def seeotheritems(): #7second delay
global taskfe
global a
global task
global yolonum
global anchor
classes = [
'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat',
'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person',
'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'
]
anchored = (1.08, 1.19, 3.42, 4.41, 6.63, 11.38, 9.42, 5.11, 16.62, 10.52)
kpu.deinit(taskfe)
kpu.deinit(task)
tasktw = kpu.load("/sd/model/20class.kmodel")
uart_B.write(" loaded 20 ")
kpu.init_yolo2(tasktw, 0.5, 0.3, 5, anchored)
imgother = sensor.snapshot()
imgother.pix_to_ai()
detectcode = kpu.run_yolo2(tasktw, imgother)
if detectcode:
led_r.value(0)
led_b.value(0)
for i in detectcode:
imgother = imgother.draw_rectangle(i.rect())
for i in detectcode:
imgother = imgother.draw_string(i.x(),
i.y(),
str(classes[i.classid()]),
color=(255, 250, 250))
imgother = imgother.draw_string(i.x(),
i.y() + 12,
'%f1.3' % i.value(),
color=(255, 250, 250))
imgother.save("/sd/yoloimages/" + str(yolonum) + ".jpg",
quality=70)
utime.sleep_ms(50)
yolonum += 1
uart_B.write(" |Yolo|> " + str(classes[i.classid()]) + " <||")
f = open("/sd/printoutput.txt", "a+")
f.write("Yolo detected: " + str(classes[i.classid()]) + "\n\r")
f.close()
del (imgother)
kpu.deinit(tasktw)
del (tasktw)
gc.collect()
uart_B.write(" killed ")
task = kpu.load("/sd/facedetect.kmodel")
taskfe = kpu.load("/sd/model/FE.smodel")
utime.sleep_ms(10)
led_r.value(1)
led_b.value(1)
kpu.init_yolo2(task, 0.5, 0.3, 5, anchor)
uart_B.write(" restarted ")
def load():
if FaceReco.is_load == False:
FaceReco.model = kpu.load(0x2C0000) # Load Model File from Flash
# Anchor data is for bbox, extracted from the training sets.
kpu.init_yolo2(FaceReco.model, 0.5, 0.3, 5, (1.889, 2.5245, 2.9465, 3.94056, 3.99987,
5.3658, 5.155437, 6.92275, 6.718375, 9.01025))
FaceReco.is_load = True
def main(model_addr=0x300000, lcd_rotation=0, sensor_hmirror=False, sensor_vflip=False):
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.set_hmirror(sensor_hmirror)
sensor.set_vflip(sensor_vflip)
sensor.run(1)
lcd.init(type=1)
lcd.rotation(lcd_rotation)
lcd.clear(lcd.WHITE)
task = kpu.load(model_addr)
anchors = (1.889, 2.5245, 2.9465, 3.94056, 3.99987, 5.3658, 5.155437, 6.92275, 6.718375, 9.01025)
kpu.init_yolo2(task, 0.5, 0.3, 5, anchors) # threshold:[0,1], nms_value: [0, 1]
try:
while(True):
img = sensor.snapshot()
t = time.ticks_ms()
objects = kpu.run_yolo2(task, img)
t = time.ticks_ms() - t
if objects:
for obj in objects:
img.draw_rectangle(obj.rect())
img.draw_string(0, 200, "t:%dms" %(t), scale=2)
lcd.display(img)
except Exception as e:
sys.print_exception(e)
finally:
kpu.deinit(task)
def __init__(self,
out_range=10,
ignore_limit=0.02,
hmirror=False,
vflip=False,
lcd_rotation=2,
lcd_mirror=True):
self.pitch = 0
self.roll = 0
self.out_range = out_range
self.ignore = ignore_limit
self.task_fd = kpu.load(0x300000) # face model addr in flash
anchor = (1.889, 2.5245, 2.9465, 3.94056, 3.99987, 5.3658,
5.155437, 6.92275, 6.718375, 9.01025)
kpu.init_yolo2(self.task_fd, 0.5, 0.3, 5, anchor)
lcd.init()
lcd.rotation(lcd_rotation)
lcd.mirror(lcd_mirror)
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
if hmirror:
sensor.set_hmirror(1)
if vflip:
sensor.set_vflip(1)
def __lazy_init(self):
err_counter = 0
while 1:
try:
sensor.reset() # Reset sensor may failed, let's try sometimes
break
except Exception:
err_counter = err_counter + 1
if err_counter == 20:
lcd.draw_string(lcd.width() // 2 - 100,
lcd.height() // 2 - 4,
"Error: Sensor Init Failed", lcd.WHITE,
lcd.RED)
time.sleep(0.1)
continue
print("progress 1 OK!")
