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8
README.md
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@ -1,8 +0,0 @@
1.2025.01.21把之前的tuoheng alg仓库代码重新开个仓库 (1)在config/service/dsp_test_service.yml里面添加参数，控制存储用的oss还是minio storage_source: 1 2.2025.02.06 (1)修改代码，把mqtt读取加入到系统中。config/service/dsp_test_service.yml，中添加mqtt_flag,决定是否启用。 (2)修改了minio情况下的，文件名命名方式。 3.2025.02.12 (1)增加了对alg算法开发的代码。可以通过配置文件config/service/dsp_test_service.yml中algSwitch: true，决定是否启用。
4、2025.07.10 周树亮 - 增加人群计数，自研车牌模型，裸土覆盖3个场景
5、江朝庆 -- 0715
1）代码整理，删除冗余代码。
2）增加requirements.txt,方便部署
3) logs

127
concurrency/FileUploadThread.py
View File

@ -19,21 +19,22 @@ from util.QueUtil import put_queue, get_no_block_queue, clear_queue
import io
from util.LocationUtils import locate_byMqtt
class FileUpload(Thread):
__slots__ = ('_fb_queue', '_context', '_image_queue', '_analyse_type', '_msg', '_mqtt_list')
__slots__ = ('_fb_queue', '_context', '_image_queue', '_analyse_type', '_msg','_mqtt_list')
def __init__(self, *args):
super().__init__()
self._fb_queue, self._context, self._msg, self._image_queue, self._analyse_type, self._mqtt_list = args
self._fb_queue, self._context, self._msg, self._image_queue, self._analyse_type,self._mqtt_list = args
self._storage_source = self._context['service']['storage_source']
self._algStatus = False # 默认关闭
self._algStatus = False # 默认关闭
# self._algStatus = True # 默认关闭
self._algSwitch = self._context['service']['algSwitch']
# 0521:
default_enabled = str(self._msg.get("defaultEnabled", "True")).lower() == "true"
self._algSwitch = self._context['service']['algSwitch']
#0521:
default_enabled = str(self._msg.get("defaultEnabled", "True")).lower() == "true"
if default_enabled:
print("执行默认程序（defaultEnabled=True）")
self._algSwitch = True
@ -43,15 +44,15 @@ class FileUpload(Thread):
# 这里放非默认逻辑的代码
self._algSwitch = False
print("---line46 :FileUploadThread.py---", self._algSwitch)
print("---line46 :FileUploadThread.py---",self._algSwitch)
# 如果任务是在线、离线处理，则用此类
#如果任务是在线、离线处理，则用此类
class ImageFileUpload(FileUpload):
__slots__ = ()
# @staticmethod
def handle_image(self, frame_msg, frame_step):
#@staticmethod
def handle_image(self,frame_msg, frame_step):
# (high_score_image["code"], all_frames, draw_config["font_config"])
# high_score_image["code"][code][cls] = (frame, frame_index_list[i], cls_list)
det_xywh, frame, current_frame, all_frames, font_config = frame_msg
@ -64,7 +65,8 @@ class ImageFileUpload(FileUpload):
模型编号：modeCode
检测目标：detectTargetCode
'''
print('*' * 100, ' mqtt_list:', len(self._mqtt_list))
print('*'*100,' mqtt_list:',len(self._mqtt_list))
model_info = []
# 更加模型编码解析数据
@ -76,10 +78,12 @@ class ImageFileUpload(FileUpload):
for target in target_list:
# 自研车牌模型判断
if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
draw_name_ocr(target[1], aFrame, target[4])
elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code) or\
ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1] == str(code):
draw_name_crowd(target[1], aFrame, target[4])
box = [target[1][0][0], target[1][0][1], target[1][3][0], target[1][3][1]]
draw_name_ocr(box, aFrame, target[4], target[0])
cls = 0
elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code):
draw_name_crowd(target[3], aFrame, target[4], cls)
cls = 0
else:
draw_painting_joint(target[1], aFrame, target[3], target[2], target[4], font_config,
target[5])
@ -111,15 +115,13 @@ class ImageFileUpload(FileUpload):
image_queue, fb_queue, analyse_type = self._image_queue, self._fb_queue, self._analyse_type
service_timeout = int(service["timeout"])
frame_step = int(service["filter"]["frame_step"]) + 120
if msg['taskType'] == 0:
self._algStatus = False
else:
self._algStatus = True
if msg['taskType']==0: self._algStatus = False
else: self._algStatus = True
try:
with ThreadPoolExecutor(max_workers=2) as t:
# 初始化oss客户端
if self._storage_source == 1:
minioSdk = MinioSdk(base_dir, env, request_id)
if self._storage_source==1:
minioSdk = MinioSdk(base_dir, env, request_id )
else:
aliyunOssSdk = AliyunOssSdk(base_dir, env, request_id)
start_time = time()
@ -135,14 +137,12 @@ class ImageFileUpload(FileUpload):
if image_msg is not None:
if image_msg[0] == 2:
logger.info("图片上传线程收到命令:{}, requestId: {}", image_msg[1], request_id)
logger.info("图片上传线程收到命令:{}, requestId: {}",image_msg[1] ,request_id)
if 'stop' == image_msg[1]:
logger.info("开始停止图片上传线程, requestId:{}", request_id)
break
if 'algStart' == image_msg[1]: self._algStatus = True; logger.info(
"图片上传线程,执行算法开启命令, requestId:{}", request_id)
if 'algStop' == image_msg[1]: self._algStatus = False; logger.info(
"图片上传线程,执行算法关闭命令, requestId:{}", request_id)
if 'algStart' == image_msg[1]: self._algStatus = True; logger.info("图片上传线程,执行算法开启命令, requestId:{}", request_id)
if 'algStop' == image_msg[1]: self._algStatus = False; logger.info("图片上传线程,执行算法关闭命令, requestId:{}", request_id)
if image_msg[0] == 1:
image_result = self.handle_image(image_msg[1], frame_step)
@ -153,8 +153,8 @@ class ImageFileUpload(FileUpload):
image_result["last_frame"],
analyse_type,
"OR", "0", "0", request_id)
if self._storage_source == 1:
or_future = t.submit(minioSdk.put_object, or_image, or_image_name)
if self._storage_source==1:
or_future = t.submit(minioSdk.put_object, or_image,or_image_name)
else:
or_future = t.submit(aliyunOssSdk.put_object, or_image_name, or_image.tobytes())
task.append(or_future)
@ -169,38 +169,38 @@ class ImageFileUpload(FileUpload):
model_info["modelCode"],
model_info["detectTargetCode"],
request_id)
if self._storage_source == 1:
if self._storage_source==1:
ai_future = t.submit(minioSdk.put_object, ai_image,
ai_image_name)
ai_image_name)
else:
ai_future = t.submit(aliyunOssSdk.put_object, ai_image_name,
ai_image.tobytes())
ai_image.tobytes())
task.append(ai_future)
# msg_list.append(message_feedback(request_id,
#msg_list.append(message_feedback(request_id,
# AnalysisStatus.RUNNING.value,
# analyse_type, "", "", "",
# or_image_name,
# ai_image_name,
# model_info['modelCode'],
# model_info['detectTargetCode']))
remote_image_list = []
remote_image_list=[]
for tk in task:
remote_image_list.append(tk.result())
remote_image_list.append( tk.result())
for ii, model_info in enumerate(model_info_list):
msg_list.append(message_feedback(request_id,
for ii,model_info in enumerate(model_info_list):
msg_list.append( message_feedback(request_id,
AnalysisStatus.RUNNING.value,
analyse_type, "", "", "",
remote_image_list[0],
remote_image_list[ii + 1],
remote_image_list[ii+1],
model_info['modelCode'],
model_info['detectTargetCode'],
longitude=model_info['gps'][0],
latitude=model_info['gps'][1],
))
) )
if (not self._algSwitch) or (self._algStatus and self._algSwitch):
if (not self._algSwitch) or ( self._algStatus and self._algSwitch):
for msg in msg_list:
put_queue(fb_queue, msg, timeout=2, is_ex=False)
del task, msg_list
@ -227,7 +227,7 @@ def build_image_name(*args):
random_num, mode_type, modeCode, target, image_type)
# 如果任务是图像处理，则用此类
#如果任务是图像处理，则用此类
class ImageTypeImageFileUpload(Thread):
__slots__ = ('_fb_queue', '_context', '_image_queue', '_analyse_type', '_msg')
@ -235,7 +235,6 @@ class ImageTypeImageFileUpload(Thread):
super().__init__()
self._fb_queue, self._context, self._msg, self._image_queue, self._analyse_type = args
self._storage_source = self._context['service']['storage_source']
@staticmethod
def handle_image(det_xywh, copy_frame, font_config):
"""
@ -257,10 +256,9 @@ class ImageTypeImageFileUpload(Thread):
for target in target_list:
# 自研车牌模型判断
if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
draw_name_ocr(target, aiFrame, font_config[cls])
elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code) or \
ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1] == str(code):
draw_name_crowd(target, aiFrame, font_config[cls])
draw_name_ocr(target[1], aiFrame, font_config[cls], target[0])
elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code):
draw_name_crowd(target[1],aiFrame,font_config[cls],target[0])
else:
draw_painting_joint(target[1], aiFrame, target[3], target[2], target[4], font_config)
@ -289,8 +287,8 @@ class ImageTypeImageFileUpload(Thread):
with ThreadPoolExecutor(max_workers=2) as t:
try:
# 初始化oss客户端
if self._storage_source == 1:
minioSdk = MinioSdk(base_dir, env, request_id)
if self._storage_source==1:
minioSdk = MinioSdk(base_dir, env, request_id )
else:
aliyunOssSdk = AliyunOssSdk(base_dir, env, request_id)
@ -315,14 +313,14 @@ class ImageTypeImageFileUpload(Thread):
ai_image_name = build_image_name(0, 0, analyse_type, "AI", result.get("modelCode"),
result.get("type"), request_id)
if self._storage_source == 1:
ai_future = t.submit(minioSdk.put_object, copy_frame, ai_image_name)
if self._storage_source==1:
ai_future = t.submit(minioSdk.put_object, copy_frame,ai_image_name)
else:
ai_future = t.submit(aliyunOssSdk.put_object, ai_image_name, copy_frame)
task.append(ai_future)
remote_names.append(ai_image_name)
# msg_list.append(message_feedback(request_id,
#msg_list.append(message_feedback(request_id,
# AnalysisStatus.RUNNING.value,
# analyse_type, "", "", "",
# image_url,
@ -338,12 +336,12 @@ class ImageTypeImageFileUpload(Thread):
if image_url is None:
or_result, or_image = cv2.imencode(".jpg", image_result.get("or_frame"))
image_url_0 = build_image_name(image_result.get("current_frame"),
image_result.get("last_frame"),
analyse_type,
"OR", "0", "O", request_id)
image_result.get("last_frame"),
analyse_type,
"OR", "0", "O", request_id)
if self._storage_source == 1:
or_future = t.submit(minioSdk.put_object, or_image, image_url_0)
if self._storage_source==1:
or_future = t.submit(minioSdk.put_object, or_image,image_url_0)
else:
or_future = t.submit(aliyunOssSdk.put_object, image_url_0,
or_image.tobytes())
@ -359,14 +357,14 @@ class ImageTypeImageFileUpload(Thread):
model_info.get("modelCode"),
model_info.get("detectTargetCode"),
request_id)
if self._storage_source == 1:
ai_future = t.submit(minioSdk.put_object, ai_image, ai_image_name)
if self._storage_source==1:
ai_future = t.submit(minioSdk.put_object, ai_image, ai_image_name)
else:
ai_future = t.submit(aliyunOssSdk.put_object, ai_image_name,
ai_image.tobytes())
task.append(ai_future)
remote_names.append(ai_image_name)
# msg_list.append(message_feedback(request_id,
#msg_list.append(message_feedback(request_id,
# AnalysisStatus.RUNNING.value,
# analyse_type, "", "", "",
# image_url,
@ -391,12 +389,12 @@ class ImageTypeImageFileUpload(Thread):
else:
if image_result:
if image_url is None:
for ii in range(len(remote_names) - 1):
for ii in range(len(remote_names)-1):
msg_list.append(message_feedback(request_id,
AnalysisStatus.RUNNING.value,
analyse_type, "", "", "",
remote_url_list[0],
remote_url_list[1 + ii],
remote_url_list[1+ii],
model_info.get('modelCode'),
model_info.get('detectTargetCode'),
analyse_results=result))
@ -408,8 +406,7 @@ class ImageTypeImageFileUpload(Thread):
image_url,
remote_url_list[ii],
model_info_list[ii].get('modelCode'),
model_info_list[ii].get(
'detectTargetCode'),
model_info_list[ii].get('detectTargetCode'),
analyse_results=result))
for msg in msg_list:

