+ {% for stream_id in streams %}
+
+
+
diff --git a/model/plugins/ModelBase.py b/model/plugins/ModelBase.py
index c643078..4c7e5d8 100644
--- a/model/plugins/ModelBase.py
+++ b/model/plugins/ModelBase.py
@@ -1,21 +1,35 @@
from abc import abstractmethod,ABC
from shapely.geometry import Point, Polygon
+from myutils.ConfigManager import myCongif
import numpy as np
import cv2
import ast
import platform
+import acl
+
+#-----acl相关------
+SUCCESS = 0 # 成功状态值
+FAILED = 1 # 失败状态值
+ACL_MEM_MALLOC_NORMAL_ONLY = 2 # 申请内存策略, 仅申请普通页
class ModelBase(ABC):
- def __init__(self):
+ def __init__(self,path):
+ '''
+ 模型类实例化
+ :param path: 模型文件本身的路径
+ :param threshold: 模型的置信阈值
+ '''
self.name = None #基于name来查询，用户对模型的配置参数，代表着模型名称需要唯一 2024-6-18 -逻辑还需要完善和验证
self.version = None
self.model_type = None # 模型类型 1-图像分类，2-目标检测（yolov5)，3-分割模型，4-关键点
- self.system = platform.system() #获取系统平台
+ self.system = myCongif.get_data("model_platform") #platform.system() #获取系统平台
self.do_map = { # 定义插件的入口函数 --
# POCType.POC: self.do_verify,
# POCType.SNIFFER: self.do_sniffer,
# POCType.BRUTE: self.do_brute
}
+ self.model_path = path # 模型路径
+
def __del__(self):
print("资源释放")
@@ -33,6 +47,167 @@ class ModelBase(ABC):
else:
return False
+ #acl ----- 相关-----
+ def _init_acl(self):
+ '''acl初始化函数'''
+ self.device_id = 0
+ #step1 初始化
+ ret = acl.init()
+ ret = acl.rt.set_device(self.device_id) # 指定运算的Device
+ if ret:
+ raise RuntimeError(ret)
+ self.context, ret = acl.rt.create_context(self.device_id) # 显式创建一个Context
+ if ret:
+ raise RuntimeError(ret)
+ print('Init ACL Successfully')
+
+ def _del_acl(self):
+ '''acl去初始化'''
+ ret = acl.rt.destroy_context(self.context) # 释放 Context
+ if ret:
+ raise RuntimeError(ret)
+ ret = acl.rt.reset_device(self.device_id) # 释放Device
+ if ret:
+ raise RuntimeError(ret)
+ ret = acl.finalize() # 去初始化
+ if ret:
+ raise RuntimeError(ret)
+ print('Deinit ACL Successfully')
+
+ def _init_resource(self):
+ ''' 初始化模型、输出相关资源。相关数据类型: aclmdlDesc aclDataBuffer aclmdlDataset'''
+ print("Init model resource")
+ # 加载模型文件
+ #self.model_path = "/home/HwHiAiUser/samples/yolo_acl_sample/yolov5s_bs1.om"
+ self.model_id, ret = acl.mdl.load_from_file(self.model_path) # 加载模型
+ if ret != 0:
+ print(f"{self.model_path}---模型加载失败！")
+ return False
+ self.model_desc = acl.mdl.create_desc() # 初始化模型信息对象
+ ret = acl.mdl.get_desc(self.model_desc, self.model_id) # 根据模型获取描述信息
+ print("[Model] Model init resource stage success")
+ # 创建模型输出 dataset 结构
+ self._gen_output_dataset() # 创建模型输出dataset结构
+ return True
+
+ def _gen_output_dataset(self):
+ ''' 组织输出数据的dataset结构 '''
+ ret = SUCCESS
+ self._output_num = acl.mdl.get_num_outputs(self.model_desc) # 获取模型输出个数
+ self.output_dataset = acl.mdl.create_dataset() # 创建输出dataset结构
+ for i in range(self._output_num):
+ temp_buffer_size = acl.mdl.get_output_size_by_index(self.model_desc, i) # 获取模型输出个数
+ temp_buffer, ret = acl.rt.malloc(temp_buffer_size, ACL_MEM_MALLOC_NORMAL_ONLY) # 为每个输出申请device内存
+ dataset_buffer = acl.create_data_buffer(temp_buffer,
