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 get_target_err(self):
img = sensor.snapshot()
code = kpu.run_yolo2(self.task_fd, img)
if code:
max_area = 0
max_i = 0
for i, j in enumerate(code):
a = j.w() * j.h()
if a > max_area:
max_i = i
max_area = a
img = img.draw_rectangle(code[max_i].rect())
self.pitch = (code[max_i].y() + code[max_i].h() /
2) / 240 * self.out_range * 2 - self.out_range
self.roll = (code[max_i].x() + code[max_i].w() /
2) / 320 * self.out_range * 2 - self.out_range
# limit
if abs(self.pitch) < self.out_range * self.ignore:
self.pitch = 0
if abs(self.roll) < self.out_range * self.ignore:
self.roll = 0
img = img.draw_cross(160, 120)
lcd.display(img)
return (self.pitch, self.roll)
else:
img = img.draw_cross(160, 120)
lcd.display(img)
return (0, 0)
def on_draw(self):
if not self.__initialized:
self.__lazy_init()
try:
while True:
img = sensor.snapshot() # Take an image from sensor
print("progress 4 OK!")
# Run the detection routine
bbox = kpu.run_yolo2(self.task, img)
if bbox:
for i in bbox:
print(i)
img.draw_rectangle(i.rect())
lcd.display(img)
home_button = self.get_system().home_button
# TODO
led_w = self.get_system().led_w
if home_button.value() == 0 and self.but_stu == 1:
if led_w.value() == 1:
led_w.value(0)
else:
led_w.value(1)
self.but_stu = 0
if home_button.value() == 1 and self.but_stu == 0:
self.but_stu = 1
except KeyboardInterrupt:
a = kpu.deinit(task)
sys.exit()
def work(img):
img.pix_to_ai()
# Run the detection routine
FaceReco.bbox = kpu.run_yolo2(FaceReco.model, img)
if FaceReco.bbox:
for i in FaceReco.bbox:
# print(i)
img.draw_rectangle(i.rect())
def updateKpu():
global g_cFiler
global g_selCnt
global g_cWav
global g_task
global g_powArr
info = g_cFiler.getInfoList()[g_selCnt]
if (g_task == None):
g_task = _resetTask()
if (g_task == None):
g_cWav.play('/sd/snd/sys_ng.wav')
g_cWav.wait()
img = sensor.snapshot()
if (info.modelType == 'yolo2'):
plist = []
for id in range(0, len(info.classList)):
plist.append(0.0)
code_obj = kpu.run_yolo2(g_task, img)
if code_obj: # object detected
for i in code_obj:
rect_size = i.w() * i.h()
if rect_size > 10:
print(len(plist))
print(i.classid())
plist[i.classid()] = 0.95
else:
fmap = kpu.forward(g_task, img, False)
plist = fmap[:]
colArr = [(255, 0, 0), (0, 255, 0), (0, 0, 255), (5, 5, 5), (0, 255, 255),
(255, 255, 0), (128, 128, 128), (50, 200, 50)]
for id in range(0, len(plist)):
if (plist[id] > 0.9):
g_powArr[id] = min((g_powArr[id] + plist[id] - 0.9) * 5.0, 100.0)
else:
g_powArr[id] -= 10.0
g_powArr[id] = max(g_powArr[id], 0.0)
img.draw_rectangle((10, 50 + 10 * id, int(g_powArr[id] * 1), 8),
colArr[id & 7], 10, True)
if (g_powArr[id] >= 100.0):
g_powArr[id] = 0.0
info = g_cFiler.getInfoList()[g_selCnt]
labels = info.classList
wavPath = info.dirName + '/' + labels[id] + '.wav'
lcd.draw_string(0, 20, wavPath)
g_cWav.play('/sd/models/' + wavPath)
g_cWav.wait()
a = lcd.display(img)
g_cButton.update()
def Object_Detection(img, obj):
global labels
code = kpu.run_yolo2(obj, img)
