昨天也是去开封结束了中国机器人大赛（~）先让我总结点话
（1）找好队友！！！！
（2）千万不要用实验室电容！！（建议全用韩佬的或者你张的）
从四月多到5.25也是很曲折啊 自己搓的第一版板子降压出问题了 第二版板子上电直接炸了 还好你张神通广大给我飞了个dc，代码也是边手写边cv边ai的真是不容易 感觉比起这种的还是更喜欢跑赛道的
就这些吧补图哩~有什么的想说的往后再补
美景起手~
陶景驰串口屏（到最后也没咋用上 起了个显示作用 串口发的也是离谱
（做了个按键控制led 虽然很抽象
k230和屏幕捏 有钱了我要买maxicam！
串口屏发的串口 自从这天开始 这玩意发的串口就不对了 设定的和发到串口助手的和发到k230的都不一样
第一版 别骂（ 虽然很抽象但是起码能用 虽然买了7个ams1117烧了六个（
某人的机械结构（懒得喷
第二版pcb 也是中看不中用了 虽然也不咋中看
二级展开柜子
第二版实物 也很抽象啦 感谢张师傅（跪谢
感谢朱佬看了两眼就发现问题所在了 让这个哪里都卡的柜子顺畅的动了起来（
十一点的101~
熬了俩通宵的114~
人体检测发的xy坐标（还是手动缩进才这么整齐的
朱姐帮我改报告
占卜的张师傅
凌晨三点的114
最后一晚（
底板的线乱的要死 但是没办法哩
25早上五点多
你康
好郑航真不比大专差
看了半天也没看懂这啥玩意
~
也是喝上老师请的水了😋
喵喵喵
喵喵喵
（你朱~
抽象机器人
no man ji~
十个狗子九个成品（
下面贴代码啦~~
from libs.PipeLine import PipeLine, ScopedTiming
from libs.AIBase import AIBase
from libs.AI2D import Ai2d
import os
import ujson
from media.media import *
from time import *
import nncase_runtime as nn
import ulab.numpy as np
import utime
import image
import aidemo
import random
import gc
import sys
import math
import aicube
from machine import Pin
from machine import FPIOA
from machine import UART
from machine import PWM
import time
# Configure pins
fpioa = FPIOA()
fpioa.set_function(42, FPIOA.PWM0)
fpioa.set_function(52, FPIOA.PWM4)
fpioa.set_function(47,FPIOA.PWM3)
fpioa.set_function(3,FPIOA.UART1_TXD)
fpioa.set_function(32, FPIOA.UART3_TXD)
fpioa.set_function(33, FPIOA.UART3_RXD)
uart = UART(UART.UART3, baudrate=115200, bits=UART.EIGHTBITS, parity=UART.PARITY_NONE, stop=UART.STOPBITS_ONE)
uart_another=UART(UART.UART1,baudrate=115200,bits=UART.EIGHTBITS,parity=UART.PARITY_NONE,stop=UART.STOPBITS_ONE)
servo = PWM(0, 50, 0, enable=True)
servo2 = PWM(4, 50, 0, enable=True)
servo3=PWM(3,50,0,enable=True)
def Servo_360(servo, angle):
angle = max(-90, min(90, angle))
duty = 5 + (angle + 90) / 36 # Original duty cycle logic
servo.duty(int(duty))
# Face Detection Classes
class FaceDetApp(AIBase):
def __init__(self, kmodel_path, model_input_size, anchors, confidence_threshold=0.25, nms_threshold=0.3, rgb888p_size=[1920,1080], display_size=[1920,1080], debug_mode=0):
super().__init__(kmodel_path, model_input_size, rgb888p_size, debug_mode)
self.kmodel_path = kmodel_path
self.model_input_size = model_input_size
self.confidence_threshold = confidence_threshold
self.nms_threshold = nms_threshold
self.anchors = anchors
self.rgb888p_size = [ALIGN_UP(rgb888p_size[0],16), rgb888p_size[1]]
self.display_size = [ALIGN_UP(display_size[0],16), display_size[1]]
self.debug_mode = debug_mode
self.ai2d = Ai2d(debug_mode)
self.ai2d.set_ai2d_dtype(nn.ai2d_format.NCHW_FMT, nn.ai2d_format.NCHW_FMT, np.uint8, np.uint8)
def config_preprocess(self, input_image_size=None):
with ScopedTiming("set preprocess config", self.debug_mode > 0):
ai2d_input_size = input_image_size if input_image_size else self.rgb888p_size
self.ai2d.pad(self.get_pad_param(), 0, [104,117,123])
self.ai2d.resize(nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel)
self.ai2d.build([1,3,ai2d_input_size[1],ai2d_input_size[0]], [1,3,self.model_input_size[1],self.model_input_size[0]])
def postprocess(self, results):
with ScopedTiming("postprocess", self.debug_mode > 0):
res = aidemo.face_det_post_process(self.confidence_threshold, self.nms_threshold, self.model_input_size[0], self.anchors, self.rgb888p_size, results)
if len(res) == 0:
