[Automatic clock-in walking timing system based on face recognition] MaixBit-K210 can easily run with face recognition function!
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Preparation
Get machine code
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Register an account on MaixHub: https://maix.sipeed.com/
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After logging in, search for the face recognition model, click the [Download] button, and you will be prompted to enter the machine code.
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- Refer to the following connection steps: Get the machine code of MaixPy series development board——MaixHub Machine code acquisition (sipeed.com)
- Download the key_gen.bin file, use kflash_gui to download the bin file to the board, and connect the serial port assistant tool (115200-8N1) to obtain a string of 32-byte machine code.
- After filling in the 32-bit machine code, you can get a compressed package. After decompression, there will be main.py and 3 smodel files (encrypted files of kmodel mode files)
Update the firmware of MaixBit
Copy the smodel file to the TF card
Code Link
- On the MaixPy IDE, enter the Python code:
import sensor
import image
import lcd
import KPU as kpu
import time
from Maix import FPIOA, GPIO
import gc
from fpioa_manager import fm
from board import board_info
import utime
# task_fd = kpu.load(0x300000)
# task_ld = kpu.load(0x400000)
# task_fe = kpu.load(0x500000)
task_fd = kpu.load("/sd/FaceDetection.smodel")
task_ld = kpu.load("/sd/FaceLandmarkDetection.smodel")
task_fe = kpu.load("/sd/FeatureExtraction.smodel")
clock = time.clock()
fm.register(board_info.BOOT_KEY, fm.fpioa.GPIOHS0)
key_gpio = GPIO(GPIO.GPIOHS0, GPIO.IN)
start_processing = False
BOUNCE_PROTECTION = 50
def set_key_state(*_):
global start_processing
start_processing = True
utime.sleep_ms(BOUNCE_PROTECTION)
key_gpio.irq(set_key_state, GPIO.IRQ_RISING, GPIO.WAKEUP_NOT_SUPPORT)
lcd.init()
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.set_hmirror(1)
sensor.set_vflip(1)
sensor.run(1)
anchor = (1.889, 2.5245, 2.9465, 3.94056, 3.99987, 5.3658, 5.155437,
6.92275, 6.718375, 9.01025) # anchor for face detect
dst_point = [(44, 59), (84, 59), (64, 82), (47, 105),
(81, 105)] # standard face key point position
a = kpu.init_yolo2(task_fd, 0.5, 0.3, 5, anchor)
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()), 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, dst_point)
a = image.warp_affine_ai(img, img_face, T)
a = img_face.ai_to_pix()
# a = img.draw_image(img_face, (128,0))
del (face_cut_128)
# calculate face feature vector
fmap = kpu.forward(task_fe, img_face)
feature = kpu.face_encode(fmap[:])
reg_flag = False
scores = []
for j in range(len(record_ftrs)):
score = kpu.face_compare(record_ftrs[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 > ACCURACY:
a = img.draw_string(i.x(), i.y(), ("%s :%2.1f" % (
names[index], max_score)), color=(0, 255, 0), scale=2)
else:
a = img.draw_string(i.x(), i.y(), ("X :%2.1f" % (
max_score)), color=(255, 0, 0), scale=2)
if start_processing:
record_ftr = feature
record_ftrs.append(record_ftr)
start_processing = False
break
fps = clock.fps()
print("%2.1f fps" % fps)
a = lcd.display(img)
gc.collect()
# kpu.memtest()
# a = kpu.deinit(task_fe)
# a = kpu.deinit(task_ld)
# a = kpu.deinit(task_fd)
Run Demo
- You can click the Boot Key button to enter the photos of the people you want to save.
- When the target person enters the camera area, "Mr. 1" can be identified (from the code part, we can see that the person is marked as 1~10: names = ['Mr.1', 'Mr.2', 'Mr.3', 'Mr.4', 'Mr.5', 'Mr.6', 'Mr.7', 'Mr.8', 'Mr.9', 'Mr.10'])
- At this point, the face recognition function has been completed!
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