Python numpy.fliplr方法代码示例

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import fliplr [as 别名]
def load_data(self, domain, batch_size=1, is_testing=False):
        data_type = "train%s" % domain if not is_testing else "test%s" % domain
        path = glob('./datasets/%s/%s/*' % (self.dataset_name, data_type))

        batch_images = np.random.choice(path, size=batch_size)

        imgs = []
        for img_path in batch_images:
            img = self.imread(img_path)
            if not is_testing:
                img = scipy.misc.imresize(img, self.img_res)

                if np.random.random() > 0.5:
                    img = np.fliplr(img)
            else:
                img = scipy.misc.imresize(img, self.img_res)
            imgs.append(img)

        imgs = np.array(imgs)/127.5 - 1.

        return imgs 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import fliplr [as 别名]
def prox_soft_symmetry(X, step, strength=1):
    """Soft version of symmetry
    Using a `strength` that varies from 0 to 1,
    with 0 meaning no symmetry enforced at all and
    1  being completely symmetric, the user can customize
    the level of symmetry required for a component
    """
    pads = [[0, 0], [0, 0]]
    slices = [slice(None), slice(None)]
    if X.shape[0] % 2 == 0:
        pads[0][1] = 1
        slices[0] = slice(0, X.shape[0])
    if X.shape[1] % 2 == 0:
        pads[1][1] = 1
        slices[1] = slice(0, X.shape[1])

    X = fft.fast_zero_pad(X, pads)
    Xs = np.fliplr(np.flipud(X))
    X = 0.5 * strength * (X + Xs) + (1 - strength) * X
    return X[tuple(slices)] 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import fliplr [as 别名]
def save_movie_to_frame(images, filename, idx=0, cmap='Blues'):
    # Collect to single image
    image = movie_to_frame(images[idx])

    # Flip it
    # image = np.fliplr(image)
    # image = np.flipud(image)

    f = plt.figure(figsize=[12, 12])
    plt.imshow(image, cmap=plt.cm.get_cmap(cmap), interpolation='none', vmin=0, vmax=1)

    plt.axis('image')
    plt.xticks([])
    plt.yticks([])
    plt.savefig(filename, format='png', bbox_inches='tight', dpi=80)
    plt.close(f) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import fliplr [as 别名]
def rotation_matrix(self):
        ''' Return rotation matrix between array indices and mm

        Note that we swap the two columns of the 'ImageOrientPatient'
        when we create the rotation matrix.  This is takes into account
        the slightly odd ij transpose construction of the DICOM
        orientation fields - see doc/theory/dicom_orientaiton.rst.
        '''
        iop = self.image_orient_patient
        s_norm = self.slice_normal
        if None in (iop, s_norm):
            return None
        R = np.eye(3)
        # np.fliplr(iop) gives matrix F in
        # doc/theory/dicom_orientation.rst The fliplr accounts for the
        # fact that the first column in ``iop`` refers to changes in
        # column index, and the second to changes in row index.
        R[:,:2] = np.fliplr(iop)
        R[:,2] = s_norm
        # check this is in fact a rotation matrix
        assert np.allclose(np.eye(3),
                           np.dot(R, R.T),
                           atol=1e-6)
        return R 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import fliplr [as 别名]
def test_flip_axis():
    a = np.arange(24).reshape((2,3,4))
    assert_array_equal(
        flip_axis(a),
        np.flipud(a))
    assert_array_equal(
        flip_axis(a, axis=0),
        np.flipud(a))
    assert_array_equal(
        flip_axis(a, axis=1),
        np.fliplr(a))
    # check accepts array-like
    assert_array_equal(
        flip_axis(a.tolist(), axis=0),
        np.flipud(a))
    # third dimension
    b = a.transpose()
    b = np.flipud(b)
    b = b.transpose()
    assert_array_equal(flip_axis(a, axis=2), b) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import fliplr [as 别名]
def decompose_projection_matrix(P, return_t=True):
  if P.shape[0] != 3 or P.shape[1] != 4:
    raise Exception('P has to be 3x4')
  M = P[:, :3]
  C = -np.linalg.inv(M) @ P[:, 3:]

  R,K = np.linalg.qr(np.flipud(M).T)
  K = np.flipud(K.T)
  K = np.fliplr(K)
  R = np.flipud(R.T)

