用 Python 开发了一个多处理代码。图像批处理流在一个进程中完成,批处理在另一个进程中完成。
一旦批处理流具有预定义数量的图像,就使用 MultiProcess.Event() 向批处理循环发送信号。所以这两个过程需要在正确的时间。
批处理时间比批处理时间长,因此处理部分没有图像丢失。
大多数情况下,批处理时间比批处理流时间短。所以我的批处理端看起来工作正常。但有时,发现批处理流重复两次,然后进行批处理,例如,
batch streaming 2.35
batch processing 2.05
batch streaming 2.25
batch processing 2.05
batch streaming 2.32 repeated
batch streaming 2.36
batch processing 3.25
batch streaming 2.35
batch processing 2.15
batch streaming 2.35
batch processing 2.25
这意味着我在处理部分有图像丢失。我该如何解决问题?
我不能发布整个代码。所以两个过程如下。
批处理流循环
while (not stopbit.is_set()):
if not cam_queue.empty():
#print('Got frame')
cmd, val = cam_queue.get()
# calculate FPS
'''diffTime = time.time() - lastFTime
fps = 1 / diffTime
print(fps)
lastFTime = time.time()'''
# if cmd == vs.StreamCommands.RESOLUTION:
# pass #print(val)
if cmd == vs.StreamCommands.FRAME:
if val is not None:
missCount=0
image = np.array(val, dtype=np.float32, order='C')
image=image.transpose([2, 0, 1])
imgrshp=image.reshape(921600)
#print(str(val.shape))
if (batch1_is_processed == False):
batch1_[count] = imgrshp#it is 921,600 flat array
batch3_[count] = val
else:
batch2_[count] = imgrshp
batch4_[count] = val
count = count + 1
if (count >= BATCHSIZE): # to start process for inference and post processing
diffTime = time.time() - lastFTime
print("batching time " + str(diffTime))
if (batch1_is_processed == False): # process batch1
q.put('batch1')
batch1_is_processed = True
#print('batch1 is set')
else: # process batch2
q.put('batch2')
batch1_is_processed = False
#print('batch2 is set')
e.set()#to signal the buffer is full
count = 0
lastFTime = time.time()
else:
missCount = missCount + 1
print("miss frame after " + str(time.time() - startTime))
if(missCount >= 10):
q.put('lostframes')
e.set()#so that immediately will go back to caller to stop with lostframes option
批量处理
while(self.stopbit is not None):
self.e.wait()
batch = self.queue.get()
lastFTime = time.time()
if(batch == 'batch1'):#process batch1
#print('batch1 is processed')
for idx in range(BATCHSIZE):
images[idx] = np.frombuffer(self.sharedbatch1[idx], dtype=np.float32)
uimg = np.frombuffer(self.sharedbatch3[idx], dtype=np.uint8)
uimgs[idx] = uimg.reshape(HEIGHT,WIDTH,CHANNEL)
elif(batch == 'batch2'):#process batch1
#print('batch2 is processed')
for idx in range(BATCHSIZE):
images[idx]=np.frombuffer(self.sharedbatch2[idx], dtype=np.float32)
uimg = np.frombuffer(self.sharedbatch4[idx], dtype=np.uint8)
uimgs[idx] = uimg.reshape(HEIGHT,WIDTH,CHANNEL)
elif(batch == 'lostframes'):
self.e.clear()
self.stopbit.set()#to stop streaming
break
#do batch processing in Nvidia's TensorRT
with engine.create_execution_context() as context:
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
inputs[0].host = np.ascontiguousarray(images, dtype=np.float32)
[outputs] = common.do_inference(context, bindings, inputs, outputs, stream, BATCHSIZE)
outputs=outputs.reshape((BATCHSIZE, 60, 80, 57))
humans=[]
for i in range(BATCHSIZE):
heat_map=outputs[i, :, :, :19]
puf_map=outputs[i, :, :, 19:]
humans.append(self.est.inference(heat_map, puf_map, 4.0))
#uimgs[i]=TfPoseEstimatorTRT.draw_humans(uimgs[i], humans[i], imgcopy=False)
#cv2.imwrite("images/image_"+str(cnt)+".jpeg", uimgs[i])
#cnt=cnt+1
hdp.ProcessHumanData(humans, uimgs)
#for i in range(BATCHSIZE):
# cv2.imwrite("images/image_"+str(cnt)+".jpeg", uimgs[i])
# cnt=cnt+1
#cv2.imshow('display',uimgs[i])
#cv2.waitKey(1)
humans.clear()
diffTime = time.time() - lastFTime
print("batch processing time "+str(diffTime))
self.e.clear()
使用来自 Multiprocessing 的 Lock 解决了该问题。有时,使用打印进行调试会产生误导。打印本身需要一些毫秒。在调试并行处理代码时需要注意这一点。
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