def measure_fps(model_file):
task = kpu.load(model_file)
kpu.set_outputs(task, 0, 1, 1, 2)
clock = time.clock()
fps_ = []
for i in range(20):
img = sensor.snapshot()
clock.tick()
fmap = kpu.forward(task, img)
lcd.display(img, oft=(0, 0))
fps_.append(clock.fps())
average_fps = sum(fps_) / len(fps_)
print(average_fps)
global fps_result
fps_result = average_fps
_ = kpu.deinit(task)
def __init__(self, detector):
self.object_detected = None
self.FaceDetector = detector
self.landmark_task = kpu.load(0x6BD000)
a = kpu.set_outputs(self.landmark_task, 0, 1, 1, 10)
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)
max_index = plist.index(pmax)
a = lcd.display(img, oft=(0, 0))
lcd.draw_string(
0, 128, "%.2f:%s " %
(pmax, labels[max_index].strip()))
_ = kpu.deinit(task)
def measure_latency(model_file):
task = kpu.load(model_file)
kpu.set_outputs(task, 0, 1, 1, 2)
clock = time.clock()
latency_ = []
for i in range(20):
img = sensor.snapshot()
clock.tick()
t1 = time.ticks_us()
fmap = kpu.forward(task, img)
t2 = time.ticks_diff(time.ticks_us(), t1) / 1000
lcd.display(img, oft=(0, 0))
latency_.append(t2)
average_latency = sum(latency_) / len(latency_)
print(average_latency)
global latency_result
latency_result = average_latency
_ = kpu.deinit(task)
def __init__(self, FileName, Label, bool=False):
self.row = global_value.row
global_value.row = global_value.row + 1
self.percent = 0
self.image_class = 0
self.file_name = FileName
self.labels = Label
if bool:
self.image_objects_task = kpu.load(self.file_name)
a = kpu.set_outputs(self.image_objects_task, 0, 1, 1,
len(self.labels))
else:
pass
def init_kpu(threshold=0.3):
classes = ["person"]
task = kpu.load(
0x300000
) #change to "/sd/name_of_the_model_file.kmodel" if loading from SD card
a = kpu.set_outputs(
task, 0, 7, 7, 30
) #the actual shape needs to match the last layer shape of your model(before Reshape)
anchor = (0.57273, 0.677385, 1.87446, 2.06253, 3.33843, 5.47434, 7.88282,
3.52778, 9.77052, 9.16828)
a = kpu.init_yolo2(
task, threshold, 0.3, 5, anchor
) #tweak the second parameter if you're getting too many false positives
return task
def __init__(self):
self.object_detected = None
self.percent = 0
self.classes = ['face']
self.anchor = (1.889, 2.5245, 2.9465, 3.94056, 3.99987, 5.3658,
5.155437, 6.92275, 6.718375, 9.01025)
self.x_center = 0
self.y_center = 0
self.object_detection_task = kpu.load(0x659000)
a = kpu.set_outputs(self.object_detection_task, 0, 7, 7, 30)
a = kpu.init_yolo2(self.object_detection_task, 0.1, 0.1, 5,
self.anchor)
def __init__(self, FileName, Classes, Anchor, bool=False):
self.row = global_value.row
global_value.row = global_value.row + 1
self.object_detected = None
self.percent = 0
self.file_name = FileName
self.classes = Classes
self.anchor = Anchor
self.x_center = 0
self.y_center = 0
if bool:
self.object_detected_task = kpu.load(self.file_name)
a = kpu.set_outputs(self.object_detected_task, 0, 7, 7,
5 * (5 + len(self.classes)))
a = kpu.init_yolo2(self.object_detected_task, 0.1, 0.3, 5,
self.anchor)
else:
pass
def __init__(self, lm_detector):
self.id = -1
self.max_score = 0
self.threshold = 85
self.LandmarkDetector = lm_detector
self.db = _read_db()
offset_x = 0
offset_y = -15
self.dst_point = [(44 + offset_x, 59 + offset_y),
(84 + offset_x, 59 + offset_y),
