Python queue.qsize方法代碼示例

# 需要導入模塊: import queue [as 別名]
# 或者: from queue import qsize [as 別名]
def grab(cam, queue, width, height, fps):
    global running
    capture = cv2.VideoCapture(cam)
    capture.set(cv2.CAP_PROP_FRAME_WIDTH, width)
    capture.set(cv2.CAP_PROP_FRAME_HEIGHT, height)
    capture.set(cv2.CAP_PROP_FPS, fps)

    while(running):
        frame = {}
        capture.grab()
        retval, img = capture.retrieve(0)
        frame["img"] = img
        frame["1"] = config["1"]
        frame["2"] = config["2"]

        blur = get_blur(img, 0.05)
        frame["blur"] = blur

        if queue.qsize() < 10:
            queue.put(frame)
        else:
            print(queue.qsize()) 

# 需要導入模塊: import queue [as 別名]
# 或者: from queue import qsize [as 別名]
def producer(pool, queue, submap_ft, refmap_ft, fname, particles,
             sx, sy, s, a, apix, coefs_method, r, nr, fftthreads=1, crop=None, pfac=2):
    log = logging.getLogger('root')
    log.debug("Producing %s" % fname)
    zreader = mrc.ZSliceReader(particles[star.UCSF.IMAGE_ORIGINAL_PATH].iloc[0])
    for i, ptcl in particles.iterrows():
        log.debug("Produce %d@%s" % (ptcl[star.UCSF.IMAGE_ORIGINAL_INDEX], ptcl[star.UCSF.IMAGE_ORIGINAL_PATH]))
        # p1r = mrc.read_imgs(stack[i], idx[i] - 1, compat="relion")
        p1r = zreader.read(ptcl[star.UCSF.IMAGE_ORIGINAL_INDEX])
        log.debug("Apply")
        ri = pool.apply_async(
            subtract_outer,
            (p1r, ptcl, submap_ft, refmap_ft, sx, sy, s, a, apix, coefs_method, r, nr),
            {"fftthreads": fftthreads, "crop": crop, "pfac": pfac})
        log.debug("Put")
        queue.put((ptcl[star.UCSF.IMAGE_INDEX], ri), block=True)
        log.debug("Queue for %s is size %d" % (ptcl[star.UCSF.IMAGE_ORIGINAL_PATH], queue.qsize()))
    zreader.close()
    log.debug("Put poison pill")
    queue.put((-1, None), block=True) 

# 需要導入模塊: import queue [as 別名]
# 或者: from queue import qsize [as 別名]
def consumer(queue, stack, apix=1.0, iothreads=None):
    log = logging.getLogger('root')
    with mrc.ZSliceWriter(stack, psz=apix) as zwriter:
        while True:
            log.debug("Get")
            i, ri = queue.get(block=True)
            log.debug("Got %d, queue for %s is size %d" %
                      (i, stack, queue.qsize()))
            if i == -1:
                break
            new_image = ri.get()
            log.debug("Result for %d was shape (%d,%d)" %
                      (i, new_image.shape[0], new_image.shape[1]))
            zwriter.write(new_image)
            queue.task_done()
            log.debug("Wrote %d to %d@%s" % (i, zwriter.i, stack))
    if iothreads is not None:
        iothreads.release() 

# 需要導入模塊: import queue [as 別名]
# 或者: from queue import qsize [as 別名]
def myPublisher(queue):
  while not queue.full():
    queue.put(1)
    print("{} Appended 1 to queue: {}".format(threading.current_thread(), queue.qsize()))
    time.sleep(1) 

# 需要導入模塊: import queue [as 別名]
# 或者: from queue import qsize [as 別名]
def mySubscriber(queue):
  while True:
    item = queue.get()
    if item is None:
      break
    print("{} removed {} from the queue".format(threading.current_thread(), item))
    print("Queue Size is now: {}".format(queue.qsize()))
    queue.task_done() 

# 需要導入模塊: import queue [as 別名]
# 或者: from queue import qsize [as 別名]
def _log_process_queue_event(queue, event):
    """Log process queue event."""
    operation = event.get("operation", "unknown")
    provider = event.get("provider")
    name = provider.name if provider else "unknown"
    LOG.info(f"Adding operation {operation} for {name} to process queue (size: {queue.qsize()})") 

# 需要導入模塊: import queue [as 別名]
# 或者: from queue import qsize [as 別名]
def __del__(self):
        self.__threadrun=False
        #clear queue
        while self._queue.qsize() > 0:
            self._queue.get_nowait() 