+ temp_buffer_size) # 创建输出的data buffer结构,将申请的内存填入data buffer
+ _, ret = acl.mdl.add_dataset_buffer(self.output_dataset, dataset_buffer) # 将 data buffer 加入输出dataset
+
+ if ret == FAILED:
+ self._release_dataset(self.output_dataset) # 失败时释放dataset
+ print("[Model] create model output dataset success")
+
+ def _gen_input_dataset(self, input_list):
+ ''' 组织输入数据的dataset结构 '''
+ ret = SUCCESS
+ self._input_num = acl.mdl.get_num_inputs(self.model_desc) # 获取模型输入个数
+ self.input_dataset = acl.mdl.create_dataset() # 创建输入dataset结构
+ for i in range(self._input_num):
+ item = input_list[i] # 获取第 i 个输入数据
+ data_ptr = acl.util.bytes_to_ptr(item.tobytes()) # 获取输入数据字节流
+ size = item.size * item.itemsize # 获取输入数据字节数
+ dataset_buffer = acl.create_data_buffer(data_ptr, size) # 创建输入dataset buffer结构, 填入输入数据
+ _, ret = acl.mdl.add_dataset_buffer(self.input_dataset, dataset_buffer) # 将dataset buffer加入dataset
+
+ if ret == FAILED:
+ self._release_dataset(self.input_dataset) # 失败时释放dataset
+ print("[Model] create model input dataset success")
+
+ def _unpack_bytes_array(self, byte_array, shape, datatype):
+ ''' 将内存不同类型的数据解码为numpy数组 '''
+ np_type = None
+
+ # 获取输出数据类型对应的numpy数组类型和解码标记
+ if datatype == 0: # ACL_FLOAT
+ np_type = np.float32
+ elif datatype == 1: # ACL_FLOAT16
+ np_type = np.float16
+ elif datatype == 3: # ACL_INT32
+ np_type = np.int32
+ elif datatype == 8: # ACL_UINT32
+ np_type = np.uint32
+ else:
+ print("unsurpport datatype ", datatype)
+ return
+
+ # 将解码后的数据组织为numpy数组,并设置shape和类型
+ return np.frombuffer(byte_array, dtype=np_type).reshape(shape)
+
+ def _output_dataset_to_numpy(self):
+ ''' 将模型输出解码为numpy数组 '''
+ dataset = []
+ # 遍历每个输出
+ for i in range(self._output_num):
+ buffer = acl.mdl.get_dataset_buffer(self.output_dataset, i) # 从输出dataset中获取buffer
+ data_ptr = acl.get_data_buffer_addr(buffer) # 获取输出数据内存地址
+ size = acl.get_data_buffer_size(buffer) # 获取输出数据字节数
+ narray = acl.util.ptr_to_bytes(data_ptr, size) # 将指针转为字节流数据
+
+ # 根据模型输出的shape和数据类型,将内存数据解码为numpy数组
+ outret = acl.mdl.get_output_dims(self.model_desc, i)[0]
+ dims = outret["dims"] # 获取每个输出的维度
+ print(f"name:{outret['name']}")
+ print(f"dimCount:{outret['dimCount']}")
+ '''
+ dims = {
+ "name": xxx, #tensor name
+ "dimCount":xxx，#shape中的维度个数
+ "dims": [xx, xx, xx] # 维度信息 --- 取的这个
+ }
+ '''
+ datatype = acl.mdl.get_output_data_type(self.model_desc, i) # 获取每个输出的数据类型 --就数据类型float16,int8等
+ output_nparray = self._unpack_bytes_array(narray, tuple(dims), datatype) # 解码为numpy数组
+ dataset.append(output_nparray)
+ return dataset
+
+ def execute(self, input_list):
+ '''创建输入dataset对象, 推理完成后, 将输出数据转换为numpy格式'''
+ self._gen_input_dataset(input_list) # 创建模型输入dataset结构
+ ret = acl.mdl.execute(self.model_id, self.input_dataset, self.output_dataset) # 调用离线模型的execute推理数据
+ out_numpy = self._output_dataset_to_numpy() # 将推理输出的二进制数据流解码为numpy数组, 数组的shape和类型与模型输出规格一致
+ return out_numpy
+
+ def release(self):
+ ''' 释放模型相关资源 '''
+ if self._is_released:
+ return
+
+ print("Model start release...")
