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Merge pull request 'zsl' (#14) from zsl into master 

Reviewed-on: #14
This commit is contained in:
jiangchaoqing 2025-08-25 11:20:27 +08:00
parent b8fc17c39f bea36e2601
commit 79a859e886
11 changed files with 605 additions and 333 deletions
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4
common/Constant.py
View File

@ -16,7 +16,6 @@ success_progess = "1.0000"
width = 1400
COLOR = (
[0, 0, 255],
[255, 0, 0],
[211, 0, 148],
[0, 127, 0],
@ -35,7 +34,8 @@ COLOR = (
[8, 101, 139],
[171, 130, 255],
[139, 112, 74],
[205, 205, 180])
[205, 205, 180],
[0, 0, 255],)
ONLINE = "online"
OFFLINE = "offline"

37
concurrency/FileUploadThread.py
View File

@ -3,6 +3,7 @@ from concurrent.futures import ThreadPoolExecutor
from threading import Thread
from time import sleep, time
from traceback import format_exc
import numpy as np
from loguru import logger
import cv2
@ -14,18 +15,18 @@ 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, draw_name_ocr, draw_name_crowd
from util.PlotsUtils import draw_painting_joint, draw_name_ocr, draw_name_crowd,draw_transparent_red_polygon
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')
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 = args
self._storage_source = self._context['service']['storage_source']
self._algStatus = False # 默认关闭
@ -64,16 +65,29 @@ class ImageFileUpload(FileUpload):
模型编号：modeCode
检测目标：detectTargetCode
'''
print('*' * 100, ' mqtt_list:', len(self._mqtt_list))
aFrame = frame.copy()
igH, igW = aFrame.shape[0:2]
model_info = []
mqttPares= det_xywh['mqttPares']
border = None
gps = [None, None]
camParas = None
if mqttPares is not None:
if mqttPares[0] == 1:
border = mqttPares[1]
elif mqttPares[0] == 0:
camParas = mqttPares[1]
if border is not None:
aFrame = draw_transparent_red_polygon(aFrame, np.array(border, np.int32), alpha=0.25)
det_xywh.pop('mqttPares')
# 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:
if camParas is not None:
gps = locate_byMqtt(target[1], igW, igH, camParas, outFormat='wgs84')
# 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])
@ -82,15 +96,7 @@ class ImageFileUpload(FileUpload):
draw_name_crowd(target[1], aFrame, target[4])
else:
draw_painting_joint(target[1], aFrame, target[3], target[2], target[4], font_config,
target[5])
igH, igW = aFrame.shape[0:2]
if len(self._mqtt_list) >= 1:
# camParas = self._mqtt_list[0]['data']
camParas = self._mqtt_list[0]
gps = locate_byMqtt(target[1], igW, igH, camParas, outFormat='wgs84')
else:
gps = [None, None]
target[5],border)
model_info.append(
{"modelCode": str(code), "detectTargetCode": str(cls), "aFrame": aFrame, 'gps': gps})
if len(model_info) > 0:
@ -133,7 +139,6 @@ class ImageFileUpload(FileUpload):
# 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:
logger.info("图片上传线程收到命令:{}, requestId: {}", image_msg[1], request_id)
if 'stop' == image_msg[1]:

21
concurrency/IntelligentRecognitionProcess.py
View File

@ -64,6 +64,7 @@ class IntelligentRecognitionProcess(Process):
self._analyse_type, progress=init_progess), timeout=2, is_ex=True)
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)
@ -127,6 +128,7 @@ 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 upload_video(base_dir, env, request_id, orFilePath, aiFilePath):
@ -306,7 +308,7 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
else:
model_param = model_conf[1]
# (modeType, model_param, allowedList, names, rainbows)
MODEL_CONFIG[code][2](frame_list[0].shape[1], frame_list[0].shape[0],
MODEL_CONFIG[code][2](frame_list[0][0].shape[1], frame_list[0][0].shape[0],
model_conf)
if draw_config.get("font_config") is None:
draw_config["font_config"] = model_param['font_config']
@ -322,7 +324,7 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
# print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno)
# 多线程并发处理, 经过测试两个线程最优
det_array = []
for i, frame in enumerate(frame_list):
for i, [frame,_] in enumerate(frame_list):
det_result = t.submit(self.obj_det, self, model_array, frame, task_status,
frame_index_list[i], tt, request_id)
det_array.append(det_result)
@ -469,6 +471,7 @@ class OfflineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
ai_url = upload_video_thread_ai.get_result()
return ai_url
'''
@staticmethod
def ai_normal_dtection(model, frame, request_id):
model_conf, code = model
@ -634,7 +637,7 @@ class OfflineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
else:
model_param = model_conf[1]
# (modeType, model_param, allowedList, names, rainbows)
MODEL_CONFIG[code][2](frame_list[0].shape[1], frame_list[0].shape[0],
MODEL_CONFIG[code][2](frame_list[0][0].shape[1], frame_list[0][0].shape[0],
model_conf)
if draw_config.get("font_config") is None:
draw_config["font_config"] = model_param['font_config']
@ -648,7 +651,7 @@ class OfflineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
det_array = []
for i, frame in enumerate(frame_list):
for i, [frame,_] in enumerate(frame_list):
det_result = t.submit(self.obj_det, self, model_array, frame, task_status,
frame_index_list[i], tt, request_id)
det_array.append(det_result)
@ -1051,6 +1054,8 @@ class PhotosIntelligentRecognitionProcess(Process):
ai_result_list = p_result[2]
for ai_result in ai_result_list:
box, score, cls = xywh2xyxy2(ai_result)
if ModelType.CITY_FIREAREA_MODEL.value[1] == str(code):
box.append(ai_result[-1])
# 如果检测目标在识别任务中,继续处理
if cls in allowedList:
label_array = label_arraylist[cls]
@ -1208,7 +1213,8 @@ class PhotosIntelligentRecognitionProcess(Process):
image_thread.setDaemon(True)
image_thread.start()
return image_thread
def check_ImageUrl_Vaild(self,url,timeout=1):
def check_ImageUrl_Vaild(self, url, timeout=1):
try:
# 发送 HTTP 请求，尝试访问图片
response = requests.get(url, timeout=timeout) # 设置超时时间为 10 秒
@ -1239,8 +1245,7 @@ class PhotosIntelligentRecognitionProcess(Process):
ExceptionType.URL_ADDRESS_ACCESS_FAILED.value[0],
ExceptionType.URL_ADDRESS_ACCESS_FAILED.value[1]), timeout=2)
return
return
with ThreadPoolExecutor(max_workers=1) as t:
try:
@ -1318,6 +1323,7 @@ class ScreenRecordingProcess(Process):
recording_feedback(self._msg["request_id"], RecordingStatus.RECORDING_WAITING.value[0]),
timeout=1, is_ex=True)
self._storage_source = self._context['service']['storage_source']
def sendEvent(self, result):
put_queue(self._event_queue, result, timeout=2, is_ex=True)
@ -1495,6 +1501,7 @@ class ScreenRecordingProcess(Process):
upload_video_thread_ai.start()
or_url = upload_video_thread_ai.get_result()
return or_url
'''
@staticmethod
def upload_video(base_dir, env, request_id, orFilePath):

