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k210中端6.0.py
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import sensor, image, lcd, time, math
import KPU as kpu
import gc, sys
from machine import UART, Timer
from fpioa_manager import fm
import utime as t
red_thresholds = (29, 97, 14, 127, -128, 127)
input_size = (224, 224)
labels = ["9", "1", "4", "2", "3", "8", "5", "6", "7"]
anchors = [0.4192, 0.3702, 0.5744, 1.6689, 0.6932, 0.6054, 1.0054, 0.9615, 2.1672, 1.6683]
num = 0
condi_flag = ""
begin_flag = 1
red_flag = 0
fm.register(18, fm.fpioa.UART1_RX, force=True)
fm.register(19, fm.fpioa.UART1_TX, force=True)
uart = UART(UART.UART1, 115200, 8, 1, 0, timeout=1000, read_buf_len=4096)
frame_head = '#'
frame_tail = ';'
task_flag = ''
condi_flag = ""#位置标志位
#出现最多的两个数字
num1=0
num2=0
roi_1 = (0,0,300,200)
task_flag = 'a'
#拼接'#0l4;'
send_num_flag1 =None
send_num_flag2 =None
#拼接'#0l4;'
send_num_flag = None
delay_num = 1
list = []
lcd.init(freq=15000000)
#找两个数字
def find_most_two_num(list):
key_list = []
value_list = []
count_dict = {}
for item in list:#遍历列表
if item in count_dict:#如果元素已经存在于字典中，则增加计数
count_dict[item] += 1#增加计数
else:#如果元素不存在于字典中，则初始化计数为1
count_dict[item] = 1#初始化计数为1’
new_count_dict = {k: v for k, v in sorted(count_dict.items(), key=lambda item: item[1], reverse=True)}
for k,v in new_count_dict.items():
key_list.append(k)
# value_list.append(v)
count_dict.clear()
return key_list[0],key_list[1]
######找出列表中出现次数最多的元素
def find_most_common(list):
count_dict = {}
for item in list:######遍历列表
if item in count_dict:######如果元素已经存在于字典中，则增加计数
count_dict[item] += 1######增加计数
else:######如果元素不存在于字典中，则初始化计数为1
count_dict[item] = 1######初始化计数为1
max_count = max(count_dict.values())######找到最大计数
most_frequent_values = [k for k, v in count_dict.items() if v == max_count]######找到所有最大计数的元素
if len(most_frequent_values) == 1:
return most_frequent_values[0]
else:
list.clear() ###### 出现最大元素个数相同，则清空传入的列表
return None
#获取数字识别结果 (这里为什么不用)
def find_most_commom_value(img,objects,num):
if objects:
list = []
for obj in objects:
pos = obj.rect()
img.draw_rectangle(pos)
img.draw_string(pos[0], pos[1], "%s : %.2f" %(labels[obj.classid()], obj.value()), scale=2, color=(255, 0, 0))
print("%s : %.2f" %(labels[obj.classid()], obj.value()))
list.append(labels[obj.classid()])#添加预测结果到列表
num += 1
print(num)
if num == 10:
print("num ==10")
num = 0
most_commom_value = find_most_common(list)
#位置检测
def find_num_condi(center_x,center_y,red_x):
if center_x > red_x:
return "#0r"
if center_x < red_x:
return "#0l"
#def find_max(orange_blobs): #寻找最大色块函数
# max_size=0
# for blob in orange_blobs:
# if blob[2]*blob[3] > max_size:
# max_blob=blob
# max_size = blob[2]*blob[3] #max_blob[2] 和 max_blob[3] 分别代表识别到的目标橙色区域的宽度和高度
# return max_blob
def find_red_max_blob(img,red_threshold): #寻找红色色块 返回其外接矩形框坐标
blobs = img.find_blobs([red_threshold])
if blobs:
max_blob = max(blobs, key=lambda b: b.pixels()) # 找到最大的色块
img.draw_rectangle(max_blob.rect(), color=(255, 0, 0)) # 在图像上画出最大色块的矩形
print("最大色块位置：", max_blob.cx(), max_blob.cy()) # 输出最大色块中心坐标到串口
return max_blob.cx()
# 如果需要通过串口发送数据，请取消下一行注释
# uart.write('X: {} Y: {}\n'.format(max_blob.cx(), max_blob.cy()).encode())
#task_flag == a
def main(anchors = None,labels = None, model_addr="/sd/demo.kmodel", sensor_window=input_size, lcd_rotation=0, sensor_hmirror=False, sensor_vflip=False):
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA) #320×240
#sensor.set_windowing(sensor_window)
sensor.set_hmirror(sensor_hmirror)
sensor.set_vflip(sensor_vflip)
sensor.set_hmirror(True)
sensor.set_vflip(True)
sensor.run(1)
sensor.skip_frames(n=10)
global num,begin_flag,task_flag,red_thresholds,red_flag,condi_flag,num1,num2,send_num_flag1,send_num_flag2,send_num_flag,list,delay_num
task = None
task = kpu.load(model_addr)
while(True):
img = sensor.snapshot()
img.copy(roi = (48,16,224,224),copy_to_fb = img)