return res, res
else:
return res[0], res[1]
def get_pad_param(self):
dst_w = self.model_input_size[0]
dst_h = self.model_input_size[1]
ratio_w = dst_w / self.rgb888p_size[0]
ratio_h = dst_h / self.rgb888p_size[1]
if ratio_w < ratio_h:
ratio = ratio_w
else:
ratio = ratio_h
new_w = (int)(ratio * self.rgb888p_size[0])
new_h = (int)(ratio * self.rgb888p_size[1])
dw = (dst_w - new_w) / 2
dh = (dst_h - new_h) / 2
top = (int)(round(0))
bottom = (int)(round(dh * 2 + 0.1))
left = (int)(round(0))
right = (int)(round(dw * 2 - 0.1))
return [0,0,0,0,top, bottom, left, right]
class FaceRegistrationApp(AIBase):
def __init__(self, kmodel_path, model_input_size, rgb888p_size=[1920,1080], display_size=[1920,1080], debug_mode=0):
super().__init__(kmodel_path, model_input_size, rgb888p_size, debug_mode)
self.kmodel_path = kmodel_path
self.model_input_size = model_input_size
self.rgb888p_size = [ALIGN_UP(rgb888p_size[0],16), rgb888p_size[1]]
self.display_size = [ALIGN_UP(display_size[0],16), display_size[1]]
self.debug_mode = debug_mode
self.umeyama_args_112 = [
38.2946, 51.6963,
73.5318, 51.5014,
56.0252, 71.7366,
41.5493, 92.3655,
70.7299, 92.2041
]
self.ai2d = Ai2d(debug_mode)
self.ai2d.set_ai2d_dtype(nn.ai2d_format.NCHW_FMT, nn.ai2d_format.NCHW_FMT, np.uint8, np.uint8)
def config_preprocess(self, landm, input_image_size=None):
with ScopedTiming("set preprocess config", self.debug_mode > 0):
ai2d_input_size = input_image_size if input_image_size else self.rgb888p_size
affine_matrix = self.get_affine_matrix(landm)
self.ai2d.affine(nn.interp_method.cv2_bilinear, 0, 0, 127, 1, affine_matrix)
self.ai2d.build([1,3,ai2d_input_size[1],ai2d_input_size[0]], [1,3,self.model_input_size[1],self.model_input_size[0]])
def postprocess(self, results):
with ScopedTiming("postprocess", self.debug_mode > 0):
return results[0][0]
def svd22(self, a):
s = [0.0, 0.0]
u = [0.0, 0.0, 0.0, 0.0]
v = [0.0, 0.0, 0.0, 0.0]
s[0] = (math.sqrt((a[0] - a[3]) ** 2 + (a[1] + a[2]) ** 2) + math.sqrt((a[0] + a[3]) ** 2 + (a[1] - a[2]) ** 2)) / 2
s[1] = abs(s[0] - math.sqrt((a[0] - a[3]) ** 2 + (a[1] + a[2]) ** 2))
v[2] = math.sin((math.atan2(2 * (a[0] * a[1] + a[2] * a[3]), a[0] ** 2 - a[1] ** 2 + a[2] ** 2 - a[3] ** 2)) / 2) if s[0] > s[1] else 0
v[0] = math.sqrt(1 - v[2] ** 2)
v[1] = -v[2]
v[3] = v[0]
u[0] = -(a[0] * v[0] + a[1] * v[2]) / s[0] if s[0] != 0 else 1
u[2] = -(a[2] * v[0] + a[3] * v[2]) / s[0] if s[0] != 0 else 0
u[1] = (a[0] * v[1] + a[1] * v[3]) / s[1] if s[1] != 0 else -u[2]
u[3] = (a[2] * v[1] + a[3] * v[3]) / s[1] if s[1] != 0 else u[0]
v[0] = -v[0]
v[2] = -v[2]
return u, s, v
def image_umeyama_112(self, src):
SRC_NUM = 5
SRC_DIM = 2
src_mean = [0.0, 0.0]
dst_mean = [0.0, 0.0]
for i in range(0, SRC_NUM * 2, 2):
src_mean[0] += src[i]
src_mean[1] += src[i + 1]
dst_mean[0] += self.umeyama_args_112[i]
dst_mean[1] += self.umeyama_args_112[i + 1]
src_mean[0] /= SRC_NUM
src_mean[1] /= SRC_NUM
dst_mean[0] /= SRC_NUM
dst_mean[1] /= SRC_NUM
src_demean = [[0.0, 0.0] for _ in range(SRC_NUM)]
dst_demean = [[0.0, 0.0] for _ in range(SRC_NUM)]
for i in range(SRC_NUM):
src_demean[i][0] = src[2 * i] - src_mean[0]
src_demean[i][1] = src[2 * i + 1] - src_mean[1]
dst_demean[i][0] = self.umeyama_args_112[2 * i] - dst_mean[0]
dst_demean[i][1] = self.umeyama_args_112[2 * i + 1] - dst_mean[1]
