Python numpy 模块，fromstring() 实例源码
我们从Python开源项目中，提取了以下50个代码示例，用于说明如何使用numpy.fromstring()。
def __init__(self, image, samplefac=10, colors=256):
# Check Numpy
if np is None:
raise RuntimeError("Need Numpy for the NeuQuant algorithm.")
# Check image
if image.size[0] * image.size[1] < NeuQuant.MAXPRIME:
raise IOError("Image is too small")
if image.mode != "RGBA":
raise IOError("Image mode should be RGBA.")
# Initialize
self.setconstants(samplefac, colors)
self.pixels = np.fromstring(image.tostring(), np.uint32)
self.setUpArrays()
self.learn()
self.fix()
self.inxbuild()
def get_example(self, i):
id = self.all_keys[i]
img = None
val = self.db.get(id.encode())
img = cv2.imdecode(np.fromstring(val, dtype=np.uint8), 1)
img = self.do_augmentation(img)
img_color = img
img_color = self.preprocess_image(img_color)
img_line = XDoG(img)
img_line = cv2.cvtColor(img_line, cv2.COLOR_GRAY2RGB)
#if img_line.ndim == 2:
# img_line = img_line[:, :, np.newaxis]
img_line = self.preprocess_image(img_line)
return img_line, img_color
def _readData1(self, fd, meta, mmap=False, **kwds):
## Read array data from the file descriptor for MetaArray v1 files
## read in axis values for any axis that specifies a length
frameSize = 1
for ax in meta['info']:
if 'values_len' in ax:
ax['values'] = np.fromstring(fd.read(ax['values_len']), dtype=ax['values_type'])
frameSize *= ax['values_len']
del ax['values_len']
del ax['values_type']
self._info = meta['info']
if not kwds.get("readAllData", True):
return
## the remaining data is the actual array
if mmap:
subarr = np.memmap(fd, dtype=meta['type'], mode='r', shape=meta['shape'])
else:
subarr = np.fromstring(fd.read(), dtype=meta['type'])
subarr.shape = meta['shape']
self._data = subarr
def _readData1(self, fd, meta, mmap=False, **kwds):
## Read array data from the file descriptor for MetaArray v1 files
## read in axis values for any axis that specifies a length
frameSize = 1
for ax in meta['info']:
if 'values_len' in ax:
ax['values'] = np.fromstring(fd.read(ax['values_len']), dtype=ax['values_type'])
frameSize *= ax['values_len']
del ax['values_len']
del ax['values_type']
self._info = meta['info']
if not kwds.get("readAllData", True):
return
## the remaining data is the actual array
if mmap:
subarr = np.memmap(fd, dtype=meta['type'], mode='r', shape=meta['shape'])
else:
subarr = np.fromstring(fd.read(), dtype=meta['type'])
subarr.shape = meta['shape']
self._data = subarr
def decode_data(obj):
"""Decode a serialised data object.
Parameter
---------
obj : Python dictionary
A dictionary describing a serialised data object.
"""
try:
if TYPES['str'] == obj[b'type']:
return decode_str(obj[b'data'])
elif TYPES['ndarray'] == obj[b'type']:
return np.fromstring(obj[b'data'], dtype=np.dtype(
obj[b'dtype'])).reshape(obj[b'shape'])
else:
# Assume the user know what they are doing
return obj
except KeyError:
# Assume the user know what they are doing
return obj
def __init__(self, feat_stride, scales, ratios, is_train=False, output_score=False):
super(ProposalOperator, self).__init__()
self._feat_stride = float(feat_stride)
self._scales = np.fromstring(scales[1:-1], dtype=float, sep=',')
self._ratios = np.fromstring(ratios[1:-1], dtype=float, sep=',').tolist()
self._anchors = generate_anchors(base_size=self._feat_stride, scales=self._scales, ratios=self._ratios)
self._num_anchors = self._anchors.shape[0]
self._output_score = output_score
if DEBUG:
print 'feat_stride: {}'.format(self._feat_stride)
print 'anchors:'
print self._anchors
if is_train:
self.cfg_key = 'TRAIN'
else:
self.cfg_key = 'TEST'
def read_uncompressed_patch(pcpatch_wkb, schema):
'''
Patch binary structure uncompressed:
byte: endianness (1 = NDR, 0 = XDR)
uint32: pcid (key to POINTCLOUD_SCHEMAS)
uint32: 0 = no compression
uint32: npoints
pointdata[]: interpret relative to pcid
'''
patchbin = unhexlify(pcpatch_wkb)
npoints = unpack("I", patchbin[9:13])[0]
dt = schema_dtype(schema)
patch = np.fromstring(patchbin[13:], dtype=dt)
# debug
# print(patch[:10])
return patch, npoints
def decompress(points, schema):
"""
Decode patch encoded with lazperf.
