import sensor, image, time, os, tf, math, uos, gc
import json
from pyb import UART
from pyb import Pin, Timer
light = Timer(2, freq=50000).channel(1, Timer.PWM, pin=Pin("P6")) #灯光基础参数引脚，频率，pwm（不用改）
light.pulse_width_percent(10) #亮度
sensor.reset() #复位和初始化传感器
sensor.set_pixformat(sensor.RGB565) #设置像素格式为RGB565(或GRAYSCALE)
sensor.set_framesize(sensor.QVGA) #设置帧大小为QVGA (320x240)
sensor.set_windowing((320, 240)) #设置320x240窗口
sensor.skip_frames(time=2000) #让相机调整一下。
uart = UART(3, 115200) #串口3，波特率115200
net = None
labels = None
min_confidence = 0.5 #最小置信度（区分识别物与背景）！！！不知道别改
#以下为空定义，防止报错
mjzb=''
xzb=''
yzb=''
rect_area=0
center_x=0
center_y=0
blobs=0
str1="A雄"
str2="B雄"
str3="A雌"
str4="B雌"
left_roi = [0,0,320,240]
try:#加载模型，在堆上分配模型文件，如果加载后我们至少有64K的空闲空间
net = tf.load("trained.tflite", load_to_fb=uos.stat('trained.tflite')[6] > (gc.mem_free() - (64*1024)))
except Exception as e:
raise Exception('Failed to load "trained.tflite", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')
try:
labels = [line.rstrip('\n') for line in open("labels.txt")]
except Exception as e:
raise Exception('Failed to load "labels.txt", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')
yellow_thresholdA = (76, 99, -25, 83, 35, 111) #色块追踪颜色阈值，可根据环境光不同修改，参数在tools-machine vision-threshold editor
#yellow_thresholdB = (34,85,-48,77,-8,87)
yellow_thresholdC = (100, 61, -105, 127, 35, 88)
white_threshold = (28, 33, -89, 127, -128, -58) #识别方框的颜色阈值
pixel_size_mm = 0.1 / 25 #0.1mm\25像素点
colors = [
(255, 0, 0),#RED
( 0, 255, 0),#GREEN
(255, 255, 0),#YELLOW
( 0, 0, 255),#BLUE
(255, 0, 255),#PINK
( 0, 255, 255),#CYAN
(255, 255, 255),#WHITE
] #如果你同时检测到7种以上的类，添加更多的颜色。
clock = time.clock()
sj=9
def modified_data(data): #整型函数，将返回主控板的面积整型为四位数（根据手眼协调函数修改）
data = int(data)
str_data=''
if data < 10:
str_data = str_data + '000' + str(data)
elif data >= 10 and data < 100:
str_data = str_data + '00' + str(data)
elif data >=100 and data <1000:
str_data = str_data + '0' + str(data)
else:
str_data = str_data + str(data)
return str_data.encode('utf-8') #返回编码通用转换格式
while(True):
clock.tick()
img = sensor.snapshot()#.lens_corr(1.8)
for i, detection_list in enumerate(net.detect(img, thresholds=[(math.ceil(min_confidence * 255), 255)])):
if (i == 0): continue
if (len(detection_list) == 0): continue
#print(labels[i])
for d in detection_list:
[x, y, w, h] = d.rect()
center_x = math.floor(x + (w / 2))
center_y = math.floor(y + (h / 2))
xzb = modified_data(center_x)
yzb = modified_data(center_y)
img.draw_circle((center_x, center_y, 8), color=(0,0,0), thickness=2)
print('识别到%s' % labels[i])
if labels[i]== 'B雄' or labels[i]== 'A雄' :
print('这是雄花')
rect_area=0
break
elif labels[i]== 'A雌' or labels[i]== 'B雌' :
blobs = img.find_blobs([yellow_thresholdA,yellow_thresholdC])
if blobs:
largest_blob = max(blobs, key = lambda b: b.pixels())
rect = largest_blob.rect()
img.draw_rectangle(rect, color = (0,0,255))
white_region = img.crop(rect).find_blobs([white_threshold], pixels_threshold=100)
if white_region:
largest_white_region = max(white_region, key = lambda b: b.pixels())
rect_area = largest_white_region.pixels()
img.draw_rectangle(largest_white_region.rect(), color = (0, 0, 255))
mjzb = modified_data(rect_area)
print('*******')
uart.write('st')
uart.write(xzb)
uart.write(yzb)
uart.write(mjzb)
print('x==%d\ty==%d\tmj==%d' % (center_x, center_y, 10*rect_area))
print(xzb, yzb, mjzb)
print('这是%s\n' % labels[i])
time.sleep(0.15)