(64 + offset_x, 82 + offset_y), (47 + offset_x, 105),
(81 + offset_x, 105)]
self.img_face = image.Image(size=(128, 128))
a = self.img_face.pix_to_ai()
self.fe_task = kpu.load(0x708000)
a = kpu.set_outputs(self.fe_task, 0, 1, 1, 128)
import sensor, image, lcd, time
import KPU as kpu
lcd.init()
lcd.rotation(2)
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.set_vflip(1)
sensor.run(1)
clock = time.clock()
classes = ['u', 'us']
task = kpu.load(0x500000)
kpu.set_outputs(task, 0, 35, 7, 7)
anchor = (1.08, 1.19, 3.42, 4.41, 6.63, 11.38, 9.42, 5.11, 16.62, 10.52)
a = kpu.init_yolo2(task, 0.5, 0.3, 5, anchor)
while (True):
clock.tick()
img = sensor.snapshot()
objects = kpu.run_yolo2(task, img)
print(clock.fps())
if objects:
for obj in objects:
img.draw_rectangle(obj.rect(), color=(0, 255, 0), thickness=3)
img.draw_string(obj.x(),
obj.y(),
classes[obj.classid()],
color=(0, 255, 0),
scale=2)
6.718375, 9.01025)
a = kpu.init_yolo2(task, 0.5, 0.3, 5, anchor)
while (True):
img = sensor.snapshot()
code = kpu.run_yolo2(task, img)
if code:
for i in code:
xx = i.x() - 10
yy = i.y() - 10
ww = i.w() + 15
hh = i.h() + 10
face_cut = img.cut(xx, yy, ww, hh)
face_cut = face_cut.resize(128, 128)
a = face_cut.pix_to_ai()
a = kpu.set_outputs(tid, 0, 1, 1, 2)
fmap = kpu.forward(tid, face_cut)
plist = fmap[:]
pmax = max(plist)
max_index = plist.index(pmax)
print(plist)
if plist[0] >= 0.94:
a = img.draw_rectangle(xx,
yy,
ww,
hh,
color=(255, 0, 0),
thickness=4)
elif plist[1] >= 0.98:
a = img.draw_rectangle(xx,
#setup LCD screen
lcd.init()
lcd.rotation(2)
#read label file
f=open("labels.txt","r")
labels=f.readlines()
f.close()
#setup CNN
task = kpu.load(0x200000)
#Kmodel V4 need set output shape manually
#set_outputs(int idx, int w, int h, int ch)
kpu.set_outputs(task,0,6,1,1)
timep = 0
while(True):
fps = 1000/(time.ticks_ms() - timep)
timep = time.ticks_ms()
img = sensor.snapshot()
img = img.resize(224,224)
a = img.pix_to_ai()
fmap = kpu.forward(task,img)
plist = fmap[:]
pmax = max(plist)
max_index = plist.index(pmax)
if (current_time - btn_time) >= 500:
btn_time = time.ticks()
fm.register(8, fm.fpioa.GPIOHS0)
btn = GPIO(GPIO.GPIOHS0, GPIO.IN)
lcd.init()
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.run(1)
task = kpu.load(
0x500000) # you need put model(face.kfpkg) in flash at address 0x500000
# task = kpu.load("/sd/face.kmodel")
a = kpu.set_outputs(task, 0, 20, 15, 30)
anchor = (1.889, 2.5245, 2.9465, 3.94056, 3.99987, 5.3658, 5.155437, 6.92275,
6.718375, 9.01025)
a = kpu.init_yolo2(task, 0.5, 0.3, 5, anchor)
while (True):
btn.irq(btn_function, GPIO.IRQ_RISING)
img = sensor.snapshot()
code = kpu.run_yolo2(task, img)
if code:
for i in code:
print(i)
a = img.draw_rectangle(i.rect())
pos(i.x() + i.w() / 2, i.y() + i.h() / 2)
a = lcd.display(img)
a = kpu.deinit(task)
def append_dataset(file_name,data):
face_dataset.append(data)
f = open(file_name,"a")
str_list_target = ["{:0.4f}".format(x) for x in data]
str_target = ','.join(str_list_target)
f.write(str_target)
f.close()
print("save to dataset success")
def send_sheet(face_id):
return
#=== AI Models ===#
task_face_detect = kpu.load(0x200000)
task_face_encode = kpu.load(0x300000)
kpu.set_outputs(task_face_encode,0,1,1,128)
anchor = (1.889, 2.5245, 2.9465, 3.94056, 3.99987, 5.3658, 5.155437, 6.92275, 6.718375, 9.01025)