'points' is a pcpatch in wkb
"""
# retrieve number of points in wkb pgpointcloud patch
npoints = patch_numpoints(points)
hexbuffer = unhexlify(points[34:])
hexbuffer += hexa_signed_int32(npoints)
# uncompress
s = json.dumps(schema).replace("\\", "")
dtype = buildNumpyDescription(json.loads(s))
lazdata = bytes(hexbuffer)
arr = np.fromstring(lazdata, dtype=np.uint8)
d = Decompressor(arr, s)
output = np.zeros(npoints * dtype.itemsize, dtype=np.uint8)
decompressed = d.decompress(output)
return decompressed
def __init__(self, image, samplefac=10, colors=256):
# Check Numpy
if np is None:
raise RuntimeError("Need Numpy for the NeuQuant algorithm.")
# Check image
if image.size[0] * image.size[1] < NeuQuant.MAXPRIME:
raise IOError("Image is too small")
if image.mode != "RGBA":
raise IOError("Image mode should be RGBA.")
# Initialize
self.setconstants(samplefac, colors)
self.pixels = np.fromstring(image.tostring(), np.uint32)
self.setUpArrays()
self.learn()
self.fix()
self.inxbuild()
def get_original_image(tfrecords_dir, is_training_data=False):
record = tf.python_io.tf_record_iterator(tfrecords_dir).next()
example = tf.train.Example()
example.ParseFromString(record)
shape = np.fromstring(example.features.feature['shape'].bytes_list.value[0], dtype=np.int32)
image = np.fromstring(example.features.feature['img_raw'].bytes_list.value[0], dtype=np.float32)
image = image.reshape(shape)
if is_training_data:
ground_truth = np.fromstring(example.features.feature['gt_raw'].bytes_list.value[0], dtype=np.uint8)
ground_truth = ground_truth.reshape(shape[:-1])
else:
ground_truth = None
return image, ground_truth
def load_bin_vec(self, fname, vocab):
"""
Loads 300x1 word vecs from Google (Mikolov) word2vec
"""
word_vecs = {}
with open(fname, "rb") as f:
header = f.readline()
vocab_size, layer1_size = map(int, header.split())
binary_len = np.dtype('float32').itemsize * layer1_size
for line in xrange(vocab_size):
word = []
while True:
ch = f.read(1)
if ch == ' ':
word = ''.join(word)
break
if ch != '\n':
word.append(ch)
if word in vocab:
word_vecs[word] = np.fromstring(f.read(binary_len), dtype='float32')
else:
f.read(binary_len)
logger.info("num words already in word2vec: " + str(len(word_vecs)))
return word_vecs
def vec2bin(input_path, output_path):
input_fd = open(input_path, "rb")
output_fd = open(output_path, "wb")
header = input_fd.readline()
output_fd.write(header)
vocab_size, vector_size = map(int, header.split())
for line in tqdm(range(vocab_size)):
word = []
while True:
ch = input_fd.read(1)
output_fd.write(ch)
if ch == b' ':
word = b''.join(word).decode('utf-8')
break
if ch != b'\n':
word.append(ch)
vector = np.fromstring(input_fd.readline(), sep=' ', dtype='float32')
output_fd.write(vector.tostring())
input_fd.close()
output_fd.close()
def get_glove_k(self, K):
assert hasattr(self, 'glove_path'), 'warning : \
you need to set_glove_path(glove_path)'
# create word_vec with k first glove vectors
k = 0
word_vec = {}
with io.open(self.glove_path) as f:
for line in f:
word, vec = line.split(' ', 1)
if k <= K:
word_vec[word] = np.fromstring(vec, sep=' ')
k += 1
if k > K:
if word in ['<s>', '</s>']:
word_vec[word] = np.fromstring(vec, sep=' ')
if k>K and all([w in word_vec for w in ['<s>', '</s>']]):
break
return word_vec
def fig2array(fig):
"""Convert a Matplotlib figure to a 4D numpy array
Params
------
fig:
A matplotlib figure
Return
------
A numpy 3D array of RGBA values
Modified version of: http://www.icare.univ-lille1.fr/node/1141
"""
# draw the renderer
fig.canvas.draw()
# Get the RGBA buffer from the figure
w, h = fig.canvas.get_width_height()
buf = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8)
buf.shape = (h, w, 3)
return buf
def _wav2array(nchannels, sampwidth, data):
"""data must be the string containing the bytes from the wav file."""
