Python numpy.fromstring() Examples

The following are 30 code examples for showing how to use numpy.fromstring(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.

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Example 1
def image(self, captcha_str):
        """
        Generate a greyscale captcha image representing number string

        Parameters
        ----------
        captcha_str: str
            string a characters for captcha image

        Returns
        -------
        numpy.ndarray
            Generated greyscale image in np.ndarray float type with values normalized to [0, 1]
        """
        img = self.captcha.generate(captcha_str)
        img = np.fromstring(img.getvalue(), dtype='uint8')
        img = cv2.imdecode(img, cv2.IMREAD_GRAYSCALE)
        img = cv2.resize(img, (self.h, self.w))
        img = img.transpose(1, 0)
        img = np.multiply(img, 1 / 255.0)
        return img 
Example 2
Project: kaldi-python-io   Author: funcwj   File: _io_kernel.py    License: Apache License 2.0 6 votes vote down vote up
def read_common_mat(fd):
    """ 
        Read common matrix(for class Matrix in kaldi setup)
        see matrix/kaldi-matrix.cc::
            void Matrix<Real>::Read(std::istream & is, bool binary, bool add)
        Return a numpy ndarray object
    """
    mat_type = read_token(fd)
    print_info(f'\tType of the common matrix: {mat_type}')
    if mat_type not in ["FM", "DM"]:
        raise RuntimeError(f"Unknown matrix type in kaldi: {mat_type}")
    float_size = 4 if mat_type == 'FM' else 8
    float_type = np.float32 if mat_type == 'FM' else np.float64
    num_rows = read_int32(fd)
    num_cols = read_int32(fd)
    print_info(f'\tSize of the common matrix: {num_rows} x {num_cols}')
    mat_data = fd.read(float_size * num_cols * num_rows)
    mat = np.fromstring(mat_data, dtype=float_type)
    return mat.reshape(num_rows, num_cols) 
Example 3
Project: kaldi-python-io   Author: funcwj   File: _io_kernel.py    License: Apache License 2.0 6 votes vote down vote up
def read_float_vec(fd, direct_access=False):
    """
        Read float vector(for class Vector in kaldi setup)
        see matrix/kaldi-vector.cc
    """
    if direct_access:
        expect_binary(fd)
    vec_type = read_token(fd)
    print_info(f'\tType of the common vector: {vec_type}')
    if vec_type not in ["FV", "DV"]:
        raise RuntimeError(f"Unknown matrix type in kaldi: {vec_type}")
    float_size = 4 if vec_type == 'FV' else 8
    float_type = np.float32 if vec_type == 'FV' else np.float64
    dim = read_int32(fd)
    print_info(f'\tDim of the common vector: {dim}')
    vec_data = fd.read(float_size * dim)
    return np.fromstring(vec_data, dtype=float_type) 
Example 4
Project: dustmaps   Author: gregreen   File: json_serializers.py    License: GNU General Public License v2.0 6 votes vote down vote up
def deserialize_ndarray(d):
    """
    Deserializes a JSONified :obj:`numpy.ndarray`. Can handle arrays serialized
    using any of the methods in this module: :obj:`"npy"`, :obj:`"b64"`,
    :obj:`"readable"`.

    Args:
        d (`dict`): A dictionary representation of an :obj:`ndarray` object.

    Returns:
        An :obj:`ndarray` object.
    """
    if 'data' in d:
        x = np.fromstring(
            base64.b64decode(d['data']),
            dtype=d['dtype'])
        x.shape = d['shape']
        return x
    elif 'value' in d:
        return np.array(d['value'], dtype=d['dtype'])
    elif 'npy' in d:
        return deserialize_ndarray_npy(d)
    else:
        raise ValueError('Malformed np.ndarray encoding.') 
Example 5
Project: AdaptiveWingLoss   Author: protossw512   File: utils.py    License: Apache License 2.0 6 votes vote down vote up
def fig2data(fig):
    """
    @brief Convert a Matplotlib figure to a 4D numpy array with RGBA channels and return it
    @param fig a matplotlib figure
    @return a numpy 3D array of RGBA values
    """
    # draw the renderer
    fig.canvas.draw ( )