+ self._release_dataset(self.input_dataset) # 释放输入数据结构
+ self.input_dataset = None # 将输入数据置空
+ self._release_dataset(self.output_dataset) # 释放输出数据结构
+ self.output_dataset = None # 将输出数据置空
+
+ if self.model_id:
+ ret = acl.mdl.unload(self.model_id) # 卸载模型
+ if self.model_desc:
+ ret = acl.mdl.destroy_desc(self.model_desc) # 释放模型描述信息
+ self._is_released = True
+ print("Model release source success")
+
+ def _release_dataset(self, dataset):
+ ''' 释放 aclmdlDataset 类型数据 '''
+ if not dataset:
+ return
+ num = acl.mdl.get_dataset_num_buffers(dataset) # 获取数据集包含的buffer个数
+ for i in range(num):
+ data_buf = acl.mdl.get_dataset_buffer(dataset, i) # 获取buffer指针
+ if data_buf:
+ ret = acl.destroy_data_buffer(data_buf) # 释放buffer
+ ret = acl.mdl.destroy_dataset(dataset) # 销毁数据集
+
+ # @abstractmethod
+ # def infer(self, inputs): # 保留接口, 子类必须重写
+ # pass
@abstractmethod
diff --git a/model/plugins/RYRQ/RYRQ_Model.py b/model/plugins/RYRQ/RYRQ_Model.py
index df0800e..edba0a6 100644
--- a/model/plugins/RYRQ/RYRQ_Model.py
+++ b/model/plugins/RYRQ/RYRQ_Model.py
@@ -5,18 +5,22 @@ from myutils.ConfigManager import myCongif
import torch
import cv2
class Model(ModelBase):
- def __init__(self,path):
- super().__init__()
- self.name = "人员入侵(ACL)"
- self.version = "V1.0"
- self.model_type = 2
- #找pt模型路径 -- 一个约束py文件和模型文件的路径关系需要固定， -- 上传模型时，要解压好路径
- dirpath,filename = os.path.split(path)
+ def __init__(self,path,threshold=0.5):
+ # 找pt模型路径 -- 一个约束py文件和模型文件的路径关系需要固定， -- 上传模型时，要解压好路径
+ dirpath, filename = os.path.split(path)
model_file = os.path.join(dirpath,"yolov5s.pt") #目前约束模型文件和py文件在同一目录
yolov5_path = myCongif.get_data("yolov5_path")
print(f"************{model_file},{yolov5_path}")
- #实例化模型
+
+ super().__init__(dirpath,threshold)
+ self.name = "人员入侵(ACL)"
+ self.version = "V1.0"
+ self.model_type = 2
+
+ #实例化模型--实例化模型没有对失败的情况进行处理
+ self.init_ok = True
self.model = torch.hub.load(yolov5_path, 'custom', path=model_file, source='local')
+ #if model 失败，inti_ok = Flase
def verify(self,image,data,isdraw=1):
diff --git a/model/plugins/RYRQ_ACL/RYRQ_Model_ACL.py b/model/plugins/RYRQ_ACL/RYRQ_Model_ACL.py
index 192c62c..fee22ec 100644
--- a/model/plugins/RYRQ_ACL/RYRQ_Model_ACL.py
+++ b/model/plugins/RYRQ_ACL/RYRQ_Model_ACL.py
@@ -1,49 +1,91 @@
import os.path
-
from model.plugins.ModelBase import ModelBase
from myutils.ConfigManager import myCongif
-import torch
+from model.base_model.ascnedcl.det_utils import get_labels_from_txt, letterbox, scale_coords, nms, draw_bbox # 模型前后处理相关函数
import cv2
+import numpy as np
+import torch # 深度学习运算框架，此处主要用来处理数据
+
class Model(ModelBase):
- def __init__(self,path):
- super().__init__()
+ def __init__(self,path,threshold=0.5):
+ # 找pt模型路径 -- 一个约束py文件和模型文件的路径关系需要固定， -- 上传模型时，要解压好路径
+ dirpath, filename = os.path.split(path)
+ self.model_file = os.path.join(dirpath, "yolov5s_bs1.om") # 目前约束模型文件和py文件在同一目录