  T = np.diag(np.sign(np.diag(K)))
  K = K @ T
  R = T @ R

  if np.linalg.det(R) < 0:
    R *= -1

  K /= K[2,2]
  if return_t:
    return K, R, cameracenter_to_translation(R, C)
  else:
    return K, R, C 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import fliplr [as 别名]
def get_symmetric_densepose(self, I, U, V, x, y, Mask):
        # This is a function to get the mirror symmetric UV labels.
        Labels_sym = np.zeros(I.shape)
        U_sym = np.zeros(U.shape)
        V_sym = np.zeros(V.shape)
        for i in (range(24)):
            if i + 1 in I:
                Labels_sym[I == (i + 1)] = self.Index_Symmetry_List[i]
                jj = np.where(I == (i + 1))
                U_loc = (U[jj] * 255).astype(np.int64)
                V_loc = (V[jj] * 255).astype(np.int64)
                V_sym[jj] = self.UV_symmetry_transformations['V_transforms'][0, i][V_loc, U_loc]
                U_sym[jj] = self.UV_symmetry_transformations['U_transforms'][0, i][V_loc, U_loc]
        Mask_flip = np.fliplr(Mask)
        Mask_flipped = np.zeros(Mask.shape)

        for i in (range(14)):
            Mask_flipped[Mask_flip == (i + 1)] = self.SemanticMaskSymmetries[i + 1]
        [y_max, x_max] = Mask_flip.shape
        y_sym = y
        x_sym = x_max - x

        return Labels_sym, U_sym, V_sym, x_sym, y_sym, Mask_flipped 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import fliplr [as 别名]
def transform(self, images):
        if self._aug_flag:
            transformed_images =\
                np.zeros([images.shape[0], self._imsize, self._imsize, 3])
            ori_size = images.shape[1]
            for i in range(images.shape[0]):
                h1 = np.floor((ori_size - self._imsize) * np.random.random())
                w1 = np.floor((ori_size - self._imsize) * np.random.random())
                cropped_image =\
                    images[i][w1: w1 + self._imsize, h1: h1 + self._imsize, :]
                if random.random() > 0.5:
                    transformed_images[i] = np.fliplr(cropped_image)
                else:
                    transformed_images[i] = cropped_image
            return transformed_images
        else:
            return images 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import fliplr [as 别名]
def __init__(self):
		nest.ResetKernel()
		nest.SetKernelStatus({"local_num_threads" : 1, "resolution" : p.time_resolution})
		self.spike_generators_l = nest.Create("poisson_generator", p.resolution[0]//2*p.resolution[1], params=p.poisson_params)
		self.spike_generators_r = nest.Create("poisson_generator", p.resolution[0]//2*p.resolution[1], params=p.poisson_params)
		self.neuron_l = nest.Create("iaf_psc_alpha", params=p.iaf_params)
		self.neuron_r = nest.Create("iaf_psc_alpha", params=p.iaf_params)
		self.spike_detector_l = nest.Create("spike_detector", params={"withtime": True})
		self.spike_detector_r = nest.Create("spike_detector", params={"withtime": True})
		self.multimeter_l = nest.Create("multimeter", params={"withtime":True, "record_from":["V_m"]})
		self.multimeter_r = nest.Create("multimeter", params={"withtime":True, "record_from":["V_m"]})
		weights_l = np.fliplr(p.weights_l.T).reshape(p.weights_l.size)
		weights_r = np.fliplr(p.weights_r.T).reshape(p.weights_r.size)
		for i in range(weights_l.size):
			syn_dict = {"model": "static_synapse", 
						"weight": weights_l[i]}
			nest.Connect([self.spike_generators_l[i]], self.neuron_l, syn_spec=syn_dict)
		for i in range(weights_r.size):
			syn_dict = {"model": "static_synapse", 
						"weight": weights_r[i]}
			nest.Connect([self.spike_generators_r[i]], self.neuron_r, syn_spec=syn_dict)
		nest.Connect(self.neuron_l, self.spike_detector_l)
		nest.Connect(self.neuron_r, self.spike_detector_r)
		nest.Connect(self.multimeter_l, self.neuron_l)
		nest.Connect(self.multimeter_r, self.neuron_r) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import fliplr [as 别名]
def center_crop(x, crop_h, crop_w=None, resize_w=64):

    if crop_w is None:
        crop_w = crop_h
    h, w = x.shape[:2]
    j = int(round((h - crop_h)/2.))
    i = int(round((w - crop_w)/2.))

