Python numpy.asfortranarray方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import asfortranarray [as 別名]
def __get_annotation__(self, mask, image=None):

        _, contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

        segmentation = []
        for contour in contours:
            # Valid polygons have >= 6 coordinates (3 points)
            if contour.size >= 6:
                segmentation.append(contour.flatten().tolist())
        RLEs = cocomask.frPyObjects(segmentation, mask.shape[0], mask.shape[1])
        RLE = cocomask.merge(RLEs)
        # RLE = cocomask.encode(np.asfortranarray(mask))
        area = cocomask.area(RLE)
        [x, y, w, h] = cv2.boundingRect(mask)

        if image is not None:
            image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
            cv2.drawContours(image, contours, -1, (0,255,0), 1)
            cv2.rectangle(image,(x,y),(x+w,y+h), (255,0,0), 2)
            cv2.imshow("", image)
            cv2.waitKey(1)

        return segmentation, [x, y, w, h], area 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import asfortranarray [as 別名]
def build_coco_results(dataset, image_ids, rois, class_ids, scores, masks):
    """Arrange resutls to match COCO specs in http://cocodataset.org/#format
    """
    # If no results, return an empty list
    if rois is None:
        return []

    results = []
    for image_id in image_ids:
        # Loop through detections
        for i in range(rois.shape[0]):
            class_id = class_ids[i]
            score = scores[i]
            bbox = np.around(rois[i], 1)
            mask = masks[:, :, i]

            result = {
                "image_id": image_id,
                "category_id": dataset.get_source_class_id(class_id, "coco"),
                "bbox": [bbox[1], bbox[0], bbox[3] - bbox[1], bbox[2] - bbox[0]],
                "score": score,
                "segmentation": maskUtils.encode(np.asfortranarray(mask))
            }
            results.append(result)
    return results 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import asfortranarray [as 別名]
def build_dataset(n_samples=50, n_features=200, n_targets=1, sparse_X=False):
    """Build samples and observation for linear regression problem."""
    random_state = np.random.RandomState(0)
    if n_targets > 1:
        w = random_state.randn(n_features, n_targets)
    else:
        w = random_state.randn(n_features)

    if sparse_X:
        X = sparse.random(n_samples, n_features, density=0.5, format='csc',
                          random_state=random_state)

    else:
        X = np.asfortranarray(random_state.randn(n_samples, n_features))

    y = X.dot(w)
    return X, y 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import asfortranarray [as 別名]
def concat_data_arrays(data_dict: Dict[str, List[ndarray]]) -> Dict[str, ndarray]:
    new_data: Dict[str, ndarray] = {}
    empty_arrs = create_empty_arrs(data_dict)
    for kind, arrs in data_dict.items():
        if len(arrs) == 1:
            arr = arrs[0]
            if arr.ndim == 1:
                arr = arr.reshape(-1, 1)
            new_data[kind] = np.asfortranarray(arr)
        else:
            data = empty_arrs[kind]
            i = 0
            for arr in arrs:
                if arr.ndim == 1:
                    data[:, i] = arr
                    i += 1
                else:
                    for j in range(arr.shape[1]):
                        data[:, i] = arr[:, j]
                        i += 1
            new_data[kind] = data
    return new_data 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import asfortranarray [as 別名]
def testCopytoExecution(self):
        a = ones((2, 3), chunk_size=1)
        b = tensor([3, -1, 3], chunk_size=2)

        copyto(a, b, where=b > 1)

        res = self.executor.execute_tensor(a, concat=True)[0]
        expected = np.array([[3, 1, 3], [3, 1, 3]])

        np.testing.assert_equal(res, expected)

        a = ones((2, 3), chunk_size=1)
        b = tensor(np.asfortranarray(np.random.rand(2, 3)), chunk_size=2)

        copyto(b, a)

        res = self.executor.execute_tensor(b, concat=True)[0]
        expected = np.asfortranarray(np.ones((2, 3)))

