def free():
mem = gc.mem_free()
b = gc.mem_free()
gc.collect()
a = gc.mem_free()
print("gc %d -> %d %d" % (b, a, len(feature_list)))
print(kpu.memtest())
def test_ai_camera():
@ui.warp_template(ui.blank_draw)
@ui.warp_template(ai_camera.ai_draw)
def app_main():
ui.display()
import time
last = time.ticks_ms()
while True:
kpu.memtest()
try:
print(time.ticks_ms() - last)
last = time.ticks_ms()
app_main()
except Exception as e:
# gc.collect()
print(e)
kpu.memtest()
def unit_test():
gc.collect()
kpu.memtest()
FaceReco.load()
kpu.memtest()
import time
last = time.ticks_ms()
i = 0
while i < 200:
i += 1
#print(i)
try:
#kpu.memtest()
gc.collect()
print(time.ticks_ms() - last)
last = time.ticks_ms()
app_main()
except Exception as e:
#
print(e)
FaceReco.free()
gc.collect()
sensor.set_vflip(1)
sensor.run(1)
sensor.skip_frames()
def get_image():
if obj.is_init == False:
obj.init()
obj.is_init = True
return sensor.snapshot()
if __name__ == "__main__":
import KPU as kpu
import gc
kpu.memtest()
lcd.init(freq=15000000)
print('ram total : ' + str(gc.mem_free() / 1024) + ' kb')
kpu.memtest()
clock = time.clock()
while (True):
clock.tick()
lcd.display(obj.get_image())
print(clock.fps())
print('ram total : ' + str(gc.mem_free() / 1024) + ' kb')
kpu.memtest()
print(e)
kpu.deinit(task)
'''
#path = "/flash/tack_picture.jpeg"
##img = sensor.snapshot()
##print("save image")
##img.save(path)
#print("read image")
#img = image.Image(path)
#lcd.display(img)
#print("ok")
'''
kpu.memtest()
task_fd = kpu.load(0x2C0000)
task_ld = kpu.load(0x580000)
task_fe = kpu.load(0x340000)
kpu.memtest()
clock = time.clock()
fm.register(23, fm.fpioa.GPIOHS0)
key_gpio = GPIO(GPIO.GPIOHS0, GPIO.IN)
start_processing = False
def set_key_state(*_):
global start_processing
start_processing = True
def setup(self):
print(kpu.memtest())
self._rec = None
self._record_count = 0
self._loop_counter = 0
self._last_100ms_cnt = 0
self._next_loop_cmd_ms = 0
self._last_active_ms = 0
self._lcd_brightness = None
self._charge_mode = None
self._timestamp = None
self._ramdisk_mount_point = "/ramdisk"
self._task = None
#self._mode = "rec"
self._mode = "auto"
self._flag_send_img_to_C = False
self._axp192 = pmu.axp192()
self._axp192.enableADCs(True)
self._axp192.enableCoulombCounter(False)
self.set_lcd_brightness(9)
fm.register(board_info.BUTTON_A, fm.fpioa.GPIO1)
self.button_a = GPIO(GPIO.GPIO1, GPIO.IN, GPIO.PULL_UP)
fm.register(board_info.BUTTON_B, fm.fpioa.GPIO2)
self.button_b = GPIO(GPIO.GPIO2, GPIO.IN, GPIO.PULL_UP)
fm.register(35, fm.fpioa.UART2_TX, force=True)
fm.register(34, fm.fpioa.UART2_RX, force=True)
baud = 115200 # 115200 1500000 3000000 4500000
self.uart = UART(UART.UART2,
baud,
8,
0,
0,
timeout=1000,
read_buf_len=4096)
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
#sensor.set_pixformat(sensor.GRAYSCALE)
#sensor.set_framesize(sensor.QVGA)
sensor.set_framesize(sensor.QQVGA)
#sensor.set_vflip(1)
#sensor.set_hmirror(1) # if set 1, storange color!!!
#sensor.set_windowing((224, 224))
sensor.run(1)
try:
stat = uos.stat(self._ramdisk_mount_point)
uos.umount(self._ramdisk_mount_point)
# print("mount_point=", mount_point, " stat=", stat)
except OSError as e:
pass
blkdev = RAMFlashDev()
vfs = uos.VfsSpiffs(blkdev)
vfs.mkfs(vfs)
uos.mount(vfs, self._ramdisk_mount_point)
lcd.init(freq=40000000)
lcd.direction(lcd.YX_RLDU)
lcd.clear(lcd.BLACK)
lcd.draw_string(10, 10, "BeetleC_AutoDrive_V", lcd.CYAN, lcd.BLACK)
if self._mode == "auto":
#self._twoWheelSteeringThrottle = TwoWheelSteeringThrottle()
print(kpu.memtest())
self._task = kpu.load("/sd/model.kmodel")
print(kpu.memtest())
lcd.clear() # Limpia la pantalla y la deja en negro
# Descripción y carga del modelo
labels = ['Acaro', 'Bueno',
'Manchado'] # Etiquetas de la ultima capa de la red
task = kpu.load(
'/sd/3clases.kmodel') # Acá va al ubicación del archivo .kmodel (CARGA)
kpu.set_outputs(task, 0, 1, 1,
3) # Aqúi van las dimensiones de la ultima capa de la red
while (True):
tick1 = utime.ticks_ms()
# Ejecucion del modelo en tiempo real
kpu.memtest() # Verifica la memoria disponible
img = sensor.snapshot() # Captura de la imagen
fmap = kpu.forward(task,
img) # Ejecuta la red neuronal con la imagen capturada
plist = fmap[:] # Extrae las probabilidades dentro de una lista
pmax = max(plist) # Escoge de las probabilidades la mayor
max_index = plist.index(
pmax) # Extrae el indice de la clase con la mayor probabilidad
tick2 = utime.ticks_ms()
etime = utime.ticks_diff(tick2, tick1)
# Impresion del resultado en la pantalla LCD (Etiqueta de la clase y probabilidad)
a = img.draw_string(0,
0,
import KPU as kpu
lcd.init()
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.set_windowing((128, 128))
sensor.set_vflip(1)
sensor.run(1)
lcd.clear()
f = open('labels.txt', 'r')
labels = f.readlines()
f.close()
print(kpu.memtest())
latency_result = None
fps_result = None
def inference(model_file):
task = kpu.load(model_file)
kpu.set_outputs(task, 0, 1, 1, 2)
clock = time.clock()
while (True):
img = sensor.snapshot()
clock.tick()
fmap = kpu.forward(task, img)
fps = clock.fps()
plist = fmap[:]
pmax = max(plist)