305
concurrency/FileUploadThread_20250106.py Normal file
View File

@ -0,0 +1,305 @@
# -*- coding: utf-8 -*-
from concurrent.futures import ThreadPoolExecutor
from threading import Thread
from time import sleep, time
from traceback import format_exc
from loguru import logger
import cv2
from entity.FeedBack import message_feedback
from enums.ExceptionEnum import ExceptionType
from exception.CustomerException import ServiceException
from util.AliyunSdk import AliyunOssSdk
from util.MinioSdk import MinioSdk
from util import TimeUtils
from enums.AnalysisStatusEnum import AnalysisStatus
from util.PlotsUtils import draw_painting_joint
from util.QueUtil import put_queue, get_no_block_queue, clear_queue
import io
class FileUpload(Thread):
__slots__ = ('_fb_queue', '_context', '_image_queue', '_analyse_type', '_msg')
def __init__(self, *args):
super().__init__()
self._fb_queue, self._context, self._msg, self._image_queue, self._analyse_type = args
self._storage_source = self._context['service']['storage_source']
class ImageFileUpload(FileUpload):
__slots__ = ()
@staticmethod
def handle_image(frame_msg, frame_step):
# (high_score_image["code"], all_frames, draw_config["font_config"])
# high_score_image["code"][code][cls] = (frame, frame_index_list[i], cls_list)
det_xywh, frame, current_frame, all_frames, font_config = frame_msg
'''
det_xywh:{
'code':{
1: [[detect_targets_code, box, score, label_array, color]]
}
}
模型编号：modeCode
检测目标：detectTargetCode
'''
model_info = []
# 更加模型编码解析数据
for code, det_list in det_xywh.items():
if len(det_list) > 0:
for cls, target_list in det_list.items():
if len(target_list) > 0:
aFrame = frame.copy()
for target in target_list:
draw_painting_joint(target[1], aFrame, target[3], target[2], target[4], font_config, target[5])
model_info.append({"modelCode": str(code), "detectTargetCode": str(cls), "aFrame": aFrame})
if len(model_info) > 0:
image_result = {
"or_frame": frame,
"model_info": model_info,
"current_frame": current_frame,
"last_frame": current_frame + frame_step
}
return image_result
return None
def run(self):
msg, context = self._msg, self._context
service = context["service"]
base_dir, env, request_id = context["base_dir"], context["env"], msg["request_id"]
logger.info("启动图片上传线程, requestId: {}", request_id)
image_queue, fb_queue, analyse_type = self._image_queue, self._fb_queue, self._analyse_type
service_timeout = int(service["timeout"])
frame_step = int(service["filter"]["frame_step"]) + 120
try:
with ThreadPoolExecutor(max_workers=2) as t:
# 初始化oss客户端
if self._storage_source==1:
minioSdk = MinioSdk(base_dir, env, request_id )
else:
aliyunOssSdk = AliyunOssSdk(base_dir, env, request_id)
start_time = time()
while True:
try:
if time() - start_time > service_timeout:
logger.error("图片上传线程运行超时, requestId: {}", request_id)
break
raise ServiceException(ExceptionType.TASK_EXCUTE_TIMEOUT.value[0],
ExceptionType.TASK_EXCUTE_TIMEOUT.value[1])
# 获取队列中的消息
image_msg = get_no_block_queue(image_queue)
if image_msg is not None:
if image_msg[0] == 2:
if 'stop' == image_msg[1]:
logger.info("开始停止图片上传线程, requestId:{}", request_id)
break
if image_msg[0] == 1:
image_result = self.handle_image(image_msg[1], frame_step)
if image_result is not None:
task = []
or_image = cv2.imencode(".jpg", image_result["or_frame"])[1]
or_image_name = build_image_name(image_result["current_frame"],
image_result["last_frame"],
analyse_type,
"OR", "0", "0", request_id)
if self._storage_source==1:
or_future = t.submit(minioSdk.put_object, or_image,or_image_name)
else:
or_future = t.submit(aliyunOssSdk.put_object, or_image_name, or_image.tobytes())
task.append(or_future)
model_info_list = image_result["model_info"]
msg_list = []
for model_info in model_info_list:
ai_image = cv2.imencode(".jpg", model_info["aFrame"])[1]
ai_image_name = build_image_name(image_result["current_frame"],
image_result["last_frame"],
analyse_type,
"AI",
model_info["modelCode"],
model_info["detectTargetCode"],
request_id)
if self._storage_source==1:
ai_future = t.submit(minioSdk.put_object, ai_image,
ai_image_name)
else:
ai_future = t.submit(aliyunOssSdk.put_object, ai_image_name,
ai_image.tobytes())
task.append(ai_future)
msg_list.append(message_feedback(request_id,
AnalysisStatus.RUNNING.value,
analyse_type, "", "", "",
or_image_name,
ai_image_name,
model_info['modelCode'],
model_info['detectTargetCode']))
for tk in task:
tk.result()
for msg in msg_list:
put_queue(fb_queue, msg, timeout=2, is_ex=False)
del task, msg_list
else:
sleep(1)
del image_msg
except Exception:
logger.error("图片上传异常：{}, requestId:{}", format_exc(), request_id)
finally:
logger.info("停止图片上传线程0, requestId:{}", request_id)
clear_queue(image_queue)
logger.info("停止图片上传线程1, requestId:{}", request_id)
def build_image_name(*args):
"""
{requestId}/{time_now}_frame-{current_frame}-{last_frame}_type_{random_num}-{mode_type}" \
"-{modeCode}-{target}_{image_type}.jpg
"""
current_frame, last_frame, mode_type, image_type, modeCode, target, request_id = args
random_num = TimeUtils.now_date_to_str(TimeUtils.YMDHMSF)
time_now = TimeUtils.now_date_to_str("%Y-%m-%d-%H-%M-%S")
return "%s/%s_frame-%s-%s_type_%s-%s-%s-%s_%s.jpg" % (request_id, time_now, current_frame, last_frame,
random_num, mode_type, modeCode, target, image_type)
class ImageTypeImageFileUpload(Thread):
__slots__ = ('_fb_queue', '_context', '_image_queue', '_analyse_type', '_msg')
def __init__(self, *args):
super().__init__()
self._fb_queue, self._context, self._msg, self._image_queue, self._analyse_type = args
self._storage_source = self._context['service']['storage_source']
@staticmethod
def handle_image(det_xywh, copy_frame, font_config):
"""
det_xywh:{
'code':{
1: [[detect_targets_code, box, score, label_array, color]]
}
}
模型编号：modeCode
检测目标：detectTargetCode
"""
model_info = []
# 更加模型编码解析数据
for code, det_info in det_xywh.items():
if det_info is not None and len(det_info) > 0:
for cls, target_list in det_info.items():
if target_list is not None and len(target_list) > 0:
aiFrame = copy_frame.copy()
for target in target_list:
draw_painting_joint(target[1], aiFrame, target[3], target[2], target[4], font_config)
model_info.append({
"modelCode": str(code),
"detectTargetCode": str(cls),
"frame": aiFrame
})
if len(model_info) > 0:
image_result = {
"or_frame": copy_frame,
"model_info": model_info,
"current_frame": 0,
"last_frame": 0
}
return image_result
return None
def run(self):
context, msg = self._context, self._msg
base_dir, env, request_id = context["base_dir"], context["env"], msg["request_id"]
logger.info("启动图片识别图片上传线程, requestId: {}", request_id)
image_queue, fb_queue, analyse_type = self._image_queue, self._fb_queue, self._analyse_type
service_timeout = int(context["service"]["timeout"])
with ThreadPoolExecutor(max_workers=2) as t:
try:
# 初始化oss客户端
if self._storage_source==1:
minioSdk = MinioSdk(base_dir, env, request_id )
else:
aliyunOssSdk = AliyunOssSdk(base_dir, env, request_id)
start_time = time()
while True:
try:
if time() - start_time > service_timeout:
logger.error("图片上传进程运行超时, requestId: {}", request_id)
break
# 获取队列中的消息
image_msg = image_queue.get()
if image_msg is not None:
if image_msg[0] == 2:
if 'stop' == image_msg[1]:
logger.info("开始停止图片上传线程, requestId:{}", request_id)
break
if image_msg[0] == 1:
task, msg_list = [], []
det_xywh, image_url, copy_frame, font_config, result = image_msg[1]
if det_xywh is None:
ai_image_name = build_image_name(0, 0, analyse_type, "AI", result.get("modelCode"),
result.get("type"), request_id)
if self._storage_source==1:
ai_future = t.submit(minioSdk.put_object, copy_frame,ai_image_name)
else:
ai_future = t.submit(aliyunOssSdk.put_object, ai_image_name, copy_frame)
task.append(ai_future)
msg_list.append(message_feedback(request_id,
AnalysisStatus.RUNNING.value,
analyse_type, "", "", "",
image_url,
ai_image_name,
result.get("modelCode"),
result.get("type"),
analyse_results=result))
else:
image_result = self.handle_image(det_xywh, copy_frame, font_config)
if image_result:
# 图片帧数编码
if image_url is None:
or_result, or_image = cv2.imencode(".jpg", image_result.get("or_frame"))
image_url = build_image_name(image_result.get("current_frame"),
image_result.get("last_frame"),
analyse_type,
"OR", "0", "O", request_id)
if self._storage_source==1:
or_future = t.submit(minioSdk.put_object, or_image,image_url)
else:
or_future = t.submit(aliyunOssSdk.put_object, image_url,
or_image.tobytes())
task.append(or_future)
model_info_list = image_result.get("model_info")
for model_info in model_info_list:
ai_result, ai_image = cv2.imencode(".jpg", model_info.get("frame"))
ai_image_name = build_image_name(image_result.get("current_frame"),
image_result.get("last_frame"),
analyse_type,
"AI",
model_info.get("modelCode"),
model_info.get("detectTargetCode"),
request_id)
if self._storage_source==1:
ai_future = t.submit(minioSdk.put_object, ai_image, ai_image_name)
else:
ai_future = t.submit(aliyunOssSdk.put_object, ai_image_name,
ai_image.tobytes())
task.append(ai_future)
msg_list.append(message_feedback(request_id,
AnalysisStatus.RUNNING.value,
analyse_type, "", "", "",
image_url,
ai_image_name,
model_info.get('modelCode'),
model_info.get('detectTargetCode'),
analyse_results=result))
for thread_result in task:
thread_result.result()
for msg in msg_list:
put_queue(fb_queue, msg, timeout=2, is_ex=False)
else:
sleep(1)
except Exception as e:
logger.error("图片上传异常：{}, requestId:{}", format_exc(), request_id)
finally:
clear_queue(image_queue)
logger.info("停止图片识别图片上传线程, requestId:{}", request_id)

51
concurrency/IntelligentRecognitionProcess.py
View File

@ -62,7 +62,7 @@ class IntelligentRecognitionProcess(Process):
# 发送waitting消息
put_queue(self._fb_queue, message_feedback(self._msg["request_id"], AnalysisStatus.WAITING.value,
self._analyse_type, progress=init_progess), timeout=2, is_ex=True)
self._storage_source = self._context['service']['storage_source']
self._storage_source = self._context['service']['storage_source']
self._algStatus = False
def sendEvent(self, eBody):
put_queue(self.event_queue, eBody, timeout=2, is_ex=True)
@ -92,6 +92,8 @@ class IntelligentRecognitionProcess(Process):
return hb_thread
class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
__slots__ = ()
@ -110,16 +112,19 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
pullProcess.start()
return pullProcess
def upload_video(self,base_dir, env, request_id, orFilePath, aiFilePath):
if self._storage_source==1:
minioSdk = MinioSdk(base_dir, env, request_id)
minioSdk = MinioSdk(base_dir, env, request_id )
upload_video_thread_or = Common(minioSdk.put_object, orFilePath, "or_online_%s.mp4" % request_id)
upload_video_thread_ai = Common(minioSdk.put_object, aiFilePath, "ai_online_%s.mp4" % request_id)
else:
else:
aliyunVodSdk = ThAliyunVodSdk(base_dir, env, request_id)
upload_video_thread_or = Common(aliyunVodSdk.get_play_url, orFilePath, "or_online_%s" % request_id)
upload_video_thread_ai = Common(aliyunVodSdk.get_play_url, aiFilePath, "ai_online_%s" % request_id)
upload_video_thread_or.setDaemon(True)
upload_video_thread_ai.setDaemon(True)
upload_video_thread_or.start()
@ -140,7 +145,7 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
or_url = upload_video_thread_or.get_result()
ai_url = upload_video_thread_ai.get_result()
return or_url, ai_url
'''
'''
@staticmethod
def ai_normal_dtection(model, frame, request_id):
@ -220,7 +225,7 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
ex = None
# 拉流进程、推流进程、心跳线程
pull_process, push_process, hb_thread = None, None, None
# 事件队列、拉流队列、心跳队列、反馈队列
event_queue, pull_queue, hb_queue, fb_queue = self.event_queue, self._pull_queue, self._hb_queue, self._fb_queue
@ -235,14 +240,14 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
# 启动拉流进程(包含拉流线程, 图片上传线程,mqtt读取线程)
# 拉流进程初始化时间长, 先启动
pull_process = self.start_pull_stream(msg, context, fb_queue, pull_queue, image_queue, analyse_type, 25)
#print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno) #7.0,
#print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno) #7.0,
# 启动心跳线程
hb_thread = self.start_heartbeat(fb_queue, hb_queue, request_id, analyse_type, context)
# print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno) #7.0,
# 加载算法模型
model_array = get_model(msg, context, analyse_type)
# print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno) #9.5
#print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno) #9.5
# 启动推流进程
push_process = self.start_push_stream(msg, push_queue, image_queue, push_ex_queue, hb_queue, context)
#print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno)
@ -295,8 +300,7 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
for i, model in enumerate(model_array):
model_conf, code = model
if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code) or \
ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code) or\
ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1] == str(code):
ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code):
if draw_config.get(code) is None:
draw_config[code] = {}
draw_config["font_config"] = model_conf[4]
@ -327,11 +331,11 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
frame_index_list[i], tt, request_id)
det_array.append(det_result)
push_objs = [det.result() for det in det_array]
# print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno)
#print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno)
put_queue(push_queue,
(1, (frame_list, frame_index_list, all_frames, draw_config, push_objs)),
timeout=2, is_ex=True)
# print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno)
#print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno)
del det_array, push_objs
del frame_list, frame_index_list, all_frames
elif pull_result[0] == 1:
@ -443,12 +447,12 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
class OfflineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
__slots__ = ()
def upload_video(self, base_dir, env, request_id, aiFilePath):
def upload_video(self,base_dir, env, request_id, aiFilePath):
aliyunVodSdk = ThAliyunVodSdk(base_dir, env, request_id)
upload_video_thread_ai = Common(aliyunVodSdk.get_play_url, aiFilePath, "ai_online_%s" % request_id)
if self._storage_source == 1:
minioSdk = MinioSdk(base_dir, env, request_id)
if self._storage_source==1:
minioSdk = MinioSdk(base_dir, env, request_id )
upload_video_thread_ai = Common(minioSdk.put_object, aiFilePath, "ai_online_%s.mp4" % request_id)
else:
aliyunVodSdk = ThAliyunVodSdk(base_dir, env, request_id)
@ -622,8 +626,7 @@ class OfflineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
for i, model in enumerate(model_array):
model_conf, code = model
if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code) or \
ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code) or\
ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1] == str(code):
ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code):
if draw_config.get(code) is None:
draw_config[code] = {}
draw_config["font_config"] = model_conf[4]
@ -763,7 +766,7 @@ class PhotosIntelligentRecognitionProcess(Process):
put_queue(self._fb_queue, message_feedback(self._msg["request_id"], AnalysisStatus.WAITING.value,
self._analyse_type, progress=init_progess), timeout=2, is_ex=True)
self.build_logo(self._msg, self._context)
self._storage_source = self._context['service']['storage_source']
self._storage_source = self._context['service']['storage_source']
@staticmethod
def build_logo(msg, context):
@ -940,7 +943,7 @@ class PhotosIntelligentRecognitionProcess(Process):
logger.error("模型分析异常: {}, requestId: {}", format_exc(), request_id)
raise e
# 自研究车牌模型
#自研究车牌模型
def carplate_rec(self, imageUrl, mod, image_queue, request_id):
try:
# model_conf： modeType, allowedList, detpar, ocrmodel, rainbows
@ -979,6 +982,7 @@ class PhotosIntelligentRecognitionProcess(Process):
# param = [image, new_device, model, par, img_type, request_id]
# model_conf, frame, device, requestId
dataBack = MODEL_CONFIG[code][3]([[modeType, device, model, postPar], image, request_id])[0][2]
logger.info("当前人数：{}", dataBack[0][0])
dets[code][0] = dataBack
if not dataBack:
logger.info("当前页面无人")
@ -1267,8 +1271,7 @@ class PhotosIntelligentRecognitionProcess(Process):
result = t.submit(self.carpalteRec, imageUrls, model, image_queue, request_id)
task_list.append(result)
# 人群计数模型
elif model[1] == ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] or \
model[1] == ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1]:
elif model[1] == ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1]:
result = t.submit(self.denscrowdcountRec, imageUrls, model, image_queue, request_id)
task_list.append(result)
else:
@ -1483,9 +1486,9 @@ class ScreenRecordingProcess(Process):
clear_queue(self._hb_queue)
clear_queue(self._pull_queue)
def upload_video(self, base_dir, env, request_id, orFilePath):
if self._storage_source == 1:
minioSdk = MinioSdk(base_dir, env, request_id)
def upload_video(self,base_dir, env, request_id, orFilePath):
if self._storage_source==1:
minioSdk = MinioSdk(base_dir, env, request_id )
upload_video_thread_ai = Common(minioSdk.put_object, aiFilePath, "%s/ai_online.mp4" % request_id)
else:
aliyunVodSdk = ThAliyunVodSdk(base_dir, env, request_id)