if code != None:
for i in code:
a = img.draw_rectangle(i.rect(), (0, 255, 0), 2)
print(i.classid())
stuff.ID = i.classid()
#uart_A.write(pack_obj_data())
print(pack_obj_data())
def findmasks():
objects = kpu.run_yolo2(task, img)
if objects:
for obj in objects:
pos = obj.rect()
if obj.classid() == 0:
img.draw_rectangle(pos, color=(255, 0, 0))
if obj.classid() == 1:
img.draw_rectangle(pos, color=(0, 255, 0))
return pos
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 work(img):
HowMany.things = kpu.run_yolo2(HowMany.task, img)
if HowMany.things:
for pos in range(len(HowMany.things)):
i = HowMany.things[pos]
img.draw_rectangle(ui.weight - (i.x() + i.w()), i.y(), i.w(), i.h())
img.draw_string(ui.weight - (i.x() + i.w()), i.y(), '%.2f:%s' % (i.value(), classes[i.classid()]), color=(0, 255, 0))
return img
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 set_face_detection(self):
img = sensor.snapshot()
code = kpu.run_yolo2(self.task, img)
if code:
for i in code:
#print(i)
a = img.draw_rectangle(i.rect())
print("find face")
self.face_detection = 1
else:
print("no face")
self.face_detection = 0
def work(img):
MaybeIs.things = kpu.run_yolo2(MaybeIs.task, img)
if MaybeIs.things:
value, obj = 0, None
for k in range(len(MaybeIs.things)):
if value < MaybeIs.things[k].value():
value, obj = MaybeIs.things[k].value(), MaybeIs.things[k]
i = MaybeIs.things[k]
MaybeIs.result = classes[i.classid()]
img.draw_rectangle(ui.weight - (i.x() + i.w()), i.y(), i.w(), i.h())
img.draw_string(ui.weight - (i.x() + i.w()), i.y(), '%.2f:%s' % (i.value(), classes[i.classid()]), color=(0, 255, 0))
return img
def main():
## main
task, graph, counter, uart = init(threshold=0.5, patience=4)
frame_idx = 0
try:
while (True):
# get image
img = sensor.snapshot().rotation_corr(z_rotation=0.0)
# detect boxes
a = img.pix_to_ai()
code = kpu.run_yolo2(task, img)
# set frame
currF = Frame(frame_idx, img, code)
# calc track
diff_idx = graph.track(currF)
# counting
counter.vanish_update(graph.F_list[-1].bboxes,
graph.F_list[-2].bboxes, graph.is_decrease)
counter.count()
# display on IDE
#img = currF.draw_frames()
#img = img.copy((32, 32, 160, 160))
#img.draw_string(0 ,0, str(counter.counter[LEFT])+","+str(counter.counter[RIGHT]),
#color=(0,255,0), scale=3)
#a = lcd.display(img)
# to Gray
msg = str(counter.counter[LEFT])+ DIV + \
str(counter.counter[RIGHT])+ DIV + str(currF.num_object)
_ = uart.write(msg)
#_ = uart.write(img)
print(counter.counter)
# finalize
frame_idx += 1
time.sleep(0.05)
except Exception as e:
# need delete kpu_task when keyboard interrupt
a = kpu.deinit(task)
del task
gc.collect()
print(e)
def detect_objects(self, threshold, return_img=False):
detected = False
img = snapshot()
img_copy = img.resize(224, 224)
a = img_copy.pix_to_ai()
code = kpu.run_yolo2(self.object_detection_task, img_copy)
if code:
for i in code:
if i.value() >= threshold:
detected = True
new_x, new_y = int(i.x() * 1.07), int(i.y() * 1.42)
roi = (new_x, new_y, int(i.w() * 1.07), int(i.h() * 1.42))
