Python numpy.dstack方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import dstack [as 別名]
def merge(self, tiles: List[np.ndarray], dtype=np.float32):
        if len(tiles) != len(self.crops):
            raise ValueError

        channels = 1 if len(tiles[0].shape) == 2 else tiles[0].shape[2]
        target_shape = self.image_height + self.margin_bottom + self.margin_top, self.image_width + self.margin_right + self.margin_left, channels

        image = np.zeros(target_shape, dtype=np.float64)
        norm_mask = np.zeros(target_shape, dtype=np.float64)

        w = np.dstack([self.weight] * channels)

        for tile, (x, y, tile_width, tile_height) in zip(tiles, self.crops):
            # print(x, y, tile_width, tile_height, image.shape)
            image[y:y + tile_height, x:x + tile_width] += tile * w
            norm_mask[y:y + tile_height, x:x + tile_width] += w

        # print(norm_mask.min(), norm_mask.max())
        norm_mask = np.clip(norm_mask, a_min=np.finfo(norm_mask.dtype).eps, a_max=None)
        normalized = np.divide(image, norm_mask).astype(dtype)
        crop = self.crop_to_orignal_size(normalized)
        return crop 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import dstack [as 別名]
def mapping(img):
    return 255.0 * (img - np.min(img)) / (np.max(img) - np.min(img))

# def read_data(path, batch_size):
#     filenames = os.listdir(path)
#     filenames_len = filenames.__len__()
#     rand_select = np.random.randint(0, filenames_len, [batch_size])
#     batch_data = np.zeros([batch_size, 256, 256, 3])
#     for i in range(batch_size):
#         img = np.array(Image.open(path + filenames[rand_select[i]]).resize([256, 256]))
#         try:
#             if img.shape.__len__() == 3:
#                 batch_data[i, :, :, :] = img[:256, :256, :3]
#             else:
#                 batch_data[i, :, :, :] = np.dstack((img, img, img))[:256, :256, :]
#         except:
#             img = np.array(Image.open(path + filenames[0]))
#             batch_data[i, :, :, :] = img[:256, :256, :3]
#     return batch_data 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import dstack [as 別名]
def train(self):

        list = os.listdir(self.path)
        nums_file = list.__len__()
        saver = tf.train.Saver()
        for i in range(10000):
            rand_select = np.random.randint(0, nums_file, [self.batch_size])
            INPUTS = np.zeros([self.batch_size, self.img_h, self.img_w, 3])
            INPUTS_CONDITION = np.zeros([self.batch_size, self.img_h, self.img_w, 3])
            for j in range(self.batch_size):
                img = np.array(Image.open(self.path + list[rand_select[j]]))
                img_h, img_w = img.shape[0], img.shape[1]
                INPUT_CON = misc.imresize(img[:, :img_w//2], [self.img_h, self.img_w]) / 127.5 - 1.0
                INPUTS_CONDITION[j] = np.dstack((INPUT_CON, INPUT_CON, INPUT_CON))
                INPUT = misc.imresize(img[:, img_w//2:], [self.img_h, self.img_w]) / 127.5 - 1.0
                INPUTS[j] = np.dstack((INPUT, INPUT, INPUT))
            self.sess.run(self.opt_dis, feed_dict={self.inputs: INPUTS, self.inputs_condition: INPUTS_CONDITION})
            self.sess.run(self.opt_gen, feed_dict={self.inputs: INPUTS, self.inputs_condition: INPUTS_CONDITION})
            if i % 10 == 0:
                [G_LOSS, D_LOSS] = self.sess.run([self.g_loss, self.d_loss], feed_dict={self.inputs: INPUTS, self.inputs_condition: INPUTS_CONDITION})
                print("Iteration: %d, d_loss: %f, g_loss: %f"%(i, D_LOSS, G_LOSS))
            if i % 100 == 0:
                saver.save(self.sess, "./save_para//model.ckpt") 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import dstack [as 別名]
def thin_plate_transform(x,y,offw,offh,imshape,shift_l=-0.05,shift_r=0.05,num_points=5,offsetMatrix=False):
    rand_p=np.random.choice(x.size,num_points,replace=False)
    movingPoints=np.zeros((1,num_points,2),dtype='float32')
    fixedPoints=np.zeros((1,num_points,2),dtype='float32')

