def showAnns(self, anns):
"""
Display the specified annotations.
:param anns (array of object): annotations to display
:return: None
"""
if len(anns) == 0:
return 0
if self.dataset['type'] == 'instances':
ax = plt.gca()
polygons = []
color = []
for ann in anns:
c = np.random.random((1, 3)).tolist()[0]
if type(ann['segmentation']) == list:
# polygon
for seg in ann['segmentation']:
poly = np.array(seg).reshape((len(seg)/2, 2))
polygons.append(Polygon(poly, True,alpha=0.4))
color.append(c)
else:
# mask
mask = COCO.decodeMask(ann['segmentation'])
img = np.ones( (mask.shape[0], mask.shape[1], 3) )
if ann['iscrowd'] == 1:
color_mask = np.array([2.0,166.0,101.0])/255
if ann['iscrowd'] == 0:
color_mask = np.random.random((1, 3)).tolist()[0]
for i in range(3):
img[:,:,i] = color_mask[i]
ax.imshow(np.dstack( (img, mask*0.5) ))
p = PatchCollection(polygons, facecolors=color, edgecolors=(0,0,0,1), linewidths=3, alpha=0.4)
ax.add_collection(p)
if self.dataset['type'] == 'captions':
for ann in anns:
print ann['caption']
python类dstack()的实例源码
def compute_eng_color(img, rgb_weights):
"""
Computes the energy of an image using its color properties
Args:
img4 (n,m,4 numpy matrix): RGB image with additional mask layer.
rgb_weights (n,m numpy matrix): img-specific weights for RBG values
Returns:
n,m numpy matrix: Color energy map of the provided image
"""
eng = np.dstack((
img[:, :, 0] * rgb_weights[0],
img[:, :, 1] * rgb_weights[1],
img[:, :, 2] * rgb_weights[2]
))
eng = np.sum(eng, axis=2)
return eng
def read_naip(file_path, bands_to_use):
"""
Read in a NAIP, based on www.machinalis.com/blog/python-for-geospatial-data-processing.
Bands_to_use is an array like [0,0,0,1], designating whether to use each band (R, G, B, IR).
"""
raster_dataset = gdal.Open(file_path, gdal.GA_ReadOnly)
bands_data = []
index = 0
for b in range(1, raster_dataset.RasterCount + 1):
band = raster_dataset.GetRasterBand(b)
if bands_to_use[index] == 1:
bands_data.append(band.ReadAsArray())
index += 1
bands_data = numpy.dstack(bands_data)
return raster_dataset, bands_data
def renderRelighting(renderer, albedo, spec, roughness, normal):
renderer.SetPointLight(0, 0.27, -0.25, 1, 0, 0.6, 0.6, 0.6)
renderer.SetAlbedoMap(albedo)
renderer.SetSpecValue(spec)
renderer.SetRoughnessValue(roughness)
normal = normal * 2.0 - 1.0
normal[0] = normal[0] * 2.5
len = np.linalg.norm(normal, axis = 2)
normal = normal / np.dstack((len, len, len))
normal = 0.5*(normal + 1.0)
renderer.SetNormalMap(normal*2.0 - 1.0)
img = renderer.Render()
renderer.SetEnvLightByID(43, 30, -10.0)
renderer.SetAlbedoMap(albedo)
renderer.SetSpecValue(spec)
renderer.SetRoughnessValue(roughness)
renderer.SetNormalMap(normal*2.0 - 1.0)
img_1 = renderer.Render()
return 1.2 * img + 0.8 * img_1
def new_image(self, image, diag=False):
if isinstance(image, str):
self.image_file = image
self.image = np.array(PIL.Image.open(image))
else:
self.image_file = None
self.image = image
# Get the image into the right format.
if self.image.dtype != np.uint8:
raise TypeError('Image %s dtype is not unsigned 8 bit integer, image.dtype is %s.'%(
'"%s"'%self.image_file if self.image_file is not None else 'argument',
self.image.dtype))
self.image = np.squeeze(self.image)
if len(self.image.shape) == 2:
self.image = np.dstack([self.image] * 3)
self.preprocess_edges()
self.randomize_view()
if diag:
plt.figure('Image')
plt.title('Image')
plt.imshow(self.image, interpolation='nearest')
plt.show()
def _fix_alpha_channel(self):
# This is a fix for a bug where the Alpha channel was dropped.
colors3to4 = [(c[:3], c[3]) for c in self.names.keys()]
colors3to4 = dict(colors3to4)
assert(len(colors3to4) == len(self.names)) # Dropped alpha channel causes colors to collide :(
for lbl in self.labels:
if lbl is None:
continue # No label file created yet.