A = [[0.0, 0.0], [0.0, 0.0]]
for i in range(SRC_DIM):
for k in range(SRC_DIM):
for j in range(SRC_NUM):
A[i][k] += dst_demean[j][i] * src_demean[j][k]
A[i][k] /= SRC_NUM
T = [[1, 0, 0], [0, 1, 0], [0, 0, 1]]
U, S, V = self.svd22([A[0][0], A[0][1], A[1][0], A[1][1]])
T[0][0] = U[0] * V[0] + U[1] * V[2]
T[0][1] = U[0] * V[1] + U[1] * V[3]
T[1][0] = U[2] * V[0] + U[3] * V[2]
T[1][1] = U[2] * V[1] + U[3] * V[3]
scale = 1.0
src_demean_mean = [0.0, 0.0]
src_demean_var = [0.0, 0.0]
for i in range(SRC_NUM):
src_demean_mean[0] += src_demean[i][0]
src_demean_mean[1] += src_demean[i][1]
src_demean_mean[0] /= SRC_NUM
src_demean_mean[1] /= SRC_NUM
for i in range(SRC_NUM):
src_demean_var[0] += (src_demean_mean[0] - src_demean[i][0]) * (src_demean_mean[0] - src_demean[i][0])
src_demean_var[1] += (src_demean_mean[1] - src_demean[i][1]) * (src_demean_mean[1] - src_demean[i][1])
src_demean_var[0] /= SRC_NUM
src_demean_var[1] /= SRC_NUM
scale = 1.0 / (src_demean_var[0] + src_demean_var[1]) * (S[0] + S[1])
T[0][2] = dst_mean[0] - scale * (T[0][0] * src_mean[0] + T[0][1] * src_mean[1])
T[1][2] = dst_mean[1] - scale * (T[1][0] * src_mean[0] + T[1][1] * src_mean[1])
T[0][0] *= scale
T[0][1] *= scale
T[1][0] *= scale
T[1][1] *= scale
return T
def get_affine_matrix(self, sparse_points):
with ScopedTiming("get_affine_matrix", self.debug_mode > 1):
matrix_dst = self.image_umeyama_112(sparse_points)
matrix_dst = [matrix_dst[0][0], matrix_dst[0][1], matrix_dst[0][2],
matrix_dst[1][0], matrix_dst[1][1], matrix_dst[1][2]]
return matrix_dst
class FaceRecognition:
def __init__(self, face_det_kmodel, face_reg_kmodel, det_input_size, reg_input_size, database_dir, anchors, confidence_threshold=0.25, nms_threshold=0.3, face_recognition_threshold=0.75, rgb888p_size=[1280,720], display_size=[1920,1080], debug_mode=0):
self.face_det_kmodel = face_det_kmodel
self.face_reg_kmodel = face_reg_kmodel
self.det_input_size = det_input_size
self.reg_input_size = reg_input_size
self.database_dir = database_dir
self.anchors = anchors
self.confidence_threshold = confidence_threshold
self.nms_threshold = nms_threshold
self.face_recognition_threshold = face_recognition_threshold
self.rgb888p_size = [ALIGN_UP(rgb888p_size[0],16), rgb888p_size[1]]
self.display_size = [ALIGN_UP(display_size[0],16), display_size[1]]
self.debug_mode = debug_mode
self.max_register_face = 100
self.feature_num = 128
self.valid_register_face = 0
self.db_name = []
self.db_data = []
self.face_det = FaceDetApp(self.face_det_kmodel, model_input_size=self.det_input_size, anchors=self.anchors, confidence_threshold=self.confidence_threshold, nms_threshold=self.nms_threshold, rgb888p_size=self.rgb888p_size, display_size=self.display_size, debug_mode=0)
self.face_reg = FaceRegistrationApp(self.face_reg_kmodel, model_input_size=self.reg_input_size, rgb888p_size=self.rgb888p_size, display_size=self.display_size)
self.face_det.config_preprocess()
self.database_init()
self.last_check_time = utime.ticks_ms()
self.print_interval = 1000
self.current_status = "no"
def run(self, input_np):
det_boxes, landms = self.face_det.run(input_np)
recg_res = []
for landm in landms:
self.face_reg.config_preprocess(landm)
feature = self.face_reg.run(input_np)
res = self.database_search(feature)
recg_res.append(res)