    movingPoints[:,:,0]=x[rand_p]
    movingPoints[:,:,1]=y[rand_p]
    fixedPoints[:,:,0]=movingPoints[:,:,0]+offw*(np.random.rand(num_points)*(shift_r-shift_l)+shift_l)
    fixedPoints[:,:,1]=movingPoints[:,:,1]+offh*(np.random.rand(num_points)*(shift_r-shift_l)+shift_l)

    tps=cv2.createThinPlateSplineShapeTransformer()
    good_matches=[cv2.DMatch(i,i,0) for i in range(num_points)]
    tps.estimateTransformation(movingPoints,fixedPoints,good_matches)

    imh,imw=imshape
    x,y=np.meshgrid(np.arange(imw),np.arange(imh))
    x,y=x.astype('float32'),y.astype('float32')
    newxy=tps.applyTransformation(np.dstack((x.ravel(),y.ravel())))[1]
    newxy=newxy.reshape([imh,imw,2])

    if offsetMatrix:
        return newxy,newxy-np.dstack((x,y))
    else:
        return newxy 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import dstack [as 別名]
def _GetTrainingInputsAndLabels(self, config):
    """Generates training inputs and labels.

    Args:
      config: Dictionary with config for this unit test.

    Returns:
      Tuple `(training_inputs, training_labels, raw_training_inputs)` where
        `training_inputs` and `training_labels` are data for training and
        `raw_training_inputs` are representation of training_inputs for
        visualisation.
    """
    raw_training_inputs = config["x_generator"](
        num_points=config["num_training_records"],
        lattice_sizes=config["lattice_sizes"])

    if isinstance(raw_training_inputs, tuple):
      # This means that raw inputs are 2-d mesh grid. Convert them into list of
      # 2-d points.
      training_inputs = list(np.dstack(raw_training_inputs).reshape((-1, 2)))
    else:
      training_inputs = raw_training_inputs

    training_labels = [config["y_function"](x) for x in training_inputs]
    return training_inputs, training_labels, raw_training_inputs 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import dstack [as 別名]
def color_grid_thresh(img, s_thresh=(170,255), sx_thresh=(20, 100)):
	img = np.copy(img)
	# Convert to HLS color space and separate the V channel
	hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
	l_channel = hls[:,:,1]
	s_channel = hls[:,:,2]
	# Sobel x
	sobelx = cv2.Sobel(l_channel, cv2.CV_64F, 1, 0) # Take the derivateive in x
	abs_sobelx = np.absolute(sobelx) # Absolute x derivateive to accentuate lines
	scaled_sobel = np.uint8(255*abs_sobelx/np.max(abs_sobelx))

	# Threshold x gradient
	sxbinary = np.zeros_like(scaled_sobel)
	sxbinary[(scaled_sobel >= sx_thresh[0]) & (scaled_sobel <= sx_thresh[1])] = 1

	# Threshold color channel
	s_binary = np.zeros_like(s_channel)
	s_binary[(s_channel >= s_thresh[0]) & (s_channel <= s_thresh[1])] = 1

	# combine the two binary
	binary = sxbinary | s_binary

	# Stack each channel (for visual check the pixal sourse)
	# color_binary = np.dstack((np.zeros_like(sxbinary), sxbinary,s_binary)) * 255
	return binary 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import dstack [as 別名]
def draw_lane_fit(undist, warped ,Minv, left_fitx, right_fitx, ploty):
	# Drawing
	# Create an image to draw the lines on
	warp_zero = np.zeros_like(warped).astype(np.uint8)
	color_warp = np.dstack((warp_zero, warp_zero, warp_zero))

	# Recast the x and y points into usable format for cv2.fillPoly()
	pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
	pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
	pts = np.hstack((pts_left, pts_right))