num_samples, remainder = divmod(len(data), sampwidth * nchannels)
if remainder > 0:
raise ValueError('The length of data is not a multiple of '
'sampwidth * num_channels.')
if sampwidth > 4:
raise ValueError("sampwidth must not be greater than 4.")
if sampwidth == 3:
a = np.empty((num_samples, nchannels, 4), dtype = np.uint8)
raw_bytes = np.fromstring(data, dtype = np.uint8)
a[:, :, :sampwidth] = raw_bytes.reshape(-1, nchannels, sampwidth)
a[:, :, sampwidth:] = (a[:, :, sampwidth - 1:sampwidth] >> 7) * 255
result = a.view('<i4').reshape(a.shape[:-1])
else:
# 8 bit samples are stored as unsigned ints; others as signed ints.
dt_char = 'u' if sampwidth == 1 else 'i'
a = np.fromstring(data, dtype='<%s%d' % (dt_char, sampwidth))
result = a.reshape(-1, nchannels)
return result
def read_array(self, dtype, count=-1, sep=""):
"""Return numpy array from file.
Work around numpy issue #2230, "numpy.fromfile does not accept
StringIO object" https://github.com/numpy/numpy/issues/2230.
"""
try:
return numpy.fromfile(self._fh, dtype, count, sep)
except IOError:
if count < 0:
size = self._size
else:
size = count * numpy.dtype(dtype).itemsize
data = self._fh.read(size)
return numpy.fromstring(data, dtype, count, sep)
def load_bin_vec(fname, vocab):
"""
Loads 300x1 word vecs from Google (Mikolov) word2vec
"""
word_vecs = {}
with open(fname, "rb") as f:
header = f.readline()
vocab_size, layer1_size = map(int, header.split())
binary_len = np.dtype('float32').itemsize * layer1_size
for line in xrange(vocab_size):
word = []
while True:
ch = f.read(1)
if ch == ' ':
word = ''.join(word)
break
if ch != '\n':
word.append(ch)
if word in vocab:
word_vecs[word] = np.fromstring(f.read(binary_len), dtype='float32')
else:
f.read(binary_len)
return word_vecs
def load_bin_vec(fname, vocab):
"""
Loads 300x1 word vecs from Google (Mikolov) word2vec
"""
word_vecs = {}
with open(fname, "rb") as f:
header = f.readline()
vocab_size, layer1_size = map(int, header.split())
binary_len = np.dtype('float32').itemsize * layer1_size
for line in xrange(vocab_size):
word = []
while True:
ch = f.read(1)
if ch == ' ':
word = ''.join(word)
break
if ch != '\n':
word.append(ch)
if word in vocab:
word_vecs[word] = np.fromstring(f.read(binary_len), dtype='float32')
else:
f.read(binary_len)
return word_vecs
def load_bin_vec(fname, vocab):
"""
Loads 300x1 word vecs from Google (Mikolov) word2vec
"""
word_vecs = {}
with open(fname, "rb") as f:
header = f.readline()
vocab_size, layer1_size = map(int, header.split())
binary_len = np.dtype('float32').itemsize * layer1_size
for line in xrange(vocab_size):
word = []
while True:
ch = f.read(1)
if ch == ' ':
word = ''.join(word)
break
if ch != '\n':
word.append(ch)
if word in vocab:
word_vecs[word] = np.fromstring(f.read(binary_len), dtype='float32')
else:
f.read(binary_len)
return word_vecs
def load_wav_file(name):
f = wave.open(name, "rb")
# print("loading %s"%name)
chunk = []
data0 = f.readframes(CHUNK)
while data0: # f.getnframes()
# data=numpy.fromstring(data0, dtype='float32')
# data = numpy.fromstring(data0, dtype='uint16')
data = numpy.fromstring(data0, dtype='uint8')
data = (data + 128) / 255. # 0-1 for Better convergence
# chunks.append(data)
chunk.extend(data)
data0 = f.readframes(CHUNK)
# finally trim:
chunk = chunk[0:CHUNK * 2] # should be enough for now -> cut
chunk.extend(numpy.zeros(CHUNK * 2 - len(chunk))) # fill with padding 0's
# print("%s loaded"%name)
return chunk
def pfmFromBuffer(buffer, reverse = 1):
sStream = cStringIO.StringIO(buffer)
color = None