+ self.coco_file = os.path.join(dirpath, "coco_names.txt")
+ super().__init__(self.model_file) #acl环境初始化基类负责类的实例化
+ self.model_id = None # 模型 id
+ self.input_dataset = None # 输入数据结构
+ self.output_dataset = None # 输出数据结构
+ self.model_desc = None # 模型描述信息
+ self._input_num = 0 # 输入数据个数
+ self._output_num = 0 # 输出数据个数
+ self._output_info = [] # 输出信息列表
+ self._is_released = False # 资源是否被释放
self.name = "人员入侵"
self.version = "V1.0"
self.model_type = 2
- #找pt模型路径 -- 一个约束py文件和模型文件的路径关系需要固定， -- 上传模型时，要解压好路径
- dirpath,filename = os.path.split(path)
- model_file = os.path.join(dirpath,"yolov5s.pt") #目前约束模型文件和py文件在同一目录
- yolov5_path = myCongif.get_data("yolov5_path")
- print(f"************{model_file},{yolov5_path}")
- #实例化模型
- self.model = torch.hub.load(yolov5_path, 'custom', path=model_file, source='local')
+
+ self.neth = 640 # 缩放的目标高度, 也即模型的输入高度
+ self.netw = 640 # 缩放的目标宽度, 也即模型的输入宽度
+ self.conf_threshold = threshold # 置信度阈值
+
+ self._init_acl()
+ self.init_ok = True
+ if not self._init_resource():
+ print("加载模型文件失败！")
+ self.init_ok = False
+
+
+ def __del__(self):
+ if self.init_ok:
+ self.release()
+ self._del_acl()
+
def verify(self,image,data,isdraw=1):
- results = self.model(image) # 进行模型检测 --- 需要统一接口
- detections = results.pandas().xyxy[0].to_dict(orient="records")
+ labels_dict = get_labels_from_txt('/mnt/zfbox/model/plugins/RYRQ_ACL/coco_names.txt') # 得到类别信息，返回序号与类别对应的字典
+ # 数据前处理
+ img, scale_ratio, pad_size = letterbox(image, new_shape=[640, 640]) # 对图像进行缩放与填充
+ img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, HWC to CHW #图片在输入时已经做了转换
+ img = np.ascontiguousarray(img, dtype=np.float32) / 255.0 # 转换为内存连续存储的数组
+
+ # 模型推理, 得到模型输出
+ output = self.execute([img,])[0]
+
+ # 后处理 -- boxout 是 tensor-list: [tensor([[],[].[]])] --[x1,y1,x2,y2,置信度,coco_index]
+ boxout = nms(torch.tensor(output), conf_thres=0.4,
+ iou_thres=0.5) # 利用非极大值抑制处理模型输出，conf_thres 为置信度阈值，iou_thres 为iou阈值
+ pred_all = boxout[0].numpy() # 转换为numpy数组 -- [[],[],[]] --[x1,y1,x2,y2,置信度,coco_index]
+ # pred_all[:, :4] 取所有行的前4列，pred_all[:,1]--第一列
+ scale_coords([640, 640], pred_all[:, :4], image.shape, ratio_pad=(scale_ratio, pad_size)) # 将推理结果缩放到原始图片大小
+
bwarn = False
warn_text = ""
#是否有检测区域，有先绘制检测区域 由于在该函数生成了polygon对象，所有需要在检测区域前调用。
if data[1] == 1:
- self.draw_polygon(image,data[2],(0, 255, 0))
+ self.draw_polygon(image,data[2],(255,0,0))
+ #过滤掉不是目标标签的数据
+ filtered_pred_all = pred_all[pred_all[:, 5] == 0]
# 绘制检测结果 --- 也需要封装在类里，
- for det in detections:
- if det['name'] == 'person': #标签是人
- x1, y1, x2, y2 = int(det['xmin']), int(det['ymin']), int(det['xmax']), int(det['ymax'])
- # 绘制目标识别的锚框
- cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
- if data[1] == 1: # 指定了检测区域
- x_center = (x1 + x2) / 2
- y_center = (y1 + y2) / 2
- #绘制中心点？
- cv2.circle(image, (int(x_center), int(y_center)), 5, (0, 0, 255), -1)
- #判断是否区域点
- if not self.is_point_in_region((x_center, y_center)):