kpu.init_yolo2(task_face_detect, 0.5, 0.3, 5, anchor)
#====== config ======#
face_threshold = 15
dataset_filename = "faces.csv"
#====================#
#=== SETUP ===#
#clear_dataset(dataset_filename,face_dataset)
face_dataset = read_dataset(dataset_filename);
corgi85.IFTTT_init("corgi_detect","0hI55mQkUiimG6RIjpWhp")
#=== wait wifi connect ===#
while corgi85.wifi_check() == 0:
import uos
import time
lcd.init()
lcd.rotation(2)
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.set_windowing((224, 224))
#sensor.set_vflip(1)
sensor.run(1)
classes = ["sakura"]
print(uos.listdir("/sd/") )
task = kpu.load("/sd/sakura_model_v01.kmodel")
kpu.set_outputs(task, 0,7,7,30)#Reshape層の内容に応じて中身を変える必要がある #the actual shape needs to match the last layer shape of your model(before Reshape)
anchor = (0.57273, 0.677385, 1.87446, 2.06253, 3.33843, 5.47434, 7.88282, 3.52778, 9.77052, 9.16828)
kpu.init_yolo2(task, 0.3, 0.3, 5, anchor)
#kpu.init_yolo2(task, 0.8, 0.9, 5, anchor)
print("start")
code=""
while(True):
img = sensor.snapshot()#.rotation_corr(z_rotation=90.0)
#a = img.pix_to_ai()
code = kpu.run_yolo2(task, img)
if code:
for i in code:
a=img.draw_rectangle(i.rect(),color = (0, 255, 0))
a = img.draw_string(i.x(),i.y(), classes[i.classid()], color=(255,0,0), scale=3)
a = lcd.display(img)
sensor.run(1)
# Init KPU
print("init KPU")
lcd.draw_string(10, 5, "init kpu")
lcd.draw_string(170, 5, "Running")
lcd.draw_string(10, 20, "load kmodel")
kpu.memtest()
task = kpu.load(0x600000)
lcd.draw_string(170, 20, "Done")
lcd.draw_string(10, 35, "set outputs")
lcd.draw_string(10, 50, "steer")
load_state = kpu.set_outputs(task, 0, 1, 1, 20)
lcd.draw_string(150, 50, ("Done" if load_state is True else "None"))
lcd.draw_string(10, 65, "throttle")
load_state = kpu.set_outputs(task, 1, 1, 1, 16)
lcd.draw_string(150, 65, ("Done" if load_state is True else "None"))
kpu.memtest()
lcd.draw_string(170, 35, "Done")
print("Done")
time.sleep_ms(1000)
lcd.draw_string(170, 5, "Done ")
time.sleep_ms(500)
lcd.draw_string(60, 119, "Setup Done! :)")
import sensor, image, lcd, time
import KPU as kpu
sensor.reset(freq=24000000, dual_buff=True)
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.set_windowing((320, 224))
sensor.set_vflip(1)
sensor.run(1)
lcd.init(type=2, freq=20000000, color=lcd.BLACK)
#lcd.rotation(2)
classes = ["license_plate"]
task = kpu.load(0x400000)
a = kpu.set_outputs(task, 0, 10, 7, 30)
anchor = (1.81, 0.85, 2.26, 1.07, 3.00, 1.46, 4.56, 1.95, 7.38, 3.45)
a = kpu.init_yolo2(task, 0.3, 0.3, 5, anchor)
while (True):
timestamp = time.ticks_ms()
img = sensor.snapshot()
a = img.pix_to_ai()
plates = kpu.run_yolo2(task, img)
if plates:
plate = plates[0]
a = img.draw_rectangle(plate.rect(), color=(0, 255, 0), thickness=5)
a = img.draw_string(70,
10,
"FPS : %.2f" % (1000 / (time.ticks_ms() - timestamp)),
color=(0, 255, 0),
scale=2)
a = lcd.display(img)
a = kpu.deinit(task)
import sensor, image, time, lcd
import KPU as kpu
lcd.init(freq=15000000)
sensor.reset() # Reset and initialize the sensor. It will
# run automatically, call sensor.run(0) to stop
sensor.set_pixformat(sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (320x240)
sensor.set_windowing((224, 224))
sensor.skip_frames(time = 2000) # Wait for settings take effect.
