自学习分类示例程序中关于保存及载入分类器的提示有误！

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在自学习分类示例程序最后，所提示的保存、载入语句为：

You can save trained data to file system by:

classifier.save("3_classes.classifier")

Then load :

model = kpu.load(0x300000)

classifier = kpu.classifier.load(model, "3_class.classifier")

保存是正确的，载入语句不对，在请教Sipeed工程师后，得知正确的应该是： classifier, class_num, sample_num = kpu.classifier.load(model, "3_class.classifier")

已经在示例程序中加上测试了，正确！ 我修改后的程序如下，供后续学习者参考（里面有些是为了理解程序增加的输出）：

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 import uos

############### config ################# class_num = 3 sample_num = 15 THRESHOLD = 11 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

print(uos.listdir("/")) print("files:", uos.listdir("/flash"))

sensor.reset() sensor.set_pixformat(sensor.RGB565) sensor.set_framesize(sensor.QVGA) sensor.set_windowing((224, 224)) sensor.set_vflip(True) #for MaixBit,20210118 sensor.set_hmirror(True) #for MaixBit if board_cube == 1: sensor.set_vflip(True) sensor.set_hmirror(True) lcd.init(type=2) lcd.rotation(2) else: lcd.init()

fm.register(board_info.BOOT_KEY, fm.fpioa.GPIOHS0) key = GPIO(GPIO.GPIOHS0, GPIO.PULL_UP)

try: del model except Exception: pass try: del classifier except Exception: pass gc.collect()

model = kpu.load(0x300000) try: classifier, class_num, sample_num = kpu.classifier.load(model, "3_classes.classifier") train_status = 1 except Exception as err: print("Exception:",err) classifier = kpu.classifier(model, class_num, sample_num) train_status = 0

print("Train:",train_status)

cap_num = 0

train_status = 0

last_cap_time = 0 last_btn_status = 1 while 1: img = sensor.snapshot() if board_cube: img = img.rotation_corr(z_rotation=90) img.pix_to_ai()

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()
        classifier.save("3_classes.classifier")
        print("train end")
        train_status = 1
else:
    res_index = -1
    try:
        res_index, min_dist = classifier.predict(img)
        print("{:.2f}".format(min_dist))
        print("index:",res_index)
    except Exception as e:
        print("predict err:", e)

    if res_index >= 0:
        if 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:
            if min_dist < (THRESHOLD+4) :
                print("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)
            else:
                img = draw_string(img, 2, 2, "unknown", 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(0x300000)

classifier, class_num, sample_num = kpu.classifier.load(model, "3_class.classifier")