opencv

OpenCV（Open Source Computer Vision Library）是一个开源的计算机视觉和机器学习软件库，具有多平台、多语言的特点。

C++

通过C++使用opencv时，由于需要配置依赖项与工具目录，笔者使用Visual Studio来构建工程，具体配置教程请参考：opencv安装教程
测试例程如下：

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#include <opencv2/opencv.hpp>

int main() {
    // 打开默认摄像头（通常是摄像头0）
    cv::VideoCapture cap(0);

    // 检查摄像头是否成功打开
    if (!cap.isOpened()) {
        std::cerr << "Error: Could not open camera." << std::endl;
        return -1;
    }

    cv::Mat frame;
    while (true) {
        // 从摄像头读取一帧
        cap >> frame;

        // 检查是否成功读取
        if (frame.empty()) {
            std::cerr << "Error: Could not grab a frame." << std::endl;
            break;
        }

        // 显示帧
        cv::imshow("Camera Feed", frame);

        // 如果按下 'q' 键，退出循环
        if (cv::waitKey(30) == 'q') {
            break;
        }
    }

    // 释放摄像头
    cap.release();

    // 销毁所有窗口
    cv::destroyAllWindows();

    return 0;
}

python

Python使用opencv相对C++就简单很多：

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pip install opencv-python

颜色追踪

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import cv2
import numpy as np

# 打开摄像头
cap = cv2.VideoCapture(0)

while True:
    # 读取摄像头的帧
    ret, frame = cap.read()

    if not ret:
        print("Failed to capture frame")
        break

    # 将图像从 BGR 转换为 HSV
    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)

    # 定义红色的 HSV 范围
    lower_red = np.array([0, 100, 100])
    upper_red = np.array([10, 255, 255])

    # 根据 HSV 范围创建掩码
    mask = cv2.inRange(hsv, lower_red, upper_red)

    # 对掩码进行模糊处理
    blurred_mask = cv2.GaussianBlur(mask, (9, 9), 0)

    # 对模糊处理后的掩码进行形态学转换
    kernel = np.ones((5, 5), np.uint8)
    morphed_mask = cv2.morphologyEx(blurred_mask, cv2.MORPH_OPEN, kernel)

    # 查找红色物体的轮廓
    contours, _ = cv2.findContours(morphed_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    # 如果找到红色物体
    if contours:
        # 获取最大轮廓
        max_contour = max(contours, key=cv2.contourArea)
        # 获取最大轮廓的外接圆
        ((x, y), radius) = cv2.minEnclosingCircle(max_contour)
        # 将浮点坐标转换为整数
        center = (int(x), int(y))
        radius = int(radius)
        # 在帧上绘制红色点的位置
        cv2.circle(frame, center, radius, (0, 0, 255), 2)

        # 打印红色点的中心坐标
        text = "Red Point Center: ({}, {})".format(center[0], center[1])
        cv2.putText(frame, text, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2, cv2.LINE_AA)

    # 显示帧
    cv2.imshow('Detected Red Point', frame)

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

# 释放摄像头并关闭窗口
cap.release()
cv2.destroyAllWindows()

霍夫圆检测

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import cv2
import numpy as np

# 打开摄像头
cap = cv2.VideoCapture(0)

if not cap.isOpened():
    print("Error: Unable to open the camera")
    exit()

while True:
    # 读取一帧
    ret, frame = cap.read()
    if not ret:
        print("Error: Unable to read frame")
        break

    # 转换为灰度图像
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    gray = cv2.medianBlur(gray, 5)

    # 使用霍夫圆变换检测圆
    circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, dp=1.2, minDist=30,
                               param1=70, param2=75 ,minRadius=1, maxRadius=100)

    # 如果检测到圆
    if circles is not None:
        circles = np.round(circles[0, :]).astype("int")
        for (x, y, r) in circles:
            # 在图像上绘制圆
            cv2.circle(frame, (x, y), r, (0, 255, 0), 4)
            # 在图像上绘制圆心
            cv2.circle(frame, (x, y), 2, (0, 128, 255), 3)
            # 显示圆心坐标
            text = f"({x}, {y})"
            cv2.putText(frame, text, (x - 40, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)