    # Get the RGB buffer from the figure
    w,h = fig.canvas.get_width_height()
    buf = np.fromstring (fig.canvas.tostring_rgb(), dtype=np.uint8)
    buf.shape = (w, h, 3)

    # canvas.tostring_argb give pixmap in ARGB mode. Roll the ALPHA channel to have it in RGBA mode
    buf = np.roll (buf, 3, axis=2)
    return buf 
Example 6
Project: ConvLab   Author: ConvLab   File: mdbt_util.py    License: MIT License 6 votes vote down vote up
def load_word_vectors(url):
    '''
    Load the word embeddings from the url
    :param url: to the word vectors
    :return: dict of word and vector values
    '''
    word_vectors = {}
    # print("Loading the word embeddings....................")
    # print('abs path: ', os.path.abspath(url))
    with open(url, mode='r', encoding='utf8') as f:
        for line in f:
            line = line.split(" ", 1)
            key = line[0]
            word_vectors[key] = np.fromstring(line[1], dtype="float32", sep=" ")
    # print("\tMDBT: The vocabulary contains about {} word embeddings".format(len(word_vectors)))
    return normalise_word_vectors(word_vectors) 
Example 7
Project: me-ica   Author: ME-ICA   File: netcdf.py    License: GNU Lesser General Public License v2.1 6 votes vote down vote up
def _read_values(self):
        nc_type = self.fp.read(4)
        n = self._unpack_int()

        typecode, size = TYPEMAP[nc_type]

        count = n*size
        values = self.fp.read(int(count))
        self.fp.read(-count % 4)  # read padding

        if typecode is not 'c':
            values = fromstring(values, dtype='>%s' % typecode)
            if values.shape == (1,): values = values[0]
        else:
            values = values.rstrip(asbytes('\x00'))
        return values 
Example 8
Project: object-detection   Author: cristianpb   File: camera_pi.py    License: MIT License 6 votes vote down vote up
def frames():
        with PiCamera() as camera:
            camera.rotation = int(str(os.environ['CAMERA_ROTATION']))
            stream = io.BytesIO()
            for _ in camera.capture_continuous(stream, 'jpeg',
                                               use_video_port=True):
                # return current frame
                stream.seek(0)
                _stream = stream.getvalue()
                data = np.fromstring(_stream, dtype=np.uint8)
                img = cv2.imdecode(data, 1)
                yield img

                # reset stream for next frame
                stream.seek(0)
                stream.truncate() 
Example 9
Project: Jamais-Vu   Author: CwbhX   File: decoder.py    License: MIT License 6 votes vote down vote up
def read(filename, limit=None):
    """
    Reads any file supported by pydub (ffmpeg) and returns the data contained
    within. If file reading fails due to input being a 24-bit wav file,
    wavio is used as a backup.

    Can be optionally limited to a certain amount of seconds from the start
    of the file by specifying the `limit` parameter. This is the amount of
    seconds from the start of the file.

    returns: (channels, samplerate)
    """
    # pydub does not support 24-bit wav files, use wavio when this occurs
    try:
        audiofile = AudioSegment.from_file(filename)

        if limit:
            audiofile = audiofile[:limit * 1000]

        data = np.fromstring(audiofile._data, np.int16)

        channels = []
        for chn in xrange(audiofile.channels):
            channels.append(data[chn::audiofile.channels])

        fs = audiofile.frame_rate
    except audioop.error:
        fs, _, audiofile = wavio.readwav(filename)

        if limit:
            audiofile = audiofile[:limit * 1000]

        audiofile = audiofile.T
        audiofile = audiofile.astype(np.int16)

        channels = []
        for chn in audiofile:
            channels.append(chn)

    return channels, audiofile.frame_rate, unique_hash(filename) 
Example 10
Project: Jamais-Vu   Author: CwbhX   File: wavio.py    License: MIT License 6 votes vote down vote up
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 
Example 11
Project: pykitti   Author: utiasSTARS   File: odometry.py    License: MIT License 6 votes vote down vote up
def _load_poses(self):
        """Load ground truth poses (T_w_cam0) from file."""
        pose_file = os.path.join(self.pose_path, self.sequence + '.txt')