303
concurrency/PullMqttThread.py
View File

@ -1,142 +1,163 @@
# -*- coding: utf-8 -*-
from threading import Thread
from time import sleep, time
from traceback import format_exc
from loguru import logger
from common.YmlConstant import mqtt_yml_path
from util.RWUtils import getConfigs
from common.Constant import init_progess
from enums.AnalysisStatusEnum import AnalysisStatus
from entity.FeedBack import message_feedback
from enums.ExceptionEnum import ExceptionType
from exception.CustomerException import ServiceException
from util.QueUtil import get_no_block_queue, put_queue, clear_queue
from multiprocessing import Process, Queue
import paho.mqtt.client as mqtt
import json,os
class PullMqtt(Thread):
__slots__ = ('__fb_queue', '__mqtt_list', '__request_id', '__analyse_type', "_context")
def __init__(self, *args):
super().__init__()
self.__fb_queue, self.__mqtt_list, self.__request_id, self.__analyse_type, self._context = args
base_dir, env = self._context["base_dir"], self._context["env"]
self.__config = getConfigs(os.path.join(base_dir, mqtt_yml_path % env))
self.__broker = self.__config["broker"]
self.__port = self.__config["port"]
self.__topic = self.__config["topic"]
self.__lengthMqttList = self.__config["length"]
def put_queue(self,__queue,data):
if __queue.full():
a = __queue.get()
__queue.put( data,block=True, timeout=2 )
def on_connect(self,client,userdata,flags,rc):
client.subscribe(self.__topic)
# 当接收到MQTT消息时，回调函数
def on_message(self,client, userdata, msg):
# 将消息解码为JSON格式
payload = msg.payload.decode('utf-8')
data = json.loads(payload)
#logger.info(str(data))
# 解析位姿信息
lon = data.get("lon")
lat = data.get("lat")
alt = data.get("alt")
yaw = data.get("yaw")
pitch = data.get("pitch")
roll = data.get("roll")
if len(self.__mqtt_list) == self.__lengthMqttList:
self.__mqtt_list.pop(0)
self.__mqtt_list.append(data)
# 打印无人机的位姿信息
#print(f"Longitude: {lon}, Latitude: {lat}, Altitude: {alt}, sat:{data.get('satcount')} , list length:{len(self.__mqtt_list)}")
def mqtt_connect(self):
# 创建客户端
self.client = mqtt.Client()
self.client.on_connect = self.on_connect
# 设置回调函数
self.client.on_message = self.on_message
# 连接到 Broker
self.client.connect(self.__broker, self.__port)
# 订阅主题
self.client.subscribe(self.__topic)
# 循环等待并处理网络事件
self.client.loop_forever()
def mqtt_disconnect(self):
start_time = time()
while True:
if time() - start_time > service_timeout:
logger.error("MQTT读取超时, requestId: %s,限定时间：%.1s , 已运行：%.1fs"%(request_id,service_timeout, time() - start_time))
raise ServiceException(ExceptionType.TASK_EXCUTE_TIMEOUT.value[0],
ExceptionType.TASK_EXCUTE_TIMEOUT.value[1])
client.loop_stop() # 停止循环
client.disconnect() # 断开连接
def run(self):
request_id, mqtt_list, progress = self.__request_id, self.__mqtt_list, init_progess
analyse_type, fb_queue = self.__analyse_type, self.__fb_queue
#service_timeout = int(self.__config["service"]["timeout"]) + 120
try:
logger.info("开始MQTT读取线程！requestId:{}", request_id)
mqtt_init_num = 0
self.mqtt_connect()
except Exception:
logger.error("MQTT线程异常:{}, requestId:{}", format_exc(), request_id)
finally:
mqtt_list = []
logger.info("MQTT线程停止完成！requestId:{}", request_id)
def start_PullMqtt(fb_queue, mqtt_list, request_id, analyse_type, context):
mqtt_thread = PullMqtt(fb_queue, mqtt_list, request_id, analyse_type, context)
mqtt_thread.setDaemon(True)
mqtt_thread.start()
return mqtt_thread
def start_PullVideo(mqtt_list):
for i in range(1000):
sleep(1)
if len(mqtt_list)>=10:
print( mqtt_list[4])
print(i,len(mqtt_list))
if __name__=="__main__":
#context = {'service':{'timeout':3600},'mqtt':{
# 'broker':"101.133.163.127",'port':1883,'topic':"test/topic","length":10}
# }
context = {
'base_dir':'/home/th/WJ/test/tuoheng_algN',
'env':'test'
}
analyse_type = '1'
request_id = '123456789'
event_queue, pull_queue, mqtt_list, image_queue, push_queue, push_ex_queue = Queue(), Queue(10), [], Queue(), Queue(), Queue()
fb_queue = Queue()
mqtt_thread = start_PullMqtt(fb_queue, mqtt_list, request_id, analyse_type, context)
start_PullVideo(mqtt_list)
print('---line117--')
#mqtt_thread.join()
# -*- coding: utf-8 -*-
from threading import Thread
from time import sleep, time
from traceback import format_exc
from loguru import logger
from common.YmlConstant import mqtt_yml_path
from util.RWUtils import getConfigs
from common.Constant import init_progess
from enums.AnalysisStatusEnum import AnalysisStatus
from entity.FeedBack import message_feedback
from enums.ExceptionEnum import ExceptionType
from exception.CustomerException import ServiceException
from util.QueUtil import get_no_block_queue, put_queue, clear_queue
from multiprocessing import Process, Queue
import paho.mqtt.client as mqtt
import json,os
class PullMqtt(Thread):
__slots__ = ('__fb_queue', '__mqtt_list', '__request_id', '__analyse_type', "_context" ,'__business')
def __init__(self, *args):
super().__init__()
self.__fb_queue, self.__mqtt_list, self.__request_id, self.__analyse_type, self._context, self.__business = args
base_dir, env = self._context["base_dir"], self._context["env"]
self.__config = getConfigs(os.path.join(base_dir, mqtt_yml_path % env))
if self.__business == 0:
self.__broker = self.__config['location']["broker"]
self.__port = self.__config['location']["port"]
self.__topic = self.__config['location']["topic"]
elif self.__business == 1:
self.__broker = self.__config['invade']["broker"]
self.__port = self.__config['invade']["port"]
self.__topic = self.__config['invade']["topic"]
self.__lengthMqttList = self.__config["length"]
def put_queue(self,__queue,data):
if __queue.full():
a = __queue.get()
__queue.put( data,block=True, timeout=2 )
def on_connect(self,client,userdata,flags,rc):
client.subscribe(self.__topic)
# 当接收到MQTT消息时，回调函数
def on_location(self,client, userdata, msg):
# 将消息解码为JSON格式
payload = msg.payload.decode('utf-8')
data = json.loads(payload)
#logger.info(str(data))
# 解析位姿信息
lon = data.get("lon")
lat = data.get("lat")
alt = data.get("alt")
yaw = data.get("yaw")
pitch = data.get("pitch")
roll = data.get("roll")
if len(self.__mqtt_list) == self.__lengthMqttList:
self.__mqtt_list.pop(0)
self.__mqtt_list.append([self.__business,data])
# 打印无人机的位姿信息
#print(f"Longitude: {lon}, Latitude: {lat}, Altitude: {alt}, sat:{data.get('satcount')} , list length:{len(self.__mqtt_list)}")
def on_invade(self, client, userdata, msg):
# 将消息解码为JSON格式
payload = msg.payload.decode('utf-8')
data = json.loads(payload)
# logger.info(str(data))
# 解析位姿信息
points = data.get("points")
if len(self.__mqtt_list) == self.__lengthMqttList:
self.__mqtt_list.pop(0)
self.__mqtt_list.append([self.__business,points])
# 打印无人机的位姿信息
# print(f"Longitude: {lon}, Latitude: {lat}, Altitude: {alt}, sat:{data.get('satcount')} , list length:{len(self.__mqtt_list)}")
def mqtt_connect(self):
# 创建客户端
self.client = mqtt.Client()
self.client.on_connect = self.on_connect
if self.__business == 0:
# 设置回调函数
self.client.on_message = self.on_location
elif self.__business == 1:
# 设置回调函数
self.client.on_message = self.on_invade
# 连接到 Broker
self.client.connect(self.__broker, self.__port)
# 订阅主题
self.client.subscribe(self.__topic)
# 循环等待并处理网络事件
self.client.loop_forever()
def mqtt_disconnect(self):
start_time = time()
while True:
if time() - start_time > service_timeout:
logger.error("MQTT读取超时, requestId: %s,限定时间：%.1s , 已运行：%.1fs"%(request_id,service_timeout, time() - start_time))
raise ServiceException(ExceptionType.TASK_EXCUTE_TIMEOUT.value[0],
ExceptionType.TASK_EXCUTE_TIMEOUT.value[1])
client.loop_stop() # 停止循环
client.disconnect() # 断开连接
def run(self):
request_id, mqtt_list, progress = self.__request_id, self.__mqtt_list, init_progess
analyse_type, fb_queue = self.__analyse_type, self.__fb_queue
#service_timeout = int(self.__config["service"]["timeout"]) + 120
try:
logger.info("开始MQTT读取线程！requestId:{}", request_id)
mqtt_init_num = 0
self.mqtt_connect()
except Exception:
logger.error("MQTT线程异常:{}, requestId:{}", format_exc(), request_id)
finally:
mqtt_list = []
logger.info("MQTT线程停止完成！requestId:{}", request_id)
def start_PullMqtt(fb_queue, mqtt_list, request_id, analyse_type, context):
mqtt_thread = PullMqtt(fb_queue, mqtt_list, request_id, analyse_type, context)
mqtt_thread.setDaemon(True)
mqtt_thread.start()
return mqtt_thread
def start_PullVideo(mqtt_list):
for i in range(1000):
sleep(1)
if len(mqtt_list)>=10:
print( mqtt_list[4])
print(i,len(mqtt_list))
if __name__=="__main__":
#context = {'service':{'timeout':3600},'mqtt':{
# 'broker':"101.133.163.127",'port':1883,'topic':"test/topic","length":10}
# }
context = {
'base_dir':'/home/th/WJ/test/tuoheng_algN',
'env':'test'
}
analyse_type = '1'
request_id = '123456789'
event_queue, pull_queue, mqtt_list, image_queue, push_queue, push_ex_queue = Queue(), Queue(10), [], Queue(), Queue(), Queue()
fb_queue = Queue()
mqtt_thread = start_PullMqtt(fb_queue, mqtt_list, request_id, analyse_type, context)
start_PullVideo(mqtt_list)
print('---line117--')
#mqtt_thread.join()

