4. 模板匹配时，代码1识别准确，在角度和摄像头参数不变、小车未动的情况下，代码2无法检测图片，需托起摄像头才能识别数字模板。
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    • 模板匹配时，代码1识别准确，在角度和摄像头参数不变、小车未动的情况下，代码2无法检测图片，需托起摄像头才能识别数字模板。

    • F

      代码1

      # This work is licensed under the MIT license.
# Copyright (c) 2013-2023 OpenMV LLC. All rights reserved.
# https://github.com/openmv/openmv/blob/master/LICENSE
#
# 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 environments
# 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 remain to show
# that the functionality exists, but, in its current state is inadequate.

import time
import sensor
import image
from image import SEARCH_EX

# from image import SEARCH_DS

# Reset sensor
'''sensor.reset()
sensor.set_vflip(True)
sensor.set_hmirror(True)
# 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)'''
sensor.reset()
sensor.set_vflip(True)
sensor.set_hmirror(True)
sensor.set_framesize(sensor.QQVGA)
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.skip_frames(50)
sensor.set_contrast(0)
sensor.set_auto_whitebal(False)    # 关闭白平衡
sensor.set_auto_gain(False)
sensor.set_gainceiling(16)
# Load template.
# Template should be a small (eg. 32x32 pixels) grayscale image.
template = image.Image("32.pgm")
template1 = image.Image("31.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.
    r = img.find_template(
        template, 0.70, step=4, search=SEARCH_EX
    )  # , roi=(10, 0, 60, 60))
    if r:
        img.draw_rectangle(r)
        print(r[2],r[3])

    r = img.find_template(
        template1, 0.70, step=4, search=SEARCH_EX
    )  # , roi=(10, 0, 60, 60))
    if r:
        img.draw_rectangle(r)
        print(22)
    print(clock.fps())

      代码2

      import sensor, image, time, pyb
import display
from image import SEARCH_EX, SEARCH_DS
from pyb import UART
# ------------------ 硬件初始化 ------------------
sensor.reset()
#sensor.set_vflip(True)
#sensor.set_hmirror(True)
sensor.set_auto_gain(False)
sensor.set_auto_whitebal(False)
lcd = display.SPIDisplay()#屏幕显示

# ------------------ 测试 ------------------

# ------------------ 模式定义 ------------------
MODE_INIT = 0        # 初始状态
MODE_LINE = 1        # 巡线模式
MODE_NUM_RECOG = 2   # 数字识别模式
current_mode = MODE_INIT

# ------------------ 通信协议定义 ------------------
HEADER = 0xAA
FOOTER = 0x55
CMD_LINE_DATA = 0x01
CMD_TURN_LEFT = 0x02
CMD_TURN_RIGHT = 0x03
CMD_RECOGNIZING = 0x04
# ------------------ 全局变量 ------------------
uart = pyb.UART(3, 115200, timeout_char=1000)
global target_num
target_num = 0  # 目标病房号
# 定义7个巡线检测区域 (x, y, w, h)
line_regions = [
    # 原QVGA(320x240) → 新QQVGA(160x120)
    (28, 0, 15, 10),    # 原(55,35,30,20)
    (43, 0, 15, 10),    # 原(85,35,30,20)
    (58, 0, 15, 10),    # 原(115,35,30,20)
    (73, 0, 15, 10),    # 原(145,35,30,20)
    (88, 0, 15, 10),    # 原(175,35,30,20)
    (103, 0, 15, 10),   # 原(205,35,30,20)
    (118, 0, 15, 10)    # 原(235,35,30,20)
]
# 红色阈值 (LAB颜色空间)
# 针对红色线调整这些值 (L: 0-100, A: -128-127, B: -128-127)
RED_THRESHOLD = (20, 100,   # L范围
                 10, 100,   # A范围 (正值表示红色)
                 -80, 100)  # B范围

# ------------------ 模板加载函数 ------------------
def load_templates():
    global templates
    global template1,template2,template3,template4
    global template5,template7,template6,template8

    template1 = image.Image("1.pgm")
    template2 = image.Image("2.pgm")
    template3 = image.Image("3.pgm")
    template4 = image.Image("4.pgm")
    template5 = image.Image("5.pgm")
    template7 = image.Image("7.pgm")
    template6 = image.Image("6.pgm")
    template8 = image.Image("8.pgm")

