下面是手势识别模型的main.py代码：
# generated by maixhub, tested on maixpy3 v0.4.8
# copy files to TF card and plug into board and power on
# 手势识别模型
import sensor, image, lcd, time
import KPU as kpu
from machine import UART
import gc, sys
from fpioa_manager import fm
input_size = (224, 224)
labels = ['go', 'stop']
anchors = [1.41, 2.13, 1.25, 1.72, 1.94, 2.53, 1.16, 2.06, 1.66, 2.19]
def lcd_show_except(e):
import uio
err_str = uio.StringIO()
sys.print_exception(e, err_str)
err_str = err_str.getvalue()
img = image.Image(size=input_size)
img.draw_string(0, 10, err_str, scale=1, color=(0xff,0x00,0x00))
lcd.display(img)
class Comm:
def __init__(self, uart):
self.uart = uart
def send_detect_result(self, objects, labels):
msg = ""
for obj in objects:
pos = obj.rect()
p = obj.value()
idx = obj.classid()
label = labels[idx]
msg += "{}:{}:{}:{}:{}:{:.2f}:{}, ".format(pos[0], pos[1], pos[2], pos[3], idx, p, label)
if msg:
msg = msg[:-2] + "\n"
self.uart.write(msg.encode())
def init_uart():
fm.register(10, fm.fpioa.UART1_TX, force=True)
fm.register(11, fm.fpioa.UART1_RX, force=True)
uart = UART(UART.UART1, 115200, 8, 0, 0, timeout=1000, read_buf_len=256)
return uart
def main(anchors, labels = None, model_addr="/sd/m.kmodel", sensor_window=input_size, 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)
uart = init_uart()
comm = Comm(uart)
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:
pos = obj.rect()
img.draw_rectangle(pos)
img.draw_string(pos[0], pos[1], "%s : %.2f" %(labels[obj.classid()], obj.value()), scale=2, color=(255, 0, 0))
comm.send_detect_result(objects, labels)
img.draw_string(0, 200, "t:%dms" %(t), scale=2, color=(255, 0, 0))
img.draw_string(0, 2, "Upgrade to MaixCAM to use YOLOv8", scale=1.2, color=(255, 0, 0))
img.draw_string(0, 30, "wiki.sipeed.com/maixcam", scale=1.2, color=(255, 0, 0))
lcd.display(img)
except Exception as e:
raise e
finally:
if not task is None:
kpu.deinit(task)
if __name__ == "__main__":
try:
# main(anchors = anchors, labels=labels, model_addr=0x300000, lcd_rotation=0)
main(anchors = anchors, labels=labels, model_addr="/sd/model-174293.kmodel")
except Exception as e:
sys.print_exception(e)
lcd_show_except(e)
finally:
gc.collect()
下面是人脸识别模型的代码：
# 人脸识别模型
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
from machine import UART
# 初始化串口
uart = UART(UART.UART1, 115200, 8, 0, 1, timeout=1000, read_buf_len=4096)
fm.register(6, fm.fpioa.UART1_TX)
fm.register(7, fm.fpioa.UART1_RX)
# 加载模型
task_fd = kpu.load("/sd/FaceDetection.smodel")
task_ld = kpu.load("/sd/FaceLandmarkDetection.smodel")#
task_fe = kpu.load("/sd/FeatureExtraction.smodel")
clock = time.clock()
# 按键初始化
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)
# 摄像头初始化
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)
# 人脸检测参数
anchor = (1.889, 2.5245, 2.9465, 3.94056, 3.99987, 5.3658, 5.155437,
6.92275, 6.718375, 9.01025)
dst_point = [(44, 59), (84, 59), (64, 82), (47, 105),
(81, 105)] # 标准人脸关键点位置
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 = ['worker1', 'worker2', 'workerr3', 'worker4', 'worker5',
'worker6', 'worker7', 'worker8', 'worker9', 'worker10']
ACCURACY = 85 # 识别准确率阈值
while True:
img = sensor.snapshot()
clock.tick()