width = None
height = None
scale = None
endian = None
header = sStream.readline().rstrip()
color = (header == 'PF')
width, height = map(int, sStream.readline().strip().split(' '))
scale = float(sStream.readline().rstrip())
endian = '<' if(scale < 0) else '>'
scale = abs(scale)
rawdata = np.fromstring(sStream.read(), endian + 'f')
shape = (height, width, 3) if color else (height, width)
sStream.close()
if(len(shape) == 3):
return rawdata.reshape(shape).astype(np.float32)[:,:,::-1]
else:
return rawdata.reshape(shape).astype(np.float32)
def sample(self, filename, save_samples):
gan = self.gan
generator = gan.generator.sample
sess = gan.session
config = gan.config
x_v, z_v = sess.run([gan.inputs.x, gan.encoder.z])
sample = sess.run(generator, {gan.inputs.x: x_v, gan.encoder.z: z_v})
plt.clf()
fig = plt.figure(figsize=(3,3))
plt.scatter(*zip(*x_v), c='b')
plt.scatter(*zip(*sample), c='r')
plt.xlim([-2, 2])
plt.ylim([-2, 2])
plt.ylabel("z")
fig.canvas.draw()
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
#plt.savefig(filename)
self.plot(data, filename, save_samples)
return [{'image': filename, 'label': '2d'}]
def _wav2array(nchannels, sampwidth, data):
"""data must be the string containing the bytes from the wav file."""
num_samples, remainder = divmod(len(data), sampwidth * nchannels)
if remainder > 0:
raise ValueError('The length of data is not a multiple of '
'sampwidth * num_channels.')
if sampwidth > 4:
raise ValueError("sampwidth must not be greater than 4.")
if sampwidth == 3:
a = np.empty((num_samples, nchannels, 4), dtype = np.uint8)
raw_bytes = np.fromstring(data, dtype = np.uint8)
a[:, :, :sampwidth] = raw_bytes.reshape(-1, nchannels, sampwidth)
a[:, :, sampwidth:] = (a[:, :, sampwidth - 1:sampwidth] >> 7) * 255
result = a.view('<i4').reshape(a.shape[:-1])
else:
# 8 bit samples are stored as unsigned ints; others as signed ints.
dt_char = 'u' if sampwidth == 1 else 'i'
a = np.fromstring(data, dtype='<%s%d' % (dt_char, sampwidth))
result = a.reshape(-1, nchannels)
return result
def load_poses(self):
"""Load ground truth poses from file."""
print('Loading poses for sequence ' + self.sequence + '...')
pose_file = os.path.join(self.pose_path, self.sequence + '.txt')
# Read and parse the poses
try:
self.T_w_cam0 = []
with open(pose_file, 'r') as f:
for line in f.readlines():
T = np.fromstring(line, dtype=float, sep=' ')
T = T.reshape(3, 4)
T = np.vstack((T, [0, 0, 0, 1]))
self.T_w_cam0.append(T)
print('done.')
except FileNotFoundError:
print('Ground truth poses are not avaialble for sequence ' +
self.sequence + '.')
def loadData(src, cimg):
gzfname, h = urlretrieve(src, './delete.me')
try:
with gzip.open(gzfname) as gz:
n = struct.unpack('I', gz.read(4))
if n[0] != 0x3080000:
raise Exception('Invalid file: unexpected magic number.')
n = struct.unpack('>I', gz.read(4))[0]
if n != cimg:
raise Exception('Invalid file: expected {0} entries.'.format(cimg))
crow = struct.unpack('>I', gz.read(4))[0]
ccol = struct.unpack('>I', gz.read(4))[0]
if crow != 28 or ccol != 28:
raise Exception('Invalid file: expected 28 rows/cols per image.')
res = np.fromstring(gz.read(cimg * crow * ccol), dtype=np.uint8)
finally:
os.remove(gzfname)
return res.reshape((cimg, crow * ccol))
def get_mnist_data(filename, num_samples, local_data_dir):
gzfname = load_or_download_mnist_files(filename, num_samples, local_data_dir)
with gzip.open(gzfname) as gz:
n = struct.unpack('I', gz.read(4))
# Read magic number.
if n[0] != 0x3080000:
raise Exception('Invalid file: unexpected magic number.')
