OpenCV及k210实现形状检测并计算面积比

Python + OpenCV

详细代码

import cv2
import numpy as np

# 打开摄像头
cap = cv2.VideoCapture(0)  # cv2.imread("path/to/image.jpg")
rectangle_area = 0
circle_area = 0
line_length = 0

while True:
    # 读取摄像头的帧
    ret, frame = cap.read()
    if not ret:
        print("Failed to read frame from the camera.")
        break

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

    # 对图像进行边缘检测
    edges = cv2.Canny(gray, 200, 400)

    # 进行轮廓检测
    contours, hierarchy = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)  # 只检测外轮廓；水平、垂直端点

    # 遍历每个轮廓并找到矩形和圆形
    for contour in contours:
        # 计算轮廓的面积
        area = cv2.contourArea(contour)

        # 如果轮廓面积小于设定阈值，则忽略
        if area < 100:
            continue

        # 使用近似多边形方法对轮廓进行逼近
        approx = cv2.approxPolyDP(contour, 0.04 * cv2.arcLength(contour, True), True)
        
        # 如果逼近后的多边形有四个顶点，则认为是矩形
        if len(approx) == 4:
            # 绘制矩形轮廓
            cv2.drawContours(frame, [approx], 0, (0, 255, 0), 2)
            cv2.putText(frame, f"Rectangle Area: {area}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
            rectangle_area = area
            
        # 如果逼近后的多边形有三个顶点，则认为是三角形
        elif len(approx) == 3:
            # 绘制三角形轮廓
            cv2.drawContours(frame, [approx], 0, (255, 0, 0), 2)
            # 计算三角形的面积
            triangle_area = cv2.contourArea(contour)
            cv2.putText(frame, f"Triangle Area: {triangle_area}", (10, 70), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)

        # 如果逼近后的多边形有更多的顶点（大致为圆形）
        elif len(approx) > 6:
            # 计算轮廓的最小外接圆
            (x, y), radius = cv2.minEnclosingCircle(contour)
            
            # 绘制圆形轮廓
            cv2.circle(frame, (int(x), int(y)), int(radius), (0, 0, 255), 2)
            
            # 计算圆形的面积
            circle_area = int(np.pi * radius * radius)
            cv2.putText(frame, f"Circle Area: {circle_area}", (10, 110), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
            all_area = rectangle_area / circle_area
            cv2.putText(frame, f"Area: {all_area}", (10, 150), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)

        # 如果逼近后的多边形有两个顶点，则认为是直线
        elif len(approx) == 2:
            x1, y1 = approx[0][0]
            x2, y2 = approx[1][0]
            # 计算直线的长度
            line_length = np.sqrt((x2 - x1)**2 + (y2 - y1)**2)
            cv2.line(frame, (x1, y1), (x2, y2), (0, 0, 255), 2)
            cv2.putText(frame, f"Line Length: {line_length}", (10, 190), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)

    # 显示结果帧
    cv2.imshow("Shapes", frame)

    # 按下 'q' 键退出循环
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# 释放摄像头和销毁窗口
cap.release()
cv2.destroyAllWindows()

实现效果

k210

详细代码

下载源码

实现效果

识别特定颜色并框出

查看代码

import cv2
import numpy as np


def red_identify(img):

    # 灰度图
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    # 转换为HSV
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

    # 二值化处理
    lower_apple = np.array([0, 100, 100])
    higher_apple = np.array([10, 200, 200])

    mask = cv2.inRange(hsv, lower_apple, higher_apple)

    # 膨胀操作
    kernel = np.ones([5, 5], dtype=np.uint8)
    dilate = cv2.dilate(mask, kernel, iterations=1)

    # 画出轮廓
    cnts, hierarchy = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)

    # 判断是否有轮廓
    if len(cnts) == 0:

        # 没有即显示原图
        cv2.imshow("red_identify", img)
        return

    max_cnt = max(cnts, key=cv2.contourArea)
    cv2.drawContours(img, max_cnt, -1, (0, 0, 255), 2)

    # 最大外接矩形
    (x, y, w, h) = cv2.boundingRect(max_cnt)
    cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255), 3)

    cv2.imshow("red_identify", img)


if __name__ == "__main__":

    # 打开摄像头
    cap = cv2.VideoCapture(0)

    # 设置摄像头参数，3和4为像素大小，5为帧率
    cap.set(3, 256)
    cap.set(4, 256)
    cap.set(5, 60)

    while True:

        # 循环读取每一帧
        flag, frame = cap.read()

        #  读取失败
        if not flag:
            print("Camera error!")
            break

        # 调用颜色识别
        red_identify(frame)

        # 若没有按下q键，则每10毫秒显示一帧（OxFF为"q"的ASCII码）
        if cv2.waitKey(10) & 0xFF == ord('q'):
            break

    cap.release()
    cv2.destroyAllWindows()