code = kpu.run_yolo2(task_fd, img)
if code:
for i in code:
# 裁剪并调整人脸大小
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()
# 人脸关键点检测
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()))
# 在图像上绘制关键点
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)
# 对齐人脸到标准位置
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()
del (face_cut_128)
# 计算人脸特征向量
fmap = kpu.forward(task_fe, img_face)
feature = kpu.face_encode(fmap[:])
# 与已记录的特征比较
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:
# 在图像上显示识别结果
a = img.draw_string(i.x(), i.y(), names[index], color=(0, 255, 0), scale=2)
# 通过串口发送识别结果
uart.write(names[index] + "\n")
print(names[index] + "\n")
else:
a = img.draw_string(i.x(), i.y(), "Unknown", color=(255, 0, 0), scale=2)
uart.write("Unknown")
print("Unknown")
#received = uart.read() # 读取回环数据
#if received:
#print("发送成功，收到回环数据:", received.decode())
#else:
#print("发送失败")
# 如果按键按下，记录当前人脸特征
if start_processing:
record_ftr = feature
record_ftrs.append(record_ftr)
start_processing = False
uart.write("New face registered\n")
print("New face registered\n")
break
# 显示帧率
fps = clock.fps()
a = lcd.display(img)
gc.collect()
我提供的是可在k210板子上的使用的模型的main.py文件，分别是手势识别模型的和人脸识别模型的，我现在想要实现先通过了人脸识别后再进行手势识别的功能，我下面的代码有没有实现这些功能###########################################################################
# Pipeline 框架（可复用）
###########################################################################
class Stage:
"""所有模型阶段的基类，返回 True 表示通过，False 表示不通过"""
def enter(self): # 第一次进入该阶段
pass
def run(self, img): # 每帧运行
return True
def exit(self): # 离开该阶段
pass
class Pipeline:
def __init__(self):
self.stages = []
self.cur = 0
def add_stage(self, stage):
self.stages.append(stage)
def run(self):
while True:
if self.cur >= len(self.stages): # 所有阶段完成，重启
self.cur = 0
stage = self.stages[self.cur]
if self.cur == 0 or self.stages[self.cur-1].__class__ != stage.__class__:
stage.enter() # 只有第一次进入时调用
img = sensor.snapshot()
ok = stage.run(img)
lcd.display(img)
if ok: # 通过，进入下一阶段
stage.exit()
self.cur += 1
gc.collect()
###########################################################################
# 人脸识别阶段
###########################################################################
import sensor, image, lcd, time, KPU as kpu, gc
from Maix import GPIO, FPIOA
from machine import UART
from board import board_info
import uos
class FaceAuthStage(Stage):
def __init__(self, threshold=80):
self.thresh = threshold
self.task_fd = None # face detect
self.task_ld = None # landmark
self.task_fe = None # feature extraction
self.record = [] # 已注册特征
self.names = ['worker1','worker2','worker3','worker4','worker5',
'worker6','worker7','worker8','worker9','worker10']
self.dst_pt = [(44,59),(84,59),(64,82),(47,105),(81,105)]
self.anchor = (1.889,2.5245,2.9465,3.94056,3.99987,5.3658,
5.155437,6.92275,6.718375,9.01025)
# 按键注册新人脸
fm.register(board_info.BOOT_KEY, fm.fpioa.GPIOHS0)
self.key = GPIO(GPIO.GPIOHS0, GPIO.IN)
self.need_reg = False
self.key.irq(self._key_cb, GPIO.IRQ_RISING)
def _key_cb(self, *_):
self.need_reg = True
def enter(self):
lcd.clear(lcd.WHITE)
lcd.draw_string(10, 10, "Face Auth Stage", lcd.RED, lcd.WHITE)
self.task_fd = kpu.load("/sd/FaceDetection.smodel")
