Python numpy.fromstring() Examples
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Example #1
Source File: captcha_generator.py From dynamic-training-with-apache-mxnet-on-aws with Apache License 2.0 | 9 votes |
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
Source File: _io_kernel.py From kaldi-python-io with Apache License 2.0 | 6 votes |
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
Source File: odometry.py From pykitti with MIT License | 6 votes |
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 #4
Source File: read.py From typhon with MIT License | 6 votes |
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 #5
Source File: _io_kernel.py From kaldi-python-io with Apache License 2.0 | 6 votes |
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 #6
Source File: wavio.py From Jamais-Vu with MIT License | 6 votes |
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 #7
Source File: read.py From typhon with MIT License | 6 votes |
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 #8
Source File: json_serializers.py From dustmaps with GNU General Public License v2.0 | 6 votes |
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 #9
Source File: cutoff.py From GST-Tacotron with MIT License | 6 votes |
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 #10
Source File: captcha_generator.py From dynamic-training-with-apache-mxnet-on-aws with Apache License 2.0 | 6 votes |
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 #11
Source File: read.py From typhon with MIT License | 6 votes |
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 #12
Source File: catalogues.py From typhon with MIT License | 6 votes |
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 #13
Source File: data_feeder.py From tf-lcnn with GNU General Public License v3.0 | 6 votes |
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
Source File: decoder.py From Jamais-Vu with MIT License | 6 votes |
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 #15
Source File: SWHear.py From Python-GUI-examples with MIT License | 6 votes |
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 #16
Source File: camera_pi.py From object-detection with MIT License | 6 votes |
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 #17
Source File: create_dataset.py From ICDAR-2019-SROIE with MIT License | 6 votes |
def checkImageIsValid(imageBin): if imageBin is None: return False imageBuf = np.fromstring(imageBin, dtype=np.uint8) img = cv2.imdecode(imageBuf, cv2.IMREAD_GRAYSCALE) imgH, imgW = img.shape[0], img.shape[1] if imgH * imgW == 0: return False return True
Example #18
Source File: utils.py From AdaptiveWingLoss with Apache License 2.0 | 6 votes |
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 #19
Source File: netcdf.py From me-ica with GNU Lesser General Public License v2.1 | 6 votes |
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 #20
Source File: mdbt_util.py From ConvLab with MIT License | 6 votes |
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 #21
Source File: test_longdouble.py From recruit with Apache License 2.0 | 5 votes |
def test_fromstring_empty(): assert_equal(np.fromstring("xxxxx", sep="x"), np.array([]))
Example #22
Source File: embedding.py From Counterfactual-StoryRW with MIT License | 5 votes |
def load_word2vec(filename, vocab, word_vecs): """Loads embeddings in the word2vec binary format which has a header line containing the number of vectors and their dimensionality (two integers), followed with number-of-vectors lines each of which is formatted as '<word-string> <embedding-vector>'. Args: filename (str): Path to the embedding file. vocab (dict): A dictionary that maps token strings to integer index. Tokens not in :attr:`vocab` are not read. word_vecs: A 2D numpy array of shape `[vocab_size, embed_dim]` which is updated as reading from the file. Returns: The updated :attr:`word_vecs`. """ with gfile.GFile(filename, "rb") as fin: header = fin.readline() vocab_size, vector_size = [int(s) for s in header.split()] if vector_size != word_vecs.shape[1]: raise ValueError("Inconsistent word vector sizes: %d vs %d" % (vector_size, word_vecs.shape[1])) binary_len = np.dtype('float32').itemsize * vector_size for _ in np.arange(vocab_size): chars = [] while True: char = fin.read(1) if char == b' ': break if char != b'\n': chars.append(char) word = b''.join(chars) word = tf.compat.as_text(word) if word in vocab: word_vecs[vocab[word]] = np.fromstring( fin.read(binary_len), dtype='float32') else: fin.read(binary_len) return word_vecs
Example #23
Source File: test_deprecations.py From recruit with Apache License 2.0 | 5 votes |
def test_fromstring(self): self.assert_deprecated(np.fromstring, args=('\x00'*80,))
Example #24
Source File: RealSenseVideo.py From laplacian-meshes with GNU General Public License v3.0 | 5 votes |
def imwritef(I, filename): IA = I.flatten().tolist() IA = struct.pack("%if"%len(IA), *IA) IA = np.fromstring(IA, dtype=np.uint8) IA = IA.reshape([I.shape[0], I.shape[1], 4]) ##Tricky!! Numpy is "low-order major" and the order I have things in is 4bytes per pixel, then columns, then rows. These are specified in reverse order print "IA.shape = ", IA.shape #Convert from RGBA format to BGRA format like the real sense saver did IA = IA[:, :, [2, 1, 0, 3]] scipy.misc.imsave(filename, IA) #Use the uv coorinates to map into the array of colors "C" #using bilinear interpolation. Out of bounds values are by default gray
Example #25
Source File: test_longdouble.py From recruit with Apache License 2.0 | 5 votes |
def test_fromstring_bogus(): assert_equal(np.fromstring("1. 2. 3. flop 4.", dtype=float, sep=" "), np.array([1., 2., 3.]))
Example #26
Source File: RealSenseVideo.py From laplacian-meshes with GNU General Public License v3.0 | 5 votes |
def imreadf(filename): #Read in file, converting image byte array to little endian float I = scipy.misc.imread(filename) #Image is stored in BGRA format so convert to RGBA I = I[:, :, [2, 1, 0, 3]] shape = I.shape I = I.flatten() IA = bytearray(I.tolist()) I = np.fromstring(IA.__str__(), dtype=np.dtype('<f4')) return np.reshape(I, shape[0:2])
Example #27
Source File: test_longdouble.py From recruit with Apache License 2.0 | 5 votes |
def test_fromstring_missing(): assert_equal(np.fromstring("1xx3x4x5x6", sep="x"), np.array([1]))
Example #28
Source File: test_longdouble.py From recruit with Apache License 2.0 | 5 votes |
def test_fromstring_best_effort_float(self): assert_equal(np.fromstring("1,234", dtype=float, sep=" "), np.array([1.]))
Example #29
Source File: dataset.py From rcan-tensorflow with MIT License | 5 votes |
def parse_tfr_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)
Example #30
Source File: test_longdouble.py From recruit with Apache License 2.0 | 5 votes |
def test_fromstring(): o = 1 + LD_INFO.eps s = (" " + repr(o))*5 a = np.array([o]*5) assert_equal(np.fromstring(s, sep=" ", dtype=np.longdouble), a, err_msg="reading '%s'" % s)