253 lines
11 KiB
Python
253 lines
11 KiB
Python
|
import os
|
||
|
|
import json
|
||
|
|
import random
|
||
|
|
from datetime import datetime
|
||
|
|
import time
|
||
|
|
from PIL import Image, ImageDraw
|
||
|
|
import numpy as np
|
||
|
|
import cv2
|
||
|
|
|
||
|
|
#pip install Pillow 2_28
|
||
|
|
#python -m pip install Pillow -i https://mirrors.aliyun.com/pypi/simple/
|
||
|
|
#python -m pip install spdlog -i https://mirrors.aliyun.com/pypi/simple/
|
||
|
|
# python -m pip install paramiko -i https://mirrors.aliyun.com/pypi/simple/
|
||
|
|
|
||
|
|
class save_data_manager:
|
||
|
|
def __init__(self,base_path,robot_id):
|
||
|
|
self.base_path_ = base_path # TF卡挂载点
|
||
|
|
self.robot_id_ = robot_id
|
||
|
|
#数据库对象
|
||
|
|
|
||
|
|
def save_data_to_tfcard_json(self,json_data,dir_name,json_type):
|
||
|
|
"""生成并保存数据到TF卡"""
|
||
|
|
now = datetime.now()
|
||
|
|
date_str = now.strftime("%Y%m%d")
|
||
|
|
hour_str = now.strftime("%H")
|
||
|
|
|
||
|
|
# 1. 创建目录
|
||
|
|
save_dir = os.path.join(self.base_path_, "data", "device_info", self.robot_id_, dir_name, date_str, f"{date_str}_{hour_str}")
|
||
|
|
os.makedirs(save_dir, exist_ok=True)
|
||
|
|
|
||
|
|
# 2. 创建文件
|
||
|
|
timestamp = None
|
||
|
|
if json_data['timestamp']:
|
||
|
|
timestamp = json_data['timestamp']
|
||
|
|
else:
|
||
|
|
timestamp = datetime.now().strftime("%Y%m%d_%H_%M_%S")
|
||
|
|
filename = f"{timestamp}_{self.robot_id_}_{json_type}.json"
|
||
|
|
|
||
|
|
# 3. 写入json
|
||
|
|
json_save_path = os.path.join(save_dir, filename)
|
||
|
|
print("json_path:",json_save_path)
|
||
|
|
with open(json_save_path, "w") as f:
|
||
|
|
json.dump(json_data, f, indent=2)
|
||
|
|
print(f"Saved face data: {filename}")
|
||
|
|
|
||
|
|
return json_save_path
|
||
|
|
|
||
|
|
|
||
|
|
def save_data_to_tfcard_jpg(self,frame_data,dir_name,jpg_type):
|
||
|
|
"""生成并保存数据到TF卡"""
|
||
|
|
now = datetime.now()
|
||
|
|
date_str = now.strftime("%Y%m%d")
|
||
|
|
hour_str = now.strftime("%H")
|
||
|
|
|
||
|
|
# 1. 创建目录
|
||
|
|
save_dir = os.path.join(self.base_path_, "data", "device_info", self.robot_id_, dir_name, date_str, f"{date_str}_{hour_str}")
|
||
|
|
os.makedirs(save_dir, exist_ok=True)
|
||
|
|
|
||
|
|
# 2. 创建文件
|
||
|
|
timestamp = datetime.now().strftime("%Y%m%d_%H_%M_%S")
|
||
|
|
filename = f"{timestamp}_{self.robot_id_}_{jpg_type}.jpg"
|
||
|
|
|
||
|
|
# 3. 写入jpg
|
||
|
|
jpg_save_path = os.path.join(save_dir, filename)
|
||
|
|
print("jpg_save_path:",jpg_save_path)
|
||
|
|
|
||
|
|
cv2.imwrite(jpg_save_path,frame_data)
|
||
|
|
|
||
|
|
return jpg_save_path
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
"""生成模拟人脸关键点数据"""
|
||
|
|
def generate_fake_face_data(self):
|
||
|
|
"""生成模拟人脸关键点数据"""
|
||
|
|
return {
|
||
|
|
"timestamp": datetime.now().strftime("%Y%m%d_%H_%M_%S"),
|
||
|
|
"robot_id": self.robot_id_,
|
||
|
|
"results": [
|
||
|
|
{"x": round(random.uniform(0, 200),0), "y": round(random.uniform(0, 1),0)} for _ in range(68)
|
||
|
|
],
|
||
|
|
"confidence": random.uniform(0.7, 0.99)
|
||
|
|
}
|
||
|
|
|
||
|
|
"""生成模拟人体姿态数据"""
|
||
|
|
def generate_fake_pose_data(self):
|
||
|
|
"""生成模拟人体姿态数据"""
|
||
|
|
keypoints = ["nose", "left_eye", "right_eye", "left_ear", "right_ear",
|
||
|
|
"left_shoulder", "right_shoulder", "left_elbow", "right_elbow",
|
||
|
|
"left_wrist", "right_wrist", "left_hip", "right_hip",