        np.testing.assert_array_equal(res, expected)
        self.assertTrue(res.flags['F_CONTIGUOUS'])
        self.assertFalse(res.flags['C_CONTIGUOUS']) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import asfortranarray [as 別名]
def testAstypeExecution(self):
        raw = np.random.random((10, 5))
        arr = tensor(raw, chunk_size=3)
        arr2 = arr.astype('i8')

        res = self.executor.execute_tensor(arr2, concat=True)
        np.testing.assert_array_equal(res[0], raw.astype('i8'))

        raw = sps.random(10, 5, density=.2)
        arr = tensor(raw, chunk_size=3)
        arr2 = arr.astype('i8')

        res = self.executor.execute_tensor(arr2, concat=True)
        self.assertTrue(np.array_equal(res[0].toarray(), raw.astype('i8').toarray()))

        raw = np.asfortranarray(np.random.random((10, 5)))
        arr = tensor(raw, chunk_size=3)
        arr2 = arr.astype('i8', order='C')

        res = self.executor.execute_tensor(arr2, concat=True)[0]
        np.testing.assert_array_equal(res, raw.astype('i8'))
        self.assertTrue(res.flags['C_CONTIGUOUS'])
        self.assertFalse(res.flags['F_CONTIGUOUS']) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import asfortranarray [as 別名]
def testArgwhereExecution(self):
        x = arange(6, chunk_size=2).reshape(2, 3)
        t = argwhere(x > 1)

        res = self.executor.execute_tensor(t, concat=True)[0]
        expected = np.argwhere(np.arange(6).reshape(2, 3) > 1)

        np.testing.assert_array_equal(res, expected)

        data = np.asfortranarray(np.random.rand(10, 20))
        x = tensor(data, chunk_size=10)

        t = argwhere(x > 0.5)

        res = self.executor.execute_tensor(t, concat=True)[0]
        expected = np.argwhere(data > 0.5)

        np.testing.assert_array_equal(res, expected)
        self.assertTrue(res.flags['F_CONTIGUOUS'])
        self.assertFalse(res.flags['C_CONTIGUOUS']) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import asfortranarray [as 別名]
def testCosOrderExecution(self):
        data = np.asfortranarray(np.random.rand(3, 5))
        x = tensor(data, chunk_size=2)

        t = cos(x)

        res = self.executor.execute_tensor(t, concat=True)[0]
        np.testing.assert_allclose(res, np.cos(data))
        self.assertFalse(res.flags['C_CONTIGUOUS'])
        self.assertTrue(res.flags['F_CONTIGUOUS'])

        t2 = cos(x, order='C')

        res2 = self.executor.execute_tensor(t2, concat=True)[0]
        np.testing.assert_allclose(res2, np.cos(data, order='C'))
        self.assertTrue(res2.flags['C_CONTIGUOUS'])
        self.assertFalse(res2.flags['F_CONTIGUOUS']) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import asfortranarray [as 別名]
def iddr_id(A, k):
    """
    Compute ID of a real matrix to a specified rank.

    :param A:
        Matrix.
    :type A: :class:`numpy.ndarray`
    :param k:
        Rank of ID.
    :type k: int

    :return:
        Column index array.
    :rtype: :class:`numpy.ndarray`
    :return:
        Interpolation coefficients.
    :rtype: :class:`numpy.ndarray`
    """
    A = np.asfortranarray(A)
    idx, rnorms = _id.iddr_id(A, k)
    n = A.shape[1]
    proj = A.T.ravel()[:k*(n-k)].reshape((k, n-k), order='F')
    return idx, proj 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import asfortranarray [as 別名]
def idd_reconid(B, idx, proj):
    """
    Reconstruct matrix from real ID.

    :param B:
        Skeleton matrix.
    :type B: :class:`numpy.ndarray`
    :param idx:
        Column index array.
    :type idx: :class:`numpy.ndarray`
    :param proj:
        Interpolation coefficients.
    :type proj: :class:`numpy.ndarray`

    :return:
        Reconstructed matrix.
    :rtype: :class:`numpy.ndarray`
    """
    B = np.asfortranarray(B)
    if proj.size > 0:
        return _id.idd_reconid(B, idx, proj)
    else:
        return B[:, np.argsort(idx)] 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import asfortranarray [as 別名]
def idd_copycols(A, k, idx):
    """
    Reconstruct skeleton matrix from real ID.