1444
concurrency/IntelligentRecognitionProcess_20250106.py Normal file
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File diff suppressed because it is too large Load Diff

83
concurrency/PushVideoStreamProcess.py
View File

@ -151,35 +151,35 @@ class OnPushStreamProcess(PushStreamProcess):
# 自研车牌模型处理
if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
cls = 0
box = xy2xyxy(qs[1])
ocrlabel, xybox = qs
box = xy2xyxy(xybox)
score = None
color = rainbows[cls]
label_array = None
rr = t.submit(draw_name_ocr, qs, copy_frame, color)
elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code) or\
ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1] == str(code):
rr = t.submit(draw_name_ocr, xybox, copy_frame, color, ocrlabel)
elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code):
cls = 0
# crowdlabel, points = qs
crowdlabel, points = qs
box = [(0, 0), (0, 0), (0, 0), (0, 0)]
score = None
color = rainbows[cls]
label_array = None
rr = t.submit(draw_name_crowd, qs, copy_frame, color)
rr = t.submit(draw_name_crowd, points, copy_frame, color, crowdlabel)
else:
try: # 应对NaN情况
box, score, cls = xywh2xyxy2(qs)
except:
continue
if cls not in allowedList or score < frame_score:
continue
label_array, color = label_arrays[cls], rainbows[cls]
if ModelType.CHANNEL2_MODEL.value[1] == str(code) and cls == 2:
rr = t.submit(draw_name_joint, box, copy_frame,
draw_config[code]["label_dict"], score, color,
font_config, qs[6])
else:
rr = t.submit(draw_painting_joint, box, copy_frame, label_array,
score, color, font_config)
try: # 应对NaN情况
box, score, cls = xywh2xyxy2(qs)
except:
continue
if cls not in allowedList or score < frame_score:
continue
label_array, color = label_arrays[cls], rainbows[cls]
if ModelType.CHANNEL2_MODEL.value[1] == str(code) and cls == 2:
rr = t.submit(draw_name_joint, box, copy_frame,
draw_config[code]["label_dict"], score, color,
font_config, qs[6])
else:
rr = t.submit(draw_painting_joint, box, copy_frame, label_array,
score, color, font_config)
thread_p.append(rr)
if det_xywh.get(code) is None:
@ -261,18 +261,17 @@ class OnPushStreamProcess(PushStreamProcess):
is_new = False
if q[11] == 1:
is_new = True
if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code) or \
ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code) or\
ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1] == str(code):
box = qs
if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
cls = ocrlabel
elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code):
cls = crowdlabel
label_array = points
if cd is None:
det_xywh2[code][cls] = [[cls, box, score, label_array, color, is_new]]
det_xywh2[code][cls] = [[cls, box, score, label_array, color, is_new]]
else:
det_xywh2[code][cls].append(
[cls, box, score, label_array, color, is_new])
det_xywh2[code][cls].append([cls, box, score, label_array, color, is_new])
if len(det_xywh2) > 0:
put_queue(image_queue, (1, [det_xywh2, frame, frame_index_list[i], all_frames,
draw_config["font_config"]]))
put_queue(image_queue, (1, [det_xywh2, frame, frame_index_list[i], all_frames, draw_config["font_config"]]))
push_p = push_stream_result.result(timeout=60)
ai_video_file = write_ai_video_result.result(timeout=60)
@ -394,21 +393,22 @@ class OffPushStreamProcess(PushStreamProcess):
# 自研车牌模型处理
if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
cls = 0
box = xy2xyxy(qs[1])
ocrlabel, xybox = qs
box = xy2xyxy(xybox)
score = None
color = rainbows[cls]
label_array = None
label_arrays = [None]
rr = t.submit(draw_name_ocr, qs, copy_frame, color)
rr = t.submit(draw_name_ocr,xybox,copy_frame,color,ocrlabel)
elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code) or\
ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1] == str(code):
elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code):
cls = 0
crowdlabel, points = qs
box = [(0,0),(0,0),(0,0),(0,0)]
score = None
color = rainbows[cls]
label_array = None
rr = t.submit(draw_name_crowd, qs, copy_frame, color)
rr = t.submit(draw_name_crowd, points, copy_frame, color, crowdlabel)
else:
box, score, cls = xywh2xyxy2(qs)
@ -500,15 +500,16 @@ class OffPushStreamProcess(PushStreamProcess):
if q[11] == 1:
is_new = True
if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code) or \
ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code) or\
ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1] == str(code):
box = qs
if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
cls = ocrlabel
elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code):
cls = crowdlabel
label_array = points
if cd is None:
det_xywh2[code][cls] = [[cls, box, score, label_array, color, is_new]]
det_xywh2[code][cls] = [[cls, box, score, label_array, color, is_new]]
else:
det_xywh2[code][cls].append(
[cls, box, score, label_array, color, is_new])
det_xywh2[code][cls].append([cls, box, score, label_array, color, is_new])
if len(det_xywh2) > 0:
put_queue(image_queue, (1, [det_xywh2, frame, frame_index_list[i], all_frames, draw_config["font_config"]]))
push_p = push_stream_result.result(timeout=60)

2
config/logger/dsp_prod_logger.yml
View File

@ -5,6 +5,6 @@ log_name: "dsp.log"
log_fmt: "{time:YYYY-MM-DD HH:mm:ss.SSS} [{level}][{process.name}-{process.id}-{thread.name}-{thread.id}][{line}] {module}-{function} - {message}"
level: "INFO"
rotation: "00:00"
retention: "15 days"
retention: "7 days"
encoding: "utf8"

2
config/logger/dsp_test_logger.yml
View File

@ -5,6 +5,6 @@ log_name: "dsp.log"
log_fmt: "{time:YYYY-MM-DD HH:mm:ss.SSS} [{level}][{process.name}-{process.id}-{thread.name}-{thread.id}][{line}] {module}-{function} - {message}"
level: "INFO"
rotation: "00:00"
retention: "7 days"
retention: "3 days"
encoding: "utf8"