    # 显示结果图像
    cv2.imshow("Detected Circles", frame)

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

# 释放摄像头和关闭窗口
cap.release()
cv2.destroyAllWindows()

2021电赛

本队使用openmv识别并输出矩形的四个顶点与红色激光的位置，使用V831检测红点与绿点的位置，并输出其差值。
题目要求

题目要求

openmv

具体的函数可在openmv官方手册中查看。

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# 使用april标签代码中的四元检测代码在图像中找到矩形。
# 四元检测算法以非常稳健的方式检测矩形，并且比基于Hough变换的方法好得多。 例如，即使镜头失真导致这些矩形看起来弯曲，它仍然可以检测到矩形。 圆角矩形是没有问题的 
# 但这个代码也会检测小半径的圆...

import sensor, image, time
from pyb import UART
import lcd

sensor.reset()
sensor.set_pixformat(sensor.RGB565) # 灰度更快(160x120 max on OpenMV-M7)
sensor.set_framesize(sensor.QVGA)
sensor.set_windowing((50,40,200,160))
sensor.skip_frames(time = 2000)

sensor.set_auto_gain(False,20) # 禁止自动增益
sensor.set_auto_whitebal(False) # 禁止自动白平衡
sensor.set_brightness(-3)
sensor.set_contrast(2) # 对比度

clock = time.clock()
lcd.init()
uart3 = UART(3, 19200)

target = [(64, 100, 8, 25, 0, 36),(64, 100, 8, 40, 0, 36),(0, 100, 8, 119, -6, 53)]

##############

def find_max(blobs):
    max_size=0
    for blob in blobs:
        if blob[2]*blob[3] > max_size:
            max_blob=blob
            max_size = blob[2]*blob[3]
    return max_blob

target_ready, rect_ready = False,False

##############

def rect_convert(img,corners):
    # 已知矩形的四个顶点坐标，坐标原点不在顶点上
    point1 = (corners[0][0], corners[0][1])
    point2 = (corners[1][0], corners[1][1])
    point3 = (corners[2][0], corners[2][1])
    point4 = (corners[3][0], corners[3][1])

    # 缩小比例
    scale = 0.94

    # 计算矩形的中心点坐标
    center = ((point1[0] + point2[0] + point3[0] + point4[0]) / 4, (point1[1] + point2[1] + point3[1] + point4[1]) / 4)

    # 将顶点坐标转换为相对中心点的坐标
    new_point1 = (point1[0] - center[0], point1[1] - center[1])
    new_point2 = (point2[0] - center[0], point2[1] - center[1])
    new_point3 = (point3[0] - center[0], point3[1] - center[1])
    new_point4 = (point4[0] - center[0], point4[1] - center[1])

    # 缩小矩形
    new_point1 = (new_point1[0] * scale, new_point1[1] * scale)
    new_point2 = (new_point2[0] * scale, new_point2[1] * scale)
    new_point3 = (new_point3[0] * scale, new_point3[1] * scale)
    new_point4 = (new_point4[0] * scale, new_point4[1] * scale)

    # 将顶点坐标转换为相对原坐标系的坐标
    new_point1 = (int(new_point1[0] + center[0]), int(new_point1[1] + center[1]))
    new_point2 = (int(new_point2[0] + center[0]), int(new_point2[1] + center[1]))
    new_point3 = (int(new_point3[0] + center[0]), int(new_point3[1] + center[1]))
    new_point4 = (int(new_point4[0] + center[0]), int(new_point4[1] + center[1]))

    #img.draw_circle(new_point1[0], new_point1[1], 5, color = (0, 255, 0))
    #img.draw_circle(new_point2[0], new_point2[1], 5, color = (0, 255, 0))
    #img.draw_circle(new_point3[0], new_point3[1], 5, color = (0, 255, 0))
    #img.draw_circle(new_point4[0], new_point4[1], 5, color = (0, 255, 0))

    img.draw_cross(new_point1[0], new_point1[1]) # cx, cy
    img.draw_cross(new_point2[0], new_point2[1]) # cx, cy
    img.draw_cross(new_point3[0], new_point3[1]) # cx, cy
    img.draw_cross(new_point4[0], new_point4[1]) # cx, cy

    after_convert = [new_point1, new_point2, new_point3, new_point4]
    return after_convert


##############

while(True):
    clock.tick()
    img = sensor.snapshot().lens_corr(strength = 1.8, zoom = 1.0) # 拍摄一张图片，并进行形变矫正