    :param A:
        Original matrix.
    :type A: :class:`numpy.ndarray`
    :param k:
        Rank of ID.
    :type k: int
    :param idx:
        Column index array.
    :type idx: :class:`numpy.ndarray`

    :return:
        Skeleton matrix.
    :rtype: :class:`numpy.ndarray`
    """
    A = np.asfortranarray(A)
    return _id.idd_copycols(A, k, idx)


#------------------------------------------------------------------------------
# idd_id2svd.f
#------------------------------------------------------------------------------ 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import asfortranarray [as 別名]
def idzr_id(A, k):
    """
    Compute ID of a complex matrix to a specified rank.

    :param A:
        Matrix.
    :type A: :class:`numpy.ndarray`
    :param k:
        Rank of ID.
    :type k: int

    :return:
        Column index array.
    :rtype: :class:`numpy.ndarray`
    :return:
        Interpolation coefficients.
    :rtype: :class:`numpy.ndarray`
    """
    A = np.asfortranarray(A)
    idx, rnorms = _id.idzr_id(A, k)
    n = A.shape[1]
    proj = A.T.ravel()[:k*(n-k)].reshape((k, n-k), order='F')
    return idx, proj 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import asfortranarray [as 別名]
def idz_reconid(B, idx, proj):
    """
    Reconstruct matrix from complex ID.

    :param B:
        Skeleton matrix.
    :type B: :class:`numpy.ndarray`
    :param idx:
        Column index array.
    :type idx: :class:`numpy.ndarray`
    :param proj:
        Interpolation coefficients.
    :type proj: :class:`numpy.ndarray`

    :return:
        Reconstructed matrix.
    :rtype: :class:`numpy.ndarray`
    """
    B = np.asfortranarray(B)
    if proj.size > 0:
        return _id.idz_reconid(B, idx, proj)
    else:
        return B[:, np.argsort(idx)] 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import asfortranarray [as 別名]
def idz_copycols(A, k, idx):
    """
    Reconstruct skeleton matrix from complex ID.

    :param A:
        Original matrix.
    :type A: :class:`numpy.ndarray`
    :param k:
        Rank of ID.
    :type k: int
    :param idx:
        Column index array.
    :type idx: :class:`numpy.ndarray`

    :return:
        Skeleton matrix.
    :rtype: :class:`numpy.ndarray`
    """
    A = np.asfortranarray(A)
    return _id.idz_copycols(A, k, idx)


#------------------------------------------------------------------------------
# idz_id2svd.f
#------------------------------------------------------------------------------ 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import asfortranarray [as 別名]
def _stft(self, data, inverse=False, length=None):
        """
        Single entrypoint for both stft and istft. This computes stft and istft with librosa on stereo data. The two
        channels are processed separately and are concatenated together in the result. The expected input formats are:
        (n_samples, 2) for stft and (T, F, 2) for istft.
        :param data: np.array with either the waveform or the complex spectrogram depending on the parameter inverse
        :param inverse: should a stft or an istft be computed.
        :return: Stereo data as numpy array for the transform. The channels are stored in the last dimension
        """
        assert not (inverse and length is None)
        data = np.asfortranarray(data)
        N = self._params["frame_length"]
        H = self._params["frame_step"]
        win = hann(N, sym=False)
        fstft = istft if inverse else stft
        win_len_arg = {"win_length": None, "length": length} if inverse else {"n_fft": N}
        n_channels = data.shape[-1]
        out = []
        for c in range(n_channels):
            d = data[:, :, c].T if inverse else data[:, c]
            s = fstft(d, hop_length=H, window=win, center=False, **win_len_arg)
            s = np.expand_dims(s.T, 2-inverse)
            out.append(s)
        if len(out) == 1:
            return out[0]
        return np.concatenate(out, axis=2-inverse)