python numpy diff_Python numpy.uint16方法代码示例
本文整理汇总了Python中numpy.uint16方法的典型用法代码示例。如果您正苦于以下问题：Python numpy.uint16方法的具体用法？Python numpy.uint16怎么用？Python numpy.uint16使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在模块numpy的用法示例。
在下文中一共展示了numpy.uint16方法的29个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的Python代码示例。
示例1: execute
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# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import uint16 [as 别名]
def execute(self):
img_h = self.img.shape[0]
img_w = self.img.shape[1]
img_c = self.img.shape[2]
gc_img = np.empty((img_h, img_w, img_c), np.uint16)
for y in range(self.img.shape[0]):
for x in range(self.img.shape[1]):
if self.mode == 'rgb':
gc_img[y, x, 0] = self.lut[self.img[y, x, 0]]
gc_img[y, x, 1] = self.lut[self.img[y, x, 1]]
gc_img[y, x, 2] = self.lut[self.img[y, x, 2]]
gc_img[y, x, :] = gc_img[y, x, :] / 4
elif self.mode == 'yuv':
gc_img[y, x, 0] = self.lut[0][self.img[y, x, 0]]
gc_img[y, x, 1] = self.lut[1][self.img[y, x, 1]]
gc_img[y, x, 2] = self.lut[1][self.img[y, x, 2]]
self.img = gc_img
return self.img
开发者ID:cruxopen，项目名称:openISP，代码行数:20，
示例2: execute
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# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import uint16 [as 别名]
def execute(self):
img_pad = self.padding()
img_pad = img_pad.astype(np.uint16)
raw_h = self.img.shape[0]
raw_w = self.img.shape[1]
nlm_img = np.empty((raw_h, raw_w), np.uint16)
kernel = np.ones((2*self.ds+1, 2*self.ds+1)) / pow(2*self.ds+1, 2)
for y in range(img_pad.shape[0] - 2 * self.Ds):
for x in range(img_pad.shape[1] - 2 * self.Ds):
center_y = y + self.Ds
center_x = x + self.Ds
sweight, average, wmax = self.calWeights(img_pad, kernel, center_y, center_x)
average = average + wmax * img_pad[center_y, center_x]
sweight = sweight + wmax
nlm_img[y,x] = average / sweight
self.img = nlm_img
return self.clipping()
开发者ID:cruxopen，项目名称:openISP，代码行数:19，
示例3: execute
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# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import uint16 [as 别名]
def execute(self):
img_pad = self.padding()
raw_h = self.img.shape[0]
raw_w = self.img.shape[1]
aaf_img = np.empty((raw_h, raw_w), np.uint16)
for y in range(img_pad.shape[0] - 4):
for x in range(img_pad.shape[1] - 4):
p0 = img_pad[y + 2, x + 2]
p1 = img_pad[y, x]
p2 = img_pad[y, x + 2]
p3 = img_pad[y, x + 4]
p4 = img_pad[y + 2, x]
p5 = img_pad[y + 2, x + 4]
p6 = img_pad[y + 4, x]
p7 = img_pad[y + 4, x + 2]
p8 = img_pad[y + 4, x + 4]
aaf_img[y, x] = (p0 * 8 + p1 + p2 + p3 + p4 + p5 + p6 + p7 + p8) / 16
self.img = aaf_img
return self.img
开发者ID:cruxopen，项目名称:openISP，代码行数:21，
示例4: test_subheader
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# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import uint16 [as 别名]
def test_subheader(self):
assert_equal(self.subhdr.get_shape() , (10,10,3))
assert_equal(self.subhdr.get_nframes() , 1)
assert_equal(self.subhdr.get_nframes(),
len(self.subhdr.subheaders))
assert_equal(self.subhdr._check_affines(), True)
assert_array_almost_equal(np.diag(self.subhdr.get_frame_affine()),
np.array([ 2.20241979, 2.20241979, 3.125, 1.]))