percent = i.value()
object_detected = self.classes[i.classid()]
if not return_img:
a = img.draw_rectangle(roi,
color=(0x1c, 0xa2, 0xff),
thickness=2)
a = img.draw_string(new_x,
new_y - 14,
("%s %%: %.2f" %
(object_detected, percent)),
color=(255, 255, 255),
scale=1.5,
mono_space=False)
self.x_center, self.y_center, self.area, self.percent, roi = _find_max(
code)
del (img_copy)
if not detected:
self.object_detected = None
self.percent = -1
if return_img:
return img, None
if return_img:
return img, roi
else:
a = lcd.display(img)
del (img)
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 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)
img_lcd = image.Image()
img_face = image.Image(size=(128, 128))
a = img_face.pix_to_ai()
record_ftr = []
record_ftrs = []
names = [
'Mr.1', 'Mr.2', 'Mr.3', 'Mr.4', 'Mr.5', 'Mr.6', 'Mr.7', 'Mr.8', 'Mr.9',
'Mr.10'
]
ACCURACY = 85
while (1):
img = sensor.snapshot()
clock.tick()
code = kpu.run_yolo2(task_fd, img)
if code:
for i in code:
# Cut face and resize to 128x128
a = img.draw_rectangle(i.rect())
face_cut = img.cut(i.x(), i.y(), i.w(), i.h())
face_cut_128 = face_cut.resize(128, 128)
a = face_cut_128.pix_to_ai()
# a = img.draw_image(face_cut_128, (0,0))
# Landmark for face 5 points
fmap = kpu.forward(task_ld, face_cut_128)
plist = fmap[:]
le = (i.x() + int(plist[0] * i.w() - 10),
i.y() + int(plist[1] * i.h()))
re = (i.x() + int(plist[2] * i.w()), i.y() + int(plist[3] * i.h()))
nose = (i.x() + int(plist[4] * i.w()),
classes = ['airplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor']
task = kpu.load("/sd/irasutoya/20class.kmodel")
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(task, 0.5, 0.3, 5, anchor)
irasutoya_icons = [image.Image("/sd/irasutoya/icons_jpeg/{}.jpeg".format(icon_class)) for icon_class in classes]
wall_img = image.Image("/sd/irasutoya/wall.jpeg")
print('[info]: Started.')
but_stu = 1
try:
while(True):
#gc.collect()
img = sensor.snapshot()
code_obj = kpu.run_yolo2(task, img)
img2show = wall_img.copy()
if code_obj: # object detected
for i in code_obj:
icon = irasutoya_icons[i.classid()]
img2show.draw_image(icon, i.x(), i.y(), x_scale=i.w()/icon.width(), y_scale=i.h()/icon.height())
lcd.display(img2show)
except KeyboardInterrupt:
kpu.deinit(task)
sys.exit()
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 = ["face", "face_mask"]
task = kpu.load(0x400000)
a = kpu.set_outputs(task, 0, 10,7,35)
anchor = (0.212104,0.261834, 0.630488,0.706821, 1.264643,1.396262, 2.360058,2.507915, 4.348460,4.007944)
a = kpu.init_yolo2(task, 0.5, 0.5, 5, anchor)
while(True):
timestamp = time.ticks_ms()
img = sensor.snapshot()
a = img.pix_to_ai()
faces = kpu.run_yolo2(task, img)
if faces:
for face in faces:
if face.classid() == 0 :
a=img.draw_rectangle(face.rect(),color = (255, 0, 0),thickness=5)
elif face.classid() == 1 :
a=img.draw_rectangle(face.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)
#====================#
#=== 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:
print("WIFI Connecting ...")