- continue #没产生报警-继续
- #产生报警
- bwarn = True
- warn_text = "Intruder detected!"
- return detections,bwarn,warn_text
+ for pred in filtered_pred_all:
+ x1, y1, x2, y2 = int(pred[0]), int(pred[1]), int(pred[2]), int(pred[3])
+ # # 绘制目标识别的锚框 --已经在draw_bbox里处理
+ # cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
+ if data[1] == 1: # 指定了检测区域
+ x_center = (x1 + x2) / 2
+ y_center = (y1 + y2) / 2
+ #绘制中心点？
+ cv2.circle(image, (int(x_center), int(y_center)), 5, (0, 0, 255), -1)
+ #判断是否区域点
+ if not self.is_point_in_region((x_center, y_center)):
+ continue #没产生报警-继续
+ #产生报警
+ bwarn = True
+ warn_text = "Intruder detected!"
+ img_dw = draw_bbox(filtered_pred_all, image, (0, 255, 0), 2, labels_dict) # 画出检测框、类别、概率
+ cv2.imwrite('img_res.png', img_dw)
+ return filtered_pred_all, False, ""
+
def testRun(self):
print("1111")
\ No newline at end of file
diff --git a/run.py b/run.py
index 82411fa..4c49222 100644
--- a/run.py
+++ b/run.py
@@ -24,8 +24,7 @@ if __name__ == '__main__':
else:
raise NotImplementedError(f"Unsupported operating system: {system}")
print(free/(1024*1024))
-
mMM.start_work() # 启动所有通道的处理
mVManager.start_check_rtsp() #线程更新视频在线情况
- web.run(debug=True,port=5001)
+ web.run(debug=True,port=5001,host="0.0.0.0")
diff --git a/web/API/channel.py b/web/API/channel.py
index 7f2880f..aeaa387 100644
--- a/web/API/channel.py
+++ b/web/API/channel.py
@@ -223,13 +223,12 @@ async def channel_model_linkmodel(): #获取算法列表 --关联算法时展示
@api.route('/channel/model/getarea',methods=['GET'])
@login_required
-async def channel_model_getarea(): #获取算法区域的备注信息
+async def channel_model_getarea(): #获取算法区域的备注信息 --- 同时获取检测阈值
ID = request.args.get('ID')
- strsql = f"select check_area,check_x,check_y,check_width,check_height from channel2model where ID={ID};"
+ strsql = f"select check_area,polygon,conf_threshold from channel2model where ID={ID};"
data = mDBM.do_select(strsql,1)
if data:
- reMsg = {'ID':ID,'check_area':data[0],'check_x':data[1],'check_y':data[2],
- 'check_width':data[3],'check_height':data[4]}
+ reMsg = {'ID':ID,'check_area':data[0],'polygon':data[1],'conf_threshold':data[2]}
else:
reMsg = {}
return jsonify(reMsg)
@@ -239,12 +238,24 @@ async def channel_model_getarea(): #获取算法区域的备注信息
async def channel_model_changearea(): #修改算法区域信息
ID = (await request.form)['ID']
check_area = (await request.form)['check_area']
- check_x = (await request.form)['check_x']
- check_y = (await request.form)['check_y']
- check_width = (await request.form)['check_width']
- check_height = (await request.form)['check_height']
- strsql = (f"update channel2model set check_area={check_area},check_x={check_x},check_y={check_y}"
- f",check_width={check_width},check_height={check_height} where ID={ID};")
+ check_x = (await request.form)['polygon']
+ strsql = (f"update channel2model set check_area={check_area},polygon={check_x} where ID={ID};")
+ ret = mDBM.do_sql(strsql)
