = 255
    #             elif (row >= p1 and row < p2):
    #                 mask_1[row, col] = 255
    #             else:
    #                 mask_2[row, col] = 255
    #
    # masks = [mask_0, mask_1, mask_2]
    res=[]
    for m in masks:
        cnts, _ = cv2.findContours(m, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

        (x, y, w, h) = cv2.boundingRect(cnts[0])  # 轮廓外界矩形
        area=cv2.contourArea(cnts[0])#面积

        # 椭圆拟合
        ellipse = cv2.fitEllipse(cnts[0])
        (x_, y_), (a, b), ang = ellipse

        #计算周长--圆度
        perimeter=cv2.arcLength(cnts[0], True)
        roundness=(4*3.1415926*area)/(perimeter*perimeter)
        re=[area,b,a,roundness]
        res.append(re)
    return res
#打印输出
def dayin(res,flg):
    for i in range(len(res)):
        if(flg==0):
            print("左脚掌区域 - ", str(i), ":   面积：", res[i][0], " 长轴：", res[i][1],
             " 短轴：", res[i][2], " 圆度：", res[i][3])
        else:
            print("右脚掌区域 - ", str(i), ":   面积：", res[i][0], " 长轴：", res[i][1], 
            " 短轴：", res[i][2], " 圆度：", res[i][3])

6.主函数

if __name__=='__main__':

    path=[".\\files\\2022-08-04-19-23-08_pressure_0.txt",
          ".\\files\\2022-08-04-19-24-45_pressure_0.txt",
          ".\\files\\2022-08-04-19-25-35_pressure_0.txt"]
    src0 = rdnumpy(path[0])
    src1 = rdnumpy(path[1])
    src2 = rdnumpy(path[2])
    srcs=[src0,src1,src2]

    #显示原图
    plt.figure("src")
    l=len(srcs)
    n = 3
    for i in range(l):
        # sole_l=np.zeros((srcs[i].shape[0],int(srcs[i].shape[1]/2)),np.uint8)
        # sole_r = np.zeros((srcs[i].shape[0],int(srcs[i].shape[1]/2)), np.uint8)

        plt.subplot(n, l, i + 1), plt.imshow(srcs[i], 'gray'), plt.title(path[i])

        ret, thresh0 = cv2.threshold(np.uint8(src0), 0, 255, cv2.THRESH_BINARY)
        plt.subplot(n, l, i + 1 + l), plt.imshow(thresh0, 'gray'), plt.title(str(i)+"- uint8")
        # 阈值分割，T=23可以调参
        ret, thresh = cv2.threshold(np.uint8(src0), 23, 255, cv2.THRESH_BINARY)
        kernel = np.ones((3, 3), np.uint8)
        bw_dilate = cv2.dilate(thresh, kernel, iterations=2)
        bw_sole=cv2.bitwise_and(thresh0,bw_dilate)
        mask=cv2.cvtColor(bw_sole,cv2.COLOR_GRAY2BGR)
        # cv2.namedWindow("jiaozhang", cv2.WINDOW_NORMAL)
        # cv2.imshow("jiaozhang", mask)

        sole_=bw_sole.copy()
        sole_l=sole_[0:thresh0.shape[0],0:26]
        sole_r=sole_[0:thresh0.shape[0],26:53]
        rect_l=get_ROI(sole_l)
        rect_r=get_ROI(sole_r)
        cv2.line(mask,(rect_l[0],rect_l[1]+int(rect_l[3]/3)),(rect_l[0]+rect_l[2],rect_l[1]+int(rect_l[3]/3)),(0,255,0),1)
        cv2.line(mask, (rect_l[0], rect_l[1] + int(rect_l[3] *2 / 3)),(rect_l[0] + rect_l[2], rect_l[1] + int(rect_l[3] *2 / 3)), (0, 255, 0), 1)
        cv2.line(mask, (rect_r[0]+26, rect_r[1] + int(rect_r[3] / 3)),(rect_r[0]+26 + rect_r[2], rect_r[1] + int(rect_r[3] / 3)), (0, 0, 255), 1)
        cv2.line(mask, (rect_r[0]+26, rect_r[1] + int(rect_r[3] * 2 / 3)),(rect_r[0]+26 + rect_r[2], rect_r[1] + int(rect_r[3] * 2 / 3)), (0, 0, 255), 1)
        plt.subplot(n, l, i + 1 + l * 2), plt.imshow(mask), plt.title(str(i) + "- jiaozhang")

        masks_l=get_seg(sole_l,rect_l)
        conts_l = get_contours(masks_l)
        # cv2.namedWindow("1",cv2.WINDOW_NORMAL)
        # cv2.imshow("1",conts)
        masks_r = get_seg(sole_r, rect_r)
        conts_r = get_contours(masks_r)
        conts=np.hstack((conts_l,conts_r))
        cv2.namedWindow("contours",cv2.WINDOW_NORMAL)
        cv2.imshow("contours",conts)

        #特征评价
        res_l = get_canshu(masks_l)
        dayin(res_l, 0)
        res_r = get_canshu(masks_r)
        dayin(res_r,1)

    plt.show()