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA) # QVGA=320x240
sensor.run(1)
print("progress 2 OK!")
self.task = kpu.load(0x300000) # Load Model File from Flash
anchor = (1.889, 2.5245, 2.9465, 3.94056, 3.99987, 5.3658, 5.155437,
6.92275, 6.718375, 9.01025)
# Anchor data is for bbox, extracted from the training sets.
print("progress 3 OK!")
kpu.init_yolo2(self.task, 0.5, 0.3, 5, anchor)
self.but_stu = 1
self.__initialized = True
def main(labels=None,
model_addr="/sd/m.kmodel",
sensor_window=(224, 224),
lcd_rotation=0,
sensor_hmirror=False,
sensor_vflip=False):
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.set_windowing(sensor_window)
sensor.set_hmirror(sensor_hmirror)
sensor.set_vflip(sensor_vflip)
sensor.run(1)
lcd.init(type=1)
lcd.rotation(lcd_rotation)
lcd.clear(lcd.WHITE)
if not labels:
with open('labels.txt', 'r') as f:
exec(f.read())
if not labels:
print("no labels.txt")
img = image.Image(size=(320, 240))
img.draw_string(90, 110, "no labels.txt", color=(255, 0, 0), scale=2)
lcd.display(img)
return 1
try:
img = image.Image("startup.jpg")
lcd.display(img)
except Exception:
img = image.Image(size=(320, 240))
img.draw_string(90,
110,
"loading model...",
color=(255, 255, 255),
scale=2)
lcd.display(img)
task = kpu.load(model_addr)
try:
while (True):
img = sensor.snapshot()
t = time.ticks_ms()
fmap = kpu.forward(task, img)
t = time.ticks_ms() - t
plist = fmap[:]
pmax = max(plist)
max_index = plist.index(pmax)
img.draw_string(0,
0,
"%.2f : %s" % (pmax, labels[max_index].strip()),
scale=2)
img.draw_string(0, 200, "t:%dms" % (t), scale=2)
lcd.display(img)
except Exception as e:
raise e
finally:
kpu.deinit(task)
def __init__(self, categoryname=[], samplesnumber=15, bool=False):
self.row = global_value.row
global_value.row = global_value.row + 1
try:
del self.model
except Exception:
pass
try:
del self.classifier
except Exception:
pass
self.classnumber = len(categoryname)
self.samplesnumber = samplesnumber
self.categoryname = categoryname
self.img_copy = None
self.cap_num = 0
self.train_status = 0
self.class_list = []
self.percent = 0
self.image_class = 0
if bool:
self.model = kpu.load(0x514000)
self.classifier = kpu.classifier(self.model, self.classnumber,
self.samplesnumber)
else:
pass
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 load():
if MaybeIs.is_load == False:
#print(MaybeIs.load)
MaybeIs.task = kpu.load(0x5C0000)
#task = kpu.load("/sd/0x5C0000_20class.kmodel")
kpu.init_yolo2(MaybeIs.task, 0.5, 0.3, 5, (1.889, 2.5245, 2.9465, 3.94056, 3.99987, 5.3658, 5.155437, 6.92275, 6.718375, 9.01025))
MaybeIs.is_load = True
def load_model_file(self, filename=''):
self.model = kpu.load(0x514000)
classifier = kpu.classifier.load(self.model, filename)
self.classifier = classifier[0]
f = open(filename.split('.')[0] + '.names', 'r')
self.class_list = ujson.loads(f.read())
#print(self.class_list)
f.close()
def load():
if MoblieNet.is_load == False:
#print(MoblieNet.load)
MoblieNet.task = kpu.load(0x720000)
#task = kpu.load("/sd/0x720000_mbnet75.kmodel")
with open(os.getcwd() + '/res/labels.txt', 'r') as f:
#with open('labels.txt', 'r') as f:
MoblieNet.labels = f.readlines()
MoblieNet.is_load = True
def load():
if FaceReco.is_load == False:
FaceReco.task_fd = kpu.load(0x2C0000)
FaceReco.task_ld = kpu.load(0x580000)
FaceReco.task_fe = kpu.load(0x340000)
a = kpu.init_yolo2(FaceReco.task_fd, 0.5, 0.3, 5, anchor)
FaceReco.img_face = image.Image(size=(128, 128))
a = FaceReco.img_face.pix_to_ai()
FaceReco.start_processing = False
from Maix import GPIO
button_io.home_button.irq(
FaceReco.set_key_state, GPIO.IRQ_RISING, GPIO.WAKEUP_NOT_SUPPORT)
FaceReco.is_load = True
def __init__(self):
self._m_fd = kpu.load(0x200000)
self._m_ld = kpu.load(0x300000)
self._m_fe = kpu.load(0x400000)
self._anchor = (1.889, 2.5245, 2.9465, 3.94056, 3.99987, 5.3658,
5.155437, 6.92275, 6.718375, 9.01025)
