圆形、矩形和三角形的识别与定位

import cv2
from scipy import *
import queue
#读入图像
img=cv2.imread(r'C:\Users\Jimmy\Desktop\wwww.png')
gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
img_Guassian = cv2.GaussianBlur(gray,(5,5),0)
thresh2 = cv2.adaptiveThreshold(img_Guassian, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 11, 2)
def cfs(img):
    """传入二值化后的图片进行连通域分割"""
    w,h = img.shape
    visited = set()
    q = queue.Queue()
    offset = [(-1,-1),(0,-1),(1,-1),(-1,0),(1,0),(-1,1),(0,1),(1,1)]
    cnts = []
    for x in range(w):
        for y in range(h):
            x_axis = []
            y_axis = []
            if img[x,y] == 0 and (x,y) not in visited:
                q.put((x,y))
                visited.add((x,y))
            while not q.empty():
                x_p,y_p = q.get()
                for x_offset,y_offset in offset:
                    x_c,y_c = x_p+x_offset,y_p+y_offset
                    if (x_c,y_c) in visited:
                        continue
                    visited.add((x_c,y_c))
                    try:
                        if img[x_c,y_c] == 0:
                            q.put((x_c,y_c))
                            x_axis.append(x_c)
                            y_axis.append(y_c)
                    except:
                        pass
            if x_axis and y_axis:
                min_x,max_x = min(x_axis),max(x_axis)
                min_y, max_y = min(y_axis), max(y_axis)
                if max_x - min_x >  2 and max_y - min_y >  2:
                    # 宽度小于3的认为是噪点，根据需要修改
                    cnts.append((min_x,max_x,min_y, max_y ))
    return cnts
def judge(img,cnts):
    num = 0
    label=[]   #存放标签
    for i in range(len(cnts)):
        # temp1 = img[cnts[i][0] - 10:cnts[i][1] + 10, cnts[i][2] - 10:cnts[i][3] + 10]  # 彩色图  四个点的顺序是左，上。右下
        temp = thresh2[cnts[i][0] - 10:cnts[i][1] + 10, cnts[i][2] - 10:cnts[i][3] + 10]  # 二值图
        a = int((cnts[i][0] + cnts[i][1]) / 2)
        b = int((cnts[i][2] + cnts[i][3]) / 2)
        img[a - 3:a + 3, b - 3:b + 3] = [0, 0, 0]  # 把中心标黑
        image, cons, hierachy = cv2.findContours(temp, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
        # img=cv2.drawContours(img,cons,1,(0,255,0),2)
        #在目标二值图上寻找边缘
        x, y, w, h = cv2.boundingRect(cons[1])
        # print(x,y,w,h)
        # img = cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 1)
        goal = []
        ding = []  # 2/3处穿线
        for j in range(w):
            if temp[int(x + h / 2)][y + j] == 0:
                goal.append(y + j)
            if temp[int(x + (2 / 3) * h)][y + j] == 0:
                ding.append(y + j)
        A = max(goal) - min(goal)
        B = max(ding) - min(ding)
        temp[int(x + h / 2)][int(y + w / 2)] = 0  # 标黑
        if abs(A - w) > 0.1 * w:
            # cv2.putText(img,"triangle",(a,b),1,(255,255,255),1)
            # print("第" + str(num) + "个目标为三角形")
            label.append(0)
        elif abs(B - w) > 0.08 * w:
            label.append(1)
        else:
            label.append(2)
        num += 1
    return label



cnts=cfs(thresh2)
print(cnts)
label= judge(img,cnts)
print(label)
# img=edages(img,thresh2)
cv2.imshow('img',img)
cv2.waitKey(0)

根据边界矩形的宽、目标中心线、目标1/3线之间的关系，判断图形。