# 需要導入模塊: import queue [as 別名]
# 或者: from queue import qsize [as 別名]
def remove_client(self, clientID):
        txThread=self._thread.pop(clientID)
        txThread.stop()
        queue=self._rxqueue.pop(clientID)
        while queue.qsize() > 0:
            queue.get_nowait()
        queue=self._txqueue.pop(clientID)
        while queue.qsize() > 0:
            queue.get_nowait()
        self._logger.info("Client-ID:{0}; removed; number of clients:{1}".format(clientID, self._clientcounter)) 

# 需要導入模塊: import queue [as 別名]
# 或者: from queue import qsize [as 別名]
def flush(self):
        """Forces a flush from the internal queue to the server"""
        queue = self.queue
        size = queue.qsize()
        queue.join()
        self.log.debug('successfully flushed {0} items.'.format(size)) 

# 需要導入模塊: import queue [as 別名]
# 或者: from queue import qsize [as 別名]
def enqueue_data(queue, capacity):  
    
    np.random.seed()
    gen = datagen()
    while True:
        try:
            data = gen.gen_srnet_data_with_background()
        except Exception as e:
            pass
        if queue.qsize() < capacity:
            queue.put(data) 

# 需要導入模塊: import queue [as 別名]
# 或者: from queue import qsize [as 別名]
def get_queue_size(self):
        
        return self.queue.qsize() 

# 需要導入模塊: import queue [as 別名]
# 或者: from queue import qsize [as 別名]
def dequeue_batch(self, batch_size, data_shape):
        
        while self.queue.qsize() < batch_size:
            pass

        i_t_batch, i_s_batch = [], []
        t_sk_batch, t_t_batch, t_b_batch, t_f_batch = [], [], [], []
        mask_t_batch = []
        
        for i in range(batch_size):
            i_t, i_s, t_sk, t_t, t_b, t_f, mask_t = self.dequeue_data()
            i_t_batch.append(i_t)
            i_s_batch.append(i_s)
            t_sk_batch.append(t_sk)
            t_t_batch.append(t_t)
            t_b_batch.append(t_b)
            t_f_batch.append(t_f)
            mask_t_batch.append(mask_t)
        
        w_sum = 0
        for t_b in t_b_batch:
            h, w = t_b.shape[:2]
            scale_ratio = data_shape[0] / h
            w_sum += int(w * scale_ratio)
        
        to_h = data_shape[0]
        to_w = w_sum // batch_size
        to_w = int(round(to_w / 8)) * 8
        to_size = (to_w, to_h) # w first for cv2
        for i in range(batch_size): 
            i_t_batch[i] = cv2.resize(i_t_batch[i], to_size)
            i_s_batch[i] = cv2.resize(i_s_batch[i], to_size)
            t_sk_batch[i] = cv2.resize(t_sk_batch[i], to_size, interpolation=cv2.INTER_NEAREST)
            t_t_batch[i] = cv2.resize(t_t_batch[i], to_size)
            t_b_batch[i] = cv2.resize(t_b_batch[i], to_size)
            t_f_batch[i] = cv2.resize(t_f_batch[i], to_size)
            mask_t_batch[i] = cv2.resize(mask_t_batch[i], to_size, interpolation=cv2.INTER_NEAREST)
            # eliminate the effect of resize on t_sk
            t_sk_batch[i] = skeletonization.skeletonization(mask_t_batch[i], 127)

        i_t_batch = np.stack(i_t_batch)
        i_s_batch = np.stack(i_s_batch)
        t_sk_batch = np.expand_dims(np.stack(t_sk_batch), axis = -1)
        t_t_batch = np.stack(t_t_batch)
        t_b_batch = np.stack(t_b_batch)
        t_f_batch = np.stack(t_f_batch)
        mask_t_batch = np.expand_dims(np.stack(mask_t_batch), axis = -1)
        
        i_t_batch = i_t_batch.astype(np.float32) / 127.5 - 1. 
        i_s_batch = i_s_batch.astype(np.float32) / 127.5 - 1. 
        t_sk_batch = t_sk_batch.astype(np.float32) / 255. 
        t_t_batch = t_t_batch.astype(np.float32) / 127.5 - 1. 
        t_b_batch = t_b_batch.astype(np.float32) / 127.5 - 1. 
        t_f_batch = t_f_batch.astype(np.float32) / 127.5 - 1.
        mask_t_batch = mask_t_batch.astype(np.float32) / 255.
        
        return [i_t_batch, i_s_batch, t_sk_batch, t_t_batch, t_b_batch, t_f_batch, mask_t_batch]