img = Image.open(lbl)
size = img.size
img = np.array(img)
if img.shape[2] == 4:
continue # Image has alpha channel, good.
elif img.shape[2] == 3:
# Lookup each (partial) color and find what its alpha should be.
alpha = np.apply_along_axis(lambda c: colors3to4[tuple(c)], 2, img)
data = np.dstack([img, np.array(alpha, dtype=np.uint8)])
new_img = Image.frombuffer("RGBA", size, data, "raw", "RGBA", 0, 1)
new_img.save(lbl)
print("FIXED", lbl)
def plot_img_with_mask(img,mask,mask2=None, line_size=2):
kernel = np.ones((line_size,line_size),dtype=np.uint8)
if np.max(img)<=1.0:
img = np.array(img*255,dtype=np.uint8);
mask = np.array(mask*255, dtype=np.uint8);
color_img = np.dstack((img,img,img));
edges = binary_dilation(canny(mask,sigma=1.0),kernel);
color_img[edges,0] = 255;
color_img[edges,1] = 0;
color_img[edges,2] = 0;
if mask2 is not None:
mask2 = np.array(mask2*255,dtype=np.uint8);
edges2 = binary_dilation(canny(mask2,sigma=1.0),kernel);
color_img[edges2,2] = 255;
color_img[edges2,0:2] = 0;
plt.imshow(color_img)
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()
def test_encode_data_roundtrip():
minrand, maxrand = np.sort(np.random.randint(-427, 8848, 2))
testdata = np.round((np.sum(
np.dstack(
np.indices((512, 512),
dtype=np.float64)),
axis=2) / (511. + 511.)) * maxrand, 2) + minrand
baseval = -1000
interval = 0.1
rtripped = _decode(data_to_rgb(testdata.copy(), baseval, interval), baseval, interval)
assert testdata.min() == rtripped.min()
assert testdata.max() == rtripped.max()
def process_one(image_dir, page_dir, output_dir, basename, colormap, color_labels):
image_filename = os.path.join(image_dir, "{}.jpg".format(basename))
page_filename = os.path.join(page_dir, "{}.xml".format(basename))
page = PAGE.parse_file(page_filename)
text_lines = [tl for tr in page.text_regions for tl in tr.text_lines]
graphic_regions = page.graphic_regions
img = imread(image_filename, mode='RGB')
gt = np.zeros_like(img[:, :, 0])
mask1 = cv2.fillPoly(gt.copy(), [PAGE.Point.list_to_cv2poly(tl.coords)
for tl in text_lines if 'comment' in tl.id], 1)
mask2 = cv2.fillPoly(gt.copy(), [PAGE.Point.list_to_cv2poly(tl.coords)
for tl in text_lines if not 'comment' in tl.id], 1)
mask3 = cv2.fillPoly(gt.copy(), [PAGE.Point.list_to_cv2poly(tl.coords)
for tl in graphic_regions], 1)
arr = np.dstack([mask1, mask2, mask3])
gt_img = convert_array_masks(arr, colormap, color_labels)
save_and_resize(img, os.path.join(output_dir, 'images', '{}.jpg'.format(basename)))
save_and_resize(gt_img, os.path.join(output_dir, 'labels', '{}.png'.format(basename)), nearest=True)
def __init__(self, m):
k = 4*m + 3
self.degree = k
theta = 2*numpy.pi * numpy.arange(1, k+2) / (k+1)
p, w = numpy.polynomial.legendre.leggauss(m+1)
# scale points to [r0, r1] (where r0 = 0, r1 = 1 for now)
p = numpy.sqrt(0.5*(p + 1.0))
p_theta = numpy.dstack(numpy.meshgrid(p, theta)).reshape(-1, 2).T
self.points = numpy.column_stack([
p_theta[0] * numpy.cos(p_theta[1]),
p_theta[0] * numpy.sin(p_theta[1]),
])