        # Read and parse the poses
        poses = []
        try:
            with open(pose_file, 'r') as f:
                lines = f.readlines()
                if self.frames is not None:
                    lines = [lines[i] for i in self.frames]

                for line in lines:
                    T_w_cam0 = np.fromstring(line, dtype=float, sep=' ')
                    T_w_cam0 = T_w_cam0.reshape(3, 4)
                    T_w_cam0 = np.vstack((T_w_cam0, [0, 0, 0, 1]))
                    poses.append(T_w_cam0)

        except FileNotFoundError:
            print('Ground truth poses are not available for sequence ' +
                  self.sequence + '.')

        self.poses = poses 
Example 12
Project: Python-GUI-examples   Author: swharden   File: SWHear.py    License: MIT License 6 votes vote down vote up
def stream_readchunk(self):
        """reads some audio and re-launches itself"""
        try:
            self.data = np.fromstring(self.stream.read(self.chunk),dtype=np.int16)
            self.fftx, self.fft = getFFT(self.data,self.rate)

        except Exception as E:
            print(" -- exception! terminating...")
            print(E,"\n"*5)
            self.keepRecording=False
        if self.keepRecording:
            self.stream_thread_new()
        else:
            self.stream.close()
            self.p.terminate()
            print(" -- stream STOPPED")
        self.chunksRead+=1 
Example 13
Project: tf-lcnn   Author: ildoonet   File: data_feeder.py    License: GNU General Public License v3.0 6 votes vote down vote up
def get_data(self):
        idxs = np.arange(len(self.train_list))
        if self.shuffle:
            self.rng.shuffle(idxs)

        caches = {}
        for i, k in enumerate(idxs):
            path = self.train_list[k]
            label = self.lb_list[k]

            if i % self.preload == 0:
                try:
                    caches = ILSVRCTenth._read_tenth_batch(self.train_list[idxs[i:i+self.preload]])
                except Exception as e:
                    logging.warning('tenth local cache failed, err=%s' % str(e))

            content = caches.get(path, '')
            if not content:
                content = ILSVRCTenth._read_tenth(path)

            img = cv2.imdecode(np.fromstring(content, dtype=np.uint8), cv2.IMREAD_COLOR)
            yield [img, label] 
Example 14
Project: typhon   Author: atmtools   File: catalogues.py    License: MIT License 6 votes vote down vote up
def from_xml(cls, xmlelement):
        """Loads a Sparse object from an existing file."""

        binaryfp = xmlelement.binaryfp
        nelem = int(xmlelement[0].attrib['nelem'])
        nrows = int(xmlelement.attrib['nrows'])
        ncols = int(xmlelement.attrib['ncols'])

        if binaryfp is None:
            rowindex = np.fromstring(xmlelement[0].text, sep=' ').astype(int)
            colindex = np.fromstring(xmlelement[1].text, sep=' ').astype(int)
            sparsedata = np.fromstring(xmlelement[2].text, sep=' ')
        else:
            rowindex = np.fromfile(binaryfp, dtype='<i4', count=nelem)
            colindex = np.fromfile(binaryfp, dtype='<i4', count=nelem)
            sparsedata = np.fromfile(binaryfp, dtype='<d', count=nelem)