# Read number of entries.
n = struct.unpack('>I', gz.read(4))[0]
if n != num_samples:
raise Exception('Invalid file: expected {0} entries.'.format(num_samples))
crow = struct.unpack('>I', gz.read(4))[0]
ccol = struct.unpack('>I', gz.read(4))[0]
if crow != 28 or ccol != 28:
raise Exception('Invalid file: expected 28 rows/cols per image.')
# Read data.
res = np.fromstring(gz.read(num_samples * crow * ccol), dtype = np.uint8)
return res.reshape((num_samples, crow * ccol))
def get_mnist_labels(filename, num_samples, local_data_dir):
gzfname = load_or_download_mnist_files(filename, num_samples, local_data_dir)
with gzip.open(gzfname) as gz:
n = struct.unpack('I', gz.read(4))
# Read magic number.
if n[0] != 0x1080000:
raise Exception('Invalid file: unexpected magic number.')
# Read number of entries.
n = struct.unpack('>I', gz.read(4))
if n[0] != num_samples:
raise Exception('Invalid file: expected {0} rows.'.format(num_samples))
# Read labels.
res = np.fromstring(gz.read(num_samples), dtype = np.uint8)
return res.reshape((num_samples, 1))
def shape(self, as_list=True):
"""
Returns the size of the self tensor as a FloatTensor (or as List).
Note:
The returned value currently is a FloatTensor because it leverages
the messaging mechanism with Unity.
Parameters
----------
as_list : bool
Value retruned as list if true; else as tensor
Returns
-------
FloatTensor
Output tensor
(or)
Iterable
Output list
"""
if (as_list):
return list(np.fromstring(self.get("shape")[:-1], sep=",").astype('int'))
else:
shape_tensor = self.no_params_func("shape", return_response=True)
return shape_tensor
def stride(self, dim=-1):
"""
Returns the stride of tensor.
Parameters
----------
dim : int
dimension of expected return
Returns
-------
FloatTensor
Output tensor.
(or)
numpy.ndarray
NumPy Array as Long
"""
if dim == -1:
return self.no_params_func("stride", return_response=True, return_type=None)
else:
strides = self.params_func("stride", [dim], return_response=True, return_type=None)
return np.fromstring(strides, sep=' ').astype('long')
def __init__(self, feat_stride, scales, ratios, output_score,
rpn_pre_nms_top_n, rpn_post_nms_top_n, threshold, rpn_min_size):
super(ProposalOperator, self).__init__()
self._feat_stride = feat_stride
self._scales = np.fromstring(scales[1:-1], dtype=float, sep=',')
self._ratios = np.fromstring(ratios[1:-1], dtype=float, sep=',')
self._anchors = generate_anchors(base_size=self._feat_stride, scales=self._scales, ratios=self._ratios)
self._num_anchors = self._anchors.shape[0]
self._output_score = output_score
self._rpn_pre_nms_top_n = rpn_pre_nms_top_n
self._rpn_post_nms_top_n = rpn_post_nms_top_n
self._threshold = threshold
self._rpn_min_size = rpn_min_size
if DEBUG:
print 'feat_stride: {}'.format(self._feat_stride)
print 'anchors:'
print self._anchors
def __init__(self, feat_stride, scales, ratios, output_score,
rpn_pre_nms_top_n, rpn_post_nms_top_n, threshold, rpn_min_size):
super(ProposalOperator, self).__init__()
self._feat_stride = feat_stride
self._scales = np.fromstring(scales[1:-1], dtype=float, sep=',')
self._ratios = np.fromstring(ratios[1:-1], dtype=float, sep=',')
self._anchors = generate_anchors(base_size=self._feat_stride, scales=self._scales, ratios=self._ratios)
self._num_anchors = self._anchors.shape[0]
self._output_score = output_score
self._rpn_pre_nms_top_n = rpn_pre_nms_top_n
self._rpn_post_nms_top_n = rpn_post_nms_top_n
self._threshold = threshold
self._rpn_min_size = rpn_min_size
if DEBUG:
print('feat_stride: {}'.format(self._feat_stride))
print('anchors:')
print(self._anchors)
def vec2bin(input_path, output_path):
input_fd = open(input_path, "rb")
output_fd = open(output_path, "wb")
header = input_fd.readline()
output_fd.write(header)
vocab_size, vector_size = map(int, header.split())
for line in tqdm(range(vocab_size)):
word = []
while True:
ch = input_fd.read(1)
output_fd.write(ch)
if ch == b' ':
word = b''.join(word).decode('utf-8')
break
if ch != b'\n':
word.append(ch)
vector = np.fromstring(input_fd.readline(), sep=' ', dtype='float32')
output_fd.write(vector.tostring())
input_fd.close()
output_fd.close()
def enwik8_raw_data(data_path=None, num_test_symbols=5000000):
"""Load raw data from data directory "data_path".
