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- # -*- coding: utf-8 -*-
-
- from concurrent.futures import ThreadPoolExecutor
- from multiprocessing import Process
- from os import getpid
-
- from os.path import join
- from time import time, sleep
- from traceback import format_exc
-
- import cv2
- import numpy as np
- import psutil
-
- from loguru import logger
-
- from enums.ExceptionEnum import ExceptionType
- from enums.ModelTypeEnum import ModelType
- from exception.CustomerException import ServiceException
- from util import ImageUtils
- from util.Cv2Utils import video_conjuncing, write_or_video, write_ai_video, push_video_stream, close_all_p
- from util.ImageUtils import url2Array, add_water_pic
- from util.LogUtils import init_log
-
- from util.PlotsUtils import draw_painting_joint, filterBox, xywh2xyxy2, draw_name_joint
-
- from util.QueUtil import get_no_block_queue, put_queue, clear_queue
-
-
- class PushStreamProcess(Process):
- __slots__ = ("_msg", "_push_queue", "_image_queue", '_push_ex_queue', '_hb_queue', "_context")
-
- def __init__(self, *args):
- super().__init__()
- # 传参
- self._msg, self._push_queue, self._image_queue, self._push_ex_queue, self._hb_queue, self._context = args
-
- def build_logo_url(self):
- logo = None
- if self._context["video"]["video_add_water"]:
- logo = self._msg.get("logo_url")
- if logo:
- logo = url2Array(logo, enable_ex=False)
- if logo is None:
- logo = cv2.imread(join(self._context['base_dir'], "image/logo.png"), -1)
- self._context["logo"] = logo
-
- @staticmethod
- def handle_image(det_xywh, det, frame_score, copy_frame, draw_config, code_list):
- 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:
- font_config, allowedList = draw_config["font_config"], draw_config[code]["allowedList"]
- rainbows, label_arrays = draw_config[code]["rainbows"], draw_config[code]["label_arrays"]
- for qs in det_result:
- box, score, cls = xywh2xyxy2(qs)
- if cls not in allowedList or score < frame_score:
- continue
- label_array, color = label_arrays[cls], rainbows[cls]
- draw_painting_joint(box, copy_frame, label_array, score, color, font_config)
- if det_xywh.get(code) is None:
- det_xywh[code], code_list[code] = {}, {}
- cd = det_xywh[code].get(cls)
- if cd is None:
- code_list[code][cls] = 1
- det_xywh[code][cls] = [[cls, box, score, label_array, color]]
- else:
- code_list[code][cls] += 1
- det_xywh[code][cls].append([cls, box, score, label_array, color])
-
-
- class OnPushStreamProcess(PushStreamProcess):
- __slots__ = ()
-
- def run(self):
- self.build_logo_url()
- msg, context = self._msg, self._context
- base_dir, env, orFilePath, aiFilePath, logo, service_timeout, frame_score = context["base_dir"], \
- context['env'], context["orFilePath"], context["aiFilePath"], context["logo"], \
- int(context["service"]["timeout"]) + 120, context["service"]["filter"]["frame_score"]
- request_id, push_url = msg["request_id"], msg["push_url"]
- push_queue, image_queue, push_ex_queue, hb_queue = self._push_queue, self._image_queue, self._push_ex_queue, \
- self._hb_queue
- or_video_file, ai_video_file, push_p, ex = None, None, None, None
- ex_status = True
- # 图片相似度开关
- picture_similarity = bool(context["service"]["filter"]["picture_similarity"])
- qs_np_tmp = None
- pix_dis = 60
- try:
- init_log(base_dir, env)
