import cv2
import numpy as np





#定义形状检测函数
def ShapeDetection(img:cv2.typing.MatLike):
    contours,hierarchy = cv2.findContours(img,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)  #寻找轮廓点
    all_cont=len(contours)
    # print(f"all_cont:{all_cont}")
    boll_count=0
    for obj in contours:
        perimeter = cv2.arcLength(obj,True)  #计算轮廓周长
        approx = cv2.approxPolyDP(obj,0.02*perimeter,True)  #获取轮廓角点坐标
        # CornerNum = len(approx)   #轮廓角点的数量
        # area = cv2.contourArea(obj)  #计算轮廓内区域的面积
        # print(f"area:{area}, perimeter:{perimeter}, CornerNum:{CornerNum}")
        x, y, w, h = cv2.boundingRect(approx)  #获取坐标值和宽度、高度
        # print(f"{x},{y},{x+w},{y+h}")
        img_obj=img[y:y+h,x:x+w]
        # print(img_obj)
        nonzero_num=np.count_nonzero(img_obj)
        percentage=nonzero_num/img_obj.size
        # print(f"nonzero_num:{nonzero_num},size={img_obj.size}, pos:{percentage}")
        # 通过计算图像白值的比例来判断实心还是空心
        if(percentage>0.55):
            boll_count+=1
    return (all_cont,boll_count)

def calc_boll_count(path:str):
    img = cv2.imread(path)
    imgGray = cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)  # 转为灰度图
    ret, binary = cv2.threshold(imgGray,60,255,cv2.THRESH_BINARY)  # 转为二值图
    return ShapeDetection(binary)  #形状检测

if __name__ == "__main__":
    ret=calc_boll_count("1.png")
    print(ret)
    ret=calc_boll_count("1644-440 172x27.png")
    print(ret)