opencv实现矿石图片检测矿石数量

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

"""原始图像导入"""      
img = cv2.imread("000166.jpg")  
#img = cv2.resize(img, (1600, 896))
#cv2.imshow("original", img)
"""转化为灰度图像"""
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) 
#cv2.imshow("gray", gray)

"""转化为二值图像
#dst0 = cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,251, 1)
cv2.imshow("black&white", dst0)"""

"""图像去躁
denose = cv2.fastNlMeansDenoising(gray,None,100,7,21)
cv2.imshow("denosing", denose)"""

"""小波图像去躁
coeffs = pywt.dwt2(gray, 'haar')
cA1,(cH1, cV1, cD1) = coeffs
cD1 = np.zeros(cD1.shape) + 255
coeffs = cA1,(cH1, cV1, cD1)
denose = pywt.idwt2(coeffs, 'haar')
cv2.imshow("denose", denose)"""

"""转化为二值图像"""
dst1 = cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,251, 1)
cv2.imshow("black&white", dst1)

"""膨胀用于排除小型黑洞"""
kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(6,6))   #(此矩阵有关于黑点、噪点的去除)
dilateImg = cv2.dilate(dst1, kernel)
#erodImg = cv2.erode(dilateImg, kernel)
cv2.imshow("erodImg&wdilateImg",dilateImg)


"""计算数目"""
dilateImg = 255 - dilateImg #黑白转换
imgs,contours,hierarchy = cv2.findContours(dilateImg,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)  #轮廓检测函数
cv2.drawContours(dilateImg,contours,-1,(120,0,0),2)  #绘制轮廓
cv2.imshow("calcuate",dilateImg)

count=0 #矿石总数
ares_avrg=0  #矿石平均
#遍历找到的所有矿石
for cont in contours:
    ares = cv2.contourArea(cont)#计算包围形状的面积
    if ares<50:   #过滤面积小于50的形状
        continue
    count+=1    #总体计数加1
    ares_avrg+=ares
    print("{}-blob:{}".format(count,ares),end="  ") #打印出每个矿石的面积
    rect = cv2.boundingRect(cont) #提取矩形坐标
    print("x:{} y:{}".format(rect[0],rect[1]))#打印坐标
    #cv2.rectangle返回值是x,y,w,h
    cv2.rectangle(img,(rect[0],rect[1]),(rect[0]+rect[2],rect[1]+rect[3]),(0,0,0xff),1)#绘制矩形
    y=10 if rect[1]<10 else rect[1] #防止编号到图片之外
    cv2.putText(img,str(count), (rect[0], y), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 3) #在矿石左上角写上编号
print("矿石平均面积:{}".format(round(ares_avrg/ares,2))) #打印出每个矿石的面积
print(count)
cv2.imshow("original", img)

cv2.waitKey()
#cv2.destroyAllWindows() # important part!