可以用翻转函数进行修改翻转
import KPU as kpu
import sensor
import lcd
from Maix import GPIO
from fpioa_manager import fm
from board import board_info
import time
import gc
from Maix import GPIO
from Maix import FPIOA
from fpioa_manager import fm
from board import board_info
############### config #################
class_num = 3 #分类
sample_num = 15 #训练次数
THRESHOLD = 11 #信任值
class_names = [‘class1’, ‘class2’, ‘class3’]
board_cube = 0
########################################
def draw_string(img, x, y, text, color, scale, bg=None ):
if bg:
img.draw_rectangle(x-2,y-2, len(text)8scale+4 , 16*scale, fill=True, color=bg)
img = img.draw_string(x, y, text, color=color,scale=scale)
return img
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.set_windowing((224, 224))
if board_cube == 1:
sensor.set_vflip(True)
sensor.set_hmirror(True)
lcd.init(type=2)
lcd.rotation(2)
else:
lcd.init()
fpioa = FPIOA()
fpioa = FPIOA()
'''
fpioa.set_function(board_info.BOOT_KEY, fm.fpioa.GPIOHS16)
pin = fpioa.get_Pin_num(fm.fpioa.GPIOHS0)
'''
#fm.register(board_info.BOOT_KEY, fm.fpioa.GPIO16, force=True)
#fm.register(board_info.BOOT_KEY, fm.fpioa.GPIOHS16)
#key = GPIO(GPIO.GPIO16, GPIO.IN)
key = GPIO(GPIO.GPIOHS0, GPIO.PULL_UP)
try:
del model
except Exception:
pass
try:
del classifier
except Exception:
pass
gc.collect()
model = kpu.load(0×300000)
classifier = kpu.classifier(model, class_num, sample_num)
cap_num = 0
train_status = 0
last_cap_time = 0
last_btn_status = 1
while 1:
img = sensor.snapshot()#从摄像头取一帧图像数据，返回值是一张图像的对象
img.rotation_corr(x_rotation=180,y_rotation=180)#x_rotation=10，#修改翻转
#img.rotation_corr()#x_rotation=10，
if board_cube:
img = img.rotation_corr(x_rotation=0)#img.rotation_corr旋转
img.rotation_corr(y_rotation=180)#x_rotation=10，
#img=img.rotation_corr([x_rotation=0.0[, y_rotation=0.0[, z_rotation=0.0[, x_translation=0.0[, y_translation=0.0[, zoom=1.0]]]]]])
img.pix_to_ai()
'''
需要执行img.pix_to_ai()来将RGB565的图像手动更新到RGB888的区域，然后才可以调用kpu相关的函数进行模型推理
'''
# capture img
if train_status == 0:
if key.value() == 0:
time.sleep_ms(30)
if key.value() == 0 and (last_btn_status == 1) and (time.ticks_ms() - last_cap_time > 500):
last_btn_status = 0
last_cap_time = time.ticks_ms()
if cap_num < class_num:
index = classifier.add_class_img(img)
cap_num += 1
print("add class img:", index)
elif cap_num < class_num + sample_num:
index = classifier.add_sample_img(img)
cap_num += 1
print("add sample img:", index)
else:
img = draw_string(img, 2, 200, "release boot key please", color=lcd.WHITE,scale=1, bg=lcd.RED)
else:
time.sleep_ms(30)
if key.value() == 1 and (last_btn_status == 0):
last_btn_status = 1
if cap_num < class_num:
img = draw_string(img, 0, 200, "press boot key to cap "+class_names[cap_num], color=lcd.WHITE,scale=1, bg=lcd.RED)
elif cap_num < class_num + sample_num:
img = draw_string(img, 0, 200, "boot key to cap sample{}".format(cap_num-class_num), color=lcd.WHITE,scale=1, bg=lcd.RED)
# train and predict
if train_status == 0:
if cap_num >= class_num + sample_num:
print("start train")
img = draw_string(img, 30, 100, "training...", color=lcd.WHITE,scale=2, bg=lcd.RED)
lcd.display(img)
classifier.train()
print("train end")
train_status = 1
else:
res_index = -1
try:
res_index, min_dist = classifier.predict(img)
print("{:.2f}".format(min_dist))
except Exception as e:
print("predict err:", e)
if res_index >= 0 and min_dist < THRESHOLD :
print("predict result:", class_names[res_index])
img = draw_string(img, 2, 2, class_names[res_index], color=lcd.WHITE,scale=2, bg=lcd.RED)
else:
print("unknown, maybe:", class_names[res_index])
img = draw_string(img, 2, 2, 'maybe {}'.format(class_names[res_index]), color=lcd.WHITE,scale=2, bg=lcd.RED)
lcd.display(img)
You can save trained data to file system by:
classifier.save(“3_classes.classifier”)
Then load :
model = kpu.load(0×300000)
classifier = kpu.classifier.load(model, “3_class.classifier”)
注意最小固件不支持