self.task_ld = kpu.load("/sd/FaceLandmarkDetection.smodel")
self.task_fe = kpu.load("/sd/FeatureExtraction.smodel")
kpu.init_yolo2(self.task_fd, 0.5, 0.3, 5, self.anchor)
# 如果 SD 根目录有 features.txt 则自动加载
if "features.txt" in uos.listdir("/sd"):
with open("/sd/features.txt") as f:
import json
data = json.load(f)
self.record = data["feats"]
self.names = data["names"]
def exit(self):
for t in (self.task_fd, self.task_ld, self.task_fe):
if t: kpu.deinit(t)
# 保存注册结果
with open("/sd/features.txt", "w") as f:
import json
json.dump({"feats": self.record, "names": self.names}, f)
def run(self, img):
code = kpu.run_yolo2(self.task_fd, img)
ok = False
if code:
for face in code:
rect = face.rect()
cut = img.cut(rect[0], rect[1], rect[2], rect[3])
cut128 = cut.resize(128,128); cut128.pix_to_ai()
# landmark
fmap = kpu.forward(self.task_ld, cut128)
pts = fmap[:]
src = [(rect[0]+int(pts[i]*rect[2]),
rect[1]+int(pts[i+1]*rect[3])) for i in range(0,10,2)]
# align
M = image.get_affine_transform(src, self.dst_pt)
warped = image.Image(size=(128,128))
image.warp_affine_ai(img, warped, M)
warped.ai_to_pix()
# feature
fmap = kpu.forward(self.task_fe, warped)
feat = kpu.face_encode(fmap[:])
if self.need_reg and len(self.record) < len(self.names):
self.record.append(feat)
self.need_reg = False
print("New face registered")
# 比对
max_score = 0
idx = 0
for j,f in enumerate(self.record):
score = kpu.face_compare(f, feat)
if score > max_score:
max_score, idx = score, j
if max_score > self.thresh:
img.draw_string(rect[0], rect[1]-20, self.names[idx], scale=2, color=(0,255,0))
ok = True # 通过认证
else:
img.draw_string(rect[0], rect[1]-20, "Unknown", scale=2, color=(255,0,0))
break
return ok
###########################################################################
# 手势识别阶段
###########################################################################
class GestureStage(Stage):
def __init__(self):
self.task = None
self.labels = ['go', 'stop']
self.anchors = [1.41,2.13,1.25,1.72,1.94,2.53,1.16,2.06,1.66,2.19]
def enter(self):
lcd.clear(lcd.WHITE)
lcd.draw_string(10, 10, "Gesture Stage", lcd.RED, lcd.WHITE)
self.task = kpu.load("/sd/model-174293.kmodel")
kpu.init_yolo2(self.task, 0.5, 0.3, 5, self.anchors)
def exit(self):
if self.task:
kpu.deinit(self.task)
def run(self, img):
objs = kpu.run_yolo2(self.task, img)
for obj in objs:
rect = obj.rect()
label = self.labels[obj.classid()]
img.draw_rectangle(rect)
img.draw_string(rect[0], rect[1], "{}:{:.2f}".format(label, obj.value()))
return True # 始终通过，如果想“按手势结束”再改
###########################################################################
# 主程序
###########################################################################
if __name__ == "__main__":
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.set_hmirror(1)
sensor.set_vflip(1)
sensor.run(1)
lcd.init()
lcd.rotation(2)
pipe = Pipeline()
pipe.add_stage(FaceAuthStage(threshold=80))
pipe.add_stage(GestureStage())
try:
pipe.run()
except KeyboardInterrupt:
pass
finally:
gc.collect()
如果实现了，是否完整实现了最开始的两个py文件的功能，请帮我解决为什么在板子上会显示灰屏，看不到画面
本文详细介绍了rising Linux系统的启动配置文件及其内容。包括boot.msg中的启动提示信息、f2中的硬盘类型提示、syslinux.cfg中的内核引导参数配置等关键信息。
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