|
||
|
|
"left_knee", "right_knee", "left_ankle", "right_ankle"]
|
||
|
|
|
||
|
|
return {
|
||
|
|
"timestamp": datetime.now().strftime("%Y%m%d_%H_%M_%S"),
|
||
|
|
"robot_id": self.robot_id_,
|
||
|
|
"results": {kp: {
|
||
|
|
"x": round(random.uniform(0, 200),0),
|
||
|
|
"y": round(random.uniform(0, 200),0),
|
||
|
|
"score": round(random.uniform(0.7, 0.99),2)
|
||
|
|
} for kp in keypoints}
|
||
|
|
}
|
||
|
|
"""生成模拟-血样-传感器数据"""
|
||
|
|
def generate_fake_sensor_001_data(self):
|
||
|
|
print("生成模拟-血样-传感器数据")
|
||
|
|
mks_data_random = {
|
||
|
|
'heart_rate': random.randint(60, 100), # 假设 心率 在60到180之间
|
||
|
|
'blood_oxygen': random.randint(85, 100), # 假设 血氧 在85到100之间
|
||
|
|
'micro_circulation': random.randint(50, 100), # 假设 微循环值 在50到100之间
|
||
|
|
'fatigue_index': random.randint(0, 100), # 假设 疲劳 指数在0到100之间
|
||
|
|
'systolic_pressure': random.randint(90, 180), # 假设 收缩压 在90到180之间
|
||
|
|
'diastolic_pressure': random.randint(60, 120), # 假设 舒张压 在60到120之间
|
||
|
|
'cardiac_output': random.randint(40, 100) # 假设 心输出 在40到100之间
|
||
|
|
}
|
||
|
|
print("mks_data_random:",mks_data_random)
|
||
|
|
return {
|
||
|
|
"timestamp": datetime.now().strftime("%Y%m%d_%H_%M_%S"),
|
||
|
|
"robot_id": self.robot_id_,
|
||
|
|
"result": mks_data_random,
|
||
|
|
}
|
||
|
|
|
||
|
|
def generate_sensor_001_data(self,mks_data_random):
|
||
|
|
print("生成模拟-血样-传感器数据")
|
||
|
|
# mks_data_random = {
|
||
|
|
# 'heart_rate': random.randint(60, 100), # 假设 心率 在60到180之间
|
||
|
|
# 'blood_oxygen': random.randint(85, 100), # 假设 血氧 在85到100之间
|
||
|
|
# 'micro_circulation': random.randint(50, 100), # 假设 微循环值 在50到100之间
|
||
|
|
# 'fatigue_index': random.randint(0, 100), # 假设 疲劳 指数在0到100之间
|
||
|
|
# 'systolic_pressure': random.randint(90, 180), # 假设 收缩压 在90到180之间
|
||
|
|
# 'diastolic_pressure': random.randint(60, 120), # 假设 舒张压 在60到120之间
|
||
|
|
# 'cardiac_output': random.randint(40, 100) # 假设 心输出 在40到100之间
|
||
|
|
# }
|
||
|
|
print("mks_data_random:",mks_data_random)
|
||
|
|
return {
|
||
|
|
"timestamp": datetime.now().strftime("%Y%m%d_%H_%M_%S"),
|
||
|
|
"robot_id": self.robot_id_,
|
||
|
|
"result": mks_data_random,
|
||
|
|
}
|
||
|
|
|
||
|
|
"""生成模拟-毫米波-传感器数据"""
|
||
|
|
def generate_fake_sensor_002_data(self):
|
||
|
|
print("生成模拟-毫米波-传感器数据")
|
||
|
|
sensor_data = {
|
||
|
|
"Part1Rst": {
|
||
|
|
"Battery": 100,
|
||
|
|
"Status": ["01"],
|
||
|
|
"CountNum": random.randint(1, 4),
|
||
|
|
"IsFall": 0
|
||
|
|
},
|
||
|
|
"Part2Rst": {
|
||
|
|
"TargetNum": 1,
|
||
|
|
"Target0": {
|
||
|
|
"ID": 16,
|
||
|
|
"PtNum": 22,
|
||
|
|
"PointCloud": [
|
||
|
|
[-0.0691750283216743, 0.13160428640200078, 1.3956739328119288],
|
||
|
|
[-0.07847705523541838, 0.15392815246426594, 1.383871249877477],
|
||
|
|
[-0.08118316058836383, 0.15254387167066272, 1.3841153359302658],
|
||
|
|
[-0.08118316058836383, 0.15254387167066272, 1.3841153359302658],
|
||
|
|
[-0.0929625, 0.17252076850321676, 1.3695799337803236],
|
||
|
|
[-0.08985277963433623, 0.1735465559405953, 1.366252506565061],
|
||
|
|
[-0.08981986049799257, 0.1729446846963683, 1.3632120794907499],
|
||
|
|
[-0.09402106608514023, 0.2008075149795186, 1.3752118342759272],
|
||
|
|
[-0.16259914164055408, 0.3659265315697133, 1.2686130237385964],
|
||
|
|
[-0.16217132883698243, 0.36379021511710175, 1.2623032883896637],
|
||
|
|