self._dst_point = [(44, 59), (84, 59), (64, 82), (47, 105), (81, 105)]
self.names = []
self.features = []
_ = kpu.init_yolo2(self._m_fd, 0.5, 0.3, 5, self._anchor)
self.img_face = image.Image(size=(128, 128))
_ = self.img_face.pix_to_ai()
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
# sensor.set_hmirror(1)
sensor.set_vflip(1)
self.show_img_timeout = 5
self._show_img_t = -5
def main(anchors, labels=None, model_addr="/sd/m.kmodel"):
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.set_windowing((224, 224))
sensor.run(1)
lcd.init(type=1)
lcd.clear(lcd.WHITE)
if not labels:
with open('labels.txt', 'r') as f:
exec(f.read())
if not labels:
print("no labels.txt")
img = image.Image(size=(320, 240))
img.draw_string(90, 110, "no labels.txt", color=(255, 0, 0), scale=2)
lcd.display(img)
return 1
try:
img = image.Image("startup.jpg")
lcd.display(img)
except Exception:
img = image.Image(size=(320, 240))
img.draw_string(90,
110,
"loading model...",
color=(255, 255, 255),
scale=2)
lcd.display(img)
task = kpu.load(model_addr)
kpu.init_yolo2(task, 0.5, 0.3, 5,
anchors) # threshold:[0,1], nms_value: [0, 1]
try:
while (True):
img = sensor.snapshot()
t = time.ticks_ms()
objects = kpu.run_yolo2(task, img)
t = time.ticks_ms() - t
if objects:
for obj in objects:
pos = obj.rect()
img.draw_rectangle(pos)
img.draw_string(pos[0],
pos[1],
"%s : %.2f" %
(labels[obj.classid()], obj.value()),
scale=2)
img.draw_string(0, 200, "t:%dms" % (t), scale=2)
lcd.display(img)
except Exception as e:
sys.print_exception(e)
finally:
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 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_Task():
global labels
global objects
objects = kpu.load("/sd/laji.kmodel")
f = open("anchors.txt", "r")
anchor_txt = f.read()
L = []
for i in anchor_txt.split(","):
L.append(float(i))
anchor = tuple(L)
f.close()
a = kpu.init_yolo2(objects, 0.6, 0.3, 5, anchor)
f = open("classes.txt", "r")
labels_txt = f.read()
labels = labels_txt.split(",")
f.close()
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, 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 find_face():
lcd.init()
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.set_vflip(1)
sensor.run(1)
task = kpu.load(0x300000)
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, 0.5, 0.3, 5, anchor)
while (True):
img = sensor.snapshot()
code = kpu.run_yolo2(task, img)
if code:
for i in code:
img.draw_rectangle(i.rect())
lcd.display(img)
kpu.deinit(task)
def main(model_addr=0x300000,
lcd_rotation=0,
sensor_hmirror=False,
sensor_vflip=False):
try:
sensor.reset()
except Exception as e:
raise Exception(
"sensor reset fail, please check hardware connection, or hardware damaged! err: {}"
.format(e))
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.set_hmirror(sensor_hmirror)
sensor.set_vflip(sensor_vflip)
sensor.run(1)
lcd.init(type=1)
lcd.rotation(lcd_rotation)
lcd.clear(lcd.WHITE)
anchors = (1.889, 2.5245, 2.9465, 3.94056, 3.99987, 5.3658, 5.155437,
6.92275, 6.718375, 9.01025)
try:
task = None
task = kpu.load(model_addr)
kpu.init_yolo2(task, 0.5, 0.3, 5,
anchors) # threshold:[0,1], nms_value: [0, 1]
while (True):
img = sensor.snapshot()
t = time.ticks_ms()
objects = kpu.run_yolo2(task, img)
t = time.ticks_ms() - t
if objects:
for obj in objects:
img.draw_rectangle(obj.rect())
img.draw_string(0, 200, "t:%dms" % (t), scale=2)
lcd.display(img)
except Exception as e:
raise e
finally:
if not task is None:
kpu.deinit(task)
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)
def _resetTask():
global g_selCnt
global g_cFiler
global g_task
info = g_cFiler.getInfoList()[g_selCnt]
fullPath = info.dirName + '/' + info.modelName
fullPath = '/sd/models/' + fullPath + '.kmodel'
try:
g_task = kpu.load(fullPath)
if (info.modelType == 'yolo2'):
anchor = (1.889, 2.5245, 2.9465, 3.94056, 3.99987, 5.3658,
5.155437, 6.92275, 6.718375, 9.01025)