    # 下面的`threshold`应设置为足够高的值，以滤除在图像中检测到的具有
    # 低边缘幅度的噪声矩形。最适用与背景形成鲜明对比的矩形。

    for r in img.find_rects(threshold = 25000):
        #for p in r.corners():
            #img.draw_circle(p[0], p[1], 5, color = (0, 255, 0))
        corners = rect_convert(img,r.corners()) # 得到转换后的矩形顶点
        rect_ready = True
    blobs = img.find_blobs(target,merge = 1,x_stride=2,y_stride=2,area_threshold=1,pixels_threshold=1 )
    if blobs:
        max_blob = find_max(blobs)
        img.draw_rectangle(max_blob.rect()) # rect
        img.draw_cross(max_blob.cx(), max_blob.cy()) # cx, cy
        target_ready =True
    if (target_ready == True) and (rect_ready == True):
        tep = "@{}, {}, {}, {}, {}, {}, {}, {}, {}, {}\r\n".format(corners[0][0], corners[0][1], corners[1][0], corners[1][1], corners[2][0], corners[2][1], corners[3][0], corners[3][1], max_blob.cx(), max_blob.cy())
        uart3.write(tep.encode("utf-8"))
        print(tep)
        target_ready, rect_ready = False,False
    img.draw_string(1, 1, "%.1f" % clock.fps(), color = (255, 0, 0), scale = 1.5, mono_space = False,
                        char_rotation = 0, char_hmirror = False, char_vflip = False,
                        string_rotation = 0, string_hmirror = False, string_vflip = False)
    lcd.display(img)
    #print("FPS %f" % clock.fps())

V831(M2Dock)

资料链接：V831官方手册
注：对于M2Dock，开机启动代码顺序是 /root/app/main.py > /root/main.py

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#!/usr/bin/python3

from maix import image, display, camera, gpio
import serial, time

camera.config(size=(240, 240))



ser = serial.Serial("/dev/ttyS1", 115200, timeout=0.2)  # 连接串口
ser.write(b" \r\n")
time.sleep(1)
ser.write(b"{V831:Ready!}\n")
# LAB阈值的初始化格式:[L_MIN,A_MIN,B_MIN,L_MAX,A_MAX,B_MAX]
set_LAB = [[(0, 97, 20, 81, 6, 68),(0, 100, 19, 97, -78, 125)],[(0, 100, -63, -21, -46, 127),(0, 100, -63, -21, -46, 127),(0, 100, -63, -21, -46, 127)]]  # 设定阈值，为了识别的稳定，每个颜色设定了多个阈值



flag = '1'
Red_ready,Green_ready=False,False
now_time = time.time()

ser.write(b"ok\n")

exp, gain = 96, 96 # 初值，exp 曝光[0, 65536]，gain 增益[16 - 1024]，随意设置得值会受到驱动限制。（80，60）（128，124）
camera.config(size=(240, 240), exp_gain=(exp, gain))

Color = [(255, 0, 0),(0, 255, 0)]

global x_center,y_center

while True:
    img = camera.capture()
    for j in range(2):
        blobs = img.find_blobs(set_LAB[j], merge=True,pixels_threshold=1,area_threshold=1, margin=10)  # 在图片中查找lab阈值内的颜色色块
        if blobs:
            for i in blobs:
                size = i["w"] * i["h"]  # 最大是240 *240也就是57600，这里用的是python中字典的键值对
                if size > 3:            # 这个设定了色块面积要大于多少才执行显示
                    x_start = i["x"]
                    x_end = i["x"] + i["w"]
                    x_center = int((x_start + x_end) / 2) # x中心坐标
                    y_start = i["y"]
                    y_end = i["y"] + i["h"]
                    y_center = int((y_start + y_end) / 2) # y中心坐标
                    m = max((x_center - i["w"] * 0.3), 0) # 往下四行都是为了确保x和y的数据在相机、屏幕显示范围内
                    n = max((y_center - i["h"] * 0.3), 0)
                    m = min((x_center - i["w"] * 0.3), 240)
                    n = min((y_center - i["h"] * 0.3), 240)
                    mk = [int(m), int(n), 20, 20]
                    if (mk[0] + mk[2]) < 260 and (mk[1] + mk[3]) < 260: # 原本的数值是220，这是为了确保下面的画框能完整显示，但当时比赛
                        img.draw_circle(x_center, y_center, int(i["h"] / 2 + 8), color = Color[j],
                                        thickness=2)  # 画一个中心点在色块中心，半径为20的空心圆
                        if (j == 0):
                            string = 'Red'
                            Red_center_x = x_center
                            Red_center_y = y_center
                            Red_ready = True
                        elif (j == 1):
                            string = 'Green'
                            Green_center_x = x_center
                            Green_center_y = y_center
                            Green_ready = True
                        str_size = image.get_string_size(string)
                        img.draw_string(x_center - int(str_size[0] / 2) - 5, y_start - 35, string, scale=1,
                                        color=Color[j], thickness=2)
                        if flag == '1':
                            flag = '0'
                            now_time = time.time()  # time.asctime()
                        if (Red_ready == True) and (Green_ready == True):
                            x_Err = Red_center_x - Green_center_x
                            y_Err = Red_center_y - Green_center_y
                            img.draw_string(0, 10,str(x_Err)+" "
                                        +str(y_Err), scale = 2, color = (255, 0, 0), thickness = 2)
#                             tep = bytearray([44, 18, x_Err, y_Err, 0, 0, 91])  # 先获得数据
                            tep = "@"+str(x_Err)+","+str(y_Err)+"\r\n" # 构造所需的字符串，
                            ser.write(tep.encode("utf-8")) # 转换为utf-8发送
                            Red_ready,Green_ready=False,False
    display.show(img)
    time_t = time.time() - now_time