40
concurrency/PullVideoStreamProcess.py
View File

@ -35,15 +35,15 @@ class PullVideoStreamProcess(Process):
put_queue(self._command_queue, result, timeout=2, is_ex=True)
@staticmethod
def start_File_upload(fb_queue, context, msg, image_queue, analyse_type,mqtt_list):
image_thread = ImageFileUpload(fb_queue, context, msg, image_queue, analyse_type,mqtt_list)
def start_File_upload(fb_queue, context, msg, image_queue, analyse_type):
image_thread = ImageFileUpload(fb_queue, context, msg, image_queue, analyse_type)
image_thread.setDaemon(True)
image_thread.start()
return image_thread
@staticmethod
def start_PullMqtt(fb_queue, mqtt_list, request_id, analyse_type, context):
mqtt_thread = PullMqtt(fb_queue, mqtt_list, request_id, analyse_type, context)
def start_PullMqtt(fb_queue, mqtt_list, request_id, analyse_type, context,business):
mqtt_thread = PullMqtt(fb_queue, mqtt_list, request_id, analyse_type, context,business)
mqtt_thread.setDaemon(True)
mqtt_thread.start()
return mqtt_thread
@ -81,13 +81,14 @@ class OnlinePullVideoStreamProcess(PullVideoStreamProcess):
# 初始化日志
init_log(base_dir, env)
logger.info("开启启动实时视频拉流进程, requestId:{},pid:{},ppid:{}", request_id,os.getpid(),os.getppid())
#开启mqtt
if service["mqtt_flag"]==1:
mqtt_thread = self.start_PullMqtt(fb_queue, mqtt_list, request_id, analyse_type, context)
# 开启mqtt
if service['mqtt']["flag"] == 1:
business = service['mqtt']["business"]
mqtt_thread = self.start_PullMqtt(fb_queue, mqtt_list, request_id, analyse_type, context, business)
# 开启图片上传线程
image_thread = self.start_File_upload(fb_queue, context, msg, image_queue, analyse_type,mqtt_list)
image_thread = self.start_File_upload(fb_queue, context, msg, image_queue, analyse_type)
cv2_init_num, init_pull_num, concurrent_frame = 0, 1, 1
start_time, pull_stream_start_time, read_start_time, full_timeout = time(), None, None, None
while True:
@ -129,7 +130,7 @@ class OnlinePullVideoStreamProcess(PullVideoStreamProcess):
frame, pull_p, width, height = pull_read_video_stream(pull_p, pull_url, width, height, width_height_3,
w_2, h_2, request_id)
if pull_queue.full():
logger.info("pull拉流队列满了:{}, requestId: {}", os.getppid(), request_id)
#logger.info("pull拉流队列满了:{}, requestId: {}", os.getppid(), request_id)
if full_timeout is None:
full_timeout = time()
if time() - full_timeout > 180:
@ -171,7 +172,7 @@ class OnlinePullVideoStreamProcess(PullVideoStreamProcess):
sleep(1)
continue
init_pull_num, read_start_time = 1, None
frame_list.append(frame)
frame_list.append([frame, mqtt_list])
frame_index_list.append(concurrent_frame)
if len(frame_list) >= frame_num:
put_queue(pull_queue, (4, (frame_list, frame_index_list, all_frames)), timeout=1, is_ex=True)
@ -222,10 +223,11 @@ class OfflinePullVideoStreamProcess(PullVideoStreamProcess):
def run(self):
msg, context, frame_num, analyse_type = self._msg, self._context, self._frame_num, self._analyse_type
request_id, base_dir, env, pull_url = msg["request_id"], context['base_dir'], context['env'], msg["pull_url"]
request_id, base_dir, env, pull_url, service = msg["request_id"], context['base_dir'], context['env'], msg["pull_url"], context["service"]
ex, service_timeout, full_timeout = None, int(context["service"]["timeout"]) + 120, None
command_queue, pull_queue, image_queue, fb_queue = self._command_queue, self._pull_queue, self._image_queue, \
self._fb_queue
command_queue, pull_queue, image_queue, fb_queue, mqtt_list = self._command_queue, self._pull_queue, self._image_queue, \
self._fb_queue, self._mqtt_list
image_thread, pull_p = None, None
width, height, width_height_3, all_frames, w_2, h_2 = None, None, None, 0, None, None
frame_list, frame_index_list = [], []
@ -235,8 +237,12 @@ class OfflinePullVideoStreamProcess(PullVideoStreamProcess):
init_log(base_dir, env)
logger.info("开启离线视频拉流进程, requestId:{}", request_id)
#开启mqtt
if service['mqtt']["flag"]==1:
business = service['mqtt']["business"]
mqtt_thread = self.start_PullMqtt(fb_queue, mqtt_list, request_id, analyse_type, context, business)
# 开启图片上传线程
image_thread = self.start_File_upload(fb_queue, context, msg, image_queue, analyse_type,[])
image_thread = self.start_File_upload(fb_queue, context, msg, image_queue, analyse_type)
# 初始化拉流工具类
cv2_init_num, concurrent_frame = 0, 1
@ -269,7 +275,7 @@ class OfflinePullVideoStreamProcess(PullVideoStreamProcess):
width, height, width_height_3, all_frames, w_2, h_2 = build_video_info(pull_url, request_id)
continue
if pull_queue.full():
logger.info("pull拉流队列满了:{}, requestId: {}", os.getppid(), request_id)
#logger.info("pull拉流队列满了:{}, requestId: {}", os.getppid(), request_id)
if full_timeout is None:
full_timeout = time()
if time() - full_timeout > 180:
@ -306,7 +312,7 @@ class OfflinePullVideoStreamProcess(PullVideoStreamProcess):
ExceptionType.READSTREAM_TIMEOUT_EXCEPTION.value[1])
logger.info("离线拉流线程结束, requestId: {}", request_id)
break
frame_list.append(frame)
frame_list.append([frame,mqtt_list])
frame_index_list.append(concurrent_frame)
if len(frame_list) >= frame_num:
put_queue(pull_queue, (4, (frame_list, frame_index_list, all_frames)), timeout=1, is_ex=True)