time.sleep(1)
while(True):
img = sensor.snapshot()
#--- face detect ---#
faces = kpu.run_yolo2(task_face_detect, img)
if faces:
#--- check face size ---#
x1 = faces[0].x() - 10
y1 = faces[0].y() - 10
w = faces[0].w() + 20
h = faces[0].h() + 10
if w > 80 and h > 80:
#--- crop target face ---#
face = img.cut(x1,y1,w,h)
face = face.resize(112,112)
a = img.draw_rectangle(x1,y1,w,h,color = (255,0,0), thickness=2)
a = face.pix_to_ai()
#--- encode face ---#
fmap = kpu.forward(task_face_encode,face)
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:
for i in detected_face:
face = img.cut(i.x(), i.y(), i.w(), i.h())
face_128 = face.resize(128, 128)
a = face_128.pix_to_ai()
fmap = kpu.forward(task_classify_face, face_128)
plist = fmap[:]
pmax = max(plist)
print("%s: %s" % (labels[plist.index(pmax)], pmax))
#将模型放在SD卡中。
task = kpu.load("/sd/20class.kmodel") #模型SD卡上
#网络参数
anchor = (1.889, 2.5245, 2.9465, 3.94056, 3.99987, 5.3658, 5.155437, 6.92275,
6.718375, 9.01025)
#初始化yolo2网络，识别可信概率为0.7（70%）
a = kpu.init_yolo2(task, 0.7, 0.3, 5, anchor)
while (True):
clock.tick()
img = sensor.snapshot()
code = kpu.run_yolo2(task, img) #运行yolo2网络
if code:
for i in code:
a = img.draw_rectangle(i.rect())
a = lcd.display(img)
lcd.draw_string(i.x(), i.y(), classes[i.classid()], lcd.RED,
lcd.WHITE)
lcd.draw_string(i.x(),
i.y() + 12, '%f1.3' % i.value(), lcd.RED,
lcd.WHITE)
#lcd.draw_string(i.x(), i.y()+12, 'test,0.618', lcd.RED, lcd.WHITE)
else:
a = lcd.display(img)
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(task, 0.5, 0.3, 5, anchor)
kpu.init_yolo2(task_face, 0.5, 0.3, 5, anchor)
print('[info]: Started.')
but_stu = 1
isButtonPressedA = 0
try:
while (True):
#gc.collect()
img = sensor.snapshot()
code_obj = kpu.run_yolo2(task, img)
code_face = kpu.run_yolo2(task_face, img)
if code_obj: # object detected
max_id = 0
max_rect = 0
for i in code_obj:
img.draw_rectangle(i.rect())
text = ' ' + classes[i.classid()] + ' (' + str(
int(i.value() * 100)) + '%) '
for x in range(-1, 2):
for y in range(-1, 2):
img.draw_string(x + i.x(),
y + i.y() + (i.h() >> 1),
text,
color=(250, 205, 137),
record_ftrs = [] #空列表 用于存储按键记录下人脸特征， 可以将特征以txt等文件形式保存到sd卡后，读取到此列表，即可实现人脸断电存储。
names = [] # 人名标签，与上面列表特征值一一对应。
with open("/sd/recordftr3.txt", "r") as f:
while (1):
line = f.readline()
if not line:
break
name = line[0:line.index('#')]
line = line[line.index('#') + 1:]
record_ftrs.append(eval(line))
names.append(name)
while (1): # 主循环
check_key() #按键检测
img = sensor.snapshot() #从摄像头获取一张图片
clock.tick() #记录时刻，用于计算帧率
code = kpu.run_yolo2(task_fd, img) # 运行人脸检测模型，获取人脸坐标位置
read_data = uart.read()
if read_data:
with open("/sd/recordftr3.txt", "a") as f:
f.write(read_data[2])
if code: # 如果检测到人脸
for i in code: # 迭代坐标框
# Cut face and resize to 128x128
a = img.draw_rectangle(i.rect()) # 在屏幕显示人脸方框
face_cut = img.cut(i.x(), i.y(), i.w(),
i.h()) # 裁剪人脸部分图片到 face_cut
face_cut_128 = face_cut.resize(128, 128) # 将裁出的人脸图片 缩放到128 * 128像素
a = face_cut_128.pix_to_ai() # 将猜出图片转换为kpu接受的格式
#a = img.draw_image(face_cut_128, (0,0))
# Landmark for face 5 points
fmap = kpu.forward(task_ld, face_cut_128) # 运行人脸5点关键点检测模型
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)
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA) #QVGA=320x240
sensor.run(1)
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.