附-txt数据

0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	
0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	
0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	
0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.7890625	2.15625	3.6015625	4.578125	4.93359375	4.53125	3.88671875	3.5390625	3.27734375	2.9140625	2.27734375	1.3203125	0.0	0.890625	3.21875	4.828125	3.21875	0.8046875	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.74609375	3.01953125	6.15625	7.015625	5.12890625	4.01171875	4.53515625	4.25	2.9609375	2.1015625	1.49609375	0.0	0.0	0.0	0.0	
0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.3046875	3.296875	4.6328125	3.5703125	1.43359375	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.03515625	5.1171875	10.1171875	10.0	5.37890625	2.7265625	2.625	1.75	0.0	0.0	0.0	0.0	0.0	0.0	0.0	
0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.671875	2.21484375	2.15625	4.3984375	10.09375	14.359375	12.125	5.6640625	1.125	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.40625	6.5234375	12.03125	11.015625	5.65625	3.5234375	3.9375	2.625	0.0	0.0	0.0	0.0	0.0	0.0	0.0	
0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.921875	1.6171875	2.28125	3.16015625	3.46875	3.234375	6.27734375	14.40625	21.8046875	20.1875	10.36328125	2.234375	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0546875	4.62890625	7.96875	6.6796875	3.1328125	2.2265625	2.94921875	2.3828125	1.0546875	0.0	0.0	0.0	0.0	0.0	0.0	
0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.48828125	3.4921875	4.7109375	4.203125	3.20703125	2.5078125	2.15625	4.34765625	11.21484375	19.3515625	19.8671875	10.91015625	2.44921875	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.98046875	3.15625	2.3515625	0.8828125	0.77734375	2.01953125	3.234375	2.9765625	1.46875	0.0	0.0	0.0	0.0	0.0	
0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.16796875	6.8125	8.05078125	5.37890625	2.20703125	1.0234375	0.9140625	1.765625	5.71875	11.9609375	13.5078125	7.70703125	1.75	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.265625	1.8984375	1.265625	0.0	0.0	2.2109375	4.859375	5.296875	2.8671875	0.0	0.0	0.0	0.0	0.0	
0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.5	4.7421875	8.71484375	9.234375	5.34765625	2.07421875	2.3359375	3.5	3.62109375	4.73828125	7.76953125	8.6015625	5.0859375	1.3125	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.00390625	2.859375	2.0625	0.80078125	0.80078125	2.53125	4.890625	5.20703125	2.859375	0.0	0.0	0.0	0.0	0.0	
0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.61328125	6.61328125	9.6171875	8.4921875	5.140625	4.91015625	8.8515625	12.50390625	12.6640625	11.109375	10.6640625	10.29296875	7.44921875	3.3203125	0.78125	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.25390625	4.33203125	5.5625	5.06640625	3.8125	3.875	5.046875	5.296875	4.06640625	2.19921875	0.0	0.0	0.0	0.0	0.0	
0.0	0.0	0.0	0.0	0.0	0.0	0.81640625	4.65234375	10.77734375	13.1484375	10.0546875	8.6484375	13.6484375	21.375	26.32421875	26.41015625	22.8203125	19.484375	18.72265625	16.4765625	9.91015625	3.3671875	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.38671875	6.3125	10.07421875	12.4140625	13.36328125	13.015625	12.67578125	11.9765625	9.46484375	6.92578125	5.46484375	3.6171875	1.58203125	0.0	0.0	0.0	
0.0	0.0	0.0	0.0	0.0	0.0	1.3125	7.125	15.55859375	18.4296875	16.34375	19.4375	29.58203125	38.40625	40.8828125	39.203125	34.80078125	29.921875	28.8984375	27.4375	18.79296875	7.5390625	1.328125	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.3203125	5.66015625	12.890625	19.1875	23.1484375	26.1328125	27.42578125	26.37890625	23.25	18.9296875	16.20703125	14.56640625	10.6484375	5.41796875	1.828125	0.0	0.0	
0.0	0.0	0.0	0.0	0.0	0.0	1.4609375	7.390625	15.63671875	20.44140625	24.390625	33.86328125	46.984375	54.72265625	53.1875	49.0546875	45.52734375	41.23828125	38.69140625	35.34765625	24.875	10.78515625	2.08984375	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.41015625	10.6953125	21.21484375	30.0546875	35.109375	38.40234375	40.11328125	39.71484375	36.984375	31.78125	26.8828125	22.51171875	15.69921875	7.97265625	2.7421875	0.0	0.0	
0.0	0.0	0.0	0.0	0.0	0.0	0.98046875	5.09375	12.38671875	21.96875	34.1640625	47.49609375	59.99609375	66.8046875	64.13671875	59.01171875	56.9296875	54.89453125	50.5703125	42.4609375	28.5390625	12.42578125	2.453125	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.953125	5.33984375	14.609375	26.8046875	37.5078125	43.75	46.1640625	46.91015625	48.3203125	48.65234375	43.421875	34.38671875	24.671875	14.359375	6.0546875	1.828125	0.0	0.0	
0.0	0.0	0.0	0.0	0.0	0.0	0.0	4.44140625	14.25	31.5625	51.21875	64.53515625	72.625	77.69921875	74.8203125	67.58203125	64.953125	64.94921875	59.8515625	48.10546875	31.203125	13.28125	2.54296875	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.84375	4.6953125	12.43359375	22.7265625	33.71875	42.16015625	45.765625	47.50390625	51.6875	55.86328125	52.8984375	41.73828125	26.80859375	12.515625	3.51171875	0.0	0.0	0.0	
0.0	0.0	0.0	0.0	0.0	0.0	1.55078125	8.984375	25.56640625	49.58984375	71.41796875	80.390625	81.29296875	81.73046875	75.95703125	64.734375	59.55078125	59.3125	53.68359375	41.8359375	26.63671875	11.140625	2.0625	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.140625	5.75390625	11.5859375	20.78125	30.3359375	36.0546875	41.1953125	49.77734375	57.69140625	57.34765625	46.828125	30.61328125	14.54296875	4.0546875	0.0	0.0	0.0	
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