    '''global templates1, templates2
    templates1 = ["1L.pgm", "1R.pgm"] #保存多个模板
    templates2 = [ "2L.pgm","2R.pgm"] #保存多个模板'''

    global templates5, templates6, templates7, templates8, templates3, templates4
    #templates3 = ["3L.pgm", "3R.pgm", "3LL.pgm", "3RR.pgm","33.pgm"] #保存多个模板
    templates3 = ["31.pgm","32.pgm","33.pgm"] #保存多个模板
    templates4 = ["41.pgm", "42.pgm", "43.pgm", "44.pgm","4.pgm"] #保存多个模板
    templates5 = ["5.pgm",  "5.pgm","5.pgm", "5.pgm","5.pgm"] #保存多个模板
    templates6 = ["6.pgm", "6.pgm", "6.pgm", "6.pgm","6.pgm"] #保存多个模板
    templates7 = ["7.pgm", "7.pgm", "7.pgm", "7.pgm","7.pgm"] #保存多个模板
    templates8 = ["81.pgm", "82.pgm", "8.pgm", "8.pgm","8.pgm"] #保存多个模板
# ------------------ 传感器模式配置 ------------------
def set_sensor_config(mode):
    if mode == MODE_LINE:  # 巡线模式配
        #sensor.reset()
        #sensor.set_vflip(True)
        #sensor.set_hmirror(True)
        sensor.set_pixformat(sensor.RGB565)
        #sensor.set_framesize(sensor.QQVGA)  # 320x240
        #sensor.skip_frames(time=50)
        sensor.set_contrast(-1)# 设置对比度-1（范围-3到+3，0为默认值，-1增强暗部细节）
        sensor.set_auto_whitebal(False)    # 关闭白平衡
        sensor.set_auto_gain(False)        # 关闭自动增益
        sensor.set_auto_exposure(False, exposure_us=25000)  # 手动设置曝光时间
        #sensor.set_windowing(0, 10, 150, 100)
    elif mode == MODE_NUM_RECOG:  # 数字识别模式配置
        sensor.reset()
        sensor.set_vflip(True)
        sensor.set_hmirror(True)
        sensor.set_framesize(sensor.QQVGA)
        sensor.set_pixformat(sensor.GRAYSCALE)
        sensor.skip_frames(50)
        sensor.set_contrast(0)
        sensor.set_auto_whitebal(False)    # 关闭白平衡
        sensor.set_auto_gain(False)
        sensor.set_gainceiling(16)
# ------------------ 核心功能函数 ------------------
def initial_target_recognition():
    """初始目标识别（全屏搜索）"""
    set_sensor_config(MODE_NUM_RECOG)
    timeout = time.ticks_ms()
    clock = time.clock()
    uart = UART(3, 115200, timeout_char=1000)
    # Run template matching
    best_num=0
    while (True):
        clock.tick()
        img = sensor.snapshot()
        lcd.write(img)
        if best_num==0:
            clock.tick()
            img = sensor.snapshot()
            r1 = img.find_template(template1, 0.70, step=4, search=SEARCH_EX)  #, roi=(10, 0, 60, 60))
            if r1:
                img.draw_rectangle(r1)
                lcd.write(img)
                best_num=1
            r2 = img.find_template(template2, 0.6, step=4, search=SEARCH_EX)  # , roi=(10, 0, 60, 60))
            if r2:
                img.draw_rectangle(r2)
                lcd.write(img)
                best_num=2
            r3 = img.find_template(template3, 0.73, step=4, search=SEARCH_EX)  # , roi=(10, 0, 60, 60))
            if r3:
                img.draw_rectangle(r3)
                lcd.write(img)
                best_num=3
            r4 = img.find_template(template4, 0.7, step=4, search=SEARCH_EX)  # , roi=(10, 0, 60, 60))
            if r4:
                img.draw_rectangle(r4)
                lcd.write(img)
                best_num=4
            r5 = img.find_template(template5, 0.7, step=4, search=SEARCH_EX)  # , roi=(10, 0, 60, 60))
            if r5:
                img.draw_rectangle(r5)
                lcd.write(img)
                best_num=5
            r6 = img.find_template(template6, 0.7, step=3, search=SEARCH_EX)  # , roi=(10, 0, 60, 60))
            if r6:
                img.draw_rectangle(r6)
                lcd.write(img)
                best_num=6
            r7 = img.find_template(template7, 0.7, step=4, search=SEARCH_EX)  # , roi=(10, 0, 60, 60))
            if r7:
                img.draw_rectangle(r7)
                lcd.write(img)
                best_num=7
            r8 = img.find_template(template8, 0.7, step=4, search=SEARCH_EX)  # , roi=(10, 0, 60, 60))
            if r8:
                img.draw_rectangle(r8)
                lcd.write(img)
                best_num=8