assert_equal(self.subhdr.get_zooms()[0], 2.20241978764534)
assert_equal(self.subhdr.get_zooms()[2], 3.125)
assert_equal(self.subhdr._get_data_dtype(0),np.uint16)
#assert_equal(self.subhdr._get_frame_offset(), 1024)
assert_equal(self.subhdr._get_frame_offset(), 1536)
dat = self.subhdr.raw_data_from_fileobj()
assert_equal(dat.shape, self.subhdr.get_shape())
scale_factor = self.subhdr.subheaders[0]['scale_factor']
assert_equal(self.subhdr.subheaders[0]['scale_factor'].item(),1.0)
ecat_calib_factor = self.hdr['ecat_calibration_factor']
assert_equal(ecat_calib_factor, 25007614.0)
开发者ID:ME-ICA，项目名称:me-ica，代码行数:21，
示例5: test_able_int_type
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# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import uint16 [as 别名]
def test_able_int_type():
# The integer type cabable of containing values
for vals, exp_out in (
([0, 1], np.uint8),
([0, 255], np.uint8),
([-1, 1], np.int8),
([0, 256], np.uint16),
([-1, 128], np.int16),
([0.1, 1], None),
([0, 2**16], np.uint32),
([-1, 2**15], np.int32),
([0, 2**32], np.uint64),
([-1, 2**31], np.int64),
([-1, 2**64-1], None),
([0, 2**64-1], np.uint64),
([0, 2**64], None)):
assert_equal(able_int_type(vals), exp_out)
开发者ID:ME-ICA，项目名称:me-ica，代码行数:19，
示例6: test_can_cast
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# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import uint16 [as 别名]
def test_can_cast():
tests = ((np.float32, np.float32, True, True, True),
(np.float64, np.float32, True, True, True),
(np.complex128, np.float32, False, False, False),
(np.float32, np.complex128, True, True, True),
(np.float32, np.uint8, False, True, True),
(np.uint32, np.complex128, True, True, True),
(np.int64, np.float32, True, True, True),
(np.complex128, np.int16, False, False, False),
(np.float32, np.int16, False, True, True),
(np.uint8, np.int16, True, True, True),
(np.uint16, np.int16, False, True, True),
(np.int16, np.uint16, False, False, True),
(np.int8, np.uint16, False, False, True),
(np.uint16, np.uint8, False, True, True),
)
for intype, outtype, def_res, scale_res, all_res in tests:
assert_equal(def_res, can_cast(intype, outtype))
assert_equal(scale_res, can_cast(intype, outtype, False, True))
assert_equal(all_res, can_cast(intype, outtype, True, True))
开发者ID:ME-ICA，项目名称:me-ica，代码行数:22，
示例7: apply_using_lut
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# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import uint16 [as 别名]
def apply_using_lut(input_band, transformation):
'''Applies a linear transformation to an array using a look up table.
This creates a uint16 array as the output and clips the output band
to the range of a uint16.
:param array input_band: A 2D array representing the image data of the
a single band
:param LinearTransformation transformation: A LinearTransformation
(gain and offset)
:returns: A 2D array of of the input_band with the transformation applied
'''
logging.info('Normalize: Applying linear transformation to band (uint16)')
def _apply_lut(band, lut):
'''Changes band intensity values based on intensity look up table (lut)
'''
if lut.dtype != band.dtype:
raise Exception(
'Band ({}) and lut ({}) must be the same data type.').format(
band.dtype, lut.dtype)
return numpy.take(lut, band, mode='clip')
lut = _linear_transformation_to_lut(transformation)
return _apply_lut(input_band, lut)
开发者ID:planetlabs，项目名称:radiometric_normalization，代码行数:27，
示例8: _uniform_weight_alpha
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# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import uint16 [as 别名]
def _uniform_weight_alpha(sum_masked_arrays, output_datatype):
'''Calculates the cumulative mask of a list of masked array
Input:
sum_masked_arrays (list of numpy masked arrays): The list of
masked arrays to find the cumulative mask of, each element
represents one band.