        return cls((sparsedata, (rowindex, colindex)), [nrows, ncols]) 
Example 15
Project: typhon   Author: atmtools   File: read.py    License: MIT License 6 votes vote down vote up
def Vector(elem):
        nelem = int(elem.attrib['nelem'])
        if nelem == 0:
            arr = np.ndarray((0,))
        else:
            # sep=' ' seems to work even when separated by newlines, see
            # http://stackoverflow.com/q/31882167/974555
            if elem.binaryfp is not None:
                arr = np.fromfile(elem.binaryfp, dtype='<d', count=nelem)
            else:
                arr = np.fromstring(elem.text, sep=' ')
            if arr.size != nelem:
                raise RuntimeError(
                    'Expected {:s} elements in Vector, found {:d}'
                    ' elements!'.format(elem.attrib['nelem'],
                                        arr.size))
        return arr 
Example 16
Project: typhon   Author: atmtools   File: read.py    License: MIT License 6 votes vote down vote up
def ComplexVector(elem):
        nelem = int(elem.attrib['nelem'])
        if nelem == 0:
            arr = np.ndarray((0,), dtype=np.complex128)
        else:
            # sep=' ' seems to work even when separated by newlines, see
            # http://stackoverflow.com/q/31882167/974555
            if elem.binaryfp is not None:
                arr = np.fromfile(elem.binaryfp, dtype=np.complex128,
                                  count=nelem)
            else:
                arr = np.fromstring(elem.text, sep=' ', dtype=np.float64)
                arr.dtype = np.complex128
            if arr.size != nelem:
                raise RuntimeError(
                    'Expected {:s} elements in Vector, found {:d}'
                    ' elements!'.format(elem.attrib['nelem'],
                                        arr.size))
        return arr 
Example 17
Project: typhon   Author: atmtools   File: read.py    License: MIT License 6 votes vote down vote up
def ComplexMatrix(elem):
        # turn dims around: in ARTS, [10 x 1 x 1] means 10 pages, 1 row, 1 col
        dimnames = [dim for dim in dimension_names
                    if dim in elem.attrib.keys()][::-1]
        dims = [int(elem.attrib[dim]) for dim in dimnames]
        if np.prod(dims) == 0:
            flatarr = np.ndarray(dims, dtype=np.complex128)
        elif elem.binaryfp is not None:
            flatarr = np.fromfile(elem.binaryfp, dtype=np.complex128,
                                  count=np.prod(np.array(dims)).item())
            flatarr = flatarr.reshape(dims)
        else:
            flatarr = np.fromstring(elem.text, sep=' ', dtype=np.float64)
            flatarr.dtype = np.complex128
            flatarr = flatarr.reshape(dims)
        return flatarr 
Example 18
Project: Caffe-Python-Data-Layer   Author: liuxianming   File: bcfstore.py    License: BSD 2-Clause "Simplified" License 5 votes vote down vote up
def __init__(self, filename):
        self._filename = filename
        print 'Loading BCF file to memory ... '+filename
        file = open(filename, 'rb')
        size = numpy.fromstring(file.read(8), dtype=numpy.uint64)
        file_sizes = numpy.fromstring(file.read(8*size), dtype=numpy.uint64)
        self._offsets = numpy.append(numpy.uint64(0),
                                     numpy.add.accumulate(file_sizes))
        self._memory = file.read()
        file.close() 
Example 19
Project: Caffe-Python-Data-Layer   Author: liuxianming   File: bcfstore.py    License: BSD 2-Clause "Simplified" License 5 votes vote down vote up
def __init__(self, filename):
        self._filename = filename
        print 'Opening BCF file ... '+filename
        self._file = open(filename, 'rb')
        size = numpy.fromstring(self._file.read(8), dtype=numpy.uint64)
        file_sizes = numpy.fromstring(self._file.read(8*size),
                                      dtype=numpy.uint64)
        self._offsets = numpy.append(numpy.uint64(0),
                                     numpy.add.accumulate(file_sizes)) 
Example 20
Project: BiblioPixelAnimations   Author: ManiacalLabs   File: system_eq.py    License: MIT License 5 votes vote down vote up
def new_frame(self, data, frame_count, time_info, status):
        data = np.fromstring(data, 'int16')
        with self.lock:
            self.frames.append(data)
            if self.stop:
                return None, pyaudio.paComplete
        return None, pyaudio.paContinue 
Example 21
Project: BiblioPixelAnimations   Author: ManiacalLabs   File: system_eq.py    License: MIT License 5 votes vote down vote up
def new_frame(self, data, frame_count, time_info, status):
        data = np.fromstring(data, 'int16')
        with self.lock:
            self.frames.append(data)
            if self.stop:
                return None, pyaudio.paComplete
        return None, pyaudio.paContinue 
Example 22
Project: disentangling_conditional_gans   Author: zalandoresearch   File: dataset_tool.py    License: MIT License 5 votes vote down vote up
def create_lsun(tfrecord_dir, lmdb_dir, resolution=256, max_images=None):
    print('Loading LSUN dataset from "%s"' % lmdb_dir)
    import lmdb # pip install lmdb
    import cv2 # pip install opencv-python
    import io
    with lmdb.open(lmdb_dir, readonly=True).begin(write=False) as txn:
        total_images = txn.stat()['entries']
        if max_images is None:
            max_images = total_images
        with TFRecordExporter(tfrecord_dir, max_images) as tfr:
            for idx, (key, value) in enumerate(txn.cursor()):
                try:
                    try:
                        img = cv2.imdecode(np.fromstring(value, dtype=np.uint8), 1)
                        if img is None:
                            raise IOError('cv2.imdecode failed')
                        img = img[:, :, ::-1] # BGR => RGB
                    except IOError:
                        img = np.asarray(PIL.Image.open(io.BytesIO(value)))
                    crop = np.min(img.shape[:2])
                    img = img[(img.shape[0] - crop) // 2 : (img.shape[0] + crop) // 2, (img.shape[1] - crop) // 2 : (img.shape[1] + crop) // 2]
                    img = PIL.Image.fromarray(img, 'RGB')
                    img = img.resize((resolution, resolution), PIL.Image.ANTIALIAS)
                    img = np.asarray(img)
                    img = img.transpose(2, 0, 1) # HWC => CHW
                    tfr.add_image(img)
                except:
                    print(sys.exc_info()[1])
                if tfr.cur_images == max_images:
                    break
        