# When integrating between 0 and 1, the weights are exactly the
# Gauss-Legendre weights, scaled according to the disk area.
self.weights = numpy.tile(0.5 * numpy.pi / (k+1) * w, k+1)
return
def _process(self, img, key=None):
if self.p.fast:
return self._fast_process(img, key)
proj = self.p.projection
if proj == img.crs:
return img
x0, x1 = img.range(0)
y0, y1 = img.range(1)
xn, yn = img.interface.shape(img, gridded=True)[:2]
px0, py0, px1, py1 = project_extents((x0, y0, x1, y1),
img.crs, proj)
src_ext, trgt_ext = (x0, x1, y0, y1), (px0, px1, py0, py1)
arrays = []
for vd in img.vdims:
arr = img.dimension_values(vd, flat=False)
projected, extents = warp_array(arr, proj, img.crs, (xn, yn),
src_ext, trgt_ext)
arrays.append(projected)
projected = np.dstack(arrays) if len(arrays) > 1 else arrays[0]
data = np.flipud(projected)
bounds = (extents[0], extents[2], extents[1], extents[3])
return img.clone(data, bounds=bounds, kdims=img.kdims,
vdims=img.vdims, crs=proj)
def geo_mesh(element):
"""
Get mesh data from a 2D Element ensuring that if the data is
on a cylindrical coordinate system and wraps globally that data
actually wraps around.
"""
if len(element.vdims) > 1:
xs, ys = (element.dimension_values(i, False, False)
for i in range(2))
zs = np.dstack([element.dimension_values(i, False, False)
for i in range(2, 2+len(element.vdims))])
else:
xs, ys, zs = (element.dimension_values(i, False, False)
for i in range(3))
lon0, lon1 = element.range(0)
if isinstance(element.crs, ccrs._CylindricalProjection) and (lon1 - lon0) == 360:
xs = np.append(xs, xs[0:1] + 360, axis=0)
zs = np.ma.concatenate([zs, zs[:, 0:1]], axis=1)
return xs, ys, zs
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
def test_100_inputs(self):
"""Test that 100 input rings work"""
def dstack_handler(*args):
"""Stack all input arrays"""
return np.dstack(tuple(args))
number_inputs = 100
connections = {'in_1': 0, 'out_1': 1}
for index in range(number_inputs):
self.blocks.append([
NumpyBlock(function=np.copy),
{'in_1': 0, 'out_1': index + 2}])
connections['in_' + str(index + 2)] = index + 2
self.blocks.append([
NumpyBlock(function=dstack_handler, inputs=len(connections) - 1),
connections])
self.expected_result = np.dstack((self.test_array,) * (len(connections) - 1)).ravel()
def find_intersections(A,B):
arrayMinimum = lambda x1, x2: np.where(x1<x2, x1, x2)
arrayMaximum = lambda x1, x2: np.where(x1>x2, x1, x2)
arrayAll = lambda abools: np.dstack(abools).all(axis=2)
slope = lambda line: (lambda d: d[:,1]/d[:,0])(np.diff(line, axis=0))
x11, x21 = np.meshgrid(A[:-1, 0], B[:-1, 0])
x12, x22 = np.meshgrid(A[1:, 0], B[1:, 0])
y11, y21 = np.meshgrid(A[:-1, 1], B[:-1, 1])
y12, y22 = np.meshgrid(A[1:, 1], B[1:, 1])
m1, m2 = np.meshgrid(slope(A), slope(B))
# Here we use masked arrays to properly treat the rare case where a line segment is perfectly vertical
_m1 = np.ma.masked_array(m1,m1==-np.inf)
_m2 = np.ma.masked_array(m2,m2==-np.inf)
yi = (_m1*(x21-x11-y21/_m2)+y11)/(1-_m1/_m2)
xi = (yi-y21)/_m2+x21