- logger.info("开始实时启动推流进程!requestId:{}", request_id)
- with ThreadPoolExecutor(max_workers=2) as t:
- # 定义三种推流、写原视频流、写ai视频流策略
- # 第一个参数时间, 第二个参数重试次数
- p_push_status, or_write_status, ai_write_status = [0, 0], [0, 0], [0, 0]
- start_time = time()
- while True:
- # 检测推流执行超时时间, 1.防止任务运行超时 2.主进程挂了,子进程运行超时
- if time() - start_time > service_timeout:
- logger.error("推流超时, requestId: {}", request_id)
- raise ServiceException(ExceptionType.TASK_EXCUTE_TIMEOUT.value[0],
- ExceptionType.TASK_EXCUTE_TIMEOUT.value[1])
- # 系统由于各种问题可能会杀死内存使用多的进程, 自己杀掉自己
- if psutil.Process(getpid()).ppid() == 1:
- logger.info("推流进程检测到父进程异常停止, 自动停止推流进程, requestId: {}", request_id)
- ex_status = False
- for q in [push_queue, image_queue, push_ex_queue, hb_queue]:
- clear_queue(q)
- break
- # 获取推流的视频帧
- push_r = get_no_block_queue(push_queue)
- 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):
- pix_dis = int((frame.shape[0]//10)*1.2)
- # 复制帧用来画图
- copy_frame = frame.copy()
- det_xywh, thread_p = {}, []
- det_xywh2 = {}
- # 所有问题的矩阵集合
- qs_np = None
- qs_reurn = []
- 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:
- font_config, allowedList = draw_config["font_config"], draw_config[code]["allowedList"]
- rainbows, label_arrays = draw_config[code]["rainbows"], draw_config[code]["label_arrays"]
- for qs in det_result:
- 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:
- det_xywh[code] = {}
- cd = det_xywh[code].get(cls)
- if not (ModelType.CHANNEL2_MODEL.value[1] == str(code) and cls == 2):
- if cd is None:
- det_xywh[code][cls] = [[cls, box, score, label_array, color]]
- 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:
- det_xywh[code][cls].append([cls, box, score, label_array, color])
- 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 logo:
- frame = add_water_pic(frame, logo, request_id)
- copy_frame = add_water_pic(copy_frame, logo, request_id)
- if len(thread_p) > 0:
- for r in thread_p:
- r.result()
- frame_merge = video_conjuncing(frame, copy_frame)
- # 写原视频到本地
- write_or_video_result = t.submit(write_or_video, frame, orFilePath, or_video_file,
- or_write_status, request_id)
- # 写识别视频到本地
- write_ai_video_result = t.submit(write_ai_video, frame_merge, aiFilePath,
- ai_video_file, ai_write_status, request_id)
- push_stream_result = t.submit(push_video_stream, frame_merge, push_p, push_url,
- p_push_status, request_id)
- # 如果有问题, 走下面的逻辑
- if qs_np is not None:
- if len(qs_np.shape) == 1:
- qs_np = qs_np[np.newaxis,...]
- qs_np_id = qs_np.copy()
- b = np.ones(qs_np_id.shape[0])
- qs_np_id = np.column_stack((qs_np_id,b))
- if qs_np_tmp is None:
- 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))
- else:
- qs_reurn = filterBox(qs_np, qs_np_tmp, pix_dis)
- if picture_similarity:
- qs_np_tmp = np.append(qs_np_tmp,qs_np_id,axis=0)
- qs_np_tmp[:, 11] += 1
- qs_np_tmp = np.delete(qs_np_tmp, np.where((qs_np_tmp[:, 11] >= 75))[0], axis=0)
- has = False
- new_lab = []
- for j in qs_reurn:
- if j[11] == 1:
- has = True
- new_lab.append(j[9])
- if has:
- for q in qs_reurn:
- if q[11] >= 1:
- cls = int(q[9])
- if not (cls in new_lab):
- continue # 为了防止其他类别被带出
- code = str(int(q[10])).zfill(3)
- if det_xywh2.get(code) is None:
- det_xywh2[code] = {}
- cd = det_xywh2[code].get(cls)
- 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]))]
- is_new = False
- if q[11] == 1:
- is_new = True