109
concurrency/PushVideoStreamProcess.py
View File

@ -23,7 +23,7 @@ from util.Cv2Utils import video_conjuncing, write_or_video, write_ai_video, push
from util.ImageUtils import url2Array, add_water_pic
from util.LogUtils import init_log
from util.PlotsUtils import draw_painting_joint, filterBox, xywh2xyxy2, xy2xyxy, draw_name_joint, plot_one_box_auto, draw_name_ocr,draw_name_crowd
from util.PlotsUtils import draw_painting_joint, filterBox, xywh2xyxy2, xy2xyxy, draw_name_joint, plot_one_box_auto, draw_name_ocr,draw_name_crowd,draw_transparent_red_polygon
from util.QueUtil import get_no_block_queue, put_queue, clear_queue
@ -36,11 +36,10 @@ class PushStreamProcess(Process):
# 传参
self._msg, self._push_queue, self._image_queue, self._push_ex_queue, self._hb_queue, self._context = args
self._algStatus = False # 默认关闭
self._algSwitch = self._context['service']['algSwitch']
self._algSwitch = self._context['service']['algSwitch']
#0521:
default_enabled = str(self._msg.get("defaultEnabled", "True")).lower() == "true"
# 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
@ -131,15 +130,26 @@ class OnPushStreamProcess(PushStreamProcess):
if push_r is not None:
if push_r[0] == 1:
frame_list, frame_index_list, all_frames, draw_config, push_objs = push_r[1]
for i, frame in enumerate(frame_list):
# 处理每1帧
for i, [frame,mqtt_list] in enumerate(frame_list):
# mqtt传参
border = None
mqttPares = None
if len(mqtt_list) >= 1:
mqttPares = mqtt_list[0]
if mqttPares[0] == 1:
border = mqttPares[1]
pix_dis = int((frame.shape[0]//10)*1.2)
# 复制帧用来画图
copy_frame = frame.copy()
if border is not None:
copy_frame = draw_transparent_red_polygon(copy_frame, np.array(border, np.int32),alpha=0.25)
det_xywh, thread_p = {}, []
det_xywh2 = {}
det_xywh2 = {'mqttPares':mqttPares}
# 所有问题的矩阵集合
qs_np = None
qs_reurn = []
bp_np = None
for det in push_objs[i]:
code, det_result = det
# 每个单独模型处理
@ -168,18 +178,21 @@ class OnPushStreamProcess(PushStreamProcess):
else:
try: # 应对NaN情况
box, score, cls = xywh2xyxy2(qs)
if cls not in allowedList or score < frame_score:
continue
if ModelType.CITY_FIREAREA_MODEL.value[1] == str(code):
# 借score作为points点集
box.append(qs[-1])
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)
rr = t.submit(draw_painting_joint, box, copy_frame, label_array, score, color, font_config, border=border)
thread_p.append(rr)
if det_xywh.get(code) is None:
@ -189,17 +202,24 @@ class OnPushStreamProcess(PushStreamProcess):
if cd is None:
det_xywh[code][cls] = [[cls, box, score, label_array, color]]
else:
det_xywh[code][cls].append([cls, box, score, label_array, color])
det_xywh[code][cls].append([cls, box, score, label_array, color])
if qs_np is None:
qs_np = np.array([box[0][0], box[0][1], box[1][0], box[1][1],
box[2][0], box[2][1], box[3][0], box[3][1],
qs_np = np.array([box[0][0], box[0][1], box[1][0], box[1][1],
box[2][0], box[2][1], box[3][0], box[3][1],
score, cls, code],dtype=np.float32)
else:
result_li = np.array([box[0][0], box[0][1], box[1][0], box[1][1],
result_li = np.array([box[0][0], box[0][1], box[1][0], box[1][1],
box[2][0], box[2][1], box[3][0], box[3][1],
score, cls, code],dtype=np.float32)
qs_np = np.row_stack((qs_np, result_li))
if ModelType.CITY_FIREAREA_MODEL.value[1] == str(code):
if bp_np is None:
bp_np = np.array([box[0][0], box[0][1], box[-1]], dtype=object)
else:
bp_li = np.array([box[0][0], box[0][1], box[-1]], dtype=object)
bp_np = np.row_stack((bp_np, bp_li))
if logo:
frame = add_water_pic(frame, logo, request_id)
copy_frame = add_water_pic(copy_frame, logo, request_id)
@ -230,7 +250,7 @@ class OnPushStreamProcess(PushStreamProcess):
if picture_similarity:
qs_np_tmp = qs_np_id.copy()
b = np.zeros(qs_np.shape[0])
qs_reurn = np.column_stack((qs_np,b))
qs_reurn = np.column_stack((qs_np,b))
else:
qs_reurn = filterBox(qs_np, qs_np_tmp, pix_dis)
if picture_similarity:
@ -256,8 +276,14 @@ class OnPushStreamProcess(PushStreamProcess):
score = q[8]
rainbows, label_arrays = draw_config[code]["rainbows"], draw_config[code]["label_arrays"]
label_array, color = label_arrays[cls], rainbows[cls]
box = [(int(q[0]), int(q[1])), (int(q[2]), int(q[3])),
(int(q[4]), int(q[5])), (int(q[6]), int(q[7]))]
box = [(int(q[0]), int(q[1])), (int(q[2]), int(q[3])),
(int(q[4]), int(q[5])), (int(q[6]), int(q[7]))]
if bp_np is not None:
if len(bp_np.shape)==1:
bp_np = bp_np[np.newaxis, ...]
for bp in bp_np:
if np.array_equal(bp[:2], np.array([int(q[0]), int(q[1])])):
box.append(bp[-1])
is_new = False
if q[11] == 1:
is_new = True
@ -281,7 +307,7 @@ class OnPushStreamProcess(PushStreamProcess):
if push_r[0] == 2:
logger.info("拉流进程收到控制命令为:{}, requestId: {}",push_r[1] ,request_id)
if 'algStart' == push_r[1]: self._algStatus = True;logger.info("算法识别开启, requestId: {}", request_id)
if 'algStop' == push_r[1]: self._algStatus = False;logger.info("算法识别关闭, requestId: {}", request_id)
if 'algStop' == push_r[1]: self._algStatus = False;logger.info("算法识别关闭, requestId: {}", request_id)
if 'stop' == push_r[1]:
logger.info("停止推流进程, requestId: {}", request_id)
break
@ -368,23 +394,33 @@ class OffPushStreamProcess(PushStreamProcess):
# [(2, 操作指令)] 指令操作
if push_r[0] == 1:
frame_list, frame_index_list, all_frames, draw_config, push_objs = push_r[1]
# 处理每一帧图片
for i, frame in enumerate(frame_list):
for i, [frame,mqtt_list] in enumerate(frame_list):
# mqtt传参
border = None
mqttPares = None
if len(mqtt_list) >= 1:
mqttPares = mqtt_list[0]
if mqttPares[0] == 1:
border = mqttPares[1]
pix_dis = int((frame.shape[0]//10)*1.2)
if frame_index_list[i] % 300 == 0 and frame_index_list[i] <= all_frames:
task_process = "%.2f" % (float(frame_index_list[i]) / float(all_frames))
put_queue(hb_queue, {"hb_value": task_process}, timeout=2)
# 复制帧用来画图
copy_frame = frame.copy()
if border is not None:
copy_frame = draw_transparent_red_polygon(copy_frame, np.array(border, np.int32),alpha=0.25)
# 所有问题记录字典
det_xywh, thread_p = {}, []
det_xywh2 = {}
det_xywh2 = {'mqttPares':mqttPares}
# 所有问题的矩阵集合
qs_np = None
qs_reurn = []
bp_np = None
for det in push_objs[i]:
code, det_result = det
# 每个单独模型处理
# 模型编号、100帧的所有问题, 检测目标、颜色、文字图片
if len(det_result) > 0:
@ -414,14 +450,16 @@ class OffPushStreamProcess(PushStreamProcess):
box, score, cls = xywh2xyxy2(qs)
if cls not in allowedList or score < frame_score:
continue
if ModelType.CITY_FIREAREA_MODEL.value[1] == str(code):
box.append(qs[-1])
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)
rr = t.submit(draw_painting_joint, box, copy_frame, label_array, score, color, font_config, border=border)
thread_p.append(rr)
if det_xywh.get(code) is None:
det_xywh[code] = {}
cd = det_xywh[code].get(cls)
@ -440,6 +478,13 @@ class OffPushStreamProcess(PushStreamProcess):
score, cls, code],dtype=np.float32)
qs_np = np.row_stack((qs_np, result_li))
if ModelType.CITY_FIREAREA_MODEL.value[1]== str(code):
if bp_np is None:
bp_np = np.array([box[0][0], box[0][1],box[-1]],dtype=object)
else:
bp_li = np.array([box[0][0], box[0][1],box[-1]],dtype=object)
bp_np = np.row_stack((bp_np, bp_li))
if logo:
frame = add_water_pic(frame, logo, request_id)
copy_frame = add_water_pic(copy_frame, logo, request_id)
@ -467,7 +512,7 @@ class OffPushStreamProcess(PushStreamProcess):
if picture_similarity:
qs_np_tmp = qs_np_id.copy()
b = np.zeros(qs_np.shape[0])
qs_reurn = np.column_stack((qs_np,b))
qs_reurn = np.column_stack((qs_np,b))
else:
qs_reurn = filterBox(qs_np, qs_np_tmp, pix_dis)
if picture_similarity:
@ -494,8 +539,14 @@ class OffPushStreamProcess(PushStreamProcess):
score = q[8]
rainbows, label_arrays = draw_config[code]["rainbows"], draw_config[code]["label_arrays"]
label_array, color = label_arrays[cls], rainbows[cls]
box = [(int(q[0]), int(q[1])), (int(q[2]), int(q[3])),
(int(q[4]), int(q[5])), (int(q[6]), int(q[7]))]
box = [(int(q[0]), int(q[1])), (int(q[2]), int(q[3])),
(int(q[4]), int(q[5])), (int(q[6]), int(q[7]))]
if bp_np is not None:
if len(bp_np.shape)==1:
bp_np = bp_np[np.newaxis, ...]
for bp in bp_np:
if np.array_equal(bp[:2], np.array([int(q[0]), int(q[1])])):
box.append(bp[-1])
is_new = False
if q[11] == 1:
is_new = True
@ -509,7 +560,7 @@ class OffPushStreamProcess(PushStreamProcess):
else:
det_xywh2[code][cls].append(
[cls, box, score, label_array, color, is_new])
if len(det_xywh2) > 0:
if len(det_xywh2) > 1:
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)
@ -517,7 +568,7 @@ class OffPushStreamProcess(PushStreamProcess):
if push_r[0] == 2:
logger.info("拉流进程收到控制命令为:{}, requestId: {}",push_r[1] ,request_id)
if 'algStart' == push_r[1]: self._algStatus = True;logger.info("算法识别开启, requestId: {}", request_id)
if 'algStop' == push_r[1]: self._algStatus = False;logger.info("算法识别关闭, requestId: {}", request_id)
if 'algStop' == push_r[1]: self._algStatus = False;logger.info("算法识别关闭, requestId: {}", request_id)
if 'stop' == push_r[1]:
logger.info("停止推流进程, requestId: {}", request_id)
break