768
enums/ModelTypeEnum-jcq.py
View File

@ -1,768 +0,0 @@
import sys
from enum import Enum, unique
from common.Constant import COLOR
sys.path.extend(['..', '../AIlib2'])
from DMPR import DMPRModel
from DMPRUtils.jointUtil import dmpr_yolo
from segutils.segmodel import SegModel
from utilsK.queRiver import riverDetSegMixProcess
from utilsK.crowdGather import gather_post_process
from segutils.trafficUtils import tracfficAccidentMixFunction
from utilsK.drownUtils import mixDrowing_water_postprocess
from utilsK.noParkingUtils import mixNoParking_road_postprocess
from utilsK.illParkingUtils import illParking_postprocess
from stdc import stdcModel
from yolov5 import yolov5Model
from DMPRUtils.jointUtil import dmpr_yolo_stdc
from AI import default_mix
from ocr import ocrModel
from utilsK.channel2postUtils import channel2_post_process
'''
参数说明
1. 编号
2. 模型编号
3. 模型名称
4. 选用的模型名称
5. 模型配置
6. 模型引用配置[Detweights文件, Segweights文件, 引用计数]
'''
@unique
class ModelType(Enum):
WATER_SURFACE_MODEL = ("1", "001", "河道模型", 'river', lambda device, gpuName: {
'device': device,
'labelnames': ["排口", "水生植被", "其它", "漂浮物", "污染排口", "菜地", "违建", "岸坡垃圾"],
'seg_nclass': 2,
'trtFlag_seg': True,
'trtFlag_det': True,
'segRegionCnt': 1,
'segPar': {
'modelSize': (640, 360),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225),
'numpy': False,
'RGB_convert_first': True,
'mixFunction': {
'function': riverDetSegMixProcess,
'pars': {
'slopeIndex': [5, 6, 7],
'riverIou': 0.1
}
}
},
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Detweights': "../AIlib2/weights/river/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../AIlib2/weights/river/stdc_360X640_%s_fp16.engine' % gpuName
})
# FOREST_FARM_MODEL = ("2", "002", "森林模型", 'forest2', lambda device, gpuName: {
# 'device': device,
# 'gpu_name': gpuName,
# 'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾","云朵"],
# 'trtFlag_det': True,
# 'trtFlag_seg': False,
# 'Detweights': "../AIlib2/weights/forest2/yolov5_%s_fp16.engine" % gpuName,
# 'seg_nclass': 2,
# 'segRegionCnt': 0,
# 'slopeIndex': [],
# 'segPar': None,
# 'postFile': {
# "name": "post_process",
# "conf_thres": 0.25,
# "iou_thres": 0.45,
# "classes": 6,
# "rainbows": COLOR
# },
# 'Segweights': None
# })
FOREST_FARM_MODEL = ("2", "002", "森林模型", 'forest2', lambda device, gpuName: {
'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾","云朵"],
'postProcess':{'function':default_mix,'pars':{}},
'models':
[
{
'weight':"../AIlib2/weights/forest2/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
'name':'yolov5',
'model':yolov5Model,
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3 } },
}
],
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6,7,8,9] ],###控制哪些检测类别显示、输出
'segRegionCnt':2,###分割模型结果需要保留的等值线数目
"pixScale": 1.2,
})
TRAFFIC_FARM_MODEL = ("3", "003", "交通模型", 'highWay2', lambda device, gpuName: {
'device': str(device),
'labelnames': ["行人", "车辆", "纵向裂缝", "横向裂缝", "修补", "网状裂纹", "坑槽", "块状裂纹", "积水", "影子", "事故"],
'trtFlag_seg': True,
'trtFlag_det': True,
'seg_nclass': 3,
'segRegionCnt': 2,
'segPar': {
'modelSize': (640, 360),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225),
'predResize': True,
'numpy': False,
'RGB_convert_first': True,
'mixFunction': {
'function': tracfficAccidentMixFunction,
'pars': {
'modelSize': (640, 360),
#'modelSize': (1920,1080),
'RoadArea': 16000,
'roadVehicleAngle': 15,
'speedRoadVehicleAngleMax': 75,
'roundness': 1.0,
'cls': 9,
'vehicleFactor': 0.1,
'confThres': 0.25,
'roadIou': 0.6,
'radius': 50,
'vehicleFlag': False,
'distanceFlag': False
}
}
},
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.25,
"classes": 10,
"rainbows": COLOR
},
'Detweights': "../AIlib2/weights/highWay2/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../AIlib2/weights/highWay2/stdc_360X640_%s_fp16.engine' % gpuName
})
EPIDEMIC_PREVENTION_MODEL = ("4", "004", "防疫模型", None, None)
PLATE_MODEL = ("5", "005", "车牌模型", None, None)
VEHICLE_MODEL = ("6", "006", "车辆模型", 'vehicle', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
'labelnames': ["车辆"],
'seg_nclass': 2,
'segRegionCnt': 0,
'slopeIndex': [],
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../AIlib2/weights/vehicle/yolov5_%s_fp16.engine" % gpuName,
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Segweights': None
})
PEDESTRIAN_MODEL = ("7", "007", "行人模型", 'pedestrian', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
'labelnames': ["行人"],
'seg_nclass': 2,
'segRegionCnt': 0,
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../AIlib2/weights/pedestrian/yolov5_%s_fp16.engine" % gpuName,
'slopeIndex': [],
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Segweights': None
})
SMOGFIRE_MODEL = ("8", "008", "烟火模型", 'smogfire', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
'labelnames': ["火焰", "烟雾"],
'seg_nclass': 2, # 分割模型类别数目，默认2类
'segRegionCnt': 0,
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../AIlib2/weights/smogfire/yolov5_%s_fp16.engine" % gpuName,
'slopeIndex': [],
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Segweights': None
})
ANGLERSWIMMER_MODEL = ("9", "009", "钓鱼游泳模型", 'AnglerSwimmer', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
'labelnames': ["钓鱼", "游泳"],
'seg_nclass': 2, # 分割模型类别数目，默认2类
'segRegionCnt': 0,
'slopeIndex': [],
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../AIlib2/weights/AnglerSwimmer/yolov5_%s_fp16.engine" % gpuName,
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Segweights': None
})
COUNTRYROAD_MODEL = ("10", "010", "乡村模型", 'countryRoad', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
'labelnames': ["违法种植"],
'seg_nclass': 2, # 分割模型类别数目，默认2类
'segRegionCnt': 0,
'slopeIndex': [],
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../AIlib2/weights/countryRoad/yolov5_%s_fp16.engine" % gpuName,
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Segweights': None
})
SHIP_MODEL = ("11", "011", "船只模型", 'ship2', lambda device, gpuName: {
'model_size': (608, 608),
'K': 100,
'conf_thresh': 0.18,
'device': 'cuda:%s' % device,
'down_ratio': 4,
'num_classes': 15,
'weights': '../AIlib2/weights/ship2/obb_608X608_%s_fp16.engine' % gpuName,
'dataset': 'dota',
'half': False,
'mean': (0.5, 0.5, 0.5),
'std': (1, 1, 1),
'heads': {'hm': None, 'wh': 10, 'reg': 2, 'cls_theta': 1},
'decoder': None,
'test_flag': True,
"rainbows": COLOR,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'drawBox': False,
'label_array': None,
'labelnames': ("0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "船只"),
})
BAIDU_MODEL = ("12", "012", "百度AI图片识别模型", None, None)
CHANNEL_EMERGENCY_MODEL = ("13", "013", "航道模型", 'channelEmergency', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
'labelnames': ["人"],
'seg_nclass': 2, # 分割模型类别数目，默认2类
'segRegionCnt': 0,
'slopeIndex': [],
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../AIlib2/weights/channelEmergency/yolov5_%s_fp16.engine" % gpuName,
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Segweights': None
})
RIVER2_MODEL = ("15", "015", "河道检测模型", 'river2', lambda device, gpuName: {
'device': device,
'labelnames': ["漂浮物", "岸坡垃圾", "排口", "违建", "菜地", "水生植物", "河湖人员", "钓鱼人员", "船只",
"蓝藻"],
'trtFlag_seg': True,
'trtFlag_det': True,
'seg_nclass': 2,
'segRegionCnt': 1,
'segPar': {
'modelSize': (640, 360),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225),
'numpy': False,
'RGB_convert_first': True,
'mixFunction': {
'function': riverDetSegMixProcess,
'pars': {
'slopeIndex': [1, 3, 4, 7],
'riverIou': 0.1
}
}
},
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.3,
"ovlap_thres_crossCategory": 0.65,
"classes": 5,
"rainbows": COLOR
},
# "../AIlib2/weights/conf/%s/yolov5.pt" % modeType.value[3]
'Detweights': "../AIlib2/weights/river2/yolov5_%s_fp16.engine" % gpuName,
# '../AIlib2/weights/conf/%s/stdc_360X640.pth' % modeType.value[3]
'Segweights': '../AIlib2/weights/river2/stdc_360X640_%s_fp16.engine' % gpuName
})
CITY_MANGEMENT_MODEL = ("16", "016", "城管模型", 'cityMangement2', lambda device, gpuName: {
'labelnames': ["车辆", "垃圾", "商贩", "违停"],
'postProcess':{
'function':dmpr_yolo_stdc,
'pars':{'carCls':0 ,'illCls':3,'scaleRatio':0.5,'border':80,'rubCls': 1, 'Rubfilter': 150}
},
'models':[
{
#'weight':'../AIlib2/weights/conf/cityMangement3/yolov5.pt',
'weight':'../AIlib2/weights/cityMangement3/yolov5_%s_fp16.engine'%(gpuName),
'name':'yolov5',
'model':yolov5Model,
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.5,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.8,"1":0.4,"2":0.5,"3":0.5 } }
},
{
'weight':'../AIlib2/weights/conf/cityMangement3/dmpr.pth',
'par':{
'depth_factor':32,'NUM_FEATURE_MAP_CHANNEL':6,'dmpr_thresh':0.1, 'dmprimg_size':640,
'name':'dmpr'
},
'model':DMPRModel,
'name':'dmpr'
},
{
'weight':'../AIlib2/weights/conf/cityMangement3/stdc_360X640.pth',
'par':{
'modelSize':(640,360),'mean':(0.485, 0.456, 0.406),'std' :(0.229, 0.224, 0.225),'predResize':True,'numpy':False, 'RGB_convert_first':True,'seg_nclass':2},###分割模型预处理参数
'model':stdcModel,
'name':'stdc'
}
],
'postFile': {
"name": "post_process",
"conf_thres": 0.5,
"iou_thres": 0.5,
"classes": 5,
"rainbows": COLOR
},
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,5,6,7,8,9] ],###控制哪些检测类别显示、输出
'segRegionCnt':2,###分割模型结果需要保留的等值线数目
"pixScale": 1.2,
})
DROWING_MODEL = ("17", "017", "人员落水模型", 'drowning', lambda device, gpuName: {
'device': device,
'labelnames': ["人头", "人", "船只"],
'trtFlag_seg': True,
'trtFlag_det': True,
'seg_nclass': 2,
'segRegionCnt': 2,
'segPar': {
'modelSize': (640, 360),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225),
'predResize': True,
'numpy': False,
'RGB_convert_first': True,
'mixFunction': {
'function': mixDrowing_water_postprocess,
'pars': {
'modelSize': (640, 360)
}
}
},
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.25,
"classes": 9,
"rainbows": COLOR
},
# "../AIlib2/weights/conf/%s/yolov5.pt" % modeType.value[3]
'Detweights': "../AIlib2/weights/drowning/yolov5_%s_fp16.engine" % gpuName,
# '../AIlib2/weights/conf/%s/stdc_360X640.pth' % modeType.value[3]
'Segweights': '../AIlib2/weights/drowning/stdc_360X640_%s_fp16.engine' % gpuName
})
NOPARKING_MODEL = (
"18", "018", "城市违章模型", 'noParking', lambda device, gpuName: {
'device': device,
'labelnames': ["车辆", "违停"],
'trtFlag_seg': True,
'trtFlag_det': True,
'seg_nclass': 4,
'segRegionCnt': 2,
'segPar': {
'modelSize': (640, 360),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225),
'predResize': True,
'numpy': False,
'RGB_convert_first': True, ###分割模型预处理参数
'mixFunction': {
'function': mixNoParking_road_postprocess,
'pars': {
'modelSize': (640, 360),
'roundness': 0.3,
'cls': 9,
'laneArea': 10,
'laneAngleCha': 5,
'RoadArea': 16000,
'fitOrder':2
}
}
},
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.25,
"classes": 9,
"rainbows": COLOR
},
'Detweights': "../AIlib2/weights/noParking/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../AIlib2/weights/noParking/stdc_360X640_%s_fp16.engine' % gpuName
})
ILLPARKING_MODEL = ("19", "019", "车辆违停模型", 'illParking', lambda device, gpuName: {
'device': device,
'labelnames': ["车", "T角点", "L角点", "违停"],
'trtFlag_seg': False,
'trtFlag_det': True,
'seg_nclass': 4,
'segRegionCnt': 2,
'segPar': {
'mixFunction': {
'function': illParking_postprocess,
'pars': {}
}
},
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.25,
"classes": 9,
"rainbows": COLOR
},
'Detweights': "../AIlib2/weights/illParking/yolov5_%s_fp16.engine" % gpuName,
'Segweights': None
})
CITYROAD_MODEL = ("20", "020", "城市公路模型", 'cityRoad', lambda device, gpuName: {
'device': device,
'labelnames': ["护栏", "交通标志", "非交通标志", "施工", "施工"],
'trtFlag_seg': False,
'trtFlag_det': True,
'slopeIndex': [],
'seg_nclass': 2,
'segRegionCnt': 0,
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.5,
"classes": 5,
"rainbows": COLOR
},
'Detweights': "../AIlib2/weights/cityRoad/yolov5_%s_fp16.engine" % gpuName,
'Segweights': None
})
POTHOLE_MODEL = ("23", "023", "坑槽检测模型", 'pothole', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
'labelnames': ["坑槽"],
'seg_nclass': 2, # 分割模型类别数目，默认2类
'segRegionCnt': 0,
'slopeIndex': [],
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../AIlib2/weights/pothole/yolov5_%s_fp16.engine" % gpuName,
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Segweights': None,
})
CHANNEL2_MODEL = ("24", "024", "船只综合检测模型", 'channel2', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
'labelnames': ["国旗", "浮标", "船名", "船只","未挂国旗船只","未封仓"], # 保持原来的标签顺序不变，方便后面业务端增加
'segRegionCnt': 0,
'postProcess':{'function':channel2_post_process,'name':'channel2','pars':{
'objs':[2],
'wRation':1/6.0,
'hRation':1/6.0,
'smallId':0, #旗帜
'bigId':3, #船只
'newId':4, #未挂国旗船只
'uncoverId':5, #未封仓标签
'recScale':1.2,
'target_cls':3.0, #目标种类
'filter_cls':4.0 #被过滤的种类
}},
'models':[
{
#'weight':'../AIlib2/weights/conf/channel2/yolov5.pt',
# 'weight':'../AIlib2/weights/channel2/yolov5_%s_fp16.engine'%(gpuName),
'weight':'/home/thsw2/jcq/test/AIlib2/weights/channel2/best.pt', # yolov5 原来模型基础上增加了未封仓
# 'weight':'../AIlib2/weights/channel2/yolov5_%s_fp16.engine'%(gpuName),
'name':'yolov5',
'model':yolov5Model,
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.1,'iou_thres':0.45,'allowedList':list(range(20)),'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.7,"1":0.7,"2":0.8,"3":0.6} }
},
{
# 'weight' : '../AIlib2/weights/ocr2/crnn_ch_4090_fp16_192X32.engine',
'weight' : '../AIlib2/weights/conf/ocr2/crnn_ch.pth',
'name':'ocr',
'model':ocrModel,
'par':{
'char_file':'../AIlib2/weights/conf/ocr2/benchmark.txt',
'mode':'ch',
'nc':3,
'imgH':32,
'imgW':192,
'hidden':256,
'mean':[0.5,0.5,0.5],
'std':[0.5,0.5,0.5],
'dynamic':False,
},
} ,
# {
# 'weight':'/home/thsw2/jcq/test/AIlib2/weights1/conf/channel2/yolov5_04.pt', # yolov5_04 添加了uncover 0 4 ；标签 yolov5_jcq
# 'name':'yolov5',
# 'model':yolov5Model,
# 'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.15,'iou_thres':0.25,'allowedList':list(range(20)),'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.7,"1":0.7,"2":0.8,"3":0.6} }
# }
],
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3]],
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Segweights': None,
})
RIVERT_MODEL = ("25", "025", "河道检测模型(T)", 'riverT', lambda device, gpuName: {
'device': device,
'labelnames': ["漂浮物", "岸坡垃圾", "排口", "违建", "菜地", "水生植物", "河湖人员", "钓鱼人员", "船只",
"蓝藻"],
'trtFlag_seg': True,
'trtFlag_det': True,
'seg_nclass': 2,
'segRegionCnt': 1,
'segPar': {
'modelSize': (640, 360),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225),
'numpy': False,
'RGB_convert_first': True,
'mixFunction': {
'function': riverDetSegMixProcess,
'pars': {
'slopeIndex': [1, 3, 4, 7],
'riverIou': 0.1
}
}
},
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.3,
"ovlap_thres_crossCategory": 0.65,
"classes": 5,
"rainbows": COLOR
},
# "../AIlib2/weights/conf/%s/yolov5.pt" % modeType.value[3]
'Detweights': "../AIlib2/weights/riverT/yolov5_%s_fp16.engine" % gpuName,
# '../AIlib2/weights/conf/%s/stdc_360X640.pth' % modeType.value[3]
'Segweights': '../AIlib2/weights/riverT/stdc_360X640_%s_fp16.engine' % gpuName
})
FORESTCROWD_FARM_MODEL = ("2", "026", "森林人群模型", 'forestCrowd', lambda device, gpuName: {
'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾","人群"],
'postProcess':{'function':gather_post_process,'pars':{'pedestrianId':2,'crowdThreshold':4,'gatherId':5,'distancePersonScale':2.0}},
'models':
[
{
'weight':"../AIlib2/weights/forestCrowd/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
'name':'yolov5',
'model':yolov5Model,
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.5,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{ "0":0.25,"1":0.25,"2":0.6,"3":0.6,'4':0.6 ,'5':0.6 } },
}
],
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6,7,8,9] ],###控制哪些检测类别显示、输出
'segRegionCnt':2,###分割模型结果需要保留的等值线数目
"pixScale": 1.2,
})
TRAFFICFORDSJ_FARM_MODEL = ("27", "027", "交通模型-大数据局", 'highWay2T', lambda device, gpuName: {
'device': str(device),
'labelnames': ["行人", "车辆", "纵向裂缝", "横向裂缝", "修补", "网状裂纹", "坑槽", "块状裂纹", "积水", "影子", "事故", "桥梁外观","设施破损缺失","龙门架","防抛网","标识牌损坏","护栏损坏","钢筋裸露" ],
'trtFlag_seg': True,
'trtFlag_det': True,
'seg_nclass': 3,
'segRegionCnt': 2,
'segPar': {
'modelSize': (640, 360),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225),
'predResize': True,
'numpy': False,
'RGB_convert_first': True,
'mixFunction': {
'function': tracfficAccidentMixFunction,
'pars': {
'modelSize': (640, 360),
#'modelSize': (1920,1080),
'RoadArea': 16000,
'roadVehicleAngle': 15,
'speedRoadVehicleAngleMax': 75,
'roundness': 1.0,
'cls': 9,
'vehicleFactor': 0.1,
'confThres': 0.25,
'roadIou': 0.6,
'radius': 50,
'vehicleFlag': False,
'distanceFlag': False
}
}
},
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.25,
"classes": 10,
"rainbows": COLOR
},
'Detweights': "../AIlib2/weights/highWay2/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../AIlib2/weights/highWay2/stdc_360X640_%s_fp16.engine' % gpuName
})
@staticmethod
def checkCode(code):
for model in ModelType:
if model.value[1] == code:
return True
return False
'''
参数1: 检测目标名称
参数2: 检测目标
参数3: 初始化百度检测客户端
'''
@unique
class BaiduModelTarget(Enum):
VEHICLE_DETECTION = (
"车辆检测", 0, lambda client0, client1, url, request_id: client0.vehicleDetectUrl(url, request_id))
HUMAN_DETECTION = (
"人体检测与属性识别", 1, lambda client0, client1, url, request_id: client1.bodyAttr(url, request_id))
PEOPLE_COUNTING = ("人流量统计", 2, lambda client0, client1, url, request_id: client1.bodyNum(url, request_id))
BAIDU_MODEL_TARGET_CONFIG = {
BaiduModelTarget.VEHICLE_DETECTION.value[1]: BaiduModelTarget.VEHICLE_DETECTION,
BaiduModelTarget.HUMAN_DETECTION.value[1]: BaiduModelTarget.HUMAN_DETECTION,
BaiduModelTarget.PEOPLE_COUNTING.value[1]: BaiduModelTarget.PEOPLE_COUNTING
}
EPIDEMIC_PREVENTION_CONFIG = {1: "行程码", 2: "健康码"}
# 模型分析方式
@unique
class ModelMethodTypeEnum(Enum):
# 方式一: 正常识别方式
NORMAL = 1
# 方式二: 追踪识别方式
TRACE = 2