委托（K210）

接委托人需求，使用基于k210的CanMv，识别倒车入库的车库，方法上本质为识别矩形，CanMv官方文档。
记录代码如下：

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#导入库
import sensor, image, time
import lcd

#对传感器进行配置、初始化
sensor.reset()
sensor.set_pixformat(sensor.RGB565)# 使用16bit全彩像素格式
sensor.set_framesize(sensor.QQVGA) # 为了保证处理速度，使用QQVGA即160*120分辨率
sensor.set_hmirror(True) # 为了在屏幕上正确显示图像，开启水平镜像
sensor.set_vflip(True) # 为了在屏幕上正确显示图像，开启垂直镜像

sensor.skip_frames(time = 2000) # 设置的生效需要时间，跳过一些帧

#初始化一些变量，包括用来计算帧率的时钟，lcd初始化，检测状态标志量
clock = time.clock()
lcd.init()
detect_status = False


#############################
"""
函数名：rect_convert
函数作用：由于通常识别的矩形为外框，对识别到的矩形进行一定的缩放，有助于正确标记顶点位置
入口参数：需要处理的图形img、原识别到的矩形顶点
返回：转换后的四个顶点坐标
"""
#############################

def rect_convert(img,corners):
    # 已知矩形的四个顶点坐标，坐标原点不在顶点上
    point1 = (corners[0][0], corners[0][1])
    point2 = (corners[1][0], corners[1][1])
    point3 = (corners[2][0], corners[2][1])
    point4 = (corners[3][0], corners[3][1])

    # 缩小比例
    scale = 0.98

    # 计算矩形的中心点坐标
    center = ((point1[0] + point2[0] + point3[0] + point4[0]) / 4, (point1[1] + point2[1] + point3[1] + point4[1]) / 4)

    # 将顶点坐标转换为相对中心点的坐标
    new_point1 = (point1[0] - center[0], point1[1] - center[1])
    new_point2 = (point2[0] - center[0], point2[1] - center[1])
    new_point3 = (point3[0] - center[0], point3[1] - center[1])
    new_point4 = (point4[0] - center[0], point4[1] - center[1])

    # 缩小矩形
    new_point1 = (new_point1[0] * scale, new_point1[1] * scale)
    new_point2 = (new_point2[0] * scale, new_point2[1] * scale)
    new_point3 = (new_point3[0] * scale, new_point3[1] * scale)
    new_point4 = (new_point4[0] * scale, new_point4[1] * scale)

    # 将顶点坐标转换为相对原坐标系的坐标
    new_point1 = (int(new_point1[0] + center[0]), int(new_point1[1] + center[1]))
    new_point2 = (int(new_point2[0] + center[0]), int(new_point2[1] + center[1]))
    new_point3 = (int(new_point3[0] + center[0]), int(new_point3[1] + center[1]))
    new_point4 = (int(new_point4[0] + center[0]), int(new_point4[1] + center[1]))

    # 在识别到的矩形中间打上"Part"标签
    img.draw_string(int(center[0])-20, int(center[1]), "Part", color = (255, 0, 0), scale = 2)

    # 将识别到的矩形各个顶点用圆圈标记起来
    img.draw_circle(new_point1[0], new_point1[1], 8, color = (0, 255, 255))
    img.draw_circle(new_point2[0], new_point2[1], 8, color = (0, 255, 255))
    img.draw_circle(new_point3[0], new_point3[1], 8, color = (0, 255, 255))
    img.draw_circle(new_point4[0], new_point4[1], 8, color = (0, 255, 255))