	# Draw the lane onto the warped blank image
	cv2.fillPoly(color_warp, np.int_([pts]), (0,255,0))

	# Warp the blank back to original image space using inverse perspective matrix(Minv)
	newwarp = cv2.warpPerspective(color_warp, Minv, (undist.shape[1], undist.shape[0]))
	# Combine the result with the original image
	result = cv2.addWeighted(undist, 1, newwarp, 0.3, 0)

	return result 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import dstack [as 別名]
def _symmetrize_correlograms(correlograms):
    """Return the symmetrized version of the CCG arrays."""

    n_clusters, _, n_bins = correlograms.shape
    assert n_clusters == _

    # We symmetrize c[i, j, 0].
    # This is necessary because the algorithm in correlograms()
    # is sensitive to the order of identical spikes.
    correlograms[..., 0] = np.maximum(
        correlograms[..., 0], correlograms[..., 0].T)

    sym = correlograms[..., 1:][..., ::-1]
    sym = np.transpose(sym, (1, 0, 2))

    return np.dstack((sym, correlograms)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import dstack [as 別名]
def test_concat(make_data):
    """Test concatenation layer."""
    x, _, X = make_data

    # This replicates the input layer behaviour
    f = ab.InputLayer('X', n_samples=3)
    g = ab.InputLayer('Y', n_samples=3)

    catlayer = ab.Concat(f, g)

    F, KL = catlayer(X=x, Y=x)

    tc = tf.test.TestCase()
    with tc.test_session():
        forked = F.eval()
        orig = X.eval()
        assert forked.shape == orig.shape[0:2] + (2 * orig.shape[2],)
        assert np.all(forked == np.dstack((orig, orig)))
        assert KL.eval() == 0.0 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import dstack [as 別名]
def plot_cost_to_go_mountain_car(env, estimator, num_tiles=20):
    x = np.linspace(env.observation_space.low[0], env.observation_space.high[0], num=num_tiles)
    y = np.linspace(env.observation_space.low[1], env.observation_space.high[1], num=num_tiles)
    X, Y = np.meshgrid(x, y)
    Z = np.apply_along_axis(lambda _: -np.max(estimator.predict(_)), 2, np.dstack([X, Y]))

    fig = plt.figure(figsize=(10, 5))
    ax = fig.add_subplot(111, projection='3d')
    surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1,
                           cmap=matplotlib.cm.coolwarm, vmin=-1.0, vmax=1.0)
    ax.set_xlabel('Position')
    ax.set_ylabel('Velocity')
    ax.set_zlabel('Value')
    ax.set_title("Mountain \"Cost To Go\" Function")
    fig.colorbar(surf)
    plt.show() 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import dstack [as 別名]
def testDStackExecution(self):
        a_data = np.random.rand(10)
        b_data = np.random.rand(10)

        a = tensor(a_data, chunk_size=4)
        b = tensor(b_data, chunk_size=4)

        c = dstack([a, b])
        res = self.executor.execute_tensor(c, concat=True)[0]
        expected = np.dstack([a_data, b_data])
        self.assertTrue(np.array_equal(res, expected))

        a_data = np.random.rand(10, 20)
        b_data = np.random.rand(10, 20)

        a = tensor(a_data, chunk_size=3)
        b = tensor(b_data, chunk_size=4)

        c = dstack([a, b])
        res = self.executor.execute_tensor(c, concat=True)[0]
        expected = np.dstack([a_data, b_data])
        self.assertTrue(np.array_equal(res, expected)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import dstack [as 別名]
def depth_im_to_dist_im(depth_im, K):
  """Converts a depth image to a distance image.
  :param depth_im: hxw ndarray with the input depth image, where depth_im[y, x]
    is the Z coordinate of the 3D point [X, Y, Z] that projects to pixel [x, y],
    or 0 if there is no such 3D point (this is a typical output of the
    Kinect-like sensors).
  :param K: 3x3 ndarray with an intrinsic camera matrix.
  :return: hxw ndarray with the distance image, where dist_im[y, x] is the
    distance from the camera center to the 3D point [X, Y, Z] that projects to
    pixel [x, y], or 0 if there is no such 3D point.
  """
  xs, ys = np.meshgrid(
    np.arange(depth_im.shape[1]), np.arange(depth_im.shape[0]))