+ if ret == True:
+ reStatus = 1
+ reMsg = '修改算法检测区域成功'
+ else:
+ reStatus = 0
+ reMsg = '新修改算法检测区域失败，请联系技术支持！'
+ return jsonify({'status': reStatus, 'msg': reMsg})
+
+@api.route('/channel/model/changethreshold',methods=['POST'])
+@login_required
+async def channel_model_changthreshold(): #修改算法区域信息
+ ID = (await request.form)['ID']
+ conf_threshold = (await request.form)['conf_threshold']
+
+ strsql = (f"update channel2model set conf_threshold={conf_threshold} where ID={ID};")
ret = mDBM.do_sql(strsql)
if ret == True:
reStatus = 1
diff --git a/web/API/user.py b/web/API/user.py
index 0995809..94ae229 100644
--- a/web/API/user.py
+++ b/web/API/user.py
@@ -24,7 +24,7 @@ async def user_login(): #用户登录
captcha = (await request.form)['captcha']
if captcha != session.get('captcha'):
#验证码验证过后，需要失效
- #？
+ print(session.get('captcha'))
return '验证码错误', 400
#比对用户名和密码
strsql = f"select password from user where username = '{username}'"
@@ -56,13 +56,14 @@ async def user_adduser(): #新增用户
people = (await request.form)['people']
tellnum = (await request.form)['tellnum']
strsql = f"select username from user where username = '{username}';"
+ password = myCongif.get_data('pw')
data = mDBM.do_select(strsql)
if data:
reStatus = 0
reMsg = '用户名重复，请重新输入！'
else:
strsql = (f"INSERT INTO user (username ,password ,status,people,tellnum ) VALUES "
- f"('{username}','{myCongif.get_data("pw")}',1,'{people}','{tellnum}');")
+ f"('{username}','{password}',1,'{people}','{tellnum}');")
ret = mDBM.do_sql(strsql)
if ret == True:
reStatus = 1
@@ -76,7 +77,8 @@ async def user_adduser(): #新增用户
@login_required
async def user_change_passwd(): #重置密码
username = (await request.form)['username']
- strsql = f"update user set password='{myCongif.get_data("pw")}' where username='{username}';"
+ password = myCongif.get_data('pw')
+ strsql = f"update user set password='{password}' where username='{username}';"
ret = mDBM.do_sql(strsql)
if ret == True:
reStatus = 1
diff --git a/web/API/viedo.py b/web/API/viedo.py
index 6faf271..2a293d3 100644
--- a/web/API/viedo.py
+++ b/web/API/viedo.py
@@ -3,7 +3,7 @@ import asyncio
import time
from . import api
from quart import jsonify, request
-from aiortc import RTCPeerConnection, RTCSessionDescription, VideoStreamTrack
+from aiortc import RTCPeerConnection, RTCSessionDescription, VideoStreamTrack,RTCConfiguration
from core.ModelManager import mMM
from core.DBManager import mDBM
from myutils.ConfigManager import myCongif
@@ -50,14 +50,12 @@ pcs = {}
# continue
#threading.Thread(target=camera_thread, daemon=True).start()
-
-
'''
---------------------传输--------------------------
'''
class VideoTransformTrack(VideoStreamTrack):
kind = "video"
- def __init__(self,cid,itype=1): #0-usb,1-RTSP,2-海康SDK
+ def __init__(self,cid,itype=1): #0-usb,1-RTSP,2-海康SDK --itype已没有作用
super().__init__()
self.channel_id = cid