27
config/mqtt/dsp_test_mqtt.yml
View File

@ -1,10 +1,21 @@
mqtt_flag: true
broker : "58.213.148.44"
port : 1883
username: "admin"
password: "admin##123"
#topic: "/topic/v1/airportFly/%s/aiDroneData"
topic: "/topic/v1/airportDrone/THJSQ03B2309TPCTD5QV/realTime/data"
# 存储多少条消息到list里
length: 10
# 业务0为经纬度定位，业务1为入侵算法开关
business: 1
# 经纬度定位
location:
broker : "58.213.148.44"
port : 1883
username: "admin"
password: "admin##123"
#topic: "/topic/v1/airportFly/%s/aiDroneData"
topic: "/topic/v1/airportDrone/THJSQ03B2309TPCTD5QV/realTime/data"
# 入侵
invade:
broker : "192.168.11.8"
port : 2883
#topic: "/topic/v1/airportFly/%s/aiDroneData"
topic: "test000/topic"
# 存储多少条消息到list里
length: 30

7
config/service/dsp_test_service.yml
View File

@ -33,7 +33,8 @@ service:
#storage source,0--aliyun,1--minio
storage_source: 0
#是否启用mqtt，0--不用，1--启用
mqtt_flag: 0
mqtt:
flag: 0
business: 1
#是否启用alg控制功能
algSwitch: False
algSwitch: False