xconds = (arrayMinimum(x11, x12) < xi, xi <= arrayMaximum(x11, x12),
arrayMinimum(x21, x22) < xi, xi <= arrayMaximum(x21, x22) )
yconds = (arrayMinimum(y11, y12) < yi, yi <= arrayMaximum(y11, y12),
arrayMinimum(y21, y22) < yi, yi <= arrayMaximum(y21, y22) )
return xi[arrayAll(xconds)], yi[arrayAll(yconds)]
def find_intersections(A,B):
arrayMinimum = lambda x1, x2: np.where(x1<x2, x1, x2)
arrayMaximum = lambda x1, x2: np.where(x1>x2, x1, x2)
arrayAll = lambda abools: np.dstack(abools).all(axis=2)
slope = lambda line: (lambda d: d[:,1]/d[:,0])(np.diff(line, axis=0))
x11, x21 = np.meshgrid(A[:-1, 0], B[:-1, 0])
x12, x22 = np.meshgrid(A[1:, 0], B[1:, 0])
y11, y21 = np.meshgrid(A[:-1, 1], B[:-1, 1])
y12, y22 = np.meshgrid(A[1:, 1], B[1:, 1])
m1, m2 = np.meshgrid(slope(A), slope(B))
# Here we use masked arrays to properly treat the rare case where a line segment is perfectly vertical
_m1 = np.ma.masked_array(m1,m1==-np.inf)
_m2 = np.ma.masked_array(m2,m2==-np.inf)
yi = (_m1*(x21-x11-y21/_m2)+y11)/(1-_m1/_m2)
xi = (yi-y21)/_m2+x21
xconds = (arrayMinimum(x11, x12) < xi, xi <= arrayMaximum(x11, x12),
arrayMinimum(x21, x22) < xi, xi <= arrayMaximum(x21, x22) )
yconds = (arrayMinimum(y11, y12) < yi, yi <= arrayMaximum(y11, y12),
arrayMinimum(y21, y22) < yi, yi <= arrayMaximum(y21, y22) )
return xi[arrayAll(xconds)], yi[arrayAll(yconds)]
def test_iou():
# 3 x 5 x 2
grid = np.dstack(np.meshgrid(10 * np.arange(5), 10 * np.arange(3)))
boxes = np.tile(
np.expand_dims(np.expand_dims(np.array([10, 10]), 0), 0),
[3, 5, 1]
)
proposals = np.reshape(np.concatenate([grid, boxes], axis=2), (-1, 4))
proposals = tf.constant(proposals, tf.float32)
ground_truth = tf.constant(np.array([
[4, 4, 10, 10],
[10, 10, 10, 10]
]), tf.float32)
iou_metric = sess.run(model.iou(ground_truth, 2, proposals, 15))
assert equal(iou_metric[0, 0], 0.2195)
assert equal(iou_metric[1, 0], 0.1363)
assert equal(iou_metric[5, 0], 0.1363)
assert equal(iou_metric[6, 0], 0.0869)
assert equal(iou_metric[6, 1], 1.0)
for (boxes, count) in [(proposals, 15), (ground_truth, 2)]:
iou_metric = sess.run(model.iou(boxes, count, boxes, count))
assert np.all(np.diag(iou_metric) == 1)
def _init_random_maze(self):
# init goal position
goal = np.zeros_like(self._level)
while True:
row_idx = np.random.randint(0, self._level.shape[0])
col_idx = np.random.randint(0, self._level.shape[1])
if self._level[row_idx, col_idx] == 0:
goal[row_idx, col_idx] = 1
self._goal_pos = np.array([row_idx, col_idx])
break
# init player position
player = np.zeros_like(self._level)
while True:
row_idx = np.random.randint(0, self._level.shape[0])
col_idx = np.random.randint(0, self._level.shape[1])
if self._level[row_idx, col_idx] == 0 and goal[row_idx, col_idx] == 0:
player[row_idx, col_idx] = 1
self._player_pos = np.array([row_idx, col_idx])
break
# stack all together in depth (along third axis)
self._maze = np.dstack((self._level, goal, player))
def draw_lines(img, lines, color=[255, 0, 0], thickness=2):
"""
averaging
&
extrapolating
lines points achieved.