sensor.set_vflip(1)
sensor.set_hmirror(1)
clock = time.clock() # Create a clock object to track the FPS.
task = kpu.load(0x300000) # mnist
a=kpu.set_outputs(task, 0, 1, 1, 2)
while(True):
clock.tick() # Update the FPS clock.
img = sensor.snapshot() # Take a picture and return the image.
#img.invert() # Use with numbers in paper
img_3 = img.resize(128, 128)
a = img_3.pix_to_ai()
fmap = kpu.forward(task, img_3)
plist = fmap[:]
pmax = max(plist)
max_index = plist.index(pmax)
#img.draw_string(5, 5, ('%s' % (max_index)), color = (255, 255, 255), scale = 2)
lcd.display(img) # Display on LCD
#print("%d: %.3f" % (max_index, pmax))
print(plist)
import sensor, image, lcd, time
import KPU as kpu
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.set_windowing((224, 224))
sensor.set_vflip(1)
sensor.run(1)
lcd.init(type=2, freq=20000000, color=lcd.BLACK)
lcd.rotation(2)
clock = time.clock()
classes = ['face', 'eye']
task = kpu.load(0x400000)
a = kpu.set_outputs(task, 0, 7, 7, 35)
anchor = (0.57273, 0.677385, 1.87446, 2.06253, 3.33843, 5.47434, 7.88282,
3.52778, 9.77052, 9.16828)
a = kpu.init_yolo2(task, 0.5, 0.3, 5, anchor)
while (True):
img = sensor.snapshot()
#a= img.resize(224,224)
a = img.pix_to_ai()
code = kpu.run_yolo2(task, img)
if code:
for i in code:
a = img.draw_rectangle(i.rect(), color=(0, 255, 0), thickness=4)
a = lcd.display(img)
a = kpu.deinit(task)
sensor.set_framesize(
sensor.QVGA) # Establece la captura de imagen como QVGA (320x240)
sensor.set_windowing(
(224, 224)) # Establece el tamaño de imagen con el que se entreno la red
sensor.set_vflip(1) # Rotación vertical de la imagen
sensor.set_saturation(-3) # Saturacion
sensor.set_brightness(-3) # brightness
sensor.set_contrast(-3) # contrast
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
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.set_windowing((224, 224))
sensor.set_vflip(1)
lcd.clear()
labels = [
'arduino_uno', 'santa_claus'
] #number of labels should match the number of labels the model was trained with
task = kpu.load(
0x200000
) #change to "/sd/name_of_the_model_file.kmodel" if loading from SD card
kpu.set_outputs(
task, 0, 1, 1,
2) #the actual shape needs to match the last layer shape of your model
while (True):
kpu.memtest()
img = sensor.snapshot()
#img = img.rotation_corr(z_rotation=90.0) uncomment if need rotation correction - only present in full maixpy firmware
a = img.pix_to_ai()
fmap = kpu.forward(task, img)
plist = fmap[:]
pmax = max(plist)
max_index = plist.index(pmax)
a = img.draw_string(0,
0,
str(labels[max_index].strip()),
color=(255, 0, 0),
tinyYolo = "/sd/models/tinyYoloMerged.kmodel" #16 fps
mbnet75 = "/sd/models/mbnet75Merged.kmodel" #12 fps
mbnet50 = "/sd/models/mbnet50Merged.kmodel" #15fps
mbnet25 = "/sd/models/mbnet25Merged.kmodel" #16fps
task = kpu.load(mbnet50)
try:
kpu.netinfo(task)
except:
print("cannot get net info, kmodel v4 not supported for netinfo!")