130
enums/ModelTypeEnum.py
View File

@ -14,6 +14,7 @@ from utilsK.drownUtils import mixDrowing_water_postprocess
from utilsK.noParkingUtils import mixNoParking_road_postprocess
from utilsK.illParkingUtils import illParking_postprocess
from utilsK.pannelpostUtils import pannel_post_process
from utilsK.securitypostUtils import security_post_process
from stdc import stdcModel
from yolov5 import yolov5Model
from p2pNet import p2NnetModel
@ -63,7 +64,7 @@ class ModelType(Enum):
"classes": 5,
"rainbows": COLOR
},
'fiterList':[2],
'Detweights': "../weights/trt/AIlib2/river/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../weights/trt/AIlib2/river/stdc_360X640_%s_fp16.engine' % gpuName
})
@ -99,10 +100,8 @@ class ModelType(Enum):
'weight':"../weights/trt/AIlib2/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 } },
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False},
}
],
'postFile': {
@ -112,11 +111,10 @@ class ModelType(Enum):
"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
"score_byClass": {0: 0.25, 1: 0.3, 2: 0.3, 3: 0.3},
'fiterList': [5],
'segRegionCnt':2,###分割模型结果需要保留的等值线数目
"pixScale": 1.2,
})
@ -162,7 +160,8 @@ class ModelType(Enum):
"classes": 10,
"rainbows": COLOR
},
'allowedList':[0,1,2,3,4,5,6,7,8,9,10,11,12,16,17,18,19,20,21,22],
'score_byClass':{11:0.75,12:0.75},
'fiterList': [13,14,15,16,17,18,19,20,21,22],
'Detweights': "../weights/trt/AIlib2/highWay2/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../weights/trt/AIlib2/highWay2/stdc_360X640_%s_fp16.engine' % gpuName
})
@ -231,7 +230,7 @@ class ModelType(Enum):
"classes": 5,
"rainbows": COLOR
},
'Segweights': None
'Segweights': None,
})
ANGLERSWIMMER_MODEL = ("9", "009", "钓鱼游泳模型", 'AnglerSwimmer', lambda device, gpuName: {
@ -345,7 +344,8 @@ class ModelType(Enum):
'function': riverDetSegMixProcess,
'pars': {
'slopeIndex': [1, 3, 4, 7],
'riverIou': 0.1
'riverIou': 0.1,
'scale': 0.25
}
}
},
@ -377,10 +377,11 @@ class ModelType(Enum):
'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 } }
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':True}
},
{
'weight':'../weights/pth/AIlib2/cityMangement3/dmpr.pth',
'weight':'../weights/trt/AIlib2/cityMangement3/dmpr_3090.engine',
#'weight':'../weights/pth/AIlib2/cityMangement3/dmpr.pth',
'par':{
'depth_factor':32,'NUM_FEATURE_MAP_CHANNEL':6,'dmpr_thresh':0.1, 'dmprimg_size':640,
'name':'dmpr'
@ -403,7 +404,7 @@ class ModelType(Enum):
"classes": 8,
"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
"score_byClass":{0:0.8, 1:0.4, 2:0.5, 3:0.5},
'segRegionCnt':2,###分割模型结果需要保留的等值线数目
"pixScale": 1.2,
})
@ -568,10 +569,10 @@ class ModelType(Enum):
'weight':'../weights/trt/AIlib2/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} }
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.1,'iou_thres':0.45,'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False}
},
{
'weight' : '../weights/pth/AIlib2/ocr2/crnn_ch.pth',
'weight' : '../weights/trt/AIlib2/ocr2/crnn_ch_%s_fp16_192X32.engine'%(gpuName),
'name':'ocr',
'model':ocrModel,
'par':{
@ -587,7 +588,6 @@ class ModelType(Enum):
},
}
],
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6]],
'segPar': None,
'postFile': {
"name": "post_process",
@ -597,6 +597,8 @@ class ModelType(Enum):
"rainbows": COLOR
},
'Segweights': None,
"score_byClass": {0: 0.7, 1: 0.7, 2: 0.8, 3: 0.6}
})
RIVERT_MODEL = ("25", "025", "河道检测模型(T)", 'riverT', lambda device, gpuName: {
@ -642,7 +644,7 @@ class ModelType(Enum):
'weight':"../weights/trt/AIlib2/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 } },
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False},
}
@ -655,7 +657,7 @@ class ModelType(Enum):
"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
"score_byClass":{0:0.25,1:0.25,2:0.6,3:0.6,4:0.6 ,5:0.6},
'segRegionCnt':2,###分割模型结果需要保留的等值线数目
"pixScale": 1.2,
@ -703,6 +705,7 @@ class ModelType(Enum):
"classes": 10,
"rainbows": COLOR
},
'fiterltList': [11,12,13,14,15,16,17],
'Detweights': "../weights/trt/AIlib2/highWay2T/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../weights/trt/AIlib2/highWay2T/stdc_360X640_%s_fp16.engine' % gpuName
})
@ -716,7 +719,7 @@ class ModelType(Enum):
'weight':"../weights/trt/AIlib2/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 } },
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':False},
}
@ -724,6 +727,7 @@ class ModelType(Enum):
'postFile': {
"rainbows": COLOR
},
"score_byClass": {0: 0.25, 1: 0.3, 2: 0.3, 3: 0.3}
})
@ -736,7 +740,7 @@ class ModelType(Enum):
'weight':"../weights/trt/AIlib2/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 } },
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':False},
}
@ -744,6 +748,7 @@ class ModelType(Enum):
'postFile': {
"rainbows": COLOR
},
"score_byClass": {0: 0.25, 1: 0.3, 2: 0.3, 3: 0.3}
})
@ -756,7 +761,7 @@ class ModelType(Enum):
'weight':"../weights/trt/AIlib2/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 } },
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':False },
}
@ -764,7 +769,6 @@ class ModelType(Enum):
'postFile': {
"rainbows": COLOR
},
})
TRAFFIC_SPILL_MODEL = ("50", "501", "高速公路抛洒物模型", 'highWaySpill', lambda device, gpuName: {
@ -806,7 +810,7 @@ class ModelType(Enum):
"classes": 2,
"rainbows": COLOR
},
'detModelpara': [{"id": str(x), "config": {"k1": "v1", "k2": "v2"}} for x in [0]],
'fiterList': [1],
###控制哪些检测类别显示、输出
'Detweights': "../weights/trt/AIlib2/highWaySpill/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../weights/trt/AIlib2/highWaySpill/stdc_360X640_%s_fp16.engine' % gpuName
@ -851,7 +855,7 @@ class ModelType(Enum):
"classes": 4,
"rainbows": COLOR
},
'detModelpara': [{"id": str(x), "config": {"k1": "v1", "k2": "v2"}} for x in [0]],
'fiterList':[1,2,3],
###控制哪些检测类别显示、输出
'Detweights': "../weights/trt/AIlib2/highWayCthc/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../weights/trt/AIlib2/highWayCthc/stdc_360X640_%s_fp16.engine' % gpuName
@ -867,14 +871,15 @@ class ModelType(Enum):
'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}},
'segRegionCnt': 1, 'trtFlag_det': True,
'trtFlag_seg': False},
}
],
'postFile': {
"rainbows": COLOR
},
'fiterList':[0]
})
@ -884,25 +889,27 @@ class ModelType(Enum):
'rainbows': COLOR,
'models': [
{
'trtFlag_det': False,
'weight': '../weights/pth/AIlib2/carplate/plate_yolov5s_v3.jit',
#'weight': '../weights/pth/AIlib2/carplate/plate_yolov5s_v3.jit',
'weight': '../weights/trt/AIlib2/carplate/yolov5_%s_fp16.engine' % (gpuName),
'name': 'yolov5',
'model': yolov5Model,
'par': {
'trtFlag_det': True,
'device': 'cuda:0',
'half': False,
'half': True,
'conf_thres': 0.4,
'iou_thres': 0.45,
'nc': 1,
'plate':8,
'plate_dilate': (0.5, 0.1)
},
},
{
'trtFlag_ocr': False,
'weight': '../weights/pth/AIlib2/ocr2/crnn_ch.pth',
'weight' : '../weights/trt/AIlib2/ocr2/crnn_ch_%s_fp16_192X32.engine'%(gpuName),
'name': 'ocr',
'model': ocrModel,
'par': {
'trtFlag_ocr': True,
'char_file': '../AIlib2/conf/ocr2/benchmark.txt',
'mode': 'ch',
'nc': 3,
@ -926,10 +933,8 @@ class ModelType(Enum):
'name': 'yolov5',
'model': yolov5Model,
'par': {'half': True, 'device': 'cuda:0', 'conf_thres': 0.50, 'iou_thres': 0.45,
'allowedList': list(range(20)), 'segRegionCnt': 1, 'trtFlag_det': True,
'trtFlag_seg': False, "score_byClass": {"0": 0.50, "1": 0.3, "2": 0.3, "3": 0.3}},
'segRegionCnt': 1, 'trtFlag_det': True,'trtFlag_seg': False},
}
],
'postFile': {
"rainbows": COLOR
@ -947,8 +952,7 @@ class ModelType(Enum):
'name': 'yolov5',
'model': yolov5Model,
'par': {'half': True, 'device': 'cuda:0', 'conf_thres': 0.50, 'iou_thres': 0.45,
'allowedList': list(range(20)), 'segRegionCnt': 1, 'trtFlag_det': True,
'trtFlag_seg': False, "score_byClass": {"0": 0.50, "1": 0.3, "2": 0.3, "3": 0.3}},
'segRegionCnt': 1, 'trtFlag_det': True, 'trtFlag_seg': False},
}
],
@ -995,8 +999,7 @@ class ModelType(Enum):
'name': 'yolov5',
'model': yolov5Model,
'par': {'half': True, 'device': 'cuda:0', 'conf_thres': 0.50, 'iou_thres': 0.45,
'allowedList': list(range(20)), 'segRegionCnt': 1, 'trtFlag_det': True,
'trtFlag_seg': False, "score_byClass": {"0": 0.50, "1": 0.3, "2": 0.3, "3": 0.3}},
'segRegionCnt': 1, 'trtFlag_det': True, 'trtFlag_seg': False},
}
],
@ -1016,8 +1019,7 @@ class ModelType(Enum):
'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}},
'segRegionCnt': 1, 'trtFlag_det': True, 'trtFlag_seg': False},
},
{
'trtFlag_det': False,
@ -1042,6 +1044,54 @@ class ModelType(Enum):
}],
})
CITY_FIREAREA_MODEL = ("30", "307", "火焰面积模型", 'FireArea', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
'labelnames': ["火焰"],
'seg_nclass': 2, # 分割模型类别数目，默认2类
'segRegionCnt': 0,
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../weights/trt/AIlib2/smogfire/yolov5_%s_fp16.engine" % gpuName, # 0：fire 1：smoke
'Samweights': "../weights/pth/AIlib2/firearea/sam_vit_b_01ec64.pth", #分割模型
'ksize':(7,7),
'sam_type':'vit_b',
'slopeIndex': [],
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Segweights': None,
'fiterList':[1],
"score_byClass": {0: 0.1}
})
CITY_SECURITY_MODEL = ("30", "308", "安防模型", 'SECURITY', lambda device, gpuName: {
'labelnames': ["带安全帽","安全帽","攀爬","斗殴","未戴安全帽"],
'postProcess': {'function': security_post_process, 'pars': {'objs': [0,1],'iou':0.25,'unhelmet':4}},
'models':
[
{
'weight': "../weights/trt/AIlib2/security/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,
'segRegionCnt': 1, 'trtFlag_det': True, 'trtFlag_seg': False},
}
],
'postFile': {
"rainbows": COLOR
},
'fiterList': [0,1],
"score_byClass": {"0": 0.50}
})
@staticmethod
def checkCode(code):
for model in ModelType:

