def hotPotato(namelist, num):
simqueue = Queue()
for name in namelist:
simqueue.enqueue(name)
while simqueue.size() > 1:
for i in range(num):
simqueue.enqueue(simqueue.dequeue())
simqueue.dequeue()
return simqueue.dequeue()
class BlobFetcher(Process):
def __init__(self, basedir, only_base = False, transform = False, jitter = True, channel_split = False):
super(BlobFetcher, self).__init__(name='blob_fetcher')
self._iqueue = Queue()
self._oqueue = Queue()
self._basedir = basedir
self._only_base = only_base
self._transform = transform
self._jitter = jitter
self._channel_split = channel_split
def channel_split(self, im):
assert im.shape[2] == 3
chn = np.random.randint(3)
return im[:,:,chn:chn+1]
def jitter_image(self, im):
#assert im.shape[2] == 3
# multiply channels
for ch in range(im.shape[2]):
mult = np.random.uniform(0.9, 1.1)
im[:,:,ch] *= mult
# shift bias
shiftVal = np.random.uniform(-0.1, 0.1)
im += shiftVal
# add sptial jitter
noise = np.random.uniform(-0.05,0.05, (im.shape[0], im.shape[1]))
for ch in range(im.shape[2]):
im[:,:,ch] += noise
# bring values to [0,1] range
im[im < 0] = 0
im[im > 1] = 1
return im
def load_image(self, imname, shape):
loc = os.path.join(self._basedir, imname)
if self._only_base:
folder = os.path.basename(os.path.dirname(os.path.dirname(os.path.dirname(imname)))) # FIXME
if folder == '2011_09_26_drive_0009_sync': folder = "2011_09_26_drive_0051_sync" # FIXME
loc = os.path.join(self._basedir, folder, os.path.basename(imname))
imrd = Image.open(loc)
if shape[1] == 1 and not self._channel_split:
imrd = imrd.convert('L')
im = np.array(imrd).astype(np.float32) / 255.0
if len(im.shape) == 2:
im = im[:, :, None]
if im.shape[2] > 3:
im = im[:, :, :3] # skip optional 4th dimension
if self._channel_split:
im = self.channel_split(im)
#print(im.shape, shape)
if not self._transform:
if not (im.shape[0] == shape[2] and im.shape[1] == shape[3] and im.shape[2] == shape[1]):
raise Exception("Incorrect image shape {} expecting {}".format(im.shape, shape))
else:
# Resize if necessary
h = im.shape[0]
w = im.shape[1]
if shape[2] != h or shape[3] != w:
# apply anti aliasing
factors = (np.asarray(im.shape, dtype=float) /
np.asarray(shape[2:] + (shape[1],), dtype=float))
anti_aliasing_sigma = np.maximum(0, (factors - 1) / 2)
im = ndimage.gaussian_filter(im, anti_aliasing_sigma)
# resize to requested size
im = transform.resize(im, shape[2:], mode = 'reflect')
# Jitter and split image
im = im.astype(np.float32)
if self._jitter:
im = self.jitter_image(im)
im = np.transpose(im, axes = (2, 0, 1))
return im
def valid(self):
return self._iqueue.empty() and self._oqueue.empty()
def size(self):
return self._iqueue.size()
def get(self):
return self._oqueue.get()
def req(self, loc, shape):
self._iqueue.put((loc, shape))
def run(self):
# load dataset
try:
# fetching blobs
while True:
loc, shape = self._iqueue.get()
self._oqueue.put(self.load_image(loc, shape))
except KeyboardInterrupt:
self.terminate()
return
#coding=utf-8
from multiprocessing import Queue
q = Queue(3) #初始化一个Queue对象,最多可接受三条put消息
q.put("消息1")
q.put("消息2")
print(q.full()) #false
q.put("消息3")
print(q.full()) #true
# 因为消息队列已满下面的try都会抛出异常,第一个try会等待2秒后再抛出异常,第二个try会立刻抛出异常
try:
q.put("消息4", True, 2)
except:
print("消息队列已满,现有消息数量:%s" % q.qsize())
try:
q.put_nowait("消息4")
except:
print("消息队列已满,现有消息数量:%s" % q.qsize())
# 推荐的方式,先判断消息队列是否已满,再写入
if not q.full():
q.put_nowait("消息4")
# 读取消息时,先判断消息队列是否为空,再读取
if not q.empty():
for i in range(q.size()):
print(q.get_nowait())
