64 lines
1.9 KiB
Python
64 lines
1.9 KiB
Python
# -*- coding:utf-8 -*-
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# @Author len
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# @Create 2023/10/28 9:28
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import cv2
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import numpy as np
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def detect_shape(contour):
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epsilon = 0.04 * cv2.arcLength(contour, True)
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approx = cv2.approxPolyDP(contour, epsilon, True)
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# 根据角点数量判断形状
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if len(approx) == 3:
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return "三角形"
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elif len(approx) == 4:
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return "四边形"
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elif len(approx) == 10:
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return "五角星"
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else:
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# 使用周长与面积的比值来判断圆形
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area = cv2.contourArea(contour)
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perimeter = cv2.arcLength(contour, True)
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radius = perimeter / (2 * np.pi)
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circle_area = np.pi * (radius ** 2)
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# 这里0.2只是一个近似值，可能需要根据你的需求进行调整
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if abs(circle_area - area) < area * 0.2:
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return "圆形"
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else:
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return "未知形状"
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# 读取图像并获取其大小
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img = cv2.imread('./data/img.png')
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h, w, _ = img.shape
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# 获取图像的中心点颜色
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center_color = img[h // 2, w // 2]
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# 将中心点的颜色转换为HSV格式
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hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
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center_color_hsv = cv2.cvtColor(np.uint8([[center_color]]), cv2.COLOR_BGR2HSV)[0][0]
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# print(center_color_hsv[0])
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# 根据中心点的颜色创建一个颜色掩码
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lower_bound = np.array([(center_color_hsv[0] - 10) % 180, 50, 50])
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upper_bound = np.array([(center_color_hsv[0] + 10) % 180, 255, 255])
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mask = cv2.inRange(hsv, lower_bound, upper_bound)
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# 寻找掩码中的轮廓
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contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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# 我们假设最大的轮廓是我们想要的
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largest_contour = max(contours, key=cv2.contourArea)
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shape = detect_shape(largest_contour)
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print(f"识别到的形状是：{shape}")
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# 显示结果
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cv2.drawContours(img, [largest_contour], -1, (0, 255, 0), 2)
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cv2.imshow('Detected Shape', img)
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cv2.waitKey(0)
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cv2.destroyAllWindows() |