Python numpy.uint16() Examples

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 

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)) 

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() 

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 

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) 

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) 

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)) 

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) 

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 

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) 

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) 

def read_tiff16(fn):
  import tifffile
  import numpy as np
  img = tifffile.imread(fn)
  if img.dtype == np.uint8:
    depth = 8
  elif img.dtype == np.uint16:
    depth = 16
  else:
    print("Warning: unsupported data type {}. Assuming 16-bit.", img.dtype)
    depth = 16

  return (img * (1.0 / (2**depth - 1))).astype(np.float32) 

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) 

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 

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 

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
        ]) 

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) 

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.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)
            if ctype in ['1', 'b']:
                assert_raises(ArithmeticError, np.cumprod, a)
                assert_raises(ArithmeticError, np.cumprod, a2, 1)
                assert_raises(ArithmeticError, np.cumprod, a)
            else:
                assert_array_equal(np.cumprod(a, axis=-1),
                                   np.array([1, 2, 20, 220,
                                             1320, 6600, 26400], ctype))
                assert_array_equal(np.cumprod(a2, axis=0),
                                   np.array([[1, 2, 3, 4],
                                             [5, 12, 21, 36],
                                             [50, 36, 84, 180]], ctype))
                assert_array_equal(np.cumprod(a2, axis=-1),
                                   np.array([[1, 2, 6, 24],
                                             [5, 30, 210, 1890],
                                             [10, 30, 120, 600]], ctype)) 

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 

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) 

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 

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) 

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 

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 

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} 

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() 

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 

def read_shapes(fin):
    return tuple(map(int, read_bytes(fin, [np.uint8, np.uint16, np.uint16]))) 

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) 

def read_padding_tuple(fin):
    return tuple(map(int, read_bytes(fin, [np.uint16, np.uint16, np.uint16, np.uint16])))