【OpenMV】AprilTag标记跟踪 NCC模板匹配测距与测量物体大小

AprilTag标记跟踪

Tag36h11，Tag25h9，Tag16h5

Tag36h11信息量更大，更准确


# AprilTags Example
#
# This example shows the power of the OpenMV Cam to detect April Tags
# on the OpenMV Cam M7. The M4 versions cannot detect April Tags.
 
import sensor, image, time, math
 
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QQVGA) # we run out of memory if the resolution is much bigger...
sensor.skip_frames(30)
sensor.set_auto_gain(False)  # must turn this off to prevent image washout...
sensor.set_auto_whitebal(False)  # must turn this off to prevent image washout...关闭白平衡
clock = time.clock()
 
while(True):
    clock.tick()
    img = sensor.snapshot()
    for tag in img.find_apriltags(): # defaults to TAG36H11 without "families".
        img.draw_rectangle(tag.rect(), color = (255, 0, 0))     #画框
        img.draw_cross(tag.cx(), tag.cy(), color = (0, 255, 0)) #画十字
        degress = 180 * tag.rotation() / math.pi                #求April Tags旋转的角度
        print(tag.id(),degress)

NCC模板匹配

使用方法：
模板匹配需要插内存卡（方向：摄像头对准自己，看到内存卡背面）
用helloworld.py例程拍照
将1 2 3 4 5 6 数字的图片拍下来，就是保留住画面，然后终止脚本
用鼠标框起来所要识别的数字，右键框框，保存到PC，选择一个位置，保存图像的格式是bmp格式
百度搜一个bmp转pgm图像格式的网站，将图像转换过去，网站如下：
https://onlineconvertfree.com/zh/convert-format/bmp-to-pgm/
将图片导出，保存到SD卡中

修改29~30行代码
template1 = image.Image("/1.pgm")
template2 = image.Image("/2.pgm")
template3 = image.Image("/3.pgm")
修改图像的名称

最后
多循环几次
r1
r2
r3


# Template Matching Example - Normalized Cross Correlation (NCC)
#
# This example shows off how to use the NCC feature of your OpenMV Cam to match
# image patches to parts of an image... expect for extremely controlled enviorments
# NCC is not all to useful.
#
# WARNING: NCC supports needs to be reworked! As of right now this feature needs
# a lot of work to be made into somethin useful. This script will reamin to show
# that the functionality exists, but, in its current state is inadequate.
 
import time, sensor, image
from image import SEARCH_EX, SEARCH_DS
 
# Reset sensor
sensor.reset()
 
# Set sensor settings
sensor.set_contrast(1)
sensor.set_gainceiling(16)
# Max resolution for template matching with SEARCH_EX is QQVGA
sensor.set_framesize(sensor.QQVGA)
# You can set windowing to reduce the search image.
#sensor.set_windowing(((640-80)//2, (480-60)//2, 80, 60))
sensor.set_pixformat(sensor.GRAYSCALE)
 
# Load template.
# Template should be a small (eg. 32x32 pixels) grayscale image.
template1 = image.Image("/1.pgm")
template2 = image.Image("/2.pgm")
template3 = image.Image("/3.pgm")
 
clock = time.clock()
 
# Run template matching
while (True):
    clock.tick()
    img = sensor.snapshot()
 
    # find_template(template, threshold, [roi, step, search])
    # ROI: The region of interest tuple (x, y, w, h).
    # Step: The loop step used (y+=step, x+=step) use a bigger step to make it faster.
    # Search is either image.SEARCH_EX for exhaustive search or image.SEARCH_DS for diamond search
    #
    # Note1: ROI has to be smaller than the image and bigger than the template.
    # Note2: In diamond search, step and ROI are both ignored.
    r1 = img.find_template(template1, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
    if r1:
        img.draw_rectangle(r1, color=(255,0,255))
    
    r2 = img.find_template(template2, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
    if r2:
        img.draw_rectangle(r2)
        
    r3 = img.find_template(template3, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
    if r3:
        img.draw_rectangle(r3)
    print(clock.fps())

学习视频链接：

OpenMV使用NCC模板匹配 | 星瞳科技 (singtown.com)
OpenMV使用AprilTag标记追踪 | 星瞳科技 (singtown.com)

测距以及测量物体大小

根据测量的像素点数，求出比例K，进而计算出距离或者物体大小

例程：

距离 = 一个常数K / 直径的像素Lm

先用这个公式求出K：

具体操作：

print(Lm)   # Lm = 67 输出像素数

print 输出 Lm=67   距离=16，所以K=16cm*67=1072

定义：（写在while(True):上面）

K_Distance = 1072

然后计算距离，输出距离：

Distance = K_Distance / Lm

print(Distance)

测量物体大小，距离摄像头越近，像素数越大，是正比关系，所以实际大小 = K*直径的像素

定义：

K_size = 0.143 #实际大小 = K*直径的像素，K=3.5cm/67            实际大小与下摄像头里的像素成正比

添加代码：

Size = K_size * Lm

        print (Size)