[-0.16910310730617623, 0.3631439575505753, 1.2691036666139888],
|
||
|
|
[-0.1638440883521219, 0.3831408593633806, 1.2404052475851655],
|
||
|
|
[-0.17067092536679368, 0.3803548338347067, 1.2408964990548133],
|
||
|
|
[-0.17067092536679368, 0.3803548338347067, 1.2408964990548133],
|
||
|
|
[-0.17294653770501756, 0.4044264626613462, 1.2315388724510081],
|
||
|
|
[-0.17294653770501756, 0.4044264626613462, 1.2315388724510081],
|
||
|
|
[-0.17235096722025173, 0.40169857121649716, 1.224546809430924],
|
||
|
|
[-0.16789502279593474, 0.4368924567706128, 1.236433858829858],
|
||
|
|
[-0.16789502279593474, 0.4368924567706128, 1.236433858829858],
|
||
|
|
[-0.16789502279593474, 0.4368924567706128, 1.236433858829858],
|
||
|
|
[-0.16719017622805205, 0.5141370904782002, 1.2188803542752917],
|
||
|
|
[-0.16758717901763867, 0.5169416889917281, 1.227432168376347]
|
||
|
|
],
|
||
|
|
"CenterPoint": [-0.12395, 0.34706, 1.27605]
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"Part3Rst": {
|
||
|
|
"TargetNum": 1,
|
||
|
|
"Target0": {
|
||
|
|
"ID": 16,
|
||
|
|
"Point": [-0.12395, 0.32227, 1.2512599999999998],
|
||
|
|
"HeartRate": random.randint(50, 65),
|
||
|
|
"BreathRate": 4
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
print("sensor_data:",sensor_data)
|
||
|
|
|
||
|
|
return {
|
||
|
|
"timestamp": datetime.now().strftime("%Y%m%d_%H_%M_%S"),
|
||
|
|
"robot_id": self.robot_id_,
|
||
|
|
"result": sensor_data,
|
||
|
|
}
|
||
|
|
|
||
|
|
"""生成模拟图片"""
|
||
|
|
def generate_fake_image(self,width=640, height=480):
|
||
|
|
"""生成模拟图片"""
|
||
|
|
img = Image.new("RGB", (width, height), color=(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)))
|
||
|
|
draw = ImageDraw.Draw(img)
|
||
|
|
|
||
|
|
# 添加一些随机形状
|
||
|
|
for _ in range(random.randint(3, 10)):
|
||
|
|
x1 = random.randint(0, width)
|
||
|
|
y1 = random.randint(0, height)
|
||
|
|
x2 = random.randint(x1, width)
|
||
|
|
y2 = random.randint(y1, height)
|
||
|
|
draw.rectangle([x1, y1, x2, y2],
|
||
|
|
fill=(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)))
|
||
|
|
|
||
|
|
# 将 PIL 图像转换为 NumPy 数组
|
||
|
|
numpy_image = np.array(img)
|
||
|
|
|
||
|
|
return numpy_image
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
|
||
|
|
base_path = "./sftp-data_1" # TF卡挂载点
|
||
|
|
robot_id = "d80801000058"
|
||
|
|
|
||
|
|
#json写入管理(完成)
|
||
|
|
test = save_data_manager(base_path,robot_id)
|
||
|
|
|
||
|
|
#数据插入管理
|
||
|
|
|
||
|
|
#数据库同步管理(完成)
|
||
|
|
|
||
|
|
#生成人脸数据
|
||
|
|
# face_data = test.generate_fake_face_data()
|
||
|
|
# test.save_data_to_tfcard_json(face_data,dir_name="face",json_type="face")
|
||
|
|
# #TODO 插入数据库
|
||
|
|
|
||
|
|
# #人体人脸数据
|
||
|
|
# pose_data = test.generate_fake_pose_data()
|
||
|
|
# test.save_data_to_tfcard_json(pose_data,dir_name="pose",json_type="pose")
|
||
|
|
# #TODO 插入数据库
|
||
|
|
|
||
|
|
#传感器数据
|
||
|
|
sensor_data = test.generate_fake_sensor_001_data()
|
||
|
|
test.save_data_to_tfcard_json(sensor_data,dir_name="sensor_001",json_type="sensor_001")
|
||
|
|
|
||
|
|
sensor_data = test.generate_fake_sensor_002_data()
|
||
|
|
test.save_data_to_tfcard_json(sensor_data,dir_name="sensor_002",json_type="sensor_002")
|
||
|
|
#TODO 插入数据
|
||
|
|
|
||
|
|
# frame_data = test.generate_fake_image()
|
||
|
|
# test.save_data_to_tfcard_jpg(frame_data,dir_name="face_jpg",jpg_type="jpg")
|
||
|
|
#TODO 插入数据库
|
||
|
|
|
||
|
|
|