@@ -69,13 +67,14 @@ class VideoTransformTrack(VideoStreamTrack):
async def recv(self):
new_frame = None
time.sleep(1.0 / self.frame_rate)
- while True:
- new_frame = mMM.verify_list[self.channel_id][4]
- #new_frame = av.VideoFrame.from_ndarray(img, format="rgb24")
- if new_frame is not None:
- break
- else:
- time.sleep(1.0 / self.frame_rate)
+ new_frame = mMM.verify_list[self.channel_id][4]
+ # while True:
+ # new_frame = mMM.verify_list[self.channel_id][4]
+ # #new_frame = av.VideoFrame.from_ndarray(img, format="rgb24")
+ # if new_frame is not None:
+ # break
+ # else:
+ # time.sleep(1.0 / self.frame_rate)
# 设置时间戳和时间基数 -- 根据耗时实时计算帧率
# elapsed_time = time.time() - self.start_time
# self.frame_count += 1
@@ -87,15 +86,24 @@ class VideoTransformTrack(VideoStreamTrack):
self.frame_count = 0
new_frame.pts = self.frame_count
new_frame.time_base = self.time_base
- #print(f"Frame pts: {new_frame.pts}, time_base: {new_frame.time_base}")
+ print(f"{self.channel_id} -- Frame pts: {new_frame.pts}, time_base: {new_frame.time_base}")
# 将检测结果图像转换为帧
# new_frame.pts = int(self.cap.get(cv2.CAP_PROP_POS_FRAMES))
# new_frame.time_base = self.time_base
# end_time = time.time() # 结束时间
# print(f"Processing time: {end_time - start_time} seconds")
+ #print("webRTC recv at:",time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))
return new_frame
+async def get_stats(peer_connection):
+ stats = await peer_connection.getStats()
+ for report in stats.values():
+ if report.type == 'outbound-rtp':
+ print(f"RTT: {report.roundTripTime} seconds")
+ print(f"Packets Sent: {report.packetsSent}")
+ print(f"Bytes Sent: {report.bytesSent}")
+
@api.route('/offer', methods=['POST'])
async def offer():
#接收客户端的连接请求
@@ -125,10 +133,15 @@ async def offer():
#提取客户端发来的SDP，生成服务器端的SDP
offer = RTCSessionDescription(sdp=params["sdp"], type=params["type"])
# # 配置 STUN 服务器
- # ice_servers = [{"urls": ["stun:stun.voipbuster.com"]}]
+ #ice_servers = [{"urls": []}]# 禁用 STUN 服务器
# pc = RTCPeerConnection(configuration={"iceServers": ice_servers})
- pc = RTCPeerConnection() #实例化一个rtcp对象
- pcs[channel_id] = pc #集合中添加一个对象，若已存在则不添加
+ config = RTCConfiguration(iceServers=[])
+ pc = RTCPeerConnection(configuration=config)
+ # t = threading.Thread(target=get_stats,args=(pc,))
+ # t.start()
+
+ #pc = RTCPeerConnection() # 实例化一个rtcp对象
+ pcs[channel_id] = pc # 集合中添加一个对象，若已存在则不添
@pc.on("datachannel")
def on_datachannel(channel):
@@ -142,6 +155,7 @@ async def offer():
elif message == 'resume':
# 执行继续操作
pass
+
#监听RTC连接状态
@pc.on("iconnectionstatechange") #当ice连接状态发生变化时
async def iconnectionstatechange():
diff --git a/web/__init__.py b/web/__init__.py
index 89e81ca..c05247f 100644
--- a/web/__init__.py
+++ b/web/__init__.py
@@ -1,27 +1,48 @@
from quart import Quart, render_template, request, jsonify,send_from_directory,session,redirect, url_for
from quart_session import Session
+from pymemcache.client import base