        if best_num!=0:
            uart.write(bytearray([HEADER, CMD_RECOGNIZING, best_num, FOOTER]))
            return best_num
        if time.ticks_diff(time.ticks_ms(), timeout) > 10000:  # 10秒超时
            return 0

def line_follow_process():
    set_sensor_config(MODE_LINE)

    bits = []
    line_detected = False

    # 检测7个巡线区域
    for idx,roi in enumerate(line_regions):
        #lcd.write(img)
        # 在ROI中寻找红色线
        # 修改后的参数（降低灵敏度）
        blobs = img.find_blobs([RED_THRESHOLD],roi=roi,
            merge=True,
            pixels_threshold=10,  # 提高最小像素点数（原50）
            area_threshold=50,    # 提高最小区域面积（原50）
            margin=10)  # 增加合并边界距离（原10）

        detected = 1 if blobs else 0
        bits.append(detected)

        # 绘制检测区域和状态
        color = (0, 255, 0) if detected else (255, 0, 0)
        img.draw_rectangle(roi, color=color)

        # 在区域中心显示检测状态
        text_x = roi[0] + roi[2]//2 - 5
        text_y = roi[1] + roi[3]//2 - 5
        img.draw_string(text_x, text_y, str(detected), color=(255, 255, 255))
        lcd.write(img)

        if detected:
            line_detected = True

    # 发送巡线数据
    if line_detected:
        dec_val = int(''.join(map(str, bits)), 2)
        if dec_val>0:
            uart.write(bytearray([HEADER, CMD_LINE_DATA, dec_val, FOOTER]))
    else:
        dec_val=0
        uart.write(bytearray([HEADER, CMD_LINE_DATA,dec_val, FOOTER]))
    print(dec_val)

    # 检测路口（全区域检测到线）
    if sum(bits) == 7:
        global current_mode
        current_mode = MODE_NUM_RECOG

def number_recognize():
    """数字识别模式处理"""
    set_sensor_config(MODE_NUM_RECOG)
    #sensor.set_windowing(0, 0, 160, 60)
    img = sensor.snapshot()
   #img = sensor.snapshot().binary([(0,64)])

    # 在左右区域识别数字
    r=None
    if target_num>2:
        while(r==None):
            clock.tick()
            img = sensor.snapshot()
            lcd.write(img)
            '''if target_num==1 :
                for t in templates1:
                    template = image.Image(t)
                    r = img.find_template(template, 0.70, step=4, search=SEARCH_EX)
                if r:
                    img.draw_rectangle(r)
                    lcd.write(img)
            elif target_num==2 :
                for t in templates2:
                    template = image.Image(t)
                    r = img.find_template(template, 0.40, step=4, search=SEARCH_EX)
                if r:
                    img.draw_rectangle(r)
                    lcd.write(img)'''
            if target_num==3 :
                for t in templates3:
                    template = image.Image(t)
                    r = img.find_template(template, 0.70, step=4, search=SEARCH_EX)