"""
if len(img.shape) == 2: # grayscale image -> make a "color" image out of it
img = np.dstack((img, img, img))
for line in lines:
for x1, y1, x2, y2 in line:
if x1 >= 0 and x1 < img.shape[1] and \
y1 >= 0 and y1 < img.shape[0] and \
x2 >= 0 and x2 < img.shape[1] and \
y2 >= 0 and y2 < img.shape[0]:
cv2.line(img, (x1, y1), (x2, y2), color, thickness)
else:
print('BAD LINE (%d, %d, %d, %d)' % (x1, y1, x2, y2))
def sort_eigensystem(parameters_dict):
eigenvectors = np.stack(tensor_spherical_to_cartesian(np.squeeze(parameters_dict['theta']),
np.squeeze(parameters_dict['phi']),
np.squeeze(parameters_dict['psi'])), axis=0)
eigenvalues = np.atleast_2d(np.squeeze(np.dstack([parameters_dict['d'],
parameters_dict['dperp0'],
parameters_dict['dperp1']])))
ranking = np.atleast_2d(np.squeeze(np.argsort(eigenvalues, axis=1, kind='mergesort')[:, ::-1]))
voxels_range = np.arange(ranking.shape[0])
sorted_eigenvalues = np.concatenate([eigenvalues[voxels_range, ranking[:, ind], None]
for ind in range(ranking.shape[1])], axis=1)
sorted_eigenvectors = np.stack([eigenvectors[ranking[:, ind], voxels_range, :]
for ind in range(ranking.shape[1])])
return sorted_eigenvalues, sorted_eigenvectors, ranking
def draw_matches(self, im1, pos1, im2, pos2, matches, filename="matches.jpg"):
self._log("drawing matches into '%s'..." % filename)
row1, col1 = im1.shape
row2, col2 = im2.shape
im_out = np.zeros((max(row1, row2), col1+col2, 3), dtype=np.uint8)
im_out[:row1, :col1] = np.dstack([im1]*3)
im_out[:row2, col1:] = np.dstack([im2]*3)
l = len(matches)
for ind, (i, j, d) in list(enumerate(matches))[::-1]:
d /= para.descr_match_threshold # map to [0, 1]
_pos1, _pos2 = pos1[i], pos2[j]
color = hsv_to_rgb(int(d * 120 - 120), 1, 1 - d / 3)
color = [int(c * 255) for c in color]
cv2.line(im_out, (_pos1[1], _pos1[0]), (_pos2[1]+col1, _pos2[0]), color, 1)
cv2.imwrite(filename, im_out)
##########################
# Utility
##########################
def get_cells_for_tile(self, tile_h, tile_v):
"""
Returns the list of cells covered by the given modis tile. The tile
is identified by its MODIS grid coordinates
"""
range_x = np.arange(tile_h * self.n_cells_per_tile_x,
(tile_h + 1) * self.n_cells_per_tile_x)
range_y = np.arange(tile_v * self.n_cells_per_tile_y,
(tile_v + 1) * self.n_cells_per_tile_y)
cells_ij = np.dstack(
np.meshgrid(range_y, range_x, indexing='ij')).reshape(-1, 2)
cells = np.ravel_multi_index(
(cells_ij[:, 0], cells_ij[:, 1]),
(self.n_cells_y, self.n_cells_x)
)
# sanity check
assert len(cells) == self.n_cells_per_tile_x * self.n_cells_per_tile_y
return cells
def test_nonrectangular_add(self):
rgba1 = np.ones((64, 1, 4))
z1 = np.expand_dims(np.arange(64.), 1)
rgba2 = np.zeros((64, 1, 4))
z2 = np.expand_dims(np.arange(63., -1., -1.), 1)
exact_rgba = np.concatenate((np.ones(32), np.zeros(32)))
exact_rgba = np.expand_dims(exact_rgba, 1)
exact_rgba = np.dstack((exact_rgba, exact_rgba, exact_rgba, exact_rgba))
exact_z = np.concatenate((np.arange(32.), np.arange(31.,-1.,-1.)))