kpu.init_yolo2(task, 0.5, 0.3, 5, anchor)
but_stu = 1
try:
while (True):
img = sensor.snapshot() # Take an image from sensor
bbox = kpu.run_yolo2(task, img) # Run the detection routine
if bbox:
bbox.sort(reverse=True, key=lambda x: x.rect()[2] * x.rect()[3])
c = bbox[0].rect()[0] + (bbox[0].rect()[2] / 2)
print(str(c) + ' ' + str(len(bbox)))
if c < 120:
print('>>>')
u_send(200)
if c > 160:
print('<<<')
u_send(201)
first = True
for i in bbox:
color = (255, 0, 0) if first else (255, 255, 255)
thick = 5 if first else 1
first = False
def run(self,
on_detect,
on_img,
on_clear,
on_people=None,
always_show_img=False):
img = sensor.snapshot()
try:
code = kpu.run_yolo2(self._m_fd, img)
except Exception:
return
if code:
for i in code:
face_cut = img.cut(i.x(), i.y(), i.w(), i.h())
face_cut_128 = face_cut.resize(128, 128)
a = face_cut_128.pix_to_ai()
#a = img.draw_image(face_cut_128, (0,0))
# Landmark for face 5 points
try:
fmap = kpu.forward(self._m_ld, face_cut_128)
except Exception:
continue
plist = fmap[:]
le = (i.x() + int(plist[0] * i.w() - 10),
i.y() + int(plist[1] * i.h()))
re = (i.x() + int(plist[2] * i.w()),
i.y() + int(plist[3] * i.h()))
nose = (i.x() + int(plist[4] * i.w()),
i.y() + int(plist[5] * i.h()))
lm = (i.x() + int(plist[6] * i.w()),
i.y() + int(plist[7] * i.h()))
rm = (i.x() + int(plist[8] * i.w()),
i.y() + int(plist[9] * i.h()))
a = img.draw_circle(le[0], le[1], 4)
a = img.draw_circle(re[0], re[1], 4)
a = img.draw_circle(nose[0], nose[1], 4)
a = img.draw_circle(lm[0], lm[1], 4)
a = img.draw_circle(rm[0], rm[1], 4)
# align face to standard position
src_point = [le, re, nose, lm, rm]
T = image.get_affine_transform(src_point, self._dst_point)
a = image.warp_affine_ai(img, self.img_face, T)
a = self.img_face.ai_to_pix()
#a = img.draw_image(img_face, (128,0))
del (face_cut_128)
# calculate face feature vector
try:
fmap = kpu.forward(self._m_fe, self.img_face)
except Exception:
continue
feature = kpu.face_encode(fmap[:])
scores = []
for j in range(len(self.features)):
score = kpu.face_compare(self.features[j], feature)
scores.append(score)
max_score = 0
index = 0
for k in range(len(scores)):
if max_score < scores[k]:
max_score = scores[k]
index = k
if max_score > 85:
a = img.draw_rectangle(i.rect(), color=(0, 255, 0))
a = img.draw_string(
i.x(),
i.y(), ("%s :%2.1f" % (self.names[index], max_score)),
color=(0, 255, 0),
scale=2)
on_detect(self.names[index], feature, max_score, img)
else:
a = img.draw_rectangle(i.rect(), color=(255, 0, 0))
# a = img.draw_string(i.x(),i.y(), ("X :%2.1f" % (max_score)), color=(255,0,0),scale=2)
on_img(img)
if on_people:
on_people(feature, img)
self._show_img_t = time.ticks_ms() / 1000.0
else:
if always_show_img:
on_img(img)
else:
if time.ticks_ms(
) - self._show_img_t * 1000 < self.show_img_timeout * 1000:
on_img(img)
else:
on_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)
def yolo(self):
self.yoloObj = kpu.run_yolo2(task, self.img)
if self.yoloObj:
for object in self.yoloObj:
self.img.draw_rectangle(object.rect(), self.color, self.border,
self.fill) # setCamera((320,240))