#---------------------------------------------------------------------------- 
Example 23
Project: disentangling_conditional_gans   Author: zalandoresearch   File: dataset.py    License: MIT License 5 votes vote down vote up
def parse_tfrecord_np(record):
    ex = tf.train.Example()
    ex.ParseFromString(record)
    shape = ex.features.feature['shape'].int64_list.value
    data = ex.features.feature['data'].bytes_list.value[0]
    return np.fromstring(data, np.uint8).reshape(shape)

#----------------------------------------------------------------------------
# Dataset class that loads data from tfrecords files. 
Example 24
Project: dynamic-training-with-apache-mxnet-on-aws   Author: awslabs   File: capsulenet.py    License: Apache License 2.0 5 votes vote down vote up
def read_data(label_url, image_url):
    with gzip.open(download_data(label_url)) as flbl:
        magic, num = struct.unpack(">II", flbl.read(8))
        label = np.fromstring(flbl.read(), dtype=np.int8)
    with gzip.open(download_data(image_url), 'rb') as fimg:
        magic, num, rows, cols = struct.unpack(">IIII", fimg.read(16))
        image = np.fromstring(fimg.read(), dtype=np.uint8).reshape(len(label), rows, cols)
    return label, image 
Example 25
def read_data(label, image):
    """
    download and read data into numpy
    """
    base_url = 'http://yann.lecun.com/exdb/mnist/'
    with gzip.open(download_file(base_url+label, os.path.join('data',label))) as flbl:
        magic, num = struct.unpack(">II", flbl.read(8))
        label = np.fromstring(flbl.read(), dtype=np.int8)
    with gzip.open(download_file(base_url+image, os.path.join('data',image)), 'rb') as fimg:
        magic, num, rows, cols = struct.unpack(">IIII", fimg.read(16))
        image = np.fromstring(fimg.read(), dtype=np.uint8).reshape(len(label), rows, cols)
    return (label, image) 
Example 26
def main():
        parser = argparse.ArgumentParser()
        parser.add_argument("font_path", help="Path to ttf font file")
        parser.add_argument("output", help="Output filename including extension (e.g. 'sample.jpg')")
        parser.add_argument("--num", help="Up to 4 digit number [Default: random]")
        args = parser.parse_args()

