opencv检测黑色轮廓：
import cv2
import numpy as np
class ShapeDetector:
def __init__(self, image_path):
self.image_path = image_path
self.img = cv2.imread(self.image_path) # 读取图像
self.imgContour = self.img.copy() # 创建一个用于绘制轮廓的副本
def detect_shapes(self):
imgGray = cv2.cvtColor(self.img, cv2.COLOR_BGR2GRAY) # 转换为灰度图像
imgBlur = cv2.GaussianBlur(imgGray, (5, 5), 1) # 高斯模糊
imgCanny = cv2.Canny(imgBlur, 60, 60) # Canny边缘检测
self.shape_detection(imgCanny) # 进行形状检测
# 显示图像
cv2.imshow("原始图像", self.img)
cv2.imshow("灰度图", imgGray)
cv2.imshow("模糊图", imgBlur)
cv2.imshow("边缘检测图", imgCanny)
cv2.imshow("形状检测", self.imgContour)
cv2.waitKey(0)
cv2.destroyAllWindows()
def shape_detection(self, img):
contours, hierarchy = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) # 寻找轮廓
for obj in contours:
area = cv2.contourArea(obj) # 计算轮廓区域面积
cv2.drawContours(self.imgContour, [obj], -1, (255, 0, 0), 4) # 绘制轮廓线
perimeter = cv2.arcLength(obj, True) # 计算轮廓周长
approx = cv2.approxPolyDP(obj, 0.02 * perimeter, True) # 近似多边形
corner_num = len(approx) # 角点数量
x, y, w, h = cv2.boundingRect(approx) # 获取边界框坐标
if corner_num == 3:
obj_type = "三角形"
elif corner_num == 4:
for i in range(0, 4):
print("矩形角坐标%d：" % (i + 1), approx[i][0][0], approx[i][0][1])
cv2.circle(self.imgContour, (approx[i][0][0], approx[i][0][1]), 5, (0, 255, 255), 3)
if w == h:
obj_type = "正方形"
else:
obj_type = "矩形"
elif corner_num > 4:
obj_type = "多边形"
else:
obj_type = "未知"
cv2.rectangle(self.imgContour, (x, y), (x + w, y + h), (0, 0, 255), 2) # 绘制边界框
cv2.putText(self.imgContour, obj_type, (x + (w // 2), y + (h // 2)), cv2.FONT_HERSHEY_COMPLEX, 0.6, (0, 0, 0), 1)
if __name__ == "__main__":
image_path = './test.jpg'
shape_detector = ShapeDetector(image_path)
shape_detector.detect_shapes()