807
enums/ModelTypeEnum-raw.py
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@ -1,807 +0,0 @@
import sys
from enum import Enum, unique
from common.Constant import COLOR
sys.path.extend(['..', '../AIlib2'])
from DMPR import DMPRModel
from DMPRUtils.jointUtil import dmpr_yolo
from segutils.segmodel import SegModel
from utilsK.queRiver import riverDetSegMixProcess
from utilsK.crowdGather import gather_post_process
from segutils.trafficUtils import tracfficAccidentMixFunction
from utilsK.drownUtils import mixDrowing_water_postprocess
from utilsK.noParkingUtils import mixNoParking_road_postprocess
from utilsK.illParkingUtils import illParking_postprocess
from stdc import stdcModel
from yolov5 import yolov5Model
from DMPRUtils.jointUtil import dmpr_yolo_stdc
from AI import default_mix
from ocr import ocrModel
from utilsK.channel2postUtils import channel2_post_process
'''
参数说明
1. 编号
2. 模型编号
3. 模型名称
4. 选用的模型名称
5. 模型配置
6. 模型引用配置[Detweights文件, Segweights文件, 引用计数]
'''
@unique
class ModelType(Enum):
WATER_SURFACE_MODEL = ("1", "001", "河道模型", 'river', lambda device, gpuName: {
'device': device,
'labelnames': ["排口", "水生植被", "其它", "漂浮物", "污染排口", "菜地", "违建", "岸坡垃圾"],
'seg_nclass': 2,
'trtFlag_seg': True,
'trtFlag_det': True,
'segRegionCnt': 1,
'segPar': {
'modelSize': (640, 360),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225),
'numpy': False,
'RGB_convert_first': True,
'mixFunction': {
'function': riverDetSegMixProcess,
'pars': {
'slopeIndex': [5, 6, 7],
'riverIou': 0.1
}
}
},
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Detweights': "../AIlib2/weights/river/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../AIlib2/weights/river/stdc_360X640_%s_fp16.engine' % gpuName
})
# FOREST_FARM_MODEL = ("2", "002", "森林模型", 'forest2', lambda device, gpuName: {
# 'device': device,
# 'gpu_name': gpuName,
# 'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾","云朵"],
# 'trtFlag_det': True,
# 'trtFlag_seg': False,
# 'Detweights': "../AIlib2/weights/forest2/yolov5_%s_fp16.engine" % gpuName,
# 'seg_nclass': 2,
# 'segRegionCnt': 0,
# 'slopeIndex': [],
# 'segPar': None,
# 'postFile': {
# "name": "post_process",
# "conf_thres": 0.25,
# "iou_thres": 0.45,
# "classes": 6,
# "rainbows": COLOR
# },
# 'Segweights': None
# })
FOREST_FARM_MODEL = ("2", "002", "森林模型", 'forest2', lambda device, gpuName: {
'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾","云朵"],
'postProcess':{'function':default_mix,'pars':{}},
'models':
[
{
'weight':"../AIlib2/weights/forest2/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
'name':'yolov5',
'model':yolov5Model,
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3 } },
}
],
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6,7,8,9] ],###控制哪些检测类别显示、输出
'segRegionCnt':2,###分割模型结果需要保留的等值线数目
"pixScale": 1.2,
})
TRAFFIC_FARM_MODEL = ("3", "003", "交通模型", 'highWay2', lambda device, gpuName: {
'device': str(device),
'labelnames': ["行人", "车辆", "纵向裂缝", "横向裂缝", "修补", "网状裂纹", "坑槽", "块状裂纹", "积水", "影子", "事故"],
'trtFlag_seg': True,
'trtFlag_det': True,
'seg_nclass': 3,
'segRegionCnt': 2,
'segPar': {
#'modelSize': (640, 360),
'modelSize': (1920, 1080),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225),
'predResize': True,
'numpy': False,
'RGB_convert_first': True,
'mixFunction': {
'function': tracfficAccidentMixFunction,
'pars': {
#'modelSize': (640, 360),
'modelSize': (1920,1080),
'RoadArea': 16000,
'roadVehicleAngle': 15,
'speedRoadVehicleAngleMax': 75,
'roundness': 1.0,
'cls': 10,
'vehicleFactor': 0.1,
'confThres': 0.25,
'roadIou': 0.6,
'radius': 50,
'vehicleFlag': False,
'distanceFlag': False
}
}
},
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.25,
"classes": 10,
"rainbows": COLOR
},
'Detweights': "../AIlib2/weights/highWay2/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../AIlib2/weights/highWay2/stdc_360X640_%s_fp16.engine' % gpuName
})
EPIDEMIC_PREVENTION_MODEL = ("4", "004", "防疫模型", None, None)
PLATE_MODEL = ("5", "005", "车牌模型", None, None)
VEHICLE_MODEL = ("6", "006", "车辆模型", 'vehicle', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
'labelnames': ["车辆"],
'seg_nclass': 2,
'segRegionCnt': 0,
'slopeIndex': [],
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../AIlib2/weights/vehicle/yolov5_%s_fp16.engine" % gpuName,
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Segweights': None
})
PEDESTRIAN_MODEL = ("7", "007", "行人模型", 'pedestrian', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
'labelnames': ["行人"],
'seg_nclass': 2,
'segRegionCnt': 0,
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../AIlib2/weights/pedestrian/yolov5_%s_fp16.engine" % gpuName,
'slopeIndex': [],
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Segweights': None
})
SMOGFIRE_MODEL = ("8", "008", "烟火模型", 'smogfire', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
'labelnames': ["火焰", "烟雾"],
'seg_nclass': 2, # 分割模型类别数目，默认2类
'segRegionCnt': 0,
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../AIlib2/weights/smogfire/yolov5_%s_fp16.engine" % gpuName,
'slopeIndex': [],
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Segweights': None
})
ANGLERSWIMMER_MODEL = ("9", "009", "钓鱼游泳模型", 'AnglerSwimmer', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
'labelnames': ["钓鱼", "游泳"],
'seg_nclass': 2, # 分割模型类别数目，默认2类
'segRegionCnt': 0,
'slopeIndex': [],
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../AIlib2/weights/AnglerSwimmer/yolov5_%s_fp16.engine" % gpuName,
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Segweights': None
})
COUNTRYROAD_MODEL = ("10", "010", "乡村模型", 'countryRoad', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
'labelnames': ["违法种植"],
'seg_nclass': 2, # 分割模型类别数目，默认2类
'segRegionCnt': 0,
'slopeIndex': [],
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../AIlib2/weights/countryRoad/yolov5_%s_fp16.engine" % gpuName,
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Segweights': None
})
SHIP_MODEL = ("11", "011", "船只模型", 'ship2', lambda device, gpuName: {
'model_size': (608, 608),
'K': 100,
'conf_thresh': 0.18,
'device': 'cuda:%s' % device,
'down_ratio': 4,
'num_classes': 15,
'weights': '../AIlib2/weights/ship2/obb_608X608_%s_fp16.engine' % gpuName,
'dataset': 'dota',
'half': False,
'mean': (0.5, 0.5, 0.5),
'std': (1, 1, 1),
'heads': {'hm': None, 'wh': 10, 'reg': 2, 'cls_theta': 1},
'decoder': None,
'test_flag': True,
"rainbows": COLOR,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'drawBox': False,
'label_array': None,
'labelnames': ("0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "船只"),
})
BAIDU_MODEL = ("12", "012", "百度AI图片识别模型", None, None)
CHANNEL_EMERGENCY_MODEL = ("13", "013", "航道模型", 'channelEmergency', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
'labelnames': ["人"],
'seg_nclass': 2, # 分割模型类别数目，默认2类
'segRegionCnt': 0,
'slopeIndex': [],
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../AIlib2/weights/channelEmergency/yolov5_%s_fp16.engine" % gpuName,
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Segweights': None
})
RIVER2_MODEL = ("15", "015", "河道检测模型", 'river2', lambda device, gpuName: {
'device': device,
'labelnames': ["漂浮物", "岸坡垃圾", "排口", "违建", "菜地", "水生植物", "河湖人员", "钓鱼人员", "船只",
"蓝藻"],
'trtFlag_seg': True,
'trtFlag_det': True,
'seg_nclass': 2,
'segRegionCnt': 1,
'segPar': {
'modelSize': (640, 360),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225),
'numpy': False,
'RGB_convert_first': True,
'mixFunction': {
'function': riverDetSegMixProcess,
'pars': {
'slopeIndex': [1, 3, 4, 7],
'riverIou': 0.1
}
}
},
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.3,
"ovlap_thres_crossCategory": 0.65,
"classes": 5,
"rainbows": COLOR
},
# "../AIlib2/weights/conf/%s/yolov5.pt" % modeType.value[3]
'Detweights': "../AIlib2/weights/river2/yolov5_%s_fp16.engine" % gpuName,
# '../AIlib2/weights/conf/%s/stdc_360X640.pth' % modeType.value[3]
'Segweights': '../AIlib2/weights/river2/stdc_360X640_%s_fp16.engine' % gpuName
})
CITY_MANGEMENT_MODEL = ("16", "016", "城管模型", 'cityMangement2', lambda device, gpuName: {
'labelnames': ["车辆", "垃圾", "商贩", "裸土","占道经营","违停"],
'postProcess':{
'function':dmpr_yolo_stdc,
'pars':{'carCls':0 ,'illCls':5,'scaleRatio':0.5,'border':80}
},
'models':[
{
#'weight':'../AIlib2/weights/conf/cityMangement3/yolov5.pt',
'weight':'../AIlib2/weights/cityMangement3/yolov5_%s_fp16.engine'%(gpuName),
'name':'yolov5',
'model':yolov5Model,
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3,4,5],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.8,"1":0.4,"2":0.5,"3":0.5,"4":0.4,"5":0.5 } }
},
{
'weight':'../AIlib2/weights/conf/cityMangement3/dmpr.pth',
'par':{
'depth_factor':32,'NUM_FEATURE_MAP_CHANNEL':6,'dmpr_thresh':0.1, 'dmprimg_size':640,
'name':'dmpr'
},
'model':DMPRModel,
'name':'dmpr'
},
{
'weight':'../AIlib2/weights/conf/cityMangement3/stdc_360X640.pth',
'par':{
'modelSize':(640,360),'mean':(0.485, 0.456, 0.406),'std' :(0.229, 0.224, 0.225),'predResize':True,'numpy':False, 'RGB_convert_first':True,'seg_nclass':2},###分割模型预处理参数
'model':stdcModel,
'name':'stdc'
}
],
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,5,6,7,8,9] ],###控制哪些检测类别显示、输出
'segRegionCnt':2,###分割模型结果需要保留的等值线数目
"pixScale": 1.2,
})
DROWING_MODEL = ("17", "017", "人员落水模型", 'drowning', lambda device, gpuName: {
'device': device,
'labelnames': ["人头", "人", "船只"],
'trtFlag_seg': True,
'trtFlag_det': True,
'seg_nclass': 2,
'segRegionCnt': 2,
'segPar': {
'modelSize': (640, 360),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225),
'predResize': True,
'numpy': False,
'RGB_convert_first': True,
'mixFunction': {
'function': mixDrowing_water_postprocess,
'pars': {
'modelSize': (640, 360)
}
}
},
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.25,
"classes": 9,
"rainbows": COLOR
},
# "../AIlib2/weights/conf/%s/yolov5.pt" % modeType.value[3]
'Detweights': "../AIlib2/weights/drowning/yolov5_%s_fp16.engine" % gpuName,
# '../AIlib2/weights/conf/%s/stdc_360X640.pth' % modeType.value[3]
'Segweights': '../AIlib2/weights/drowning/stdc_360X640_%s_fp16.engine' % gpuName
})
NOPARKING_MODEL = (
"18", "018", "城市违章模型", 'noParking', lambda device, gpuName: {
'device': device,
'labelnames': ["车辆", "违停"],
'trtFlag_seg': True,
'trtFlag_det': True,
'seg_nclass': 4,
'segRegionCnt': 2,
'segPar': {
'modelSize': (640, 360),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225),
'predResize': True,
'numpy': False,
'RGB_convert_first': True, ###分割模型预处理参数
'mixFunction': {
'function': mixNoParking_road_postprocess,
'pars': {
'modelSize': (640, 360),
'roundness': 0.3,
'cls': 9,
'laneArea': 10,
'laneAngleCha': 5,
'RoadArea': 16000,
'fitOrder':2
}
}
},
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.25,
"classes": 9,
"rainbows": COLOR
},
'Detweights': "../AIlib2/weights/noParking/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../AIlib2/weights/noParking/stdc_360X640_%s_fp16.engine' % gpuName
})
ILLPARKING_MODEL = ("19", "019", "车辆违停模型", 'illParking', lambda device, gpuName: {
'device': device,
'labelnames': ["车", "T角点", "L角点", "违停"],
'trtFlag_seg': False,
'trtFlag_det': True,
'seg_nclass': 4,
'segRegionCnt': 2,
'segPar': {
'mixFunction': {
'function': illParking_postprocess,
'pars': {}
}
},
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.25,
"classes": 9,
"rainbows": COLOR
},
'Detweights': "../AIlib2/weights/illParking/yolov5_%s_fp16.engine" % gpuName,
'Segweights': None
})
CITYROAD_MODEL = ("20", "020", "城市公路模型", 'cityRoad', lambda device, gpuName: {
'device': device,
'labelnames': ["护栏", "交通标志", "非交通标志", "施工", "施工"],
'trtFlag_seg': False,
'trtFlag_det': True,
'slopeIndex': [],
'seg_nclass': 2,
'segRegionCnt': 0,
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.8,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Detweights': "../AIlib2/weights/cityRoad/yolov5_%s_fp16.engine" % gpuName,
'Segweights': None
})
POTHOLE_MODEL = ("23", "023", "坑槽检测模型", 'pothole', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
'labelnames': ["坑槽"],
'seg_nclass': 2, # 分割模型类别数目，默认2类
'segRegionCnt': 0,
'slopeIndex': [],
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../AIlib2/weights/pothole/yolov5_%s_fp16.engine" % gpuName,
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Segweights': None,
})
CHANNEL2_MODEL = ("24", "024", "船只综合检测模型", 'channel2', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
'labelnames': ["国旗", "浮标", "船名", "船只","未挂国旗船只"],
'segRegionCnt': 0,
'postProcess':{'function':channel2_post_process,'name':'channel2','pars':{
'objs':[2],
'wRation':1/6.0,
'hRation':1/6.0,
'smallId':0,
'bigId':3,
'newId':4,
'recScale':1.2}},
'models':[
{
#'weight':'../AIlib2/weights/conf/channel2/yolov5.pt',
'weight':'../AIlib2/weights/channel2/yolov5_%s_fp16.engine'%(gpuName),
'name':'yolov5',
'model':yolov5Model,
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.1,'iou_thres':0.45,'allowedList':list(range(20)),'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.7,"1":0.7,"2":0.8,"3":0.6} }
},
{
# 'weight' : '../AIlib2/weights/ocr2/crnn_ch_4090_fp16_192X32.engine',
'weight' : '../AIlib2/weights/conf/ocr2/crnn_ch.pth',
'name':'ocr',
'model':ocrModel,
'par':{
'char_file':'../AIlib2/weights/conf/ocr2/benchmark.txt',
'mode':'ch',
'nc':3,
'imgH':32,
'imgW':192,
'hidden':256,
'mean':[0.5,0.5,0.5],
'std':[0.5,0.5,0.5],
'dynamic':False,
},
}
],
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3]],
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Segweights': None,
})
RIVERT_MODEL = ("25", "025", "河道检测模型(T)", 'riverT', lambda device, gpuName: {
'device': device,
'labelnames': ["漂浮物", "岸坡垃圾", "排口", "违建", "菜地", "水生植物", "河湖人员", "钓鱼人员", "船只",
"蓝藻"],
'trtFlag_seg': True,
'trtFlag_det': True,
'seg_nclass': 2,
'segRegionCnt': 1,
'segPar': {
'modelSize': (640, 360),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225),
'numpy': False,
'RGB_convert_first': True,
'mixFunction': {
'function': riverDetSegMixProcess,
'pars': {
'slopeIndex': [1, 3, 4, 7],
'riverIou': 0.1
}
}
},
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.3,
"ovlap_thres_crossCategory": 0.65,
"classes": 5,
"rainbows": COLOR
},
# "../AIlib2/weights/conf/%s/yolov5.pt" % modeType.value[3]
'Detweights': "../AIlib2/weights/riverT/yolov5_%s_fp16.engine" % gpuName,
# '../AIlib2/weights/conf/%s/stdc_360X640.pth' % modeType.value[3]
'Segweights': '../AIlib2/weights/riverT/stdc_360X640_%s_fp16.engine' % gpuName
})
FORESTCROWD_FARM_MODEL = ("26", "026", "森林人群模型", 'forestCrowd', lambda device, gpuName: {
'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾","人群"],
'postProcess':{'function':gather_post_process,'pars':{'pedestrianId':2,'crowdThreshold':4,'gatherId':5,'distancePersonScale':2.0}},
'models':
[
{
'weight':"../AIlib2/weights/forestCrowd/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
'name':'yolov5',
'model':yolov5Model,
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{ "0":0.25,"1":0.25,"2":0.6,"3":0.6,'4':0.6 ,'5':0.6 } },
}
],
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6,7,8,9] ],###控制哪些检测类别显示、输出
'segRegionCnt':2,###分割模型结果需要保留的等值线数目
"pixScale": 1.2,
})
TRAFFICFORDSJ_FARM_MODEL = ("27", "027", "交通模型-大数据局", 'highWay2T', lambda device, gpuName: {
'device': str(device),
'labelnames': ["行人", "车辆", "纵向裂缝", "横向裂缝", "修补", "网状裂纹", "坑槽", "块状裂纹", "积水", "影子", "事故", "桥梁外观","设施破损缺失","龙门架","防抛网","标识牌损坏","护栏损坏","钢筋裸露" ],
'trtFlag_seg': True,
'trtFlag_det': True,
'seg_nclass': 3,
'segRegionCnt': 2,
'segPar': {
'modelSize': (640, 360),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225),
'predResize': True,
'numpy': False,
'RGB_convert_first': True,
'mixFunction': {
'function': tracfficAccidentMixFunction,
'pars': {
'modelSize': (640, 360),
#'modelSize': (1920,1080),
'RoadArea': 16000,
'roadVehicleAngle': 15,
'speedRoadVehicleAngleMax': 75,
'roundness': 1.0,
'cls': 9,
'vehicleFactor': 0.1,
'confThres': 0.25,
'roadIou': 0.6,
'radius': 50,
'vehicleFlag': False,
'distanceFlag': False
}
}
},
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.25,
"classes": 10,
"rainbows": COLOR
},
'Detweights': "../AIlib2/weights/highWay2/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../AIlib2/weights/highWay2/stdc_360X640_%s_fp16.engine' % gpuName
})
SMARTSITE_MODEL = ("28", "028", "智慧工地模型", 'smartSite', lambda device, gpuName: {
'labelnames': [ "工人","塔式起重机","悬臂","起重机","压路机","推土机","挖掘机","卡车","装载机","泵车","混凝土搅拌车","打桩","其他车辆" ],
'postProcess':{'function':default_mix,'pars':{}},
'models':
[
{
'weight':"../AIlib2/weights/smartSite/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
'name':'yolov5',
'model':yolov5Model,
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':list(range(20)),'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3 } },
}
],
'postFile': {
"rainbows": COLOR
},
})
RUBBISH_MODEL = ("29", "029", "垃圾模型", 'rubbish', lambda device, gpuName: {
'labelnames': [ "建筑垃圾","白色垃圾","其他垃圾"],
'postProcess':{'function':default_mix,'pars':{}},
'models':
[
{
'weight':"../AIlib2/weights/rubbish/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
'name':'yolov5',
'model':yolov5Model,
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':list(range(20)),'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3 } },
}
],
'postFile': {
"rainbows": COLOR
},
})
FIREWORK_MODEL = ("30", "030", "烟花模型", 'firework', lambda device, gpuName: {
'labelnames': [ "烟花"],
'postProcess':{'function':default_mix,'pars':{}},
'models':
[
{
'weight':"../AIlib2/weights/firework/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
'name':'yolov5',
'model':yolov5Model,
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':list(range(20)),'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3 } },
}
],
'postFile': {
"rainbows": COLOR
},
})
@staticmethod
def checkCode(code):
for model in ModelType:
if model.value[1] == code:
return True
return False
'''
参数1: 检测目标名称
参数2: 检测目标
参数3: 初始化百度检测客户端
'''
@unique
class BaiduModelTarget(Enum):
VEHICLE_DETECTION = (
"车辆检测", 0, lambda client0, client1, url, request_id: client0.vehicleDetectUrl(url, request_id))
HUMAN_DETECTION = (
"人体检测与属性识别", 1, lambda client0, client1, url, request_id: client1.bodyAttr(url, request_id))
PEOPLE_COUNTING = ("人流量统计", 2, lambda client0, client1, url, request_id: client1.bodyNum(url, request_id))
BAIDU_MODEL_TARGET_CONFIG = {
BaiduModelTarget.VEHICLE_DETECTION.value[1]: BaiduModelTarget.VEHICLE_DETECTION,
BaiduModelTarget.HUMAN_DETECTION.value[1]: BaiduModelTarget.HUMAN_DETECTION,
BaiduModelTarget.PEOPLE_COUNTING.value[1]: BaiduModelTarget.PEOPLE_COUNTING
}
EPIDEMIC_PREVENTION_CONFIG = {1: "行程码", 2: "健康码"}
# 模型分析方式
@unique
class ModelMethodTypeEnum(Enum):
# 方式一: 正常识别方式
NORMAL = 1
# 方式二: 追踪识别方式
TRACE = 2