The raw Hutter prize data is at:
http://mattmahoney.net/dc/enwik8.zip
Args:
data_path: string path to the directory where simple-examples.tgz has
been extracted.
num_test_symbols: number of symbols at the end that make up the test set
Returns:
tuple (train_data, valid_data, test_data, unique)
where each of the data objects can be passed to hutter_iterator.
"""
data_path = os.path.join(data_path, "enwik8")
raw_data = _read_symbols(data_path)
raw_data = np.fromstring(raw_data, dtype=np.uint8)
unique, data = np.unique(raw_data, return_inverse=True)
train_data = data[: -2 * num_test_symbols]
valid_data = data[-2 * num_test_symbols: -num_test_symbols]
test_data = data[-num_test_symbols:]
return train_data, valid_data, test_data, unique
def text8_raw_data(data_path=None, num_test_symbols=5000000):
"""Load raw data from data directory "data_path".
The raw text8 data is at:
http://mattmahoney.net/dc/text8.zip
Args:
data_path: string path to the directory where simple-examples.tgz has
been extracted.
num_test_symbols: number of symbols at the end that make up the test set
Returns:
tuple (train_data, valid_data, test_data, unique)
where each of the data objects can be passed to text8_iterator.
"""
data_path = os.path.join(data_path, "text8")
raw_data = _read_symbols(data_path)
raw_data = np.fromstring(raw_data, dtype=np.uint8)
unique, data = np.unique(raw_data, return_inverse=True)
train_data = data[: -2 * num_test_symbols]
valid_data = data[-2 * num_test_symbols: -num_test_symbols]
test_data = data[-num_test_symbols:]
return train_data, valid_data, test_data, unique
def load_bin_vec(fname, vocab):
"""
Loads word vecs from word2vec bin file
"""
word_vecs = OrderedDict()
with open(fname, "rb") as f:
header = f.readline()
vocab_size, layer1_size = map(int, header.split())
binary_len = np.dtype('float32').itemsize * layer1_size
for line in xrange(vocab_size):
word = []
while True:
ch = f.read(1)
if ch == ' ':
word = ''.join(word)
break
if ch != '\n':
word.append(ch)
if word in vocab:
idx = vocab[word]
word_vecs[idx] = np.fromstring(f.read(binary_len), dtype='float32')
else:
f.read(binary_len)
return word_vecs
def test_if_items_patch_updates_stock_filter(self, init_db, headers, redis, session, client, api):
body = [{
'name': 'test',
'stores': [{'id': 1}],
'schema': {'properties': {'id': {'type': 'string'}}, 'type': 'object', 'id_names': ['id']}
}]
client = await client
await client.post('/item_types/', headers=headers, data=ujson.dumps(body))
body = [{'id': 'test'}]
resp = await client.post('/item_types/1/items?store_id=1', headers=headers, data=ujson.dumps(body))
assert resp.status == 201
test_model = _all_models['store_items_test_1']
await ItemsIndicesMap(test_model).update(session)
body = [{'id': 'test', '_operation': 'delete'}]
resp = await client.patch('/item_types/1/items?store_id=1', headers=headers, data=ujson.dumps(body))
stock_filter = np.fromstring(await redis.get('store_items_test_1_stock_filter'), dtype=np.bool).tolist()
assert stock_filter == [False]
def predict(self, input_file):
# img = base64.b64decode(input_base64)
# img_array = np.fromstring(img, np.uint8)
# input_file = cv2.imdecode(img_array, 1)
# ip_converted = preprocessing.resizing(input_base64)
segmented_image = preprocessing.image_segmentation(
preprocessing.resizing(input_file)
)
# processed_image = preprocessing.removebg(segmented_image)
detect = pycolor.detect_color(
segmented_image,
self._mapping_file
)
return (detect)
def load_poses(self):
"""Load ground truth poses from file."""