  Xs = np.multiply(xs - K[0, 2], depth_im) * (1.0 / K[0, 0])
  Ys = np.multiply(ys - K[1, 2], depth_im) * (1.0 / K[1, 1])

  dist_im = np.sqrt(
    Xs.astype(np.float64)**2 +
    Ys.astype(np.float64)**2 +
    depth_im.astype(np.float64)**2)
  # dist_im = np.linalg.norm(np.dstack((Xs, Ys, depth_im)), axis=2)  # Slower.

  return dist_im 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import dstack [as 別名]
def compute_history_3d(self, pos_history):
        """Compute a 3D position matrix

        The first two columns are the 2D position in the x and y axes
        respectively, while the third column is the fitness on that given
        position.

        Parameters
        ----------
        pos_history : numpy.ndarray
            Two-dimensional position matrix history of shape
            :code:`(iterations, n_particles, 2)`

        Returns
        -------
        numpy.ndarray
            3D position matrix of shape :code:`(iterations, n_particles, 3)`
        """
        fitness = np.array(list(map(self.func, pos_history)))
        return np.dstack((pos_history, fitness)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import dstack [as 別名]
def test_luminance():
    source = sn.load('tests/sobel_input.png')[:,:,:3]

    L = rgb2gray(source)
    skresult = np.dstack([L, L, L])
    small_skresult = sn.resize(skresult, width=256)

    L = sn.rgb_to_luminance(source)
    snresult = np.dstack([L, L, L])
    small_snresult = sn.resize(snresult, width=256)

    L = skimage_sobel(source)
    sksobel = np.dstack([L, L, L])
    small_sksobel = sn.resize(sksobel, width=256)

    L = sn.rgb_to_luminance(source)
    L = sn.compute_sobel(L)
    snsobel = np.dstack([L, L, L])
    small_snsobel = sn.resize(snsobel, width=256)

    sn.show(np.hstack([
        small_skresult,
        small_snresult,
        small_sksobel,
        small_snsobel])) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import dstack [as 別名]
def elastic_transform(image,elastic_value_x ,elastic_value_y):
    """Elastic deformation of images as described in [Simard2003]_ (with modifications JUST in Y-DIRECTION).
    .. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
         Convolutional Neural Networks applied to Visual Document Analysis", in
         Proc. of the International Conference on Document Analysis and
         Recognition, 2003.

     Based on https://gist.github.com/erniejunior/601cdf56d2b424757de5
    """
    shape = image.shape
    random_state = np.random.RandomState(None)
    nY = shape[0] // 25
    nX = shape[1] // 25
    sigma = min(shape[1], shape[0]) * 0.0025
    alpha_X = elastic_value_x * min(shape[0], shape[1])
    alpha_Y = elastic_value_y * min(shape[0], shape[1])
    dx = gaussian_filter((random_state.rand(nY, nX) * 2 - 1), sigma)
    dy = gaussian_filter((random_state.rand(nY, nX) * 2 - 1), sigma)
    x, y, z = np.meshgrid(np.arange(shape[1]), np.arange(shape[0]), np.arange(shape[2]))
    dx = misc.imresize(dx, [shape[0], shape[1]], interp='bicubic')
    dy = misc.imresize(dy, [shape[0], shape[1]], interp='bicubic')
    # plt.imshow(dx, cmap=plt.cm.gray)
    # plt.show()
    dxT = []
    dyT = []
    for dummy in range(shape[2]):
        dxT.append(dx)
        dyT.append(dy)
    dx = np.dstack(dxT)
    dy = np.dstack(dyT)
    dx = dx * alpha_X
    dy = dy * alpha_Y
    indices = np.reshape(y + dy, (-1, 1)), np.reshape(x + dx, (-1, 1)), np.reshape(z, (-1, 1))
    image = map_coordinates(image, indices, order=1).reshape(shape)
    return image