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

"""原始圖像導(dǎo)入""" ? ? ?
img = cv2.imread("000166.jpg") ?
#img = cv2.resize(img, (1600, 896))
#cv2.imshow("original", img)
"""轉(zhuǎn)化為灰度圖像"""
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)?
#cv2.imshow("gray", gray)

"""轉(zhuǎn)化為二值圖像
#dst0 = cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,251, 1)
cv2.imshow("black&white", dst0)"""

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

"""小波圖像去躁
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)"""

"""轉(zhuǎn)化為二值圖像"""
dst1 = cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,251, 1)
cv2.imshow("black&white", dst1)

"""膨脹用于排除小型黑洞"""
kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(6,6)) ? #(此矩陣有關(guān)于黑點(diǎn)、噪點(diǎn)的去除)
dilateImg = cv2.dilate(dst1, kernel)
#erodImg = cv2.erode(dilateImg, kernel)
cv2.imshow("erodImg&wdilateImg",dilateImg)


"""計(jì)算數(shù)目"""
dilateImg = 255 - dilateImg #黑白轉(zhuǎn)換
imgs,contours,hierarchy = cv2.findContours(dilateImg,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) ?#輪廓檢測(cè)函數(shù)
cv2.drawContours(dilateImg,contours,-1,(120,0,0),2) ?#繪制輪廓
cv2.imshow("calcuate",dilateImg)

count=0 #礦石總數(shù)
ares_avrg=0 ?#礦石平均
#遍歷找到的所有礦石
for cont in contours:
? ? ares = cv2.contourArea(cont)#計(jì)算包圍形狀的面積
? ? if ares<50: ? #過(guò)濾面積小于50的形狀
? ? ? ? continue
? ? count+=1 ? ?#總體計(jì)數(shù)加1
? ? ares_avrg+=ares
? ? print("{}-blob:{}".format(count,ares),end=" ?") #打印出每個(gè)礦石的面積
? ? rect = cv2.boundingRect(cont) #提取矩形坐標(biāo)
? ? print("x:{} y:{}".format(rect[0],rect[1]))#打印坐標(biāo)
? ? #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)#繪制矩形
? ? y=10 if rect[1]<10 else rect[1] #防止編號(hào)到圖片之外
? ? cv2.putText(img,str(count), (rect[0], y), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 3) #在礦石左上角寫上編號(hào)
print("礦石平均面積:{}".format(round(ares_avrg/ares,2))) #打印出每個(gè)礦石的面積
print(count)
cv2.imshow("original", img)

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