exact_z = np.expand_dims(exact_z, 1)
buff1 = ZBuffer(rgba1, z1)
buff2 = ZBuffer(rgba2, z2)
buff = buff1 + buff2
assert_almost_equal(buff.rgba, exact_rgba)
assert_almost_equal(buff.z, exact_z)
est_rel_entro_HJW.py 文件源码
项目：HJW_KL_divergence_estimator
作者: Mathegineer
项目源码
文件源码
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def log_mat(x, n, g_coeff, c_1, const):
with np.errstate(divide='ignore', invalid='ignore'):
K = g_coeff.shape[0] - 1
thres = 2 * c_1 * math.log(n) / n
[T, X] = np.meshgrid(thres, x)
ratio = np.clip(2*X/T - 1, 0, 1)
# force MATLAB-esque behavior with NaN, inf
ratio[T == 0] = 1.0
ratio[X == 0] = 0.0
q = np.reshape(np.arange(K), [1, 1, K])
g = np.tile(np.reshape(g_coeff, [1, 1, K + 1]), [c_1.shape[1], 1])
g[:, :, 0] = g[:, :, 0] + np.log(thres)
MLE = np.log(X) + (1-X) / (2*X*n)
MLE[X == 0] = -np.log(n) - const
tmp = (n*X[:,:,np.newaxis] - q)/(T[:,:,np.newaxis]*(n - q))
polyApp = np.sum(np.cumprod(np.dstack([np.ones(T.shape + (1,)), tmp]),
axis=2) * g, axis=2)
polyFail = np.logical_or(np.isnan(polyApp), np.isinf(polyApp))
polyApp[polyFail] = MLE[polyFail]
return ratio*MLE + (1-ratio)*polyApp
def getNeighborsSorted(self, id, objType):
if id not in self.objectDict: return None
distColumn, idColumn = self.kdTrees[objType][1].query(self.get(id).pos, k=self.maxSearch[objType])
indexToID = self.kdTrees[objType][0]
for i in range(0, idColumn.size):
if distColumn[i] == float('inf'):
idColumn = idColumn[:i]
distColumn = distColumn[:i]
break
idColumn[i] = indexToID[idColumn[i]]
return np.dstack([idColumn, distColumn])[0]
def MakePaddedSequenceTensorFromListArray( in_arr, pad_value=0., doWhitening=False, maxlen=None, padding = 'pre'):
seq_list = numpy.array([])
arr = in_arr
if len(in_arr.shape)==1:
arr = numpy.array([ in_arr ])
for i in range( arr.shape[1] ):
current = convertSequencesFromListArray( arr[:,i], dopad=True, doWhitening=doWhitening, maxlen=maxlen, padding = padding )
if len(seq_list)==0:
seq_list = current
else:
seq_list = numpy.dstack((seq_list, current) )
return seq_list
def MakePaddedSequenceTensor( filename_list, doWhitening=False, maxlen=None):
seq_list = numpy.array([])
for fn in filename_list:
current = convertSequences( fn, dopad=True, doWhitening= doWhitening, maxlen=maxlen)
if len(seq_list)==0:
seq_list = current
else:
seq_list = numpy.dstack((seq_list, current) )
return seq_list
###################################################################################################
# maniplation functions
###################################################################################################
def ensurebuf(self, invalidate=True):
if self.dbuf is None:
if self.dpil is not None:
self.dbuf = self.dpil.tostring("raw", "RGBX", 0, 1)
elif self.darr is not None:
data = self.scaledpixelarray(0,255.999)
self.dbuf = np.dstack(( np.flipud(np.rollaxis(data,1)).astype(np.uint8),
np.zeros(self.shape[::-1],np.uint8) )).tostring()
else:
raise ValueError("No source data for conversion to buffer")
if invalidate:
self.dpil = None
self.darr = None
self.rangearr = None
## This private function ensures that there is a valid numpy array representation, converting from
# one of the other representations if necessary, and invalidating the other representations if requested.
def asarray(self, axis=3):
"""
This function ...
:return:
"""
# Get a list that contains the frames
frame_list = self.frames.as_list()
# Stack the frames into a 3D numpy array
if axis == 3: return np.dstack(frame_list)
elif axis == 2: return np.hstack(frame_list)
elif axis == 1: return np.vstack(frame_list)
elif axis == 0: return np.stack(frame_list)
else: raise ValueError("'axis' parameter should be integer 0-3")
# -----------------------------------------------------------------