img.pix_to_ai()
data = uart.read()
if data:
data_decoded = data.decode('utf-8')
if data_decoded[0] == frame_head and data_decoded[2] == frame_tail:
task_flag = data_decoded[1]
if task_flag == 'a':
num = 0
print(task_flag)
#task1
if task_flag == 'a':
kpu.init_yolo2(task, 0.5, 0.3, 5, anchors) # threshold:[0,1], nms_value: [0, 1]
img.draw_string(0, 0, "task1", color=(255, 0, 0), scale=2)
#print("正在执行task1")
objects = kpu.run_yolo2(task, img)
if objects:
for obj in objects:
#red_flag =find_red_blob(img,red_threshold) # 识别红色
pos = obj.rect()
img.draw_rectangle(pos)
img.draw_string(pos[0], pos[1], "%s : %.2f" %(labels[obj.classid()], obj.value()), scale=2, color=(255, 255, 255))
print("%s : %.2f" %(labels[obj.classid()], obj.value()))
list.append(labels[obj.classid()])#添加预测结果到列表
num +=1
print(num)
img.draw_string(0, 100, "num:%d"%(num) , scale=2, color=(255, 255, 255))
if num == 10:
print("num == 10")
print("list:",list)
img.draw_string(0, 100, "num:%d"%(num) , scale=2, color=(255, 255, 255))
num = 0
most_commom_value = find_most_common(list)
list.clear()
if most_commom_value != None:#判断是否返回最大值
if begin_flag == 1:
begin_flag = begin_flag + 1
uart.write("#0b"+str(most_commom_value)+';')
print("#0b")
print("begin")
else:
uart.write("#0c"+str(most_commom_value)+';')
img.draw_string(0, 200, "finish" , scale=2, color=(255, 255, 255))
print("most_commom_value:",most_commom_value)
print("#0c"+str(most_commom_value))
else:
print("no most_commom_value")
img.draw_string(0, 150, "none_finish" , scale=2, color=(255, 255, 255))
#K210找十字路口(已调)
red_blobs_crossroad = img.find_blobs([(0, 100, 12, 127, -124, 117)],roi=(0,80,500,30),x_stride=20, y_stride=20, pixels_threshold=3000)
if not red_blobs_crossroad:
print("none_crossroad;")
else:
for blob in red_blobs_crossroad:
img.draw_rectangle(blob.rect())
img.draw_cross(blob.cx(), blob.cy())
uart.write('#b;')#k210识别到十字路口
img.draw_string(0, 0, "crossroad", color=(255, 0, 0), scale=2)
#print("#0c;")
#task2
elif task_flag == 'b':
if delay_num == 1:
t.sleep_ms(500)
delay_num = 0
kpu.init_yolo2(task, 0.4, 0.4, 5, anchors) # threshold:[0,1], nms_value: [0, 1]
img.draw_string(0, 0, "task2", color=(255, 0, 0), scale=2)
#print("正在执行task2")
objects = kpu.run_yolo2(task, img)
if objects:
for obj in objects:
red_x = find_red_max_blob(img,red_thresholds) #识别最大红色色块 (是否需要roi)
pos = obj.rect()
img.draw_rectangle(pos)
img.draw_string(pos[0], pos[1], "%s : %.2f" %(labels[obj.classid()], obj.value()), scale=2, color=(255, 255, 255))
center_x = pos[0] + pos[2] / 2
center_y = pos[1] + pos[3] / 2
if labels[obj.classid()] == 1 or labels[obj.classid()] == 2:
continue
print("%s : %.2f" %(labels[obj.classid()], obj.value()))
list.append(labels[obj.classid()])#添加预测结果到列表
num +=1
print(num)
img.draw_string(0, 100, "num:%d"%(num) , scale=2, color=(255, 255, 255))
#识别
if num == 20:
print("num == 20")
print(list)
img.draw_string(0, 100, "num:%d"%(num) , scale=2, color=(255, 255, 255))
num = 0
num1,num2 = find_most_two_num(list)
list.clear()
print("num1:%d,num2:%d")
#拼接数字
if labels[obj.classid()] == num1:
condi_flag = find_num_condi(center_x,center_y,red_x) #如：#0l
send_num_flag1 = str(condi_flag)+str(num1) # 如#0l3
print("send_num_flag1:%s"%send_num_flag1)
if labels[obj.classid()] == num2:
condi_flag = find_num_condi(center_x,center_y,red_x)
send_num_flag2 = str(condi_flag)+str(num2)#如#0r4
print("send_num_flag2:%s"%send_num_flag2)
if send_num_flag1 is not None and send_num_flag2 is not None:
send_num_flag = send_num_flag1+send_num_flag2
send_num_flag = send_num_flag[:4] + send_num_flag[6:] + ";"#如 #0l3r4;
uart.write(send_num_flag)
print("send_num_flag:%s"%send_num_flag)
lcd.display(img)
if __name__ == "__main__":
# main(anchors = anchors, labels=labels, model_addr=0x300000, lcd_rotation=0)
main(anchors = anchors, labels=labels, model_addr="/sd/demo.kmodel")