print('Loading poses for sequence ' + self.sequence + '...')
pose_file = os.path.join(self.pose_path, self.sequence + '.txt')
# Read and parse the poses
try:
self.T_w_cam0 = []
with open(pose_file, 'r') as f:
for line in f.readlines():
T = np.fromstring(line, dtype=float, sep=' ')
T = T.reshape(3, 4)
T = np.vstack((T, [0, 0, 0, 1]))
self.T_w_cam0.append(T)
print('done.')
except FileNotFoundError:
print('Ground truth poses are not avaialble for sequence ' +
self.sequence + '.')
def enwik8_raw_data(data_path=None, num_test_symbols=5000000):
"""Load raw data from data directory "data_path".
The raw Hutter prize data is at:
http://mattmahoney.net/dc/enwik8.zip
Args:
data_path: string path to the directory where simple-examples.tgz has
been extracted.
num_test_symbols: number of symbols at the end that make up the test set
Returns:
tuple (train_data, valid_data, test_data, unique)
where each of the data objects can be passed to hutter_iterator.
"""
data_path = os.path.join(data_path, "enwik8")
raw_data = _read_symbols(data_path)
raw_data = np.fromstring(raw_data, dtype=np.uint8)
unique, data = np.unique(raw_data, return_inverse=True)
train_data = data[: -2 * num_test_symbols]
valid_data = data[-2 * num_test_symbols: -num_test_symbols]
test_data = data[-num_test_symbols:]
return train_data, valid_data, test_data, unique
def text8_raw_data(data_path=None, num_test_symbols=5000000):
"""Load raw data from data directory "data_path".
The raw text8 data is at:
http://mattmahoney.net/dc/text8.zip
Args:
data_path: string path to the directory where simple-examples.tgz has
been extracted.
num_test_symbols: number of symbols at the end that make up the test set
Returns:
tuple (train_data, valid_data, test_data, unique)
where each of the data objects can be passed to text8_iterator.
"""
data_path = os.path.join(data_path, "text8")
raw_data = _read_symbols(data_path)
raw_data = np.fromstring(raw_data, dtype=np.uint8)
unique, data = np.unique(raw_data, return_inverse=True)
train_data = data[: -2 * num_test_symbols]
valid_data = data[-2 * num_test_symbols: -num_test_symbols]
test_data = data[-num_test_symbols:]
return train_data, valid_data, test_data, unique
def load_word_vectors(file_destination):
"""
This method loads the word vectors from the supplied file destination.
It loads the dictionary of word vectors and prints its size and the vector dimensionality.
"""
print "Loading pretrained word vectors from", file_destination
word_dictionary = {}
try:
f = codecs.open(file_destination, 'r', 'utf-8')
for line in f:
line = line.split(" ", 1)
key = unicode(line[0].lower())
word_dictionary[key] = numpy.fromstring(line[1], dtype="float32", sep=" ")
except:
print "Word vectors could not be loaded from:", file_destination
return {}
print len(word_dictionary), "vectors loaded from", file_destination
return word_dictionary
def checkImageIsValid(imageBin):
if imageBin is None:
return False
try:
imageBuf = np.fromstring(imageBin, dtype=np.uint8)
img = cv2.imdecode(imageBuf, cv2.IMREAD_GRAYSCALE)
imgH, imgW = img.shape[0], img.shape[1]
except:
return False
else:
if imgH * imgW == 0:
return False
return True
def get_frame_input_feature(input_file):
features = []
record_iterator = tf.python_io.tf_record_iterator(path=input_file)
for i, string_record in enumerate(record_iterator):
example = tf.train.SequenceExample()
example.ParseFromString(string_record)
# traverse the Example format to get data
video_id = example.context.feature['video_id'].bytes_list.value[0]
label = example.context.feature['labels'].int64_list.value[:]
rgbs = []
audios = []
rgb_feature = example.feature_lists.feature_list['rgb'].feature
for i in range(len(rgb_feature)):
rgb = np.fromstring(rgb_feature[i].bytes_list.value[0], dtype=np.uint8).astype(np.float32)
rgb = utils.Dequantize(rgb, 2, -2)
rgbs.append(rgb)