return det_boxes, recg_res
def database_init(self):
with ScopedTiming("database_init", self.debug_mode > 1):
db_file_list = os.listdir(self.database_dir)
for db_file in db_file_list:
if not db_file.endswith('.bin'):
continue
if self.valid_register_face >= self.max_register_face:
break
valid_index = self.valid_register_face
full_db_file = self.database_dir + db_file
with open(full_db_file, 'rb') as f:
data = f.read()
feature = np.frombuffer(data, dtype=np.float)
self.db_data.append(feature)
name = db_file.split('.')[0]
self.db_name.append(name)
self.valid_register_face += 1
def database_search(self, feature):
with ScopedTiming("database_search", self.debug_mode > 1):
v_id = -1
v_score_max = 0.0
feature /= np.linalg.norm(feature)
for i in range(self.valid_register_face):
db_feature = self.db_data[i]
db_feature /= np.linalg.norm(db_feature)
v_score = np.dot(feature, db_feature)/2 + 0.5
if v_score > v_score_max:
v_score_max = v_score
v_id = i
if v_id == -1:
self.current_status = "no"
return 'unknown'
elif v_score_max < self.face_recognition_threshold:
self.current_status = "no"
return 'unknown'
else:
if self.db_name[v_id] == "111":
self.current_status = "yes"
else:
self.current_status = "no"
result = 'name: {}, score:{}'.format(self.db_name[v_id], v_score_max)
return result
def check_status(self):
current_time = utime.ticks_ms()
if utime.ticks_diff(current_time, self.last_check_time) >= self.print_interval:
self.last_check_time = current_time
return self.current_status
return None
def draw_result(self, pl, dets, recg_results):
pl.osd_img.clear()
if dets:
for i, det in enumerate(dets):
x1, y1, w, h = map(lambda x: int(round(x, 0)), det[:4])
x1 = x1 * self.display_size[0]//self.rgb888p_size[0]
y1 = y1 * self.display_size[1]//self.rgb888p_size[1]
w = w * self.display_size[0]//self.rgb888p_size[0]
h = h * self.display_size[1]//self.rgb888p_size[1]
pl.osd_img.draw_rectangle(x1, y1, w, h, color=(255,0,0,255), thickness=4)
recg_text = recg_results[i]
pl.osd_img.draw_string_advanced(x1, y1, 32, recg_text, color=(255,255,0,0))
# Person Detection Class
class PersonDetectionApp(AIBase):
def __init__(self, kmodel_path, model_input_size, labels, anchors, confidence_threshold=0.2, nms_threshold=0.5, nms_option=False, strides=[8,16,32], rgb888p_size=[224,224], display_size=[1920,1080], debug_mode=0):
super().__init__(kmodel_path, model_input_size, rgb888p_size, debug_mode)
self.last_boxes = []
self.kmodel_path = kmodel_path
self.model_input_size = model_input_size
self.labels = labels
self.anchors = anchors
self.strides = strides
self.confidence_threshold = confidence_threshold
self.nms_threshold = nms_threshold
self.nms_option = nms_option
self.rgb888p_size = [ALIGN_UP(rgb888p_size[0],16), rgb888p_size[1]]
self.display_size = [ALIGN_UP(display_size[0],16), display_size[1]]
self.debug_mode = debug_mode
self.ai2d = Ai2d(debug_mode)
self.ai2d.set_ai2d_dtype(nn.ai2d_format.NCHW_FMT, nn.ai2d_format.NCHW_FMT, np.uint8, np.uint8)
def config_preprocess(self, input_image_size=None):
with ScopedTiming("set preprocess config", self.debug_mode > 0):
ai2d_input_size = input_image_size if input_image_size else self.rgb888p_size
top, bottom, left, right = self.get_padding_param()
self.ai2d.pad([0,0,0,0,top,bottom,left,right], 0, [0,0,0])
