圆形检测算法-基于颜色和形状（opencv）

最新推荐文章于 2023-02-20 09:00:00 发布

肆十二

最新推荐文章于 2023-02-20 09:00:00 发布

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本文介绍了一种基于Python OpenCV的彩色圆检测算法，该算法能够识别并标记图像中的红色、白色和蓝色圆圈。通过HSV颜色空间转换和轮廓提取，算法实现了不同颜色圆圈的有效检测。

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import cv2
import numpy as np

# 圆检测算法
def detect(img):

    # 定义红色、蓝色、白色的hsv区间文件
    red1 = np.array([0, 100, 46])
    red2 = np.array([8, 255, 255])

    white1 = np.array([0, 0, 221])
    white2 = np.array([180, 30, 255])

    blue1 = np.array([100, 43, 46])
    blue2 = np.array([124, 255, 255])
    # 从rgb转到hsv颜色空间
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    # 找到每个像素的值，得到对应的单色图像
    mask_red = cv2.inRange(hsv, red1, red2)
    mask_white = cv2.inRange(hsv, white1, white2)
    mask_blue = cv2.inRange(hsv, blue1, blue2)

    result_red = cv2.bitwise_and(img, img, mask=mask_red)
    result_white = cv2.bitwise_and(img, img, mask=mask_white)
    result_blue = cv2.bitwise_and(img, img, mask=mask_blue)

    # 对图像进行灰度化
    gray_red = cv2.cvtColor(result_red, cv2.COLOR_BGR2GRAY)
    gray_white = cv2.cvtColor(result_white, cv2.COLOR_BGR2GRAY)
    gray_blue = cv2.cvtColor(result_blue, cv2.COLOR_BGR2GRAY)
    # 对灰度图像进行轮廓提取，更加面积，去除较大或较小面积，保存和圆相同的面积轮廓
    _, contours, heridency = cv2.findContours(gray_red, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    _, contours2, heridency = cv2.findContours(gray_white, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    # 定义绘制字体
    font = cv2.FONT_HERSHEY_SIMPLEX
    # 红色轮廓
    for i in contours:
        # 计算轮廓面积
        area = cv2.contourArea(i)
        # 当面积在500-2000之间，才进行圆检测
        if 500 < area < 2000:
            circles_red = cv2.HoughCircles(gray_red, cv2.HOUGH_GRADIENT, 1,
                                           50, param1=150, param2=20, minRadius=5, maxRadius=14)
            # 若不为空，则进行绘制
            if circles_red is not None:
                circles_1 = np.uint16(np.around(circles_red))
                for i in circles_1[0, :]:
                    cv2.circle(img, (i[0], i[1]), i[2], (0, 0, 255), 2)  # 画圆
                    cv2.circle(img, (i[0], i[1]), 2, (0, 0, 255), 2)  # 画圆心
                    cv2.putText(img, 'red', (i[0], i[1]), font, 0.8, (0, 0, 255), 2)
    # 白色轮廓
    for i in contours2:
        area = cv2.contourArea(i)
        if 350 < area < 1500:

            circles_white = cv2.HoughCircles(gray_white, cv2.HOUGH_GRADIENT, 1,
                            20, param1=35, param2=20, minRadius=0, maxRadius=30)

            if circles_white is not None:
                circles_2 = np.uint16(np.around(circles_white))
                for i in circles_2[0, :]:
                    cv2.circle(img, (i[0], i[1]), i[2], (255, 255, 255), 2)  # 画圆
                    cv2.circle(img, (i[0], i[1]), 2, (255, 255, 255), 2)  # 画圆心
                    cv2.putText(img, 'white', (i[0], i[1]), font, 0.8, (255, 255, 255), 2)

    # 因蓝色轮廓较少，所以不需要进行轮廓提取
    circles_blue = cv2.HoughCircles(gray_blue, cv2.HOUGH_GRADIENT, 1,
                            50, param1=35, param2=20, minRadius=5, maxRadius=25)

    if circles_blue is not None:
        circles_3 = np.uint16(np.around(circles_blue))
        for i in circles_3[0, :]:
            cv2.circle(img, (i[0], i[1]), i[2], (255, 0, 0), 2)  # 画圆
            cv2.circle(img, (i[0], i[1]), 2, (255, 0, 0), 2)  # 画圆心
            cv2.putText(img, 'blue', (i[0], i[1]), font, 0.8, (255, 0, 0), 2)

    return img

cap = cv2.VideoCapture('demo/DSC_0001.MOV')

while(cap.isOpened()):
    ret, frame = cap.read()
    frame = cv2.resize(frame, (640, 480))
    result = detect(frame)
    cv2.imshow('frame', result)
    if cv2.waitKey(25) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()