audio_feature = example.feature_lists.feature_list['audio'].feature
for i in range(len(audio_feature)):
audio = np.fromstring(audio_feature[i].bytes_list.value[0], dtype=np.uint8).astype(np.float32)
audio = utils.Dequantize(audio, 2, -2)
audios.append(audio)
rgbs = np.array(rgbs)
audios = np.array(audios)
features.append((video_id, label, rgbs, audios))
return features
def get_frame_input_feature(input_file):
features = []
record_iterator = tf.python_io.tf_record_iterator(path=input_file)
for i, string_record in enumerate(record_iterator):
example = tf.train.SequenceExample()
example.ParseFromString(string_record)
# traverse the Example format to get data
video_id = example.context.feature['video_id'].bytes_list.value[0]
label = example.context.feature['labels'].int64_list.value[:]
rgbs = []
audios = []
rgb_feature = example.feature_lists.feature_list['rgb'].feature
for i in range(len(rgb_feature)):
rgb = np.fromstring(rgb_feature[i].bytes_list.value[0], dtype=np.uint8).astype(np.float32)
rgb = utils.Dequantize(rgb, 2, -2)
rgbs.append(rgb)
audio_feature = example.feature_lists.feature_list['audio'].feature
for i in range(len(audio_feature)):
audio = np.fromstring(audio_feature[i].bytes_list.value[0], dtype=np.uint8).astype(np.float32)
audio = utils.Dequantize(audio, 2, -2)
audios.append(audio)
rgbs = np.array(rgbs)
audios = np.array(audios)
features.append((video_id, label, rgbs, audios))
return features
def get_frame_input_feature(input_file):
features = []
record_iterator = tf.python_io.tf_record_iterator(path=input_file)
for i, string_record in enumerate(record_iterator):
example = tf.train.SequenceExample()
example.ParseFromString(string_record)
# traverse the Example format to get data
video_id = example.context.feature['video_id'].bytes_list.value[0]
label = example.context.feature['labels'].int64_list.value[:]
rgbs = []
audios = []
rgb_feature = example.feature_lists.feature_list['rgb'].feature
for i in range(len(rgb_feature)):
rgb = np.fromstring(rgb_feature[i].bytes_list.value[0], dtype=np.uint8).astype(np.float32)
rgb = utils.Dequantize(rgb, 2, -2)
rgbs.append(rgb)
audio_feature = example.feature_lists.feature_list['audio'].feature
for i in range(len(audio_feature)):
audio = np.fromstring(audio_feature[i].bytes_list.value[0], dtype=np.uint8).astype(np.float32)
audio = utils.Dequantize(audio, 2, -2)
audios.append(audio)
rgbs = np.array(rgbs)
audios = np.array(audios)
features.append((video_id, label, rgbs, audios))
return features
def _unpack_data_block(f, blocksize, packing):
"""
Private method to read a block from a file into a NumPy array.
"""
return numpy.fromstring(f.read(blocksize), packing)
def get_full_alignment_base_quality_scores(read):
"""
Returns base quality scores for the full read alignment, inserting zeroes for deletions and removing
inserted and soft-clipped bases. Therefore, only returns quality for truly aligned sequenced bases.
Args:
read (pysam.AlignedSegment): read to get quality scores for
Returns:
np.array: numpy array of quality scores
"""
quality_scores = np.fromstring(read.qual, dtype=np.byte) - tk_constants.ILLUMINA_QUAL_OFFSET
start_pos = 0
for operation,length in read.cigar:
operation = cr_constants.cigar_numeric_to_category_map[operation]
if operation == 'D':
quality_scores = np.insert(quality_scores, start_pos, [0] * length)
elif operation == 'I' or operation == 'S':
quality_scores = np.delete(quality_scores, np.s_[start_pos:start_pos + length])
if not operation == 'I' and not operation == 'S':
start_pos += length
return start_pos, quality_scores
def get_qvs(qual):
if qual is None:
return None
return numpy.fromstring(qual, dtype=numpy.byte) - ILLUMINA_QUAL_OFFSET
def get_bases_qual(qual, cutoff):
if qual is None:
return None
qvs = numpy.fromstring(qual, dtype=numpy.byte) - ILLUMINA_QUAL_OFFSET
return numpy.count_nonzero(qvs[qvs > cutoff])