lastLayerShape = (len(classes) + 5) * 5
#from documentation Kmodel V4 need set output shape manually
a = kpu.set_outputs(task, 0, 7, 7, lastLayerShape)
anchor = (0.57273, 0.677385, 1.87446, 2.06253, 3.33843, 5.47434, 7.88282,
3.52778, 9.77052, 9.16828) #yolo v2 tiny
def doOverlap(boxDict, box1Dict):
box = boxDict['rect']
box1 = box1Dict['rect']
print("checking overlapping boxes:", box, " ", box1)
box_x = box[0]
box_y = box[1]
box_x2 = box[0] + box[2]
box_y2 = box[1] + box[3]
box1_x = box1[0]
box1_y = box1[1]
Scale = 2
sensor.set_windowing((window_height, window_width))
sensor.skip_frames(100)
sensor.run(1)
print("init kpu")
lcd.draw_string(10, 10, "init kpu")
lcd.draw_string(170, 10, "Running")
lcd.draw_string(10, 30, "load kmodel")
kpu.memtest()
task = kpu.load(0x500000)
lcd.draw_string(170, 30, "Done")
lcd.draw_string(10, 50, "set outputs")
fmap = kpu.set_outputs(task, 0, window_height, window_width, classes)
kpu.memtest()
lcd.draw_string(170, 50, "Done")
print("Done")
time.sleep_ms(1000)
lcd.draw_string(170, 10, "Done ")
time.sleep_ms(500)
lcd.draw_string(60, 70, "Setup Done! :)")
clock = time.clock()
seg_img = image.Image()
output_array = np.array([0, 0, 0, 0, 0])
color_dict = {0: (0, 0, 0), # 0 in VOC2012 BG,VOid
1: (192, 0, 0), # 9 in VOC2012 CHAIR, SOFA
2: (64, 128, 0), # 11 in VOC2012 TABLE
sensor.set_pixformat(
sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (320x240)
#sensor.set_windowing((320, 240))
sensor.skip_frames(time=2000) # Wait for settings take effect.
sensor.set_vflip(1)
sensor.set_hmirror(1)
clock = time.clock() # Create a clock object to track the FPS.
print('loading face detect model')
task_detect_face = kpu.load(0x300000) # Charge face detect model into KPU
print('loading face expresion classify model')
task_classify_face = kpu.load(
0x500000) # Charge face classification model into KPU
a = kpu.set_outputs(task_classify_face, 0, 1, 1, 2)
anchor = (1.889, 2.5245, 2.9465, 3.94056, 3.99987, 5.3658, 5.155437, 6.92275,
6.718375, 9.01025)
a = kpu.init_yolo2(task_detect_face, 0.5, 0.3, 5, anchor)
labels = ['happy', 'sad'] # Facial expression labels
print('configuration complete')
while (True):
clock.tick() # Update the FPS clock.
img = sensor.snapshot() # Take a picture and return the image.