(sums_masked_array.mask has a 1 for a no data pixel and
a 0 otherwise)
output_datatype (numpy datatype): The output datatype
Output:
output_alpha (numpy uint16 array): The output mask
(0 for a no data pixel, uint16 max value otherwise)
'''
output_alpha = numpy.ones(sum_masked_arrays[0].shape)
for band_sum_masked_array in sum_masked_arrays:
output_alpha[numpy.nonzero(band_sum_masked_array.mask == 1)] = 0
output_alpha = output_alpha.astype(output_datatype) * \
numpy.iinfo(output_datatype).max
return output_alpha
开发者ID:planetlabs，项目名称:radiometric_normalization，代码行数:26，
示例9: test_save_with_compress
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# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import uint16 [as 别名]
def test_save_with_compress(self):
output_file = 'test_save_with_compress.tif'
test_band = numpy.array([[5, 2, 2], [1, 6, 8]], dtype=numpy.uint16)
test_alpha = numpy.array([[0, 0, 0], [1, 1, 1]], dtype=numpy.bool)
test_gimage = gimage.GImage([test_band, test_band, test_band],
test_alpha, self.metadata)
gimage.save(test_gimage, output_file, compress=True)
result_gimg = gimage.load(output_file)
numpy.testing.assert_array_equal(result_gimg.bands[0], test_band)
numpy.testing.assert_array_equal(result_gimg.bands[1], test_band)
numpy.testing.assert_array_equal(result_gimg.bands[2], test_band)
numpy.testing.assert_array_equal(result_gimg.alpha, test_alpha)
self.assertEqual(result_gimg.metadata, self.metadata)
os.unlink(output_file)
开发者ID:planetlabs，项目名称:radiometric_normalization，代码行数:18，
示例10: format_time
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# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import uint16 [as 别名]
def format_time(running_time):
"""Format time in seconds as hours:minutes:seconds.
PARAMETERS
----------
running_time : float
Time in seconds.
RETURNS
----------
running_time : str
The time formatted as hours:minutes:seconds.
"""
hrs = np.uint16(np.floor(running_time/(60.**2)))
mts = np.uint16(np.floor(running_time/60.-hrs*60))
sec = np.uint16(np.round(running_time-hrs*60.**2-mts*60.))
return "{:02d}:{:02d}:{:02d}".format(hrs,mts,sec)
开发者ID:simnibs，项目名称:simnibs，代码行数:20，
示例11: squeeze_bits
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# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import uint16 [as 别名]
def squeeze_bits(arr: numpy.ndarray) -> numpy.ndarray:
"""Return a copy of an integer numpy array with the minimum bitness."""
assert arr.dtype.kind in ("i", "u")
if arr.size == 0:
return arr
if arr.dtype.kind == "i":
assert arr.min() >= 0
mlbl = int(arr.max()).bit_length()
if mlbl <= 8:
dtype = numpy.uint8
elif mlbl <= 16:
dtype = numpy.uint16
elif mlbl <= 32:
dtype = numpy.uint32
else:
dtype = numpy.uint64
return arr.astype(dtype)
开发者ID:src-d，项目名称:modelforge，代码行数:19，
示例12: group_years
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# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import uint16 [as 别名]
def group_years(years, interval=3):
""" Return integers representing sequential groupings of years
Note: years specified must be sorted
Args:
years (np.ndarray): the year corresponding to each EVI value
interval (int, optional): number of years to group together
(default: 3)
Returns:
np.ndarray: integers representing sequential year groupings
"""
n_groups = math.ceil((years.max() - years.min()) / interval)
if n_groups <= 1:
return np.zeros_like(years, dtype=np.uint16)
splits = np.array_split(np.arange(years.min(), years.max() + 1), n_groups)
groups = np.zeros_like(years, dtype=np.uint16)
for i, s in enumerate(splits):
groups[np.in1d(years, s)] = i
return groups
开发者ID:ceholden，项目名称:yatsm，代码行数:26，
示例13: ordinal2yeardoy
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# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import uint16 [as 别名]
def ordinal2yeardoy(ordinal):
""" Convert ordinal dates to two arrays of year and doy
Args:
ordinal (np.ndarray): ordinal dates
Returns:
np.ndarray: nobs x 2 np.ndarray containing the year and DOY for each
ordinal date
"""
_date = [dt.fromordinal(_d) for _d in ordinal]
yeardoy = np.empty((ordinal.size, 2), dtype=np.uint16)
yeardoy[:, 0] = np.array([int(_d.strftime('%Y')) for _d in _date])
yeardoy[:, 1] = np.array([int(_d.strftime('%j')) for _d in _date])
return yeardoy
开发者ID:ceholden，项目名称:yatsm，代码行数:19，
示例14: test_padded_union
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# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import uint16 [as 别名]
def test_padded_union(self):
dt = np.dtype(dict(
names=['a', 'b'],
offsets=[0, 0],
formats=[np.uint16, np.uint32],
itemsize=5,
))
ct = np.ctypeslib.as_ctypes_type(dt)
assert_(issubclass(ct, ctypes.Union))
assert_equal(ctypes.sizeof(ct), dt.itemsize)
