特征识别-圆和矩形（Identify circles and rectangles）

基于OpenMV平台进行开发

识别原理：

定义矩形度的公式：

Rectangularity = area_real/area_external
矩形度 = 图形面积/外接矩形面积

圆和矩形的矩形度值不同

识别结果：

程序代码：

import sensor, image, time, math, pyb

# 全局变量

GRAYSCALE_THRESHOLD = [(0, 100)]

Rectangularity_Threshold = 0.9            # 设定阈值

# 调用硬件

led = pyb.LED(3)                          # Red LED = 1, Green LED = 2, Blue LED = 3, IR LEDs = 4.

clock = time.clock()                      # Tracks FPS.

def senser_init():                         # 摄像头传感器初始化函数

    sensor.reset()                         # Initialize the camera sensor.

    sensor.set_pixformat(sensor.GRAYSCALE) # use grayscale.

    sensor.set_framesize(sensor.QQVGA)     # use QQVGA for speed.

    sensor.skip_frames(30)                 # Let new settings take affect.

    sensor.set_auto_gain(True)             # must be turned off for color tracking

    sensor.set_auto_whitebal(True)         # must be turned off for color tracking

    return;

def hardware_init():

    led.on()            #亮灯

    time.sleep(100)     #延时150ms

    led.off()           #暗灯

    time.sleep(100)

    return;

def Maximum_interclass_variance_method(grayFrame):  # 最大类间方差法

    w = 0

    avgValue = 0

    t = 0

    maxVariance = 0

    statistics_data = grayFrame.get_statistics()

    u = statistics_data.mean()

    histogram_data = grayFrame.get_histogram()      # 获取灰度直方图

    histogram_bins = histogram_data.bins()          # 获取灰度直方图的列表

    for i in range(len(histogram_bins)):

        w += (histogram_bins[i]+0.0000001)

        avgValue  += i * (histogram_bins[i]+0.0000001)

        t = avgValue/w - u

        variance = t * t * w /(1 - w)

        if(variance > maxVariance):

            maxVariance = variance;

            threshold = i;

    return threshold

# 主函数

hardware_init();

senser_init();

while(True):

    img = sensor.snapshot()                                                  # Take a picture and return the image.

    h = Maximum_interclass_variance_method(img);                             # 最大类间方差法计算阈值

    GRAYSCALE_THRESHOLD =[(0, h)]

    blobs = img.find_blobs(GRAYSCALE_THRESHOLD, merge=True) # 图像分割

    if blobs:

        for i in range(len(blobs)):

            area_real = blobs[i].pixels()                                    # 实际面积

            area_external = blobs[i].w() * blobs[i].h()                      # 外接矩形的面积

            Rectangularity = area_real/area_external                         # 计算矩形度

            if Rectangularity > Rectangularity_Threshold:                    # 大于阈值为矩形

                img.draw_rectangle(blobs[i].rect())

            elif Rectangularity < Rectangularity_Threshold:                  # 小于阈值为圆型

                img.draw_circle(blobs[i].x()+int(blobs[i].w()/2), blobs[i].y()+int(blobs[i].w()/2),int(blobs[i].w()/2), color=255)