76
util/ModelUtils.py
View File

@ -27,6 +27,7 @@ import torch
import tensorrt as trt
from utilsK.jkmUtils import pre_process, post_process, get_return_data
from DMPR import DMPRModel
from segment_anything import SamPredictor, sam_model_registry
FONT_PATH = "../AIlib2/conf/platech.ttf"
@ -36,6 +37,7 @@ class OneModel:
def __init__(self, device, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None, env=None):
try:
start = time.time()
logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
requestId)
par = modeType.value[4](str(device), gpu_name)
@ -68,10 +70,11 @@ class OneModel:
'ovlap_thres_crossCategory': postFile.get("ovlap_thres_crossCategory"),
'iou_thres': postFile["iou_thres"],
# 对高速模型进行过滤
'allowedList': par['allowedList'] if modeType.value[0] == '3' else [],
'segRegionCnt': par['segRegionCnt'],
'trtFlag_det': par['trtFlag_det'],
'trtFlag_seg': par['trtFlag_seg']
'trtFlag_seg': par['trtFlag_seg'],
'score_byClass':par['score_byClass'] if 'score_byClass' in par.keys() else None,
'fiterList': par['fiterList'] if 'fiterList' in par.keys() else []
}
model_param = {
"model": model,
@ -86,6 +89,7 @@ class OneModel:
logger.error("模型加载异常：{}, requestId:{}", format_exc(), requestId)
raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
logger.info("模型初始化时间：{}, requestId:{}", time.time() - start, requestId)
# 纯分类模型
class cityManagementModel:
__slots__ = "model_conf"
@ -103,6 +107,8 @@ class cityManagementModel:
model_param = {
"modelList": modelList,
"postProcess": postProcess,
"score_byClass":par['score_byClass'] if 'score_byClass' in par.keys() else None,
"fiterList":par['fiterList'] if 'fiterList' in par.keys() else [],
}
self.model_conf = (modeType, model_param, allowedList, names, rainbows)
except Exception:
@ -111,15 +117,14 @@ class cityManagementModel:
ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
def detSeg_demo2(args):
model_conf, frame, request_id = args
modelList, postProcess = model_conf[1]['modelList'], model_conf[1]['postProcess']
modelList, postProcess,score_byClass,fiterList = (
model_conf[1]['modelList'], model_conf[1]['postProcess'],model_conf[1]['score_byClass'], model_conf[1]['fiterList'])
try:
result = [[ None, None, AI_process_N([frame], modelList, postProcess)[0] ] ] # 为了让返回值适配统一的接口而写的shi
result = [[ None, None, AI_process_N([frame], modelList, postProcess,score_byClass,fiterList)[0] ] ] # 为了让返回值适配统一的接口而写的shi
return result
except ServiceException as s:
raise s
except Exception:
# self.num += 1
# cv2.imwrite('/home/th/tuo_heng/dev/img%s.jpg' % str(self.num), frame)
logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), request_id)
raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
@ -127,11 +132,6 @@ def detSeg_demo2(args):
def model_process(args):
model_conf, frame, request_id = args
model_param, names, rainbows = model_conf[1], model_conf[3], model_conf[4]
# modeType, model_param, allowedList, names, rainbows = model_conf
# segmodel, names, label_arraylist, rainbows, objectPar, font, segPar, mode, postPar, requestId = args
# model_param['digitFont'] = digitFont
# model_param['label_arraylist'] = label_arraylist
# model_param['font_config'] = font_config
try:
return AI_process([frame], model_param['model'], model_param['segmodel'], names, model_param['label_arraylist'],
rainbows, objectPar=model_param['objectPar'], font=model_param['digitFont'],
@ -164,7 +164,13 @@ class TwoModel:
Detweights = par['Detweights']
with open(Detweights, "rb") as f, trt.Runtime(trt.Logger(trt.Logger.ERROR)) as runtime:
model = runtime.deserialize_cuda_engine(f.read())
segmodel = None
if modeType == ModelType.CITY_FIREAREA_MODEL:
sam = sam_model_registry[par['sam_type']](checkpoint=par['Samweights'])
sam.to(device=device)
segmodel = SamPredictor(sam)
else:
segmodel = None
postFile = par['postFile']
conf_thres = postFile["conf_thres"]
iou_thres = postFile["iou_thres"]
@ -178,7 +184,10 @@ class TwoModel:
"conf_thres": conf_thres,
"iou_thres": iou_thres,
"trtFlag_det": par['trtFlag_det'],
"otc": otc
"otc": otc,
"ksize":par['ksize'] if 'ksize' in par.keys() else None,
"score_byClass": par['score_byClass'] if 'score_byClass' in par.keys() else None,
"fiterList": par['fiterList'] if 'fiterList' in par.keys() else []
}
self.model_conf = (modeType, model_param, allowedList, names, rainbows)
except Exception:
@ -186,16 +195,15 @@ class TwoModel:
raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
logger.info("模型初始化时间：{}, requestId:{}", time.time() - s, requestId)
def forest_process(args):
model_conf, frame, request_id = args
model_param, names, rainbows = model_conf[1], model_conf[3], model_conf[4]
try:
return AI_process_forest([frame], model_param['model'], model_param['segmodel'], names,
model_param['label_arraylist'], rainbows, model_param['half'], model_param['device'],
model_param['conf_thres'], model_param['iou_thres'], [], font=model_param['digitFont'],
trtFlag_det=model_param['trtFlag_det'], SecNms=model_param['otc'])
model_param['conf_thres'], model_param['iou_thres'],font=model_param['digitFont'],
trtFlag_det=model_param['trtFlag_det'], SecNms=model_param['otc'],ksize = model_param['ksize'],
score_byClass=model_param['score_byClass'],fiterList=model_param['fiterList'])
except ServiceException as s:
raise s
except Exception:
@ -204,7 +212,6 @@ def forest_process(args):
logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), request_id)
raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
class MultiModel:
__slots__ = "model_conf"
@ -223,6 +230,8 @@ class MultiModel:
model_param = {
"modelList": modelList,
"postProcess": postProcess,
"score_byClass": par['score_byClass'] if 'score_byClass' in par.keys() else None,
"fiterList": par['fiterList'] if 'fiterList' in par.keys() else []
}
self.model_conf = (modeType, model_param, allowedList, names, rainbows)
except Exception:
@ -230,13 +239,13 @@ class MultiModel:
raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
logger.info("模型初始化时间：{}, requestId:{}", time.time() - s, requestId)
def channel2_process(args):
model_conf, frame, request_id = args
modelList, postProcess = model_conf[1]['modelList'], model_conf[1]['postProcess']
modelList, postProcess,score_byClass,fiterList = (
model_conf[1]['modelList'], model_conf[1]['postProcess'],model_conf[1]['score_byClass'], model_conf[1]['fiterList'])
try:
start = time.time()
result = [[None, None, AI_process_C([frame], modelList, postProcess)[0]]] # 为了让返回值适配统一的接口而写的shi
result = [[None, None, AI_process_C([frame], modelList, postProcess,score_byClass,fiterList)[0]]] # 为了让返回值适配统一的接口而写的shi
# print("AI_process_C use time = {}".format(time.time()-start))
return result
except ServiceException as s:
@ -245,7 +254,6 @@ def channel2_process(args):
logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), request_id)
raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
def get_label_arraylist(*args):
width, height, names, rainbows = args
# line = int(round(0.002 * (height + width) / 2) + 1)
@ -266,8 +274,6 @@ def get_label_arraylist(*args):
'label_location': 'leftTop'}
label_arraylist = get_label_arrays(names, rainbows, fontSize=text_height, fontPath=FONT_PATH)
return digitFont, label_arraylist, (line, text_width, text_height, fontScale, tf)
# 船只模型
class ShipModel:
__slots__ = "model_conf"
@ -293,8 +299,6 @@ class ShipModel:
raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
logger.info("模型初始化时间：{}, requestId:{}", time.time() - s, requestId)
def obb_process(args):
model_conf, frame, request_id = args
model_param = model_conf[1]
@ -309,7 +313,6 @@ def obb_process(args):
logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), request_id)
raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
# 车牌分割模型、健康码、行程码分割模型
class IMModel:
__slots__ = "model_conf"
@ -333,7 +336,7 @@ class IMModel:
new_device = torch.device(par['device'])
model = torch.jit.load(par[img_type]['weights'])
logger.info("########################加载 jit 模型成功 成功 ########################, requestId:{}",
logger.info("########################加载 jit 模型成功 成功 ########################, requestId:{}",
requestId)
self.model_conf = (modeType, allowedList, new_device, model, par, img_type)
except Exception:
@ -387,15 +390,12 @@ class CARPLATEModel:
detpar = par['models'][0]['par']
# new_device = torch.device(par['device'])
# modelList=[ modelPar['model'](weights=modelPar['weight'],par=modelPar['par']) for modelPar in par['models'] ]
logger.info("########################加载 plate_yolov5s_v3.jit 成功 ########################, requestId:{}",
requestId)
self.model_conf = (modeType, device, modelList, detpar, par['rainbows'])
except Exception:
logger.error("模型加载异常：{}, requestId:{}", format_exc(), requestId)
raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
class DENSECROWDCOUNTModel:
__slots__ = "model_conf"
@ -763,4 +763,18 @@ MODEL_CONFIG = {
None,
lambda x: cc_process(x)
),
# 加载火焰面积模型
ModelType.CITY_FIREAREA_MODEL.value[1]: (
lambda x, y, r, t, z, h: TwoModel(x, y, r, ModelType.CITY_FIREAREA_MODEL, t, z, h),
ModelType.CITY_FIREAREA_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: forest_process(x)
),
# 加载安防模型
ModelType.CITY_SECURITY_MODEL.value[1]: (
lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.CITY_SECURITY_MODEL, t, z, h),
ModelType.CITY_SECURITY_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: detSeg_demo2(x)
),
}