assert_equal(ct._fields_, [
('a', ctypes.c_uint16),
('b', ctypes.c_uint32),
('', ctypes.c_char * 5), # padding
])
开发者ID:Frank-qlu，项目名称:recruit，代码行数:18，
示例15: test_basic
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# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import uint16 [as 别名]
def test_basic(self):
ba = [1, 2, 10, 11, 6, 5, 4]
ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
for ctype in [np.int8, np.uint8, np.int16, np.uint16, np.int32,
np.uint32, np.float32, np.float64, np.complex64,
np.complex128]:
a = np.array(ba, ctype)
a2 = np.array(ba2, ctype)
tgt = np.array([1, 3, 13, 24, 30, 35, 39], ctype)
assert_array_equal(np.cumsum(a, axis=0), tgt)
tgt = np.array(
[[1, 2, 3, 4], [6, 8, 10, 13], [16, 11, 14, 18]], ctype)
assert_array_equal(np.cumsum(a2, axis=0), tgt)
tgt = np.array(
[[1, 3, 6, 10], [5, 11, 18, 27], [10, 13, 17, 22]], ctype)
assert_array_equal(np.cumsum(a2, axis=1), tgt)
开发者ID:Frank-qlu，项目名称:recruit，代码行数:21，
示例16: test_half_conversion_denormal_round_even
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# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import uint16 [as 别名]
def test_half_conversion_denormal_round_even(self, float_t, uint_t, bits):
# Test specifically that all bits are considered when deciding
# whether round to even should occur (i.e. no bits are lost at the
# end. Compare also gh-12721. The most bits can get lost for the
# smallest denormal:
smallest_value = np.uint16(1).view(np.float16).astype(float_t)
assert smallest_value == 2**-24
# Will be rounded to zero based on round to even rule:
rounded_to_zero = smallest_value / float_t(2)
assert rounded_to_zero.astype(np.float16) == 0
# The significand will be all 0 for the float_t, test that we do not
# lose the lower ones of these:
for i in range(bits):
# slightly increasing the value should make it round up:
larger_pattern = rounded_to_zero.view(uint_t) | uint_t(1 << i)
larger_value = larger_pattern.view(float_t)
assert larger_value.astype(np.float16) == smallest_value
开发者ID:Frank-qlu，项目名称:recruit，代码行数:21，
示例17: test_half_values
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# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import uint16 [as 别名]
def test_half_values(self):
"""Confirms a small number of known half values"""
a = np.array([1.0, -1.0,
2.0, -2.0,
0.0999755859375, 0.333251953125, # 1/10, 1/3
65504, -65504, # Maximum magnitude
2.0**(-14), -2.0**(-14), # Minimum normal
2.0**(-24), -2.0**(-24), # Minimum subnormal
0, -1/1e1000, # Signed zeros
np.inf, -np.inf])
b = np.array([0x3c00, 0xbc00,
0x4000, 0xc000,
0x2e66, 0x3555,
0x7bff, 0xfbff,
0x0400, 0x8400,
0x0001, 0x8001,
0x0000, 0x8000,
0x7c00, 0xfc00], dtype=uint16)
b.dtype = float16
assert_equal(a, b)
开发者ID:Frank-qlu，项目名称:recruit，代码行数:22，
示例18: _toscalar
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# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import uint16 [as 别名]
def _toscalar(v):
if isinstance(v, (np.float16, np.float32, np.float64,
np.uint8, np.uint16, np.uint32, np.uint64,
np.int8, np.int16, np.int32, np.int64)):
return np.asscalar(v)
else:
return v
开发者ID:mme，项目名称:vergeml，代码行数:9，
示例19: __init__
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# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import uint16 [as 别名]
def __init__(self, server):
super().__init__()
self.setDaemon(True)
self._stop = threading.Event()
self._reading = thread_lock()
self.dt = np.dtype(np.uint16)
self.dt = self.dt.newbyteorder('
self._data = [np.zeros(WS_FRAME_SIZE, self.dt),
np.zeros(WS_FRAME_SIZE, self.dt)]
self._buf = False
self.ws = create_connection("ws://{}/".format(server))
开发者ID:ManiacalLabs，项目名称:BiblioPixelAnimations，代码行数:13，
示例20: step
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# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import uint16 [as 别名]
def step(self, amt=1):
d = self._ws_thread.get_frame()
d = d.reshape(WS_FRAME_HEIGHT, WS_FRAME_WIDTH)
if self.mirror:
d = np.fliplr(d)
d = rebin(d, (self.height, self.width)).astype(np.uint16)
self.shader.render(d)
开发者ID:ManiacalLabs，项目名称:BiblioPixelAnimations，代码行数:11，
示例21: _load_annotation
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# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import uint16 [as 别名]
def _load_annotation(self, index):
# store original annotation as orig_objs
height, width, orig_objs = self._parse_voc_anno(self._image_anno_tmpl.format(index))
# filter difficult objects
if not self._config['use_diff']:
non_diff_objs = [obj for obj in orig_objs if obj['difficult'] == 0]
objs = non_diff_objs
else:
objs = orig_objs
num_objs = len(objs)
boxes = np.zeros((num_objs, 4), dtype=np.uint16)
gt_classes = np.zeros((num_objs,), dtype=np.int32)
