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()