                '''rr1 = img.find_template(templates3[0], 0.70, step=4, search=SEARCH_EX)  #, roi=(10, 0, 60, 60))
                if rr1:
                    img.draw_rectangle(rr1)


                rr2 = img.find_template(templates3[1], 0.7, step=4, search=SEARCH_EX)  # , roi=(10, 0, 60, 60))
                if rr2:
                    img.draw_rectangle(rr2)'''


                if r:
                    print(r[2],r[3])
                    
                    img.draw_rectangle(r)
                    lcd.write(img)
            elif target_num==4 :
                for t in templates4:
                    template = image.Image(t)
                    r = img.find_template(template, 0.70, step=4, search=SEARCH_EX)
                if r:
                    print(44444)
                    img.draw_rectangle(r)
                    lcd.write(img)
            elif target_num==5 :
                for t in templates5:
                    template = image.Image(t)
                    r = img.find_template(template, 0.72, step=4, search=SEARCH_EX)
                if r:
                    img.draw_rectangle(r)
                    lcd.write(img)
            elif target_num==6 :
                for t in templates6:
                    template = image.Image(t)
                    r = img.find_template(template, 0.70, step=4, search=SEARCH_EX)
                if r:
                    img.draw_rectangle(r)
                    lcd.write(img)
            elif target_num==7 :
                for t in templates7:
                    template = image.Image(t)
                    r = img.find_template(template, 0.70, step=4, search=SEARCH_EX)
                if r:
                    img.draw_rectangle(r)
                    lcd.write(img)
            elif target_num==8 :
                for t in templates8:
                    template = image.Image(t)
                    r = img.find_template(template, 0.70, step=4, search=SEARCH_EX)
                if r:
                    img.draw_rectangle(r)
                    lcd.write(img)
            elif target_num==0:
                r=None

    # 发送转向指令
    #if target_num in found_nums:
        #turn_cmd = CMD_TURN_LEFT if found_nums.index(target_num)==0 else CMD_TURN_RIGHT
        if r is not None: # 只有匹配成功时才处理A
            print(r[0])
            if r[0] >= 65:
               turn_cmd = CMD_TURN_RIGHT #是右
            elif 0< r[0] <65:
                turn_cmd = CMD_TURN_LEFT #是左
        else:
            turn_cmd = CMD_LINE_DATA
    elif target_num==1:
        turn_cmd = CMD_TURN_LEFT#是左
    elif target_num==2:
        turn_cmd = CMD_TURN_RIGHT #是右
    uart.write(bytearray([HEADER, turn_cmd,0,FOOTER]))

    # 返回巡线模式
    global current_mode
    current_mode = MODE_LINE
    set_sensor_config(MODE_LINE)

# ------------------ 主程序初始化 ------------------
templates = load_templates()
set_sensor_config(MODE_LINE)
# ------------------ 主循环 ------------------
clock = time.clock()
while True:
        clock.tick()
        # 状态机处理
        if current_mode == MODE_INIT:
            target_num = initial_target_recognition()
            if target_num > 0:
                current_mode = MODE_LINE
                set_sensor_config(MODE_LINE)
            else:
                current_mode = MODE_INIT

        elif current_mode == MODE_LINE:
                line_follow_process()

        elif current_mode == MODE_NUM_RECOG:
                number_recognize()

        # 显示帧率1
        img = sensor.snapshot()
        img.draw_string(5,20, "FPS:%.1f" % clock.fps(), color=(255,0,0))

        # 显示当前模式
        mode_text = "LINE" if current_mode == MODE_LINE else "NUMBER"


        img.draw_string(250, 5, mode_text, color=(0,255,0))

        # 短延时
        pyb.delay(10)
        print(clock.fps()

      15_1751532674230_82.png 14_1751532674230_81.png 13_1751532674230_72.png 12_1751532674230_71.png 11_1751532674230_62.png 10_1751532674229_61.png 9_1751532674229_52.png 8_1751532674229_51.png 7_1751532674229_44.png 6_1751532674229_43.png 5_1751532674229_42.png 4_1751532674229_41.png 3_1751532674229_34.png 2_1751532674229_33.png 1_1751532674229_32.png 0_1751532674228_31.png
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    • 尽量更换位神经网络方法来识别数字，模板匹配的局限性太大，神经网络教程：
      ① https://singtown.com/cn/learn/1
      ② https://singtown.com/cn/learn/2

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