self.ai2d.resize(nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel)
self.ai2d.build([1,3,ai2d_input_size[1],ai2d_input_size[0]], [1,3,self.model_input_size[1],self.model_input_size[0]])
def postprocess(self, results):
with ScopedTiming("postprocess", self.debug_mode > 0):
dets = aicube.anchorbasedet_post_process(results[0], results[1], results[2], self.model_input_size, self.rgb888p_size, self.strides, len(self.labels), self.confidence_threshold, self.nms_threshold, self.anchors, self.nms_option)
return dets
def draw_result(self, pl, dets):
with ScopedTiming("display_draw", self.debug_mode >0):
self.last_boxes = []
if dets:
pl.osd_img.clear()
for det_box in dets:
x1, y1, x2, y2 = det_box[2], det_box[3], det_box[4], det_box[5]
w = float(x2 - x1) * self.display_size[0] // self.rgb888p_size[0]
h = float(y2 - y1) * self.display_size[1] // self.rgb888p_size[1]
x1 = int(x1 * self.display_size[0] // self.rgb888p_size[0])
y1 = int(y1 * self.display_size[1] // self.rgb888p_size[1])
x2 = int(x2 * self.display_size[0] // self.rgb888p_size[0])
y2 = int(y2 * self.display_size[1] // self.rgb888p_size[1])
self.last_boxes.append((x1,y1,x2,y2))
if (h < (0.1*self.display_size[0])):
continue
if (w < (0.25*self.display_size[0]) and ((x1 < (0.03*self.display_size[0])) or (x2 > (0.97*self.display_size[0])))):
continue
if (w<(0.15*self.display_size[0]) and ((x1<(0.01*self.display_size[0])) or (x2>(0.99*self.display_size[0])))):
continue
pl.osd_img.draw_rectangle(x1, y1, int(w), int(h), color=(255,0,255,0), thickness=2)
pl.osd_img.draw_string_advanced(x1, y1-50, 32, " " + self.labels[det_box[0]] + " " + str(round(det_box[1],2)), color=(255,0,255,0))
else:
pl.osd_img.clear()
def get_padding_param(self):
dst_w = self.model_input_size[0]
dst_h = self.model_input_size[1]
input_width = self.rgb888p_size[0]
input_high = self.rgb888p_size[1]
ratio_w = dst_w / input_width
ratio_h = dst_h / input_high
if ratio_w < ratio_h:
ratio = ratio_w
else:
ratio = ratio_h
new_w = (int)(ratio * input_width)
new_h = (int)(ratio * input_high)
dw = (dst_w - new_w) / 2
dh = (dst_h - new_h) / 2
top = int(round(dh - 0.1))
bottom = int(round(dh + 0.1))
left = int(round(dw - 0.1))
right = int(round(dw - 0.1))
return top, bottom, left, right
def main():
# Display mode configuration
display_mode = "lcd"
if display_mode == "hdmi":
display_size = [1920, 1080]
else:
display_size = [800, 480]
# Common configuration
rgb888p_size = [1920, 1080]
# Initialize Pipeline
pl = PipeLine(rgb888p_size=rgb888p_size, display_size=display_size, display_mode=display_mode)
pl.create()
# Initialize Face Recognition
face_det_kmodel_path = "/sdcard/examples/kmodel/face_detection_320.kmodel"
face_reg_kmodel_path = "/sdcard/examples/kmodel/face_recognition.kmodel"
anchors_path = "/sdcard/examples/utils/prior_data_320.bin"
database_dir = "/sdcard/examples/utils/db/"
face_det_input_size = [320, 320]
face_reg_input_size = [112, 112]
confidence_threshold = 0.5
nms_threshold = 0.2
anchor_len = 4200
det_dim = 4
anchors = np.fromfile(anchors_path, dtype=np.float)
anchors = anchors.reshape((anchor_len, det_dim))
face_recognition_threshold = 0.75
fr = FaceRecognition(
face_det_kmodel_path,
face_reg_kmodel_path,
det_input_size=face_det_input_size,
reg_input_size=face_reg_input_size,