- if cd is None:
- 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])
- 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)
- ai_video_file = write_ai_video_result.result(timeout=60)
- or_video_file = write_or_video_result.result(timeout=60)
- # 接收停止指令
- if push_r[0] == 2:
- if 'stop' == push_r[1]:
- logger.info("停止推流进程, requestId: {}", request_id)
- break
- if 'stop_ex' == push_r[1]:
- ex_status = False
- logger.info("停止推流进程, requestId: {}", request_id)
- break
- del push_r
- else:
- sleep(1)
- except ServiceException as s:
- logger.error("推流进程异常:{}, requestId:{}", s.msg, request_id)
- ex = s.code, s.msg
- except Exception:
- logger.error("推流进程异常:{}, requestId:{}", format_exc(), request_id)
- ex = ExceptionType.SERVICE_INNER_EXCEPTION.value[0], ExceptionType.SERVICE_INNER_EXCEPTION.value[1]
- finally:
- # 关闭推流管, 原视频写对象, 分析视频写对象
- close_all_p(push_p, or_video_file, ai_video_file, request_id)
- if ex:
- code, msg = ex
- put_queue(push_ex_queue, (1, code, msg), timeout=2)
- else:
- if ex_status:
- # 关闭推流的时候, 等待1分钟图片队列处理完,如果1分钟内没有处理完, 清空图片队列, 丢弃没有上传的图片
- c_time = time()
- while time() - c_time < 60:
- if image_queue.qsize() == 0 or image_queue.empty():
- break
- sleep(2)
- for q in [push_queue, image_queue, hb_queue]:
- clear_queue(q)
- logger.info("推流进程停止完成!图片队列大小: {}, requestId:{}", image_queue.qsize(), request_id)
-
-
- class OffPushStreamProcess(PushStreamProcess):
- __slots__ = ()
-
- def run(self):
- self.build_logo_url()
- msg, context = self._msg, self._context
- request_id = msg["request_id"]
- base_dir, env = context["base_dir"], context['env']
- push_queue, image_queue, push_ex_queue, hb_queue = self._push_queue, self._image_queue, self._push_ex_queue, \
- self._hb_queue
- aiFilePath, logo = context["aiFilePath"], context["logo"]
- ai_video_file, push_p, push_url = None, None, msg["push_url"]
- service_timeout = int(context["service"]["timeout"]) + 120
- frame_score = context["service"]["filter"]["frame_score"]
- ex = None
- ex_status = True
- # 图片相似度开关
- picture_similarity = bool(context["service"]["filter"]["picture_similarity"])
- qs_np_tmp = None
- pix_dis = 60
- try:
- init_log(base_dir, env)
- logger.info("开始启动离线推流进程!requestId:{}", request_id)
- with ThreadPoolExecutor(max_workers=2) as t:
- # 定义三种推流、写原视频流、写ai视频流策略
- # 第一个参数时间, 第二个参数重试次数
- p_push_status, ai_write_status = [0, 0], [0, 0]
- start_time = time()
- while True:
- # 检测推流执行超时时间
- if time() - start_time > service_timeout:
- logger.error("离线推流超时, requestId: {}", request_id)
- raise ServiceException(ExceptionType.TASK_EXCUTE_TIMEOUT.value[0],
- ExceptionType.TASK_EXCUTE_TIMEOUT.value[1])
- # 系统由于各种问题可能会杀死内存使用多的进程, 自己杀掉自己
- if psutil.Process(getpid()).ppid() == 1:
- logger.info("离线推流进程检测到父进程异常停止, 自动停止推流进程, requestId: {}", request_id)
- ex_status = False
- for q in [push_queue, image_queue, push_ex_queue, hb_queue]:
- clear_queue(q)
- break
- # 获取推流的视频帧
- push_r = get_no_block_queue(push_queue)
- if push_r is not None:
- # [(1, ...] 视频帧操作
- # [(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):
- 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()
- # 所有问题记录字典
- det_xywh, thread_p = {}, []
- det_xywh2 = {}
- # 所有问题的矩阵集合
- qs_np = None
- qs_reurn = []
- 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:
- font_config, allowedList = draw_config["font_config"], draw_config[code]["allowedList"]