识别乒乓球的距离与大小


黄色球形例程代码：
# Measure the distance
#
# This example shows off how to measure the distance through the size in imgage
# This example in particular looks for yellow pingpong ball.
import sensor, image, time
# For color tracking to work really well you should ideally be in a very, very,
# very, controlled enviroment where the lighting is constant...
yellow_threshold   = (57, 100, 17, 88, 99, 32)  #(37, 92, -5, 52, 89, 37)
# You may need to tweak the above settings for tracking green things...
# Select an area in the Framebuffer to copy the color settings.
sensor.reset() # Initialize the camera sensor.
sensor.set_pixformat(sensor.RGB565) # use RGB565.
sensor.set_framesize(sensor.QVGA) # use QQVGA for speed.
sensor.skip_frames(10) # Let new settings take affect.
sensor.set_auto_whitebal(False) # turn this off.    用颜色识别进行测量，需要关闭白平衡！
clock = time.clock() # Tracks FPS.
K_Distance = 1072           # K = 距离*像素 = 16cm * 67       实际长度和摄像头里的像素成反比
K_size = 0.0522  #实际大小 = K*直径的像素，K=3.5cm/67           实际大小与下摄像头里的像素成正比
while(True):
    clock.tick() # Track elapsed milliseconds between snapshots().
    img = sensor.snapshot() # Take a picture and return the image.
    blobs = img.find_blobs([yellow_threshold])
    if len(blobs) == 1:
        # Draw a rect around the blob.
        b = blobs[0]
        img.draw_rectangle(b[0:4]) # rect
        img.draw_cross(b[5], b[6]) # cx, cy
        Lm = (b[2]+b[3])/2		   #(长+宽)/2 = 像素数Lm
        Distance = K_Distance / Lm
 
        #print(Lm)   # Lm = 67 输出像素数
        print(Distance)     #输出距离
 
        Size = K_size * Lm
        print (Size)        #输出大小
 
    #print(clock.fps()) # Note: Your OpenMV Cam runs about half as fast while
    # connected to your computer. The FPS should increase once disconnected.

红色矩形与蓝色矩形同时识别

这里我拿了魔方，拧成上面是6个红色色块，下面三个是蓝色色块，只识别这一面即可


红色矩形的识别代码
蓝色矩形的识别代码，返回高度和宽度
 
 
# Measure the distance
#
# This example shows off how to measure the distance through the size in imgage
# This example in particular looks for yellow pingpong ball.
import sensor, image, time
# For color tracking to work really well you should ideally be in a very, very,
# very, controlled enviroment where the lighting is constant...
yellow_threshold   = (57, 100, 17, 88, 99, 32)  #(37, 92, -5, 52, 89, 37)
red_threshold   = (15, 61, 48, 116, 53, 19) #红色阈值
blue_threshold   = (31, 47, 26, -28, -51, -15) #蓝色阈值
# You may need to tweak the above settings for tracking green things...
# Select an area in the Framebuffer to copy the color settings.
sensor.reset() # Initialize the camera sensor.
sensor.set_pixformat(sensor.RGB565) # use RGB565.
sensor.set_framesize(sensor.QVGA) # use QQVGA for speed.
sensor.skip_frames(10) # Let new settings take affect.
sensor.set_auto_whitebal(False) # turn this off.    用颜色识别进行测量，需要关闭白平衡！
clock = time.clock() # Tracks FPS.
K_Distance = 1072           # K = 距离*像素 = 16cm * 67       实际长度和摄像头里的像素成反比
K_size = 0.0522  #实际大小 = K*直径的像素，K=3.5cm/67           实际大小与下摄像头里的像素成正比
while(True):
    clock.tick() # Track elapsed milliseconds between snapshots().
    img = sensor.snapshot() # Take a picture and return the image.
    blobs = img.find_blobs([yellow_threshold])  #识别黄色
    if len(blobs) == 1:
        # Draw a rect around the blob.
        b = blobs[0]
        img.draw_rectangle(b[0:4]) # rect
        img.draw_cross(b[5], b[6]) # cx, cy
        Lm = (b[2]+b[3])/2		   #(长+宽)/2 = 像素数Lm
        Distance = K_Distance / Lm
 
        #print(Lm)   # Lm = 67 输出像素数
        print(Distance)     #输出距离
        Size = K_size * Lm
        print (Size)        #输出大小
 
    Bolb2 = img.find_blobs([red_threshold]) #识别红色
    if len(Bolb2) == 1:
        # Draw a rect around the blob.
        blocation = Bolb2[0]
        img.draw_rectangle(blocation[0:4]) # rect
        img.draw_cross(blocation[5], blocation[6]) # cx, cy
        h = K_size * blocation[3]
        w = K_size * blocation[2]
        print("红色高:%s cm, 红色宽:%s cm " %(h,w))
 
    Bolb3 = img.find_blobs([blue_threshold])    #识别蓝色
    if len(Bolb3) == 1:
        # Draw a rect around the blob.
        local_blue = Bolb3[0]
        img.draw_rectangle(local_blue[0:4]) # rect
        img.draw_cross(local_blue[5], local_blue[6]) # cx, cy
        h = K_size * local_blue[3]
        w = K_size * local_blue[2]
        print("蓝色高:%s cm, 蓝色宽:%s cm " %(h,w))
 
    #print(clock.fps()) # Note: Your OpenMV Cam runs about half as fast while
    # connected to your computer. The FPS should increase once disconnected.

这样就可以同时识别红色和蓝色色块了

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原文地址：https://blog.csdn.net/weixin_63135906/article/details/131493825

【OpenMV】AprilTag标记跟踪 NCC模板匹配 测距与测量物体大小

AprilTag标记跟踪

NCC模板匹配

测距以及测量物体大小

识别乒乓球的距离与大小

红色矩形与蓝色矩形同时识别

【OpenMV】AprilTag标记跟踪 NCC模板匹配测距与测量物体大小