48
enums/ModelTypeEnum.py
View File

@ -374,8 +374,8 @@ class ModelType(Enum):
},
'models':[
{
'weight':'../weights/trt/AIlib2/cityMangement3/yolov5_%s_fp16.engine'%(gpuName),
'name':'yolov5',
'weight':'../weights/trt/AIlib2/cityMangement3/yolov5_%s_fp16.engine'%(gpuName),
'name':'yolov5',
'model':yolov5Model,
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3,4,5,6,7],'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':True, "score_byClass":{"0":0.8,"1":0.4,"2":0.5,"3":0.5 } }
},
@ -633,6 +633,8 @@ class ModelType(Enum):
'Segweights': '../weights/trt/AIlib2/riverT/stdc_360X640_%s_fp16.engine' % gpuName
})
FORESTCROWD_FARM_MODEL = ("26", "026", "森林人群模型", 'forestCrowd', lambda device, gpuName: {
'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾","人群"],
'postProcess':{'function':gather_post_process,'pars':{'pedestrianId':2,'crowdThreshold':4,'gatherId':5,'distancePersonScale':2.0}},
@ -974,14 +976,12 @@ class ModelType(Enum):
'row': 2,
'line': 2,
'point_loss_coef': 0.45,
'conf': 0.65,
'conf': 0.25,
'gpu_id': 0,
'eos_coef': '0.5',
'set_cost_class': 1,
'set_cost_point': 0.05,
'backbone': 'vgg16_bn',
'expend': 10,
'psize': 2,
'backbone': 'vgg16_bn'
},
}],
})
@ -1006,42 +1006,6 @@ class ModelType(Enum):
})
CITY_UNDERBUILDCOUNT_MODEL = ("30", "306", "建筑物下人群计数", 'perUnderBuild', lambda device, gpuName: {
'labelnames': ["建筑物下人群"],
'device': str(device),
'rainbows': COLOR,
'models': [
{
'weight': "../weights/trt/AIlib2/perUnderBuild/yolov5_%s_fp16.engine" % (gpuName), ###检测模型路径
'name': 'yolov5',
'model': yolov5Model,
'par': {'half': True, 'device': 'cuda:0', 'conf_thres': 0.25, 'iou_thres': 0.45,
'allowedList': [0,1,2], 'segRegionCnt': 1, 'trtFlag_det': True,
'trtFlag_seg': False, "score_byClass": {"0": 0.25, "1": 0.3, "2": 0.3, "3": 0.3}},
},
{
'trtFlag_det': False,
'weight': "../weights/pth/AIlib2/DenseCrowd/SHTechA.pth", ###检测模型路径
'vggweight': "../weights/pth/AIlib2/DenseCrowd/vgg16_bn-6c64b313.pth", ###检测模型路径
'name': 'p2pnet',
'model': p2NnetModel,
'par': {
'device': 'cuda:0',
'row': 2,
'line': 2,
'point_loss_coef': 0.45,
'conf': 0.50,
'gpu_id': 0,
'eos_coef': '0.5',
'set_cost_class': 1,
'set_cost_point': 0.05,
'backbone': 'vgg16_bn',
'expend': 10,
'psize': 5
},
}],
})
@staticmethod
def checkCode(code):
for model in ModelType:

8
readme.md Normal file
View File

@ -0,0 +1,8 @@
1.2025.01.21把之前的tuoheng alg仓库代码重新开个仓库
(1)在config/service/dsp_test_service.yml里面添加参数，控制存储用的oss还是minio
storage_source: 1
2.2025.02.06
(1)修改代码，把mqtt读取加入到系统中。config/service/dsp_test_service.yml，中添加mqtt_flag,决定是否启用。
(2)修改了minio情况下的，文件名命名方式。
3.2025.02.12
(1)增加了对alg算法开发的代码。可以通过配置文件config/service/dsp_test_service.yml中algSwitch: true，决定是否启用。

