# -*- coding:utf-8 -*-
# @Author len
# @Create 2023/10/28 12:18

import cv2
import numpy as np

def detect_shape(contour):
    epsilon = 0.04 * cv2.arcLength(contour, True)
    approx = cv2.approxPolyDP(contour, epsilon, True)

    if len(approx) == 3:
        return "三角形"
    elif len(approx) == 4:
        return "四边形"
    elif len(approx) == 10:
        return "五角星"
    else:
        area = cv2.contourArea(contour)
        perimeter = cv2.arcLength(contour, True)
        radius = perimeter / (2 * np.pi)
        circle_area = np.pi * (radius ** 2)
        if abs(circle_area - area) < area * 0.2:
            return "圆形"
        else:
            return "未知形状"

img = cv2.imread('exp4/crops/polygon/new_36.jpg')

# 转换为灰度图像
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# 使用高斯模糊减少噪声
blurred = cv2.GaussianBlur(gray, (5, 5), 0)

# Canny边缘检测
edges = cv2.Canny(blurred, 50, 150)

# 寻找轮廓
contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

# 删除面积小于阈值的轮廓
filtered_contours = [c for c in contours if cv2.contourArea(c) > 500]

# 检查是否有有效的轮廓
if len(filtered_contours) == 0:
    print("没有找到有效的轮廓")
else:
    # 找到面积最大的轮廓
    largest_contour = max(filtered_contours, key=cv2.contourArea)
    shape = detect_shape(largest_contour)
    print(f"识别到的形状是：{shape}")

    # 显示结果
    cv2.drawContours(img, [largest_contour], -1, (0, 255, 0), 2)
    cv2.imshow('Detected Shape', img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()