detected_face = kpu.run_yolo2(task_detect_face, img)
if detected_face:
import KPU as kpu task = kpu.load(0x400000) kpu.set_outputs(task, 0, 1,3,1) info = kpu.netinfo(task) layerbottom = info[-1] print(info, layerbottom)
return data_packet
#classes = ["smallfish","mebaru","azi","hugu","ishidai","kasago","bera","kawahagi","tai","hamati","kurodai","kisu","gure","sayori","suzuki"]
classes = ["sakura"]
print(uos.listdir("/sd/"))
# KPU setting
#kpu.deinit(task)
try:
task = kpu.load(modelpath)
except:
kpu.deinit(task)
task = kpu.load(modelpath)
#kpu.set_outputs(task, 0,7,7,100)#Reshape層の内容に応じて中身を変える必要がある #the actual shape needs to match the last layer shape of your model(before Reshape)
kpu.set_outputs(task, 0, 7, 7, 30)
anchor = (0.57273, 0.677385, 1.87446, 2.06253, 3.33843, 5.47434, 7.88282,
3.52778, 9.77052, 9.16828)
kpu.init_yolo2(task, 0.3, 0.3, 5, anchor)
pic_filepath = "/sd/sakura_pic"
try:
uos.mkdir(pic_filepath)
except:
print(uos.listdir(pic_filepath))
cnt = 0
while (True):
try:
img = sensor.snapshot()
code = kpu.run_yolo2(task, img)
i2c.scan() # INITIALIZE CAMERA sensor.reset() sensor.set_pixformat(sensor.RGB565) sensor.set_framesize(sensor.QVGA) sensor.run(1) # INITIALIZE LCD lcd.init() lcd.rotation(2) lcd.clear() # INITIALIZE MODEL task = kpu.load(0x200000) kpu.set_outputs(task, 0, 1, 1, 5) # INITIALIZE FILE HANDLER # create file and include column headers if file doesn't exist if FILE_NAME not in os.listdir(): with open(FILE_NAME, "w") as fout: column_headers = "name|contact_number|email|address|date_time|temperature|classification|confidence_level|cough_others|fever_others|headache_others|difficulty_breathing_others|cough|fever|headache|difficulty_breathing\n" fout.write(column_headers) # create image directory if it doesn't exist if IMAGE_DIRECTORY not in os.listdir(): os.mkdir(IMAGE_DIRECTORY) # RESET WHEN WIFI OR SOCKET CONNECTION IS LOST def reset(): lcd.clear()
#tested with frimware 5-0.22
import sensor,image,lcd
import KPU as kpu
lcd.init()
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.set_windowing((224, 224))
sensor.set_vflip(1)
sensor.run(1)
classes = ["racoon"]
task = kpu.load(0x200000) #change to "/sd/name_of_the_model_file.kmodel" if loading from SD card
a = kpu.set_outputs(task, 0, 7,7,30) #the actual shape needs to match the last layer shape of your model(before Reshape)
anchor = (0.57273, 0.677385, 1.87446, 2.06253, 3.33843, 5.47434, 7.88282, 3.52778, 9.77052, 9.16828)
a = kpu.init_yolo2(task, 0.3, 0.3, 5, anchor) #tweak the second parameter if you're getting too many false positives
while(True):
img = sensor.snapshot().rotation_corr(z_rotation=90.0)
a = img.pix_to_ai()
code = kpu.run_yolo2(task, img)
if code:
for i in code:
a=img.draw_rectangle(i.rect(),color = (0, 255, 0))
a = img.draw_string(i.x(),i.y(), classes[i.classid()], color=(255,0,0), scale=3)
a = lcd.display(img)
else:
a = lcd.display(img)
a = kpu.deinit(task)
import os, image, time
import KPU as kpu
md = kpu.load(0x400000)
kpu.set_outputs(md, 0, 1, 3, 1)
img = image.Image("/sd/3.jpg")
print(img)
#img = img.rgb_to_grayscale()
img.pix_to_ai()
a = kpu.forward(md, img)
fmap = kpu.get_output(md, 0)
print("fmap", fmap[:])
#print(os.listdir())