507
service/Dispatcher-raw.py Normal file
View File

@ -0,0 +1,507 @@
# -*- coding: utf-8 -*-
import time,os
from os.path import join
from traceback import format_exc
import json
from cerberus import Validator
from common.Constant import ONLINE_START_SCHEMA, ONLINE_STOP_SCHEMA, OFFLINE_START_SCHEMA, OFFLINE_STOP_SCHEMA, \
IMAGE_SCHEMA, RECORDING_START_SCHEMA, RECORDING_STOP_SCHEMA, PULL2PUSH_START_SCHEMA, PULL2PUSH_STOP_SCHEMA
from common.YmlConstant import service_yml_path, kafka_yml_path
from concurrency.FeedbackThread import FeedbackThread
from concurrency.uploadGPU import uploadGPUinfos
from concurrency.IntelligentRecognitionProcess2 import OnlineIntelligentRecognitionProcess2, \
OfflineIntelligentRecognitionProcess2, PhotosIntelligentRecognitionProcess2
from concurrency.Pull2PushStreamProcess import PushStreamProcess
from entity.FeedBack import message_feedback, recording_feedback, pull_stream_feedback
from enums.AnalysisStatusEnum import AnalysisStatus
from enums.AnalysisTypeEnum import AnalysisType
from enums.ExceptionEnum import ExceptionType
from enums.ModelTypeEnum import ModelMethodTypeEnum, ModelType
from enums.RecordingStatusEnum import RecordingStatus
from enums.StatusEnum import PushStreamStatus, ExecuteStatus
from exception.CustomerException import ServiceException
from loguru import logger
from multiprocessing import Queue
from concurrency.IntelligentRecognitionProcess import OnlineIntelligentRecognitionProcess, \
OfflineIntelligentRecognitionProcess, PhotosIntelligentRecognitionProcess, ScreenRecordingProcess
from util.CpuUtils import print_cpu_ex_status
from util.FileUtils import create_dir_not_exist
from util.GPUtils import get_first_gpu_name, print_gpu_ex_status, check_cude_is_available,select_best_server
from util.KafkaUtils import CustomerKafkaConsumer
from util.QueUtil import put_queue
from util.RWUtils import getConfigs
from kafka import KafkaProducer, KafkaConsumer
'''
分发服务
'''
class DispatcherService:
__slots__ = ('__context', '__feedbackThread', '__listeningProcesses', '__fbQueue', '__topics','__taskType', '__task_type',
'__kafka_config', '__recordingProcesses', '__pull2PushProcesses','__topicsPort','__gpuTopic','__role','__uploadGPUThread','__gpuDics','__producer')
def __init__(self, base_dir, env):
# 检测cuda是否活动
check_cude_is_available()
# 获取全局上下文配置
self.__context = getConfigs(join(base_dir, service_yml_path % env))
# 创建任务执行, 视频保存路径
create_dir_not_exist(join(base_dir, self.__context["video"]["file_path"]))
# 将根路径和环境设置到上下文中
self.__context["base_dir"], self.__context["env"] = base_dir, env
# 问题反馈线程
self.__feedbackThread,self.__uploadGPUThread, self.__fbQueue = None,None, Queue()
# 实时、离线、图片任务进程字典
self.__listeningProcesses = {}
# 录屏任务进程字典
self.__recordingProcesses = {}
# 转推流任务进程字典
self.__pull2PushProcesses = {}
self.__kafka_config = getConfigs(join(base_dir, kafka_yml_path % env))
self.__producer = KafkaProducer(
bootstrap_servers=self.__kafka_config['bootstrap_servers'],#tencent yun
value_serializer=lambda v: v.encode('utf-8'))
self.__gpuDics = { }#用于存储gpu信息的字典
self.__role = self.__context["role"]
self.__topics = [
self.__kafka_config["topic"]["dsp-alg-online-tasks-topic"], # 实时监听topic
self.__kafka_config["topic"]["dsp-alg-offline-tasks-topic"], # 离线监听topic
self.__kafka_config["topic"]["dsp-alg-image-tasks-topic"], # 图片监听topic
self.__kafka_config["topic"]["dsp-recording-task-topic"], # 录屏监听topic
self.__kafka_config["topic"]["dsp-push-stream-task-topic"] # 推流监听topic
]
self.__topicsPort = [
self.__kafka_config["topicPort"]["dsp-alg-online-tasks-topic"], # 实时监听topic
self.__kafka_config["topicPort"]["dsp-alg-offline-tasks-topic"], # 离线监听topic
self.__kafka_config["topicPort"]["dsp-alg-image-tasks-topic"], # 图片监听topic
self.__kafka_config["topicPort"]["dsp-recording-task-topic"], # 录屏监听topic
self.__kafka_config["topicPort"]["dsp-push-stream-task-topic"] # 推流监听topic
]
self.__gpuTopic = [self.__kafka_config["topicGPU"]]
if self.__role==1:
self.__topics = self.__topics + self.__topicsPort + self.__gpuTopic
# 对应topic的各个lambda表达式
self.__task_type = {
self.__topics[0]: (AnalysisType.ONLINE.value, lambda x, y: self.online(x, y),
lambda x, y, z: self.identify_method(x, y, z)),
self.__topics[1]: (AnalysisType.OFFLINE.value, lambda x, y: self.offline(x, y),
lambda x, y, z: self.identify_method(x, y, z)),
self.__topics[2]: (AnalysisType.IMAGE.value, lambda x, y: self.image(x, y),
lambda x, y, z: self.identify_method(x, y, z)),
self.__topics[3]: (AnalysisType.RECORDING.value, lambda x, y: self.recording(x, y),
lambda x, y, z: self.recording_method(x, y, z)),
self.__topics[4]: (AnalysisType.PULLTOPUSH.value, lambda x, y: self.pullStream(x, y),
lambda x, y, z: self.push_stream_method(x, y, z))
}
self.__taskType={
self.__kafka_config["topic"]["dsp-alg-online-tasks-topic"]:0, # 实时监听topic
self.__kafka_config["topic"]["dsp-alg-offline-tasks-topic"]:1, # 离线监听topic
self.__kafka_config["topic"]["dsp-alg-image-tasks-topic"]:2, # 图片监听topic
self.__kafka_config["topic"]["dsp-recording-task-topic"]:3, # 录屏监听topic
self.__kafka_config["topic"]["dsp-push-stream-task-topic"]:4 # 推流监听topic
}
gpu_name_array = get_first_gpu_name()
gpu_array = [g for g in ('3090', '2080', '4090', 'A10') if g in gpu_name_array]
gpu_name = '2080Ti'
if len(gpu_array) > 0:
if gpu_array[0] != '2080':
gpu_name = gpu_array[0]
else:
raise Exception("GPU资源不在提供的模型所支持的范围内！请先提供对应的GPU模型！")
logger.info("当前服务环境为: {}, 服务器GPU使用型号: {}", env, gpu_name)
self.__context["gpu_name"] = gpu_name
self.start_service()
# 服务调用启动方法
def start_service(self):
# 初始化kafka监听者
customerKafkaConsumer = CustomerKafkaConsumer(self.__kafka_config, topics=self.__topics)
####增加一个线程，用于试试监控和发送gpu状态####
####
logger.info("(♥◠‿◠)ﾉﾞ DSP【算法调度服务】启动成功 服务器IP:{}".format(self.__kafka_config['bootstrap_servers'] ))
while True:
try:
# 检查任务进程运行情况，去除结束的任务
self.check_process_task()
# 启动反馈线程
self.start_feedback_thread()
self.start_uploadGPU_thread()
msg = customerKafkaConsumer.poll()
if msg is not None and len(msg) > 0:
for k, v in msg.items():
for m in v:
message = m.value
#如果收到的信息是gpu状态的话，收到信息后，更新自己的gpu服务器状态，下面不再执行
if m.topic in self.__gpuTopic:
customerKafkaConsumer.commit_offset(m,'x'*16,False)
#更新机器资源现状
ip = message['System']['Local IP Address']
self.__gpuDics[ip]=message
continue
#如果收到的信息是门户消息，收到信息后，要根据Gpu状态，转发到对应的机器。
elif m.topic in self.__topicsPort:
customerKafkaConsumer.commit_offset(m, 'y'*16)
#状态分析
#recondGpu={'hostname':'thsw2','IP':'192.168.10.66','gpuId':0}
recondGpu= select_best_server(self.__gpuDics)
if recondGpu is None:
print( 'recondGpu：',recondGpu, ' self.__gpuDics: ',self.__gpuDics,' topic:',m.topic, ' message:',message )
continue
#转发消息
message['transmit_topic'] = m.topic + '-' + recondGpu['IP']
transmitMsg={'transmit':message}
msg_json = json.dumps( message )
future = self.__producer.send( message['transmit_topic'] ,msg_json)
try:
future.get(timeout=2)
logger.info( "转发消息成功，消息topic:{},消息内容:{}",message['transmit_topic'],message )
except kafka_errors as e:
print('------transmitted error:',e)
logger.info("转发消息失败")
traceback.format_exc()
else:
requestId = message.get("request_id")
if requestId is None:
logger.error("请求参数格式错误, 请检查请求体格式是否正确！message:%s"%(message))
continue
customerKafkaConsumer.commit_offset(m, requestId)
logger.info("当前拉取到的消息, topic:{}, offset:{}, partition: {}, body: {}, requestId:{}",
m.topic, m.offset, m.partition, message, requestId)
message['taskType']=self.__taskType[m.topic]
topic_method = self.__task_type[m.topic]
topic_method[2](topic_method[1], message, topic_method[0])
else:
print_gpu_ex_status()
print_cpu_ex_status(self.__context["base_dir"])
time.sleep(1)
except Exception:
logger.error("主线程异常：{}", format_exc())
def identify_method(self, handle_method, message, analysisType):
try:
check_cude_is_available()
handle_method(message, analysisType)
except ServiceException as s:
logger.error("消息监听异常：{}, requestId: {}", s.msg, message["request_id"])
put_queue(self.__fbQueue, message_feedback(message["request_id"], AnalysisStatus.FAILED.value, analysisType,
s.code, s.msg), timeout=1)
except Exception:
logger.error("消息监听异常：{}, requestId: {}", format_exc(), message["request_id"])
put_queue(self.__fbQueue, message_feedback(message["request_id"], AnalysisStatus.FAILED.value, analysisType,
ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[1]), timeout=1)
finally:
del message
def push_stream_method(self, handle_method, message, analysisType):
try:
check_cude_is_available()
handle_method(message, analysisType)
except ServiceException as s:
logger.error("消息监听异常：{}, requestId: {}", s.msg, message['request_id'])
videoInfo = [{"id": url.get("id"), "status": PushStreamStatus.FAILED.value[0]} for url in
message.get("video_urls", []) if url.get("id") is not None]
put_queue(self.__fbQueue, pull_stream_feedback(message['request_id'], ExecuteStatus.FAILED.value[0],
s.code, s.msg, videoInfo), timeout=1)
except Exception:
logger.error("消息监听异常：{}, requestId: {}", format_exc(), message['request_id'])
videoInfo = [{"id": url.get("id"), "status": PushStreamStatus.FAILED.value[0]} for url in
message.get("video_urls", []) if url.get("id") is not None]
put_queue(self.__fbQueue, pull_stream_feedback(message.get("request_id"), ExecuteStatus.FAILED.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[1], videoInfo),
timeout=1)
finally:
del message
def recording_method(self, handle_method, message, analysisType):
try:
check_cude_is_available()
handle_method(message, analysisType)
except ServiceException as s:
logger.error("消息监听异常：{}, requestId: {}", s.msg, message["request_id"])
put_queue(self.__fbQueue,
recording_feedback(message["request_id"], RecordingStatus.RECORDING_FAILED.value[0],
error_code=s.code, error_msg=s.msg), timeout=1)
except Exception:
logger.error("消息监听异常：{}, requestId: {}", format_exc(), message["request_id"])
put_queue(self.__fbQueue,
recording_feedback(message["request_id"], RecordingStatus.RECORDING_FAILED.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[1]), timeout=1)
finally:
del message
# 开启实时进程
def startOnlineProcess(self, msg, analysisType):
if self.__listeningProcesses.get(msg["request_id"]):
logger.warning("实时重复任务，请稍后再试！requestId:{}", msg["request_id"])
return
model_type = self.__context["service"]["model"]["model_type"]
codes = [model.get("code") for model in msg["models"] if model.get("code")]
if ModelMethodTypeEnum.NORMAL.value == model_type or ModelType.ILLPARKING_MODEL.value[1] in codes:
coir = OnlineIntelligentRecognitionProcess(self.__fbQueue, msg, analysisType, self.__context)
else:
coir = OnlineIntelligentRecognitionProcess2(self.__fbQueue, msg, analysisType, self.__context)
coir.start()
logger.info("开始实时进程！requestId:{},pid:{}, ppid:{}", msg["request_id"],os.getpid(),os.getppid())
self.__listeningProcesses[msg["request_id"]] = coir
# 结束实时进程
def stopOnlineProcess(self, msg):
ps = self.__listeningProcesses.get(msg["request_id"])
if ps is None:
logger.warning("未查询到该任务，无法停止任务！requestId:{}", msg["request_id"])
return
ps.sendEvent({"command": "stop"})
# 新增该函数用于，向子任务发送命令（algStart，algStop）
def sendCmdToChildProcess(self, msg,cmd="algStart"):
ps = self.__listeningProcesses.get(msg["request_id"])
if ps is None:
logger.warning("未查询到该任务，无法停止任务！requestId:{}", msg["request_id"])
return
ps.sendEvent({"command": cmd})
@staticmethod
def check_process(listeningProcess):
for requestId in list(listeningProcess.keys()):
if not listeningProcess[requestId].is_alive():
del listeningProcess[requestId]
def check_process_task(self):
self.check_process(self.__listeningProcesses)
self.check_process(self.__recordingProcesses)
self.check_process(self.__pull2PushProcesses)
# 开启离线进程
def startOfflineProcess(self, msg, analysisType):
if self.__listeningProcesses.get(msg["request_id"]):
logger.warning("离线重复任务，请稍后再试！requestId:{}", msg["request_id"])
return
model_type = self.__context["service"]["model"]["model_type"]
codes = [model.get("code") for model in msg["models"] if model.get("code")]
if ModelMethodTypeEnum.NORMAL.value == model_type:
first = OfflineIntelligentRecognitionProcess(self.__fbQueue, msg, analysisType, self.__context)
else:
first = OfflineIntelligentRecognitionProcess2(self.__fbQueue, msg, analysisType, self.__context)
first.start()
self.__listeningProcesses[msg["request_id"]] = first
# 结束离线进程
def stopOfflineProcess(self, msg):
ps = self.__listeningProcesses.get(msg["request_id"])
if ps is None:
logger.warning("未查询到该任务，无法停止任务！requestId:{}", msg["request_id"])
return
ps.sendEvent({"command": "stop"})
# 开启图片分析进程
def startImageProcess(self, msg, analysisType):
pp = self.__listeningProcesses.get(msg["request_id"])
if pp is not None:
logger.warning("重复任务，请稍后再试！requestId:{}", msg["request_id"])
return
model_type = self.__context["service"]["model"]["model_type"]
codes = [model.get("code") for model in msg["models"] if model.get("code")]
if ModelMethodTypeEnum.NORMAL.value == model_type or ModelType.ILLPARKING_MODEL.value[1] in codes:
imaged = PhotosIntelligentRecognitionProcess(self.__fbQueue, msg, analysisType, self.__context)
else:
imaged = PhotosIntelligentRecognitionProcess2(self.__fbQueue, msg, analysisType, self.__context)
# 创建在线识别进程并启动
imaged.start()
self.__listeningProcesses[msg["request_id"]] = imaged
'''
校验kafka消息
'''
@staticmethod
def check_msg(msg, schema):
try:
v = Validator(schema, allow_unknown=True)
result = v.validate(msg)
if not result:
logger.error("参数校验异常: {}, requestId: {}", v.errors, msg["request_id"])
raise ServiceException(ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[0],
ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[1])
except ServiceException as s:
raise s
except Exception:
logger.error("参数校验异常: {}, requestId: {}", format_exc(), msg["request_id"])
raise ServiceException(ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[0],
ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[1])
'''
开启反馈线程，用于发送消息
'''
def start_feedback_thread(self):
if self.__feedbackThread is None:
self.__feedbackThread = FeedbackThread(self.__fbQueue, self.__kafka_config)
self.__feedbackThread.setDaemon(True)
self.__feedbackThread.start()
time.sleep(1)
if self.__feedbackThread and not self.__feedbackThread.is_alive():
logger.error("反馈线程异常停止, 开始重新启动反馈线程！！！！！")
self.__feedbackThread = FeedbackThread(self.__fbQueue, self.__kafka_config)
self.__feedbackThread.setDaemon(True)
self.__feedbackThread.start()
time.sleep(1)
def start_uploadGPU_thread(self):
if self.__uploadGPUThread is None:
self.__uploadGPUThread = uploadGPUinfos(self.__context, self.__kafka_config)
self.__uploadGPUThread.setDaemon(True)
self.__uploadGPUThread.start()
time.sleep(1)
if self.__uploadGPUThread and not self.__uploadGPUThread.is_alive():
logger.error("反馈线程异常停止, 开始重新启动反馈线程！！！！！")
self.__uploadGPUThread = uploadGPUinfos(self.__context, self.__kafka_config)
self.__uploadGPUThread.setDaemon(True)
self.__uploadGPUThread.start()
time.sleep(1)
'''
在线分析逻辑
'''
def online0(self, message, analysisType):
if "start" == message.get("command"):
self.check_msg(message, ONLINE_START_SCHEMA)
if len(self.__listeningProcesses) >= int(self.__context['service']["task"]["limit"]):
raise ServiceException(ExceptionType.NO_RESOURCES.value[0],
ExceptionType.NO_RESOURCES.value[1])
self.startOnlineProcess(message, analysisType)
elif message.get("command") in ["algStart","algStop"]:
self.sendCmdToChildProcess(message,cmd=message.get("command"))
elif "stop" == message.get("command"):
self.check_msg(message, ONLINE_STOP_SCHEMA)
self.stopOnlineProcess(message)
else:
raise ServiceException(ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[0],
ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[1])
def online(self, message, analysisType):
if "start" == message.get("command"):
self.check_msg(message, ONLINE_START_SCHEMA)
if len(self.__listeningProcesses) >= int(self.__context['service']["task"]["limit"]):
raise ServiceException(ExceptionType.NO_RESOURCES.value[0],
ExceptionType.NO_RESOURCES.value[1])
self.startOnlineProcess(message, analysisType)
elif message.get("command") in ["algStart","algStop"]:
if message.get("defaultEnabled",True):
self.sendCmdToChildProcess(message,cmd=message.get("command"))
elif "stop" == message.get("command"):
self.check_msg(message, ONLINE_STOP_SCHEMA)
self.stopOnlineProcess(message)
else:
raise ServiceException(ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[0],
ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[1])
def offline(self, message, analysisType):
if "start" == message.get("command"):
self.check_msg(message, OFFLINE_START_SCHEMA)
if len(self.__listeningProcesses) >= int(self.__context['service']["task"]["limit"]):
raise ServiceException(ExceptionType.NO_RESOURCES.value[0],
ExceptionType.NO_RESOURCES.value[1])
self.startOfflineProcess(message, analysisType)
elif message.get("command") in ["algStart","algStop"]:
self.sendCmdToChildProcess( message,cmd=message.get("command"))
elif "stop" == message.get("command"):
self.check_msg(message, OFFLINE_STOP_SCHEMA)
self.stopOfflineProcess(message)
else:
raise ServiceException(ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[0],
ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[1])
def image(self, message, analysisType):
if "start" == message.get("command"):
self.check_msg(message, IMAGE_SCHEMA)
if len(self.__listeningProcesses) >= int(self.__context['service']["task"]["image"]["limit"]):
raise ServiceException(ExceptionType.NO_RESOURCES.value[0],
ExceptionType.NO_RESOURCES.value[1])
self.startImageProcess(message, analysisType)
else:
raise ServiceException(ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[0],
ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[1])
def recording(self, message, analysisType):
if "start" == message.get("command"):
self.check_msg(message, RECORDING_START_SCHEMA)
if len(self.__recordingProcesses) >= int(self.__context['service']["task"]["limit"]):
raise ServiceException(ExceptionType.NO_RESOURCES.value[0],
ExceptionType.NO_RESOURCES.value[1])
self.startRecordingProcess(message, analysisType)
elif "stop" == message.get("command"):
self.check_msg(message, RECORDING_STOP_SCHEMA)
self.stopRecordingProcess(message)
else:
raise ServiceException(ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[0],
ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[1])
# 开启录屏进程
def startRecordingProcess(self, msg, analysisType):
if self.__listeningProcesses.get(msg["request_id"]):
logger.warning("重复任务，请稍后再试！requestId:{}", msg["request_id"])
return
srp = ScreenRecordingProcess(self.__fbQueue, self.__context, msg, analysisType)
srp.start()
self.__recordingProcesses[msg["request_id"]] = srp
# 结束录屏进程
def stopRecordingProcess(self, msg):
rdp = self.__recordingProcesses.get(msg["request_id"])
if rdp is None:
logger.warning("未查询到该任务，无法停止任务！requestId:{}", msg["request_id"])
return
rdp.sendEvent({"command": "stop"})
def pullStream(self, message, analysisType):
if "start" == message.get("command"):
self.check_msg(message, PULL2PUSH_START_SCHEMA)
if len(self.__pull2PushProcesses) >= int(self.__context['service']["task"]["limit"]):
raise ServiceException(ExceptionType.NO_RESOURCES.value[0],
ExceptionType.NO_RESOURCES.value[1])
self.startPushStreamProcess(message, analysisType)
elif "stop" == message.get("command"):
self.check_msg(message, PULL2PUSH_STOP_SCHEMA)
self.stopPushStreamProcess(message)
else:
raise ServiceException(ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[0],
ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[1])
def startPushStreamProcess(self, msg, analysisType):
if self.__pull2PushProcesses.get(msg["request_id"]):
logger.warning("重复任务，请稍后再试！requestId:{}", msg["request_id"])
return
srp = PushStreamProcess(self.__fbQueue, self.__context, msg, analysisType)
srp.start()
self.__pull2PushProcesses[msg["request_id"]] = srp
# 结束录屏进程
def stopPushStreamProcess(self, msg):
srp = self.__pull2PushProcesses.get(msg["request_id"])
if srp is None:
logger.warning("未查询到该任务，无法停止任务！requestId:{}", msg["request_id"])
return
srp.sendEvent({"command": "stop", "videoIds": msg.get("video_ids", [])})