184
util/PlotsUtils.py
View File

@ -5,7 +5,7 @@ import unicodedata
from loguru import logger
FONT_PATH = "../AIlib2/conf/platech.ttf"
zhFont = ImageFont.truetype(FONT_PATH, 20, encoding="utf-8")
zhFont = ImageFont.truetype(FONT_PATH, 20, encoding="utf-8")
def get_label_array(color=None, label=None, font=None, fontSize=40, unify=False):
if unify:
@ -24,7 +24,6 @@ def get_label_array(color=None, label=None, font=None, fontSize=40, unify=False)
im_array = cv2.resize(im_array, (0, 0), fx=scale, fy=scale)
return im_array
def get_label_arrays(labelNames, colors, fontSize=40, fontPath="platech.ttf"):
font = ImageFont.truetype(fontPath, fontSize, encoding='utf-8')
label_arraylist = [get_label_array(colors[i % 20], label_name, font, fontSize) for i, label_name in
@ -50,6 +49,48 @@ def get_label_array_dict(colors, fontSize=40, fontPath="platech.ttf"):
zh_dict[code] = arr
return zh_dict
def get_label_left(x0,y1,label_array,img):
imh, imw = img.shape[0:2]
lh, lw = label_array.shape[0:2]
# x1 框框左上x位置 + 描述的宽
# y0 框框左上y位置 - 描述的高
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
return x0,y0,x1,y1
def get_label_right(x1,y0,label_array):
lh, lw = label_array.shape[0:2]
# x1 框框右上x位置 + 描述的宽
# y0 框框右上y位置 - 描述的高
x0, y1 = x1 - lw, y0 - lh
# 如果y0小于0, 说明超过上边框
if y0 < 0 or y1 < 0:
y1 = 0
# y1等于文字高度
y0 = y1 + lh
# 如果x0小于0
if x0 < 0 or x1 < 0:
x0 = 0
x1 = x0 + lw
return x0,y1,x1,y0
def xywh2xyxy(box):
if not isinstance(box[0], (list, tuple, np.ndarray)):
@ -75,42 +116,24 @@ def xy2xyxy(box):
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):
def draw_painting_joint(box, img, label_array, score=0.5, color=None, config=None, isNew=False, border=None):
# 识别问题描述图片的高、宽
lh, lw = label_array.shape[0:2]
# 图片的长度和宽度
imh, imw = img.shape[0:2]
if border is not None:
border = np.array(border,np.int32)
color,label_array=draw_name_border(box,color,label_array,border)
#img = draw_transparent_red_polygon(img,border,'',alpha=0.1)
lh, lw = label_array.shape[0:2]
tl = config[0]
if isinstance(box[-1], np.ndarray):
return draw_name_points(img,box,color)
label = ' %.2f' % score
box = xywh2xyxy(box)
# 框框左上的位置
x0, y1 = box[0][0], box[0][1]
# if score_location == 'leftTop':
# x0, y1 = box[0][0], box[0][1]
# # 框框左下的位置
# elif score_location == 'leftBottom':
# x0, y1 = box[3][0], box[3][1]
# else:
# x0, y1 = box[0][0], box[0][1]
# x1 框框左上x位置 + 描述的宽
# y0 框框左上y位置 - 描述的高
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
x0, y0, x1, y1 = get_label_left(x0, y1, label_array, img)
# 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
@ -120,14 +143,12 @@ def draw_painting_joint(box, img, label_array, score=0.5, color=None, config=Non
5. thickness（int）：画线的粗细
6. shift：顶点坐标中小数的位数
'''
tl = config[0]
img[y0:y1, x0:x1, :] = label_array
box1 = np.asarray(box, np.int32)
cv2.polylines(img, [box1], True, color, tl)
img[y0:y1, x0:x1, :] = label_array
pts_cls = [(x0, y0), (x1, y1)]
# 把英文字符score画到类别旁边
# tl = max(int(round(imw / 1920 * 3)), 1) or round(0.002 * (imh + imw) / 2) + 1
label = ' %.2f' % score
# tf = max(tl, 1)
# fontScale = float(format(imw / 1920 * 1.1, '.2f')) or tl * 0.33
# fontScale = tl * 0.33
@ -230,7 +251,6 @@ def draw_name_ocr(box, img, color, line_thickness=2, outfontsize=40):
# (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
# det1为 (m2, 12) 矩阵
@ -276,6 +296,7 @@ def plot_one_box_auto(box, img, color=None, line_thickness=2, label_array=None):
# 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]
points = None
box = xy2xyxy(box)
# 框框左上的位置
x0, y1 = box[0][0], box[0][1]
@ -316,7 +337,6 @@ def plot_one_box_auto(box, img, color=None, line_thickness=2, label_array=None):
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:
@ -367,4 +387,90 @@ def draw_name_crowd(dets, img, color, outfontsize=20):
img[y0:y1, x0:x1, :] = label_arr
return img, dets
return img, dets
def draw_name_points(img,box,color):
font = ImageFont.truetype(FONT_PATH, 6, encoding='utf-8')
points = box[-1]
arrea = cv2.contourArea(points)
label = '火焰'
arealabel = '面积:%s' % f"{arrea:.1e}"
label_array_area = get_label_array(color, arealabel, font, 10)
label_array = get_label_array(color, label, font, 10)
lh_area, lw_area = label_array_area.shape[0:2]
box = box[:4]
# 框框左上的位置
x0, y1 = box[0][0], max(box[0][1] - lh_area - 3, 0)
x1, y0 = box[1][0], box[1][1]
x0_label, y0_label, x1_label, y1_label = get_label_left(x0, y1, label_array, img)
x0_area, y0_area, x1_area, y1_area = get_label_right(x1, y0, label_array_area)
img[y0_label:y1_label, x0_label:x1_label, :] = label_array
img[y0_area:y1_area, x0_area:x1_area, :] = label_array_area
# cv2.drawContours(img, points, -1, color, tl)
cv2.polylines(img, [points], False, color, 2)
if lw_area < box[1][0] - box[0][0]:
box = [(x0, y1), (x1, y1), (x1, box[2][1]), (x0, box[2][1])]
else:
box = [(x0_label, y1), (x1, y1), (x1, box[2][1]), (x0_label, box[2][1])]
box = np.asarray(box, np.int32)
cv2.polylines(img, [box], True, color, 2)
return img, box
def draw_name_border(box,color,label_array,border):
box = xywh2xyxy(box[:4])
cx, cy = int((box[0][0] + box[2][0]) / 2), int((box[0][1] + box[2][1]) / 2)
flag = cv2.pointPolygonTest(border, (int(cx), int(cy)),
False) # 若为False，会找点是否在内，外，或轮廓上
if flag == 1:
color = [0, 0, 255]
# 纯白色是(255, 255, 255)，根据容差定义白色范围
lower_white = np.array([255 - 30] * 3, dtype=np.uint8)
upper_white = np.array([255, 255, 255], dtype=np.uint8)
# 创建白色区域的掩码（白色区域为True，非白色为False）
white_mask = cv2.inRange(label_array, lower_white, upper_white)
# 创建与原图相同大小的目标颜色图像
target_img = np.full_like(label_array, color, dtype=np.uint8)
# 先将非白色区域设为目标颜色，再将白色区域覆盖回原图颜色
label_array = np.where(white_mask[..., None], label_array, target_img)
return color,label_array
def draw_transparent_red_polygon(img, points, alpha=0.5):
"""
在图像中指定的多边形区域绘制半透明红色
参数:
image_path: 原始图像路径
points: 多边形顶点坐标列表，格式为[(x1,y1), (x2,y2), ..., (xn,yn)]
output_path: 输出图像路径
alpha: 透明度系数，0-1之间，值越小透明度越高
"""
# 读取原始图像
if img is None:
raise ValueError(f"无法读取图像")
# 创建与原图大小相同的透明图层（RGBA格式）
overlay = np.zeros((img.shape[0], img.shape[1], 4), dtype=np.uint8)
# 将点列表转换为适合cv2.fillPoly的格式
#pts = np.array(points, np.int32)
pts = points.reshape((-1, 1, 2))
# 在透明图层上绘制红色多边形（BGR为0,0,255）
# 最后一个通道是Alpha值，控制透明度，黄色rgb
cv2.fillPoly(overlay, [pts], (255, 0, 0, int(alpha * 255)))
# 将透明图层转换为BGR格式（用于与原图混合）
overlay_bgr = cv2.cvtColor(overlay, cv2.COLOR_RGBA2BGR)
# 创建掩码，用于提取红色区域
mask = overlay[:, :, 3] / 255.0
mask = np.stack([mask] * 3, axis=-1) # 转换为3通道
# 混合原图和透明红色区域
img = img * (1 - mask) + overlay_bgr * mask
img = img.astype(np.uint8)
# # 保存结果
# cv2.imwrite(output_path, result)
return img