    #img.draw_cross(new_point1[0], new_point1[1]) # cx, cy
    #img.draw_cross(new_point2[0], new_point2[1]) # cx, cy
    #img.draw_cross(new_point3[0], new_point3[1]) # cx, cy
    #img.draw_cross(new_point4[0], new_point4[1]) # cx, cy

    # 将矩形各个顶点连线
    img.draw_line(new_point1[0], new_point1[1], new_point2[0], new_point2[1], thickness = 2, color = (255, 0, 0))
    img.draw_line(new_point2[0], new_point2[1], new_point3[0], new_point3[1], thickness = 2, color = (255, 0, 0))
    img.draw_line(new_point3[0], new_point3[1], new_point4[0], new_point4[1], thickness = 2, color = (255, 0, 0))
    img.draw_line(new_point4[0], new_point4[1], new_point1[0], new_point1[1], thickness = 2, color = (255, 0, 0))

    after_convert = [new_point1, new_point2, new_point3, new_point4]
    return after_convert


#############################

"""
主函数
"""

#############################

while(True):
    clock.tick()
    img = sensor.snapshot()

    img = img.erode(2, threshold = 3)#图像滤波中的腐蚀算法，用于去除黑色矩形框身边的白色噪点

    for r in img.find_rects(threshold = 25000,roi=(20,0,130,120)):
        corners = rect_convert(img,r.corners())#新增的
        detect_status = True                   #使能检测状态标志量
    # 画框以标记ROI（Region of Interest，即感兴趣区域）
    img.draw_rectangle(20,0,130,120, color = (255, 0, 0), thickness = 2, fill = False)
    img.draw_string(40, 0, "%s" % "Detect Area", color = (255, 0, 0), scale = 1.5)
    # 如果检测到矩形就在屏幕中显示检测成功
    if (detect_status == True):
        img.draw_string(10, 50, "%s" % "Detect successfully", color = (0, 255, 0), scale = 1.4)
    # 在屏幕左上角显示帧率
    img.draw_string(1, 1, "%.1f" % clock.fps(), color = (255, 0, 0), scale = 1.5)
    # 在屏幕上显示图像
    lcd.display(img)

测试

在开发测试中的一些小例子，留档以便不时之需

霍夫圆变换

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from maix import image, display, camera

while True:
    img = camera.capture()
    # 霍夫圆变换查找圆形
    circles = img.find_circles(threshold = 2000, x_margin = 10, y_margin = 10, r_margin = 10)
    for circle in circles:
        x, y, r = circle[0], circle[1], circle[2]
        # 绘制圆形，使用圆心坐标和半径
        img.draw_circle(x, y, r, color=(255, 0, 0), thickness=2)
    display.show(img)

霍夫圆变换的基本原理如下：

  1. 边缘检测：首先，对图像进行边缘检测（通常使用 Canny 边缘检测算法），以得到图像中的边缘点。

  2. 参数空间：对于每个边缘点，假设它是一个圆的一部分。圆可以由三个参数确定：圆心的两个坐标(a,b)和半径 r。因此，霍夫圆变换会在一个三维参数空间（两维表示圆心位置，一维表示半径）中进行投票。

  3. 投票机制：对于每个边缘点 (x,y)，假设它属于一个半径为 r 的圆，那么圆心 (a,b) 可以通过以下公式计算：

f(x)={a=x−r⋅cos⁡(θ)b=y−r⋅sin⁡(θ)

其中，θ 为角度，范围从 0 到 360 度变化。对于每个可能的 θ 值，计算对应的 a 和 b，并在参数空间中对这些 (a,b,r) 进行投票。

  1. 峰值检测：在参数空间中，投票数最多的点对应于检测到的圆。

矩形查找

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from maix import image, display, camera
import time

while True:
    img = camera.capture()
    rects = img.find_rects(threshold = 5000,is_xywh=0)
    for i in rects:
        img.draw_circle(i[0], i[1], 10, color=(255, 0, 0),thickness=2)
        img.draw_circle(i[2], i[3], 10, color=(255, 0, 0),thickness=2) 
        img.draw_line(i[0], i[1], i[2], i[3], color = (127, 127, 127), thickness = 1)
        w = i[2] - i[0]
        h = i[3] - i[1]
    display.show(img)