0
requirements.txt → t.txt
View File

19
util/ModelUtils.py
View File

@ -46,12 +46,8 @@ class OneModel:
new_device = select_device(par.get('device'))
half = new_device.type != 'cpu'
Detweights = par['Detweights']
if par['trtFlag_det']:
with open(Detweights, "rb") as f, trt.Runtime(trt.Logger(trt.Logger.ERROR)) as runtime:
model = runtime.deserialize_cuda_engine(f.read())
else:
model = attempt_load(Detweights, map_location=new_device) # load FP32 model
if half: model.half()
with open(Detweights, "rb") as f, trt.Runtime(trt.Logger(trt.Logger.ERROR)) as runtime:
model = runtime.deserialize_cuda_engine(f.read())
par['segPar']['seg_nclass'] = par['seg_nclass']
Segweights = par['Segweights']
if Segweights:
@ -406,8 +402,8 @@ class DENSECROWDCOUNTModel:
par = modeType.value[4](str(device), gpu_name)
rainbows = par["rainbows"]
models=[ modelPar['model'](weights=modelPar['weight'],par=modelPar['par']) for modelPar in par['models'] ]
postPar = [pp['par'] for pp in par['models']]
self.model_conf = (modeType, device, models, postPar, rainbows)
postPar = par['models'][0]['par']
self.model_conf = (modeType, device, models[0], postPar, rainbows)
except Exception:
logger.error("模型加载异常：{}, requestId:{}", format_exc(), requestId)
raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
@ -756,11 +752,4 @@ MODEL_CONFIG = {
None,
lambda x: cc_process(x)
),
# 加载建筑物下行人检测模型
ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1]: (
lambda x, y, r, t, z, h: DENSECROWDCOUNTModel(x, y, r, ModelType.CITY_UNDERBUILDCOUNT_MODEL, t, z, h),
ModelType.CITY_UNDERBUILDCOUNT_MODEL,
None,
lambda x: cc_process(x)
),
}

116
util/PlotsUtils.py
View File

@ -2,7 +2,6 @@ import cv2
import numpy as np
from PIL import Image, ImageDraw, ImageFont
import unicodedata
from loguru import logger
FONT_PATH = "../AIlib2/conf/platech.ttf"
zhFont = ImageFont.truetype(FONT_PATH, 20, encoding="utf-8")
@ -68,12 +67,6 @@ def xywh2xyxy2(param):
# return [(lt, yt), (rt, yt), (rt, yr), (lt, yr)]
return np.asarray(param[0][0:4], np.int32), float(param[1]), int(param[2])
def xy2xyxy(box):
if not isinstance(box[0], (list, tuple, np.ndarray)):
x1, y1, x2, y2 = int(box[0]), int(box[1]), int(box[2]), int(box[3])
# 顺时针
box = [(x1, y1), (x2, y1), (x2, y2), (x1, y2)]
return box
def draw_painting_joint(box, img, label_array, score=0.5, color=None, config=None, isNew=False):
# 识别问题描述图片的高、宽
@ -225,11 +218,6 @@ def draw_name_joint(box, img, label_array_dict, score=0.5, color=None, config=No
cv2.putText(img, label, p3, 0, config[3], [225, 255, 255], thickness=config[4], lineType=cv2.LINE_AA)
return img, box
def draw_name_ocr(box, img, color, line_thickness=2, outfontsize=40):
font = ImageFont.truetype(FONT_PATH, outfontsize, encoding='utf-8')
# (color=None, label=None, font=None, fontSize=40, unify=False)
label_zh = get_label_array(color, box[0], font, outfontsize)
return plot_one_box_auto(box[1], img, color, line_thickness, label_zh)
def filterBox(det0, det1, pix_dis):
# det0为 (m1, 11) 矩阵
@ -263,108 +251,8 @@ def filterBox(det0, det1, pix_dis):
x_c, y_c = (x3+x4)//2, (y3+y4)//2
dis = (x2_c - x_c)**2 + (y2_c - y_c)**2
mask = (joint_det[..., 9] == joint_det[..., 20]) & (dis <= pix_dis**2)
# 类别相同 & 中心点在上一帧的框内 判断为True
res = np.sum(mask, axis=1)
det0_copy[..., -1] = res
return det0_copy
def plot_one_box_auto(box, img, color=None, line_thickness=2, label_array=None):
# print("省略 :%s, box:%s"%('+++' * 10, box))
# 识别问题描述图片的高、宽
lh, lw = label_array.shape[0:2]
# print("省略 :%s, lh:%s, lw:%s"%('+++' * 10, lh, lw))
# 图片的长度和宽度
imh, imw = img.shape[0:2]
box = xy2xyxy(box)
# 框框左上的位置
x0, y1 = box[0][0], box[0][1]
# print("省略 :%s, x0:%s, y1:%s"%('+++' * 10, x0, y1))
x1, y0 = x0 + lw, y1 - lh
# 如果y0小于0, 说明超过上边框
if y0 < 0:
y0 = 0
# y1等于文字高度
y1 = y0 + lh
# 如果y1框框的高大于图片高度
if y1 > imh:
# y1等于图片高度
y1 = imh
# y0等于y1减去文字高度
y0 = y1 - lh
# 如果x0小于0
if x0 < 0:
x0 = 0
x1 = x0 + lw
if x1 > imw:
x1 = imw
x0 = x1 - lw
# box_tl = max(int(round(imw / 1920 * 3)), 1) or round(0.002 * (imh + imw) / 2) + 1
'''
1. img（array） 为ndarray类型（可以为cv.imread）直接读取的数据
2. box（array）：为所画多边形的顶点坐标
3. 所画四边形是否闭合，通常为True
4. color（tuple）：BGR三个通道的值
5. thickness（int）：画线的粗细
6. shift：顶点坐标中小数的位数
'''
# Plots one bounding box on image img
tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness
box1 = np.asarray(box, np.int32)
cv2.polylines(img, [box1], True, color, tl)
img[y0:y1, x0:x1, :] = label_array
return img, box
def draw_name_crowd(dets, img, color, outfontsize=20):
font = ImageFont.truetype(FONT_PATH, outfontsize, encoding='utf-8')
if len(dets) == 2:
label = '当前人数：%d'%len(dets[0])
detP = dets[0]
line = dets[1]
for p in detP:
img = cv2.circle(img, (int(p[0]), int(p[1])), line, color, -1)
label_arr = get_label_array(color, label, font, outfontsize)
lh, lw = label_arr.shape[0:2]
img[0:lh, 0:lw, :] = label_arr
elif len(dets) == 3:
detP = dets[1]
line = dets[2]
for p in detP:
img = cv2.circle(img, (int(p[0]), int(p[1])), line, color, -1)
detM = dets[0]
h, w = img.shape[:2]
for b in detM:
label = '该建筑下行人及数量：%d'%(int(b[4]))
label_arr = get_label_array(color, label, font, outfontsize)
lh, lw = label_arr.shape[0:2]
# 框框左上的位置
x0, y1 = int(b[0]), int(b[1])
# print("省略 :%s, x0:%s, y1:%s"%('+++' * 10, x0, y1))
x1, y0 = x0 + lw, y1 - lh
# 如果y0小于0, 说明超过上边框
if y0 < 0:
y0 = 0
# y1等于文字高度
y1 = y0 + lh
# 如果y1框框的高大于图片高度
if y1 > h:
# y1等于图片高度
y1 = h
# y0等于y1减去文字高度
y0 = y1 - lh
# 如果x0小于0
if x0 < 0:
x0 = 0
x1 = x0 + lw
if x1 > w:
x1 = w
x0 = x1 - lw
cv2.polylines(img, [np.asarray(xy2xyxy(b), np.int32)], True, (0, 128, 255), 2)
img[y0:y1, x0:x1, :] = label_arr
return img, dets
return det0_copy