【发布时间】：2021-08-02 15:30:53
【问题描述】：
我正在从事一个使用 OpenVino 和 Python3 从图像中提取信息的项目。由于项目的扩展，决定我们的人脸检测模块在多处理模块的支持下独立工作。
不幸的是，我无法在这个框架内进行人脸检测，因为来自 IeCore 的 infer() 方法似乎会阻塞（没有主要的实现问题，因为我可以使提到的方法在使用踩模块而不是进程时正常工作）。
因此，欢迎任何有关如何解决此问题的帮助。
下面是我尝试使用多处理模块运行的代码的主要元素。
import os
import cv2
import time
import numpy as np
from pathlib import Path
from multiprocessing import Process
from modules.face.abstract.abcopenvino import ABCOpenVino
from modules.utils.blob2frame import blob2frame
from openvino.inference_engine import IECore, Blob, TensorDesc
YAXIS = 0
XAXIS = 1
class FaceDetection(ABCOpenVino):
def __init__(self, frame_queue, face_queue):
super().__init__()
if not hasattr(self, 'confidence'):
raise ValueError('Class attribute confidence must be defined')
self._smoothed = np.zeros((4, )).astype('float32')
self.frame_queue = frame_queue
self.face_queue = face_queue
ie_core_handle = IECore()
self._network = ie_core_handle.read_network(self.file_xml, self.file_bin)
key = next(iter(self._network.input_info))
self.tensor_description = self._network.input_info[key].tensor_desc
self._executable_network = ie_core_handle.load_network(self._network, device_name='CPU', num_requests=1)
self.inference_request = self._executable_network.requests[0]
self.input_blob_name = next(iter(self.inference_request.input_blobs))
self.output_blob_name = next(iter(self.inference_request.output_blobs))
self.p = Process(target=self._process_exec, args=())
self.p.name = '{}_{}'.format(__name__, 0)
self.p.daemon = True
self.p.start()
time.sleep(0.01)
# With treading it works
#import threading
#t = threading.Thread(target=self._process_exec, args=())
#t.name = '{}_{}'.format(__name__, 0)
#t.daemon = True
#t.start()
#time.sleep(0.01)
def _process_exec(self):
self.myprint("Running! pid: {}".format(os.getpid()))
while True:
if self._stop:
self.myprint('Stopping! pid: {}'.format(os.getpid()))
time.sleep(0.05)
break
elem = self.frame_queue.get()
if elem:
frame, = elem
face_bbox_lst = self._network_infer(frame)
if face_bbox_lst:
elem = (frame, face_bbox_lst, )
self.face_queue.put(elem)
self.myprint('Done')
return
def _network_infer(self, frame):
N, C, H, W = self.tensor_description.dims
hwc = cv2.resize(frame, (H, W,)).astype('float32')
chw = hwc.transpose((2, 0, 1))
input_blob = Blob(self.tensor_description, chw)
self.inference_request.set_blob(blob_name=self.input_blob_name, blob=input_blob)
self.inference_request.infer()
output = self.inference_request.output_blobs[self.output_blob_name].buffer
return output
def terminate(self):
self._stop = True
【问题讨论】：
标签： python-3.x multiprocessing openvino