from .main import main
from .API import api
from functools import wraps
-
from quart_sqlalchemy import SQLAlchemy
from flask_migrate import Migrate
-
#app.config['SECRET_KEY'] = 'mysecret' #密钥 --需要放配置文件
#socketio = SocketIO(app)
+# Create the custom backend for quart-session
+class MemcachedSessionInterface: #只是能用，不明所以
+ def __init__(self, client):
+ self.client = client
+
+ async def open_session(self, app, request):
+ sid = request.cookies.get(app.session_cookie_name)
+ if not sid:
+ sid = self._generate_sid()
+ val = await self.client.get(self.key_prefix + sid)
+ if val is not None:
+ return self._deserialize(val)
+ return self._get_default_session()
+
+ async def save_session(self, app, session, response):
+ val = self._serialize(session)
+ await self.client.set(self.key_prefix + session.sid, val, self.expire)
+
def create_app():
app = Quart(__name__)
#相关配置--设置各种配置选项，这些选项会在整个应用程序中被访问和使用。
- app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///app.db'
- app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False
+ # app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///app.db'
+ # app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False
app.config['SECRET_KEY'] = 'zfxxkj_2024_!@#'
- app.config['SESSION_TYPE'] = 'redis' # session类型
- app.config['SESSION_FILE_DIR'] = './sessions' # session保存路径
+ app.config['SESSION_TYPE'] = 'memcached' # session类型
+ #app.config['SESSION_FILE_DIR'] = './sessions' # session保存路径
+ #app.config['SESSION_MEMCACHED'] = base.Client(('localhost', 11211))
app.config['SESSION_PERMANENT'] = True # 如果设置为True，则关闭浏览器session就失效。
app.config['SESSION_USE_SIGNER'] = False # 是否对发送到浏览器上session的cookie值进行加密
+
+ memcached_client = base.Client(('localhost', 11211))
+ app.session_interface = MemcachedSessionInterface(memcached_client)
Session(app)
#ORM数据库管理
diff --git a/web/main/routes.py b/web/main/routes.py
index d5025b4..583a15b 100644
--- a/web/main/routes.py
+++ b/web/main/routes.py
@@ -21,9 +21,9 @@ def login_required(f):
@main.route('/')
async def index():
- # error = request.args.get('error')
+ #error = request.args.get('error')
return await render_template('实时预览.html')
- # return await render_template('登录.html',error=error)
+ #return await render_template('登录.html',error=error)
#return await render_template('index_webrtc.html')
# @main.route('/', methods=['GET', 'POST'])
diff --git a/web/main/templates/index.html b/web/main/templates/index.html
deleted file mode 100644
index 9015b2c..0000000
--- a/web/main/templates/index.html
+++ /dev/null
@@ -1,12 +0,0 @@
-
-
-
-
-
-
+  }})
+
+ {% endfor %}
+ YOLOv5 Real-Time Detection
-
- 
-
+
-
+
-
-
+
-
+
-
-
+
diff --git a/web/main/templates/通道管理.html b/web/main/templates/通道管理.html
index dd7af56..659c131 100644
--- a/web/main/templates/通道管理.html
+++ b/web/main/templates/通道管理.html
@@ -273,7 +273,6 @@
-
+
-
rtsp://www.easydarwin.org:554/your_stream_id