database_dir=database_dir,
anchors=anchors,
confidence_threshold=confidence_threshold,
nms_threshold=nms_threshold,
face_recognition_threshold=face_recognition_threshold,
rgb888p_size=rgb888p_size,
display_size=display_size
)
# Initialize Person Detection
person_kmodel_path = "/sdcard/examples/kmodel/person_detect_yolov5n.kmodel"
person_labels = ["person"]
person_anchors = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326]
person_confidence_threshold = 0.2
person_nms_threshold = 0.6
person_det = PersonDetectionApp(
person_kmodel_path,
model_input_size=[640,640],
labels=person_labels,
anchors=person_anchors,
confidence_threshold=person_confidence_threshold,
nms_threshold=person_nms_threshold,
nms_option=False,
strides=[8,16,32],
rgb888p_size=rgb888p_size,
display_size=display_size,
debug_mode=0
)
person_det.config_preprocess()
# Main loop
face_recognition_active = True
last_time = utime.ticks_ms()
current_mode = None # None, 'face', or 'person'
try:
while True:
servo.enable(False)
servo2.enable(False)
servo3.enable(False)
os.exitpoint()
# Check for UART commands
cmd = uart.read(1)
if cmd == b'\xd1': # Face detection command
current_mode = 'face'
face_recognition_active=True
print("Switching to face detection mode")
elif cmd == b'\xd2': # Person detection command
current_mode = 'person'
print("Switching to person detection mode")
elif cmd==b'\xe1':
servo3.enable(False)
servo.enable(True)
Servo_360(servo,90)
servo2.enable(False)
time.sleep(1.6)
servo.enable(False)
servo2.enable(True)
Servo_360(servo2,-90)
time.sleep(1.8)
elif cmd==b'\xe2':
servo2.enable(True)
Servo_360(servo2,90)
time.sleep(2)
servo2.enable(False)
servo.enable(True)
Servo_360(servo,-90)
time.sleep(1.9)
servo.enable(False)
elif cmd==b'\xe3':
servo3.enable(True)
Servo_360(servo3,90)
time.sleep(1)
servo3.enable(False)
elif cmd==b'\xe4':
servo.enable(False)
servo2.enable(False)
servo3.enable (True)
Servo_360(servo3,-90)
time.sleep(2)
elif cmd==b'\x06':
uart_another.write(bytes([0xff]))
img = pl.get_frame()
if current_mode == 'face':
# Run face detection and recognition
det_boxes, recg_res = fr.run(img)
fr.draw_result(pl, det_boxes, recg_res)
# Check status and send UART commands if needed
status = fr.check_status()
if status == 'yes':
uart.write(bytes([0xAA, 0x00, 0x01]))
current_mode=None
elif status == 'no':
uart.write(bytes([0xAA, 0x00, 0x02]))
time.sleep(3)
elif current_mode == 'person':
# Run person detection
res = person_det.run(img)
person_det.draw_result(pl, res)
# Send person detection results via UART
current_time = utime.ticks_ms()
if current_time - last_time >= 100:
if not person_det.last_boxes:
uart_another.write(bytes([0xff]))
else:
for box in person_det.last_boxes:
x1, y1, x2, y2 = box
x = (x1 + x2) / 2
y = (y1 + y2) / 2
uart_another.write("{},{},{},{} ".format(x1, x2, y1, y2))
if x > 300 and x < 650:
uart_another.write(bytes([0x02]))
if x < 350:
uart_another.write(bytes([0x04]))
if x > 650:
uart_another.write(bytes([0x05]))
pl.show_image()
gc.collect()
finally:
# Cleanup
if 'fr' in locals():
del fr
if 'person_det' in locals():
person_det.deinit()
pl.destroy()
if __name__ == "__main__":
main()
由于主包心急 把keil代码删了 虽然也没什么好看的吧 （其实主播把语音模块的也删了 服le）
过程是曲折的（其实只有折），好在结果很满意 下次比赛希望跟队友合作能更好点 取得好成绩
over~
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