        captcha = ImageCaptcha(fonts=[args.font_path])
        captcha_str = args.num if args.num else DigitCaptcha.get_rand(3, 4)
        img = captcha.generate(captcha_str)
        img = np.fromstring(img.getvalue(), dtype='uint8')
        img = cv2.imdecode(img, cv2.IMREAD_GRAYSCALE)
        cv2.imwrite(args.output, img)
        print("Captcha image with digits {} written to {}".format([int(c) for c in captcha_str], args.output)) 
Example 27
def __init__(self, num_classes='21', batch_images='1', batch_rois='128', fg_fraction='0.25',
                 fg_overlap='0.5', box_stds='(0.1, 0.1, 0.2, 0.2)'):
        super(ProposalTargetProp, self).__init__(need_top_grad=False)
        self._num_classes = int(num_classes)
        self._batch_images = int(batch_images)
        self._batch_rois = int(batch_rois)
        self._fg_fraction = float(fg_fraction)
        self._fg_overlap = float(fg_overlap)
        self._box_stds = tuple(np.fromstring(box_stds[1:-1], dtype=float, sep=',')) 
Example 28
Project: GST-Tacotron   Author: KinglittleQ   File: cutoff.py    License: MIT License 5 votes vote down vote up
def cutoff(input_wav, output_wav):
    '''
    input_wav --- input wav file path
    output_wav --- output wav file path
    '''

    # read input wave file and get parameters.
    with wave.open(input_wav, 'r') as fw:
        params = fw.getparams()
        # print(params)
        nchannels, sampwidth, framerate, nframes = params[:4]

        strData = fw.readframes(nframes)
        waveData = np.fromstring(strData, dtype=np.int16)

        max_v = np.max(abs(waveData))
        for i in range(waveData.shape[0]):
            if abs(waveData[i]) > 0.08 * max_v:
                break

        for j in range(waveData.shape[0] - 1, 0, -1):
            if abs(waveData[j]) > 0.08 * max_v:
                break

    # write new wav file
    with wave.open(output_wav, 'w') as fw:
        params = list(params)
        params[3] = nframes - i - (waveData.shape[0] - 1 - j)
        fw.setparams(params)
        fw.writeframes(strData[2 * i:2 * (j + 1)]) 
Example 29
Project: DOTA_models   Author: ringringyi   File: plot_lfads.py    License: Apache License 2.0 5 votes vote down vote up
def plot_lfads(train_bxtxd, train_model_vals,
               train_ext_input_bxtxi=None, train_truth_bxtxd=None,
               valid_bxtxd=None, valid_model_vals=None,
               valid_ext_input_bxtxi=None, valid_truth_bxtxd=None,
               bidx=None, cf=1.0, output_dist='poisson'):

  # Plotting
  f = plt.figure(figsize=(18,20), tight_layout=True)
  plot_lfads_timeseries(train_bxtxd, train_model_vals,
                        train_ext_input_bxtxi,
                        truth_bxtxn=train_truth_bxtxd,
                        conversion_factor=cf, bidx=bidx,
                        output_dist=output_dist, col_title='Train')
  plot_lfads_timeseries(valid_bxtxd, valid_model_vals,
                        valid_ext_input_bxtxi,
                        truth_bxtxn=valid_truth_bxtxd,
                        conversion_factor=cf, bidx=bidx,
                        output_dist=output_dist,
                        subplot_cidx=1, col_title='Valid')

  # Convert from figure to an numpy array width x height x 3 (last for RGB)
  f.canvas.draw()
  data = np.fromstring(f.canvas.tostring_rgb(), dtype=np.uint8, sep='')
  data_wxhx3 = data.reshape(f.canvas.get_width_height()[::-1] + (3,))
  plt.close()

  return data_wxhx3 
Example 30
Project: deep-models   Author: LaurentMazare   File: rhn-text8.py    License: Apache License 2.0 5 votes vote down vote up
def load(filename):
  with open(filename, 'r') as f:
    data = f.read()
  data = np.fromstring(data, dtype=np.uint8)
  unique, data = np.unique(data, return_inverse=True)
  return data, len(unique)