- rainbows, label_arrays = draw_config[code]["rainbows"], draw_config[code]["label_arrays"]
- for qs in det_result:
- box, score, cls = xywh2xyxy2(qs)
- 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:
- det_xywh[code] = {}
- cd = det_xywh[code].get(cls)
- if not (ModelType.CHANNEL2_MODEL.value[1] == str(code) and cls == 2):
- if cd is None:
- det_xywh[code][cls] = [[cls, box, score, label_array, color]]
- 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:
- det_xywh[code][cls].append([cls, box, score, label_array, color])
- 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 logo:
- frame = add_water_pic(frame, logo, request_id)
- copy_frame = add_water_pic(copy_frame, logo, request_id)
- if len(thread_p) > 0:
- for r in thread_p:
- r.result()
- frame_merge = video_conjuncing(frame, copy_frame)
- # 写识别视频到本地
- write_ai_video_result = t.submit(write_ai_video, frame_merge, aiFilePath,
- ai_video_file,
- ai_write_status, request_id)
- push_stream_result = t.submit(push_video_stream, frame_merge, push_p, push_url,
- p_push_status, request_id)
-
- if qs_np is not None:
- if len(qs_np.shape) == 1:
- qs_np = qs_np[np.newaxis,...]
- qs_np_id = qs_np.copy()
- b = np.ones(qs_np_id.shape[0])
- qs_np_id = np.column_stack((qs_np_id,b))
- if qs_np_tmp is None:
- 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))
- else:
- qs_reurn = filterBox(qs_np, qs_np_tmp, pix_dis)
- if picture_similarity:
- qs_np_tmp = np.append(qs_np_tmp,qs_np_id,axis=0)
- qs_np_tmp[:, 11] += 1
- qs_np_tmp = np.delete(qs_np_tmp, np.where((qs_np_tmp[:, 11] >= 75))[0], axis=0)
- has = False
- new_lab = []
- for j in qs_reurn:
- if j[11] == 1:
- has = True
- new_lab.append(j[9])
- if has:
- for q in qs_reurn:
- if q[11] >= 1:
- cls = int(q[9])
- if not (cls in new_lab):
- continue # 为了防止其他类别被带出
- code = str(int(q[10])).zfill(3)
- if det_xywh2.get(code) is None:
- det_xywh2[code] = {}
- cd = det_xywh2[code].get(cls)
- 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]))]
- is_new = False
- if q[11] == 1:
- is_new = True
- if cd is None:
- 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])
- 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)
- ai_video_file = write_ai_video_result.result(timeout=60)
- # 接收停止指令
- if push_r[0] == 2:
- if 'stop' == push_r[1]:
- logger.info("停止推流进程, requestId: {}", request_id)
- break
- if 'stop_ex' == push_r[1]:
- logger.info("停止推流进程, requestId: {}", request_id)
- ex_status = False
- break
- del push_r
- else:
- sleep(1)
- except ServiceException as s:
- logger.error("推流进程异常:{}, requestId:{}", s.msg, request_id)
- ex = s.code, s.msg
- except Exception:
- logger.error("推流进程异常:{}, requestId:{}", format_exc(), request_id)
- ex = ExceptionType.SERVICE_INNER_EXCEPTION.value[0], ExceptionType.SERVICE_INNER_EXCEPTION.value[1]
- finally:
- # 关闭推流管, 分析视频写对象
- close_all_p(push_p, None, ai_video_file, request_id)
- if ex:
- code, msg = ex
- put_queue(push_ex_queue, (1, code, msg), timeout=2)
- else:
- if ex_status:
- # 关闭推流的时候, 等待1分钟图片队列处理完,如果1分钟内没有处理完, 清空图片队列, 丢弃没有上传的图片
- c_time = time()
- while time() - c_time < 60:
- if image_queue.qsize() == 0 or image_queue.empty():
- break
- sleep(2)
- for q in [push_queue, image_queue, hb_queue]:
- clear_queue(q)
- logger.info("推流进程停止完成!requestId:{}", request_id)
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