# Load object bounding boxes into a data frame.
for ix, obj in enumerate(objs):
# Make pixel indexes 0-based
x1 = obj['bbox'][0] - 1
y1 = obj['bbox'][1] - 1
x2 = obj['bbox'][2] - 1
y2 = obj['bbox'][3] - 1
cls = self._class_to_ind[obj['name'].lower().strip()]
boxes[ix, :] = [x1, y1, x2, y2]
gt_classes[ix] = cls
roi_rec = {'index': index,
'objs': orig_objs,
'image': self._image_file_tmpl.format(index),
'height': height,
'width': width,
'boxes': boxes,
'gt_classes': gt_classes,
'flipped': False}
return roi_rec
开发者ID:awslabs，项目名称:dynamic-training-with-apache-mxnet-on-aws，代码行数:36，
示例22: nb_process_label
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# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import uint16 [as 别名]
def nb_process_label(processed_label,sorted_label_voxel_pair):
label_size = 256
counter = np.zeros((label_size,),dtype = np.uint16)
counter[sorted_label_voxel_pair[0,3]] = 1
cur_sear_ind = sorted_label_voxel_pair[0,:3]
for i in range(1,sorted_label_voxel_pair.shape[0]):
cur_ind = sorted_label_voxel_pair[i,:3]
if not np.all(np.equal(cur_ind,cur_sear_ind)):
processed_label[cur_sear_ind[0],cur_sear_ind[1],cur_sear_ind[2]] = np.argmax(counter)
counter = np.zeros((label_size,),dtype = np.uint16)
cur_sear_ind = cur_ind
counter[sorted_label_voxel_pair[i,3]] += 1
processed_label[cur_sear_ind[0],cur_sear_ind[1],cur_sear_ind[2]] = np.argmax(counter)
return processed_label
开发者ID:edwardzhou130，项目名称:PolarSeg，代码行数:16，
示例23: _load_imagenet_annotation
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# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import uint16 [as 别名]
def _load_imagenet_annotation(self, index):
"""
Load image and bounding boxes info from txt files of imagenet.
"""
filename = os.path.join(self._data_path, 'Annotations', self._image_set, index + '.xml')
# print 'Loading: {}'.format(filename)
def get_data_from_tag(node, tag):
return node.getElementsByTagName(tag)[0].childNodes[0].data
with open(filename) as f:
data = minidom.parseString(f.read())
objs = data.getElementsByTagName('object')
num_objs = len(objs)
boxes = np.zeros((num_objs, 4), dtype=np.uint16)
gt_classes = np.zeros((num_objs), dtype=np.int32)
overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
# Load object bounding boxes into a data frame.
for ix, obj in enumerate(objs):
x1 = float(get_data_from_tag(obj, 'xmin'))
y1 = float(get_data_from_tag(obj, 'ymin'))
x2 = float(get_data_from_tag(obj, 'xmax'))
y2 = float(get_data_from_tag(obj, 'ymax'))
cls = self._wnid_to_ind[
str(get_data_from_tag(obj, "name")).lower().strip()]
boxes[ix, :] = [x1, y1, x2, y2]
gt_classes[ix] = cls
overlaps[ix, cls] = 1.0
overlaps = scipy.sparse.csr_matrix(overlaps)
return {'boxes' : boxes,
'gt_classes': gt_classes,
'gt_overlaps' : overlaps,
'flipped' : False}
开发者ID:guoruoqian，项目名称:cascade-rcnn_Pytorch，代码行数:40，
示例24: execute
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# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import uint16 [as 别名]
def execute(self):
img_pad = self.padding()
raw_h = self.img.shape[0]
raw_w = self.img.shape[1]
dpc_img = np.empty((raw_h, raw_w), np.uint16)
for y in range(img_pad.shape[0] - 4):
for x in range(img_pad.shape[1] - 4):
p0 = img_pad[y + 2, x + 2]
p1 = img_pad[y, x]
p2 = img_pad[y, x + 2]
p3 = img_pad[y, x + 4]
p4 = img_pad[y + 2, x]
p5 = img_pad[y + 2, x + 4]
p6 = img_pad[y + 4, x]
p7 = img_pad[y + 4, x + 2]
p8 = img_pad[y + 4, x + 4]
if (abs(p1 - p0) > self.thres) and (abs(p2 - p0) > self.thres) and (abs(p3 - p0) > self.thres) \
and (abs(p4 - p0) > self.thres) and (abs(p5 - p0) > self.thres) and (abs(p6 - p0) > self.thres) \
and (abs(p7 - p0) > self.thres) and (abs(p8 - p0) > self.thres):
if self.mode == 'mean':
p0 = (p2 + p4 + p5 + p7) / 4
elif self.mode == 'gradient':
dv = abs(2 * p0 - p2 - p7)
dh = abs(2 * p0 - p4 - p5)
ddl = abs(2 * p0 - p1 - p8)
ddr = abs(2 * p0 - p3 - p6)
if (min(dv, dh, ddl, ddr) == dv):
p0 = (p2 + p7 + 1) / 2
elif (min(dv, dh, ddl, ddr) == dh):
p0 = (p4 + p5 + 1) / 2
elif (min(dv, dh, ddl, ddr) == ddl):
p0 = (p1 + p8 + 1) / 2
else:
p0 = (p3 + p6 + 1) / 2
dpc_img[y, x] = p0
self.img = dpc_img
return self.clipping()
开发者ID:cruxopen，项目名称:openISP，代码行数:39，
示例25: write_shape
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# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import uint16 [as 别名]
def write_shape(shape, fout):
"""
Write tuple (C,H,W) to file, given shape 1CHW.
:return number of bytes written
"""
assert len(shape) == 4 and shape[0] == 1, shape
shape = shape[1:]
assert shape[0] < 2**8, shape
assert shape[1] < 2**16, shape
assert shape[2] < 2**16, shape
assert len(shape) == 3, shape
write_bytes(fout, [np.uint8, np.uint16, np.uint16], shape)
return 5
开发者ID:fab-jul，项目名称:L3C-PyTorch，代码行数:15，
示例26: read_shapes
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# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import uint16 [as 别名]
def read_shapes(fin):
return tuple(map(int, read_bytes(fin, [np.uint8, np.uint16, np.uint16])))
开发者ID:fab-jul，项目名称:L3C-PyTorch，代码行数:4，
示例27: write_padding_tuple
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# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import uint16 [as 别名]
def write_padding_tuple(padding_tuple, fout):
assert len(padding_tuple) == 4
write_bytes(fout,
[np.uint16, np.uint16, np.uint16, np.uint16],
padding_tuple)
开发者ID:fab-jul，项目名称:L3C-PyTorch，代码行数:7，
示例28: read_padding_tuple
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# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import uint16 [as 别名]
def read_padding_tuple(fin):
return tuple(map(int, read_bytes(fin, [np.uint16, np.uint16, np.uint16, np.uint16])))
开发者ID:fab-jul，项目名称:L3C-PyTorch，代码行数:4，
示例29: test_bytes
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# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import uint16 [as 别名]
def test_bytes(tmpdir):
shape = (3, 512, 768)
p = str(tmpdir.mkdir('test').join('hi.l3c'))
with open(p, 'wb') as f:
write_bytes(f, [np.uint8, np.uint16, np.uint16], shape)
with open(p, 'rb') as f:
c, h, w = read_bytes(f, [np.uint8, np.uint16, np.uint16])
assert (c, h, w) == shape
开发者ID:fab-jul，项目名称:L3C-PyTorch，代码行数:10，
注：本文中的numpy.uint16方法示例整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。