# Anchor data is for bbox, extracted from the training sets.
kpu.init_yolo2(g_task, 0.5, 0.3, 5, anchor)
except:
lcd.draw_string(0, 20, "Err:" + info.modelName + " not find.",
lcd.WHITE, lcd.RED)
g_task = None
return g_task
def checkCamera():
global x, y
x = 0
y = 0
showInfo("camera starting", True)
img = image.Image()
try:
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.skip_frames(time=2000)
lcd.rotation(2)
except:
showInfo("camera init failed", False)
return
else:
showInfo("camera init done", True)
try:
task = kpu.load(0x300000)
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:
img = sensor.snapshot()
code = kpu.run_yolo2(task, img)
if code:
for i in code:
a = img.draw_rectangle(i.rect(), color=lcd.BLUE)
new = img.copy(roi=(0, 0, 239, 239))
lcd.display(new)
except:
print('kup load fialed')
while True:
img = sensor.snapshot()
new = img.copy(roi=(0, 0, 239, 239))
lcd.display(new)
def main(labels = None, model_addr="/sd/m.kmodel", lcd_rotation=0, sensor_hmirror=False, sensor_vflip=False):
gc.collect()
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.set_windowing((224, 224))
sensor.set_hmirror(sensor_hmirror)
sensor.set_vflip(sensor_vflip)
sensor.run(1)
lcd.init(type=1)
lcd.rotation(lcd_rotation)
lcd.clear(lcd.WHITE)
if not labels:
raise Exception("no labels.txt")
task = kpu.load(model_addr)
try:
while(True):
img = sensor.snapshot()
t = time.ticks_ms()
fmap = kpu.forward(task, img)
t = time.ticks_ms() - t
plist=fmap[:]
pmax=max(plist)
max_index=plist.index(pmax)
img.draw_string(0,0, "%.2f\n%s" %(pmax, labels[max_index].strip()), scale=2, color=(255, 0, 0))
img.draw_string(0, 200, "t:%dms" %(t), scale=2, color=(255, 0, 0))
lcd.display(img)
except Exception as e:
sys.print_exception(e)
finally:
kpu.deinit(task)
#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 sensor, image, lcd, utime # import 相关库
from fpioa_manager import *
import KPU as kpu
import time, uos
from Maix import FPIOA, GPIO
from fpioa_manager import fm, board_info
from Maix import GPIO
from machine import UART
task_fd = kpu.load(0x200000) # 从flash 0x200000 加载人脸检测模型
task_ld = kpu.load(0x300000) # 从flash 0x300000 加载人脸五点关键点检测模型
task_fe = kpu.load(0x400000) # 从flash 0x400000 加载人脸196维特征值模型
clock = time.clock() # 初始化系统时钟，计算帧率
key_pin = 16 # 设置按键引脚 FPIO16
fpioa = FPIOA()
fpioa.set_function(key_pin, FPIOA.GPIO7)
key_gpio = GPIO(GPIO.GPIO7, GPIO.IN)
last_key_state = 1
key_pressed = 0 # 初始化按键引脚 分配GPIO7 到 FPIO16
def check_key(): # 按键检测函数，用于在循环中检测按键是否按下，下降沿有效
global last_key_state
global key_pressed
val = key_gpio.value()
if last_key_state == 1 and val == 0:
key_pressed = 1
else:
key_pressed = 0
last_key_state = val