    rate = np.random.uniform(0, 1, size=1)

    if rate < 0.5:
        x = np.fliplr(x)

    #first crop tp 178x178 and resize to 128x128
    return scipy.misc.imresize(x[20:218-20, 0: 178], [resize_w, resize_w])

    #Another cropped method

    # return scipy.misc.imresize(x[j:j+crop_h, i:i+crop_w],
    #                            [resize_w, resize_w]) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import fliplr [as 别名]
def load_image_array(image_file, image_size):
	img = skimage.io.imread(image_file)
	# GRAYSCALE
	if len(img.shape) == 2:
		img_new = np.ndarray( (img.shape[0], img.shape[1], 3), dtype = 'uint8')
		img_new[:,:,0] = img
		img_new[:,:,1] = img
		img_new[:,:,2] = img
		img = img_new

	img_resized = skimage.transform.resize(img, (image_size, image_size))

	# FLIP HORIZONTAL WIRH A PROBABILITY 0.5
	if random.random() > 0.5:
		img_resized = np.fliplr(img_resized)
	
	
	return img_resized.astype('float32') 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import fliplr [as 别名]
def update(self):
        if self.inky_colour is None:
            raise RuntimeError("You must specify which colour of Inky pHAT you're using: inkyphat.set_colour('red', 'black' or 'yellow')")

        self._display_init()

        x1, x2 = self.update_x1, self.update_x2
        y1, y2 = self.update_y1, self.update_y2

        region = self.buffer[y1:y2, x1:x2]

        if self.v_flip:
            region = numpy.fliplr(region)

        if self.h_flip:
            region = numpy.flipud(region)

        buf_red = numpy.packbits(numpy.where(region == RED, 1, 0)).tolist()
        if self.inky_version == 1:
            buf_black = numpy.packbits(numpy.where(region == 0, 0, 1)).tolist()
        else:
            buf_black = numpy.packbits(numpy.where(region == BLACK, 0, 1)).tolist()

        self._display_update(buf_black, buf_red)
        self._display_fini() 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import fliplr [as 别名]
def __flip(self, image, landmarks, run_prob=0.5):
        """
        Do image flop. Only for horizontal
        Args:
            image: a numpy type
            landmarks: face landmarks with format [(x1, y1), (x2, y2), ...]
            run_prob: probability to do this operate. 0.0-1.0
        Return:
            an image and landmarks will be returned
        Raises:
            Unsupport count of landmarks
        """
        if np.random.rand() < run_prob:
            return image, landmarks
        image = np.fliplr(image)
        landmarks[:, 0] = image.shape[1] - landmarks[:, 0]
        landmarks = LandmarkHelper.flip(landmarks, landmarks.shape[0])
        return image, landmarks 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import fliplr [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) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import fliplr [as 别名]
def _recalls(all_ious, proposal_nums, thrs):

    img_num = all_ious.shape[0]
    total_gt_num = sum([ious.shape[0] for ious in all_ious])

    _ious = np.zeros((proposal_nums.size, total_gt_num), dtype=np.float32)
    for k, proposal_num in enumerate(proposal_nums):
        tmp_ious = np.zeros(0)
        for i in range(img_num):
            ious = all_ious[i][:, :proposal_num].copy()
            gt_ious = np.zeros((ious.shape[0]))
            if ious.size == 0:
                tmp_ious = np.hstack((tmp_ious, gt_ious))
                continue
            for j in range(ious.shape[0]):
                gt_max_overlaps = ious.argmax(axis=1)
                max_ious = ious[np.arange(0, ious.shape[0]), gt_max_overlaps]
                gt_idx = max_ious.argmax()
                gt_ious[j] = max_ious[gt_idx]
                box_idx = gt_max_overlaps[gt_idx]
                ious[gt_idx, :] = -1
                ious[:, box_idx] = -1
            tmp_ious = np.hstack((tmp_ious, gt_ious))
        _ious[k, :] = tmp_ious

    _ious = np.fliplr(np.sort(_ious, axis=1))
    recalls = np.zeros((proposal_nums.size, thrs.size))
    for i, thr in enumerate(thrs):
        recalls[:, i] = (_ious >= thr).sum(axis=1) / float(total_gt_num)

    return recalls