def _convert(matrix, arr):
"""Do the color space conversion.
Parameters
----------
matrix : array_like
The 3x3 matrix to use.
arr : array_like
The input array.
Returns
-------
out : ndarray, dtype=float
The converted array.
"""
arr = _prepare_colorarray(arr)
arr = np.swapaxes(arr, 0, -1)
oldshape = arr.shape
arr = np.reshape(arr, (3, -1))
out = np.dot(matrix, arr)
out.shape = oldshape
out = np.swapaxes(out, -1, 0)
return np.ascontiguousarray(out)
python类ascontiguousarray()的实例源码
def filter_sort_unique(self, max_objval=float('Inf')):
# filter
if max_objval < float('inf'):
good_idx = self.objvals <= max_objval
self.objvals = self.objvals[good_idx]
self.solutions = self.solutions[good_idx]
if len(self.objvals) > 0:
sort_idx = np.argsort(self.objvals)
self.objvals = self.objvals[sort_idx]
self.solutions = self.solutions[sort_idx]
# unique
b = np.ascontiguousarray(self.solutions).view(
np.dtype((np.void, self.solutions.dtype.itemsize * self.P)))
_, unique_idx = np.unique(b, return_index=True)
self.objvals = self.objvals[unique_idx]
self.solutions = self.solutions[unique_idx]
def get_screen(self):
screen = self.env.render(mode='rgb_array').transpose(
(2, 0, 1)) # transpose into torch order (CHW)
# Strip off the top and bottom of the screen
screen = screen[:, 160:320]
view_width = 320
cart_location = self.get_cart_location()
if cart_location < view_width // 2:
slice_range = slice(view_width)
elif cart_location > (self.screen_width - view_width // 2):
slice_range = slice(-view_width, None)
else:
slice_range = slice(cart_location - view_width // 2,
cart_location + view_width // 2)
# Strip off the edges, so that we have a square image centered on a cart
screen = screen[:, :, slice_range]
# Convert to float, rescare, convert to torch tensor
# (this doesn't require a copy)
screen = np.ascontiguousarray(screen, dtype=np.float32) / 255
screen = torch.from_numpy(screen)
# Resize, and add a batch dimension (BCHW)
return self.resize(screen).numpy()
def _compute_targets(rois, overlaps, labels):
"""Compute bounding-box regression targets for an image."""
# Indices of ground-truth ROIs
gt_inds = np.where(overlaps == 1)[0]
if len(gt_inds) == 0:
# Bail if the image has no ground-truth ROIs
return np.zeros((rois.shape[0], 5), dtype=np.float32)
# Indices of examples for which we try to make predictions
ex_inds = np.where(overlaps >= cfg.TRAIN.BBOX_THRESH)[0]
# Get IoU overlap between each ex ROI and gt ROI
ex_gt_overlaps = bbox_overlaps(
np.ascontiguousarray(rois[ex_inds, :], dtype=np.float),
np.ascontiguousarray(rois[gt_inds, :], dtype=np.float))
# Find which gt ROI each ex ROI has max overlap with:
# this will be the ex ROI's gt target
gt_assignment = ex_gt_overlaps.argmax(axis=1)
gt_rois = rois[gt_inds[gt_assignment], :]
ex_rois = rois[ex_inds, :]
targets = np.zeros((rois.shape[0], 5), dtype=np.float32)
targets[ex_inds, 0] = labels[ex_inds]
targets[ex_inds, 1:] = bbox_transform(ex_rois, gt_rois)
return targets
def fit(self, X, y):
"""
Estimate the topic distributions per document (theta), term
distributions per topic (phi), and regression coefficients (eta).
Parameters
----------
X : array-like, shape = (n_docs, n_terms)
The document-term matrix.
y : array-like, shape = (n_docs,)
Response values for each document.
"""
self.doc_term_matrix = X
self.n_docs, self.n_terms = X.shape
self.n_tokens = X.sum()
doc_lookup, term_lookup = self._create_lookups(X)
# iterate
self.theta, self.phi, self.eta, self.loglikelihoods = gibbs_sampler_slda(
self.n_iter, self.n_report_iter,
self.n_topics, self.n_docs, self.n_terms, self.n_tokens,
self.alpha, self.beta, self.mu, self.nu2, self.sigma2,
doc_lookup, term_lookup,
np.ascontiguousarray(y, dtype=np.float64), self.seed)
def fit(self, X, y):
"""
Estimate the topic distributions per document (theta), term
distributions per topic (phi), and regression coefficients (eta).
Parameters
----------
X : array-like, shape = (n_docs, n_terms)
The document-term matrix.
y : array-like, shape = (n_docs,)
Response values for each document.
"""
self.doc_term_matrix = X
self.n_docs, self.n_terms = X.shape
self.n_tokens = X.sum()
doc_lookup, term_lookup = self._create_lookups(X)
# iterate
self.theta, self.phi, self.eta, self.loglikelihoods = gibbs_sampler_blslda(
self.n_iter, self.n_report_iter,
self.n_topics, self.n_docs, self.n_terms, self.n_tokens,
self.alpha, self.beta, self.mu, self.nu2, self.b,
doc_lookup, term_lookup,
np.ascontiguousarray(y, dtype=np.float64), self.seed)
def fit(self, X, y):
"""
Estimate the topic distributions per document (theta), term
distributions per topic (phi), and regression coefficients (eta).
Parameters
----------
X : array-like, shape = (n_docs, n_terms)
The document-term matrix.
y : array-like, shape = (n_docs,)
Response values for each document.
"""
self.doc_term_matrix = X
self.n_docs, self.n_terms = X.shape
self.n_tokens = X.sum()
doc_lookup, term_lookup = self._create_lookups(X)
# iterate
self.theta, self.phi, self.eta, self.loglikelihoods = gibbs_sampler_slda(
self.n_iter, self.n_report_iter,
self.n_topics, self.n_docs, self.n_terms, self.n_tokens,
self.alpha, self.beta, self.mu, self.nu2, self.sigma2,
doc_lookup, term_lookup,
np.ascontiguousarray(y, dtype=np.float64), self.seed)
def fit(self, X, y):
"""
Estimate the topic distributions per document (theta), term
distributions per topic (phi), and regression coefficients (eta).
Parameters
----------
X : array-like, shape = (n_docs, n_terms)
The document-term matrix.
y : array-like, shape = (n_docs,)
Response values for each document.
"""
self.doc_term_matrix = X
self.n_docs, self.n_terms = X.shape
self.n_tokens = X.sum()
doc_lookup, term_lookup = self._create_lookups(X)
# iterate
self.theta, self.phi, self.eta, self.loglikelihoods = gibbs_sampler_blslda(
self.n_iter, self.n_report_iter,
self.n_topics, self.n_docs, self.n_terms, self.n_tokens,
self.alpha, self.beta, self.mu, self.nu2, self.b,
doc_lookup, term_lookup,
np.ascontiguousarray(y, dtype=np.float64), self.seed)
def _compute_targets(rois, overlaps, labels):
"""Compute bounding-box regression targets for an image."""
# Indices of ground-truth ROIs
gt_inds = np.where(overlaps == 1)[0]
if len(gt_inds) == 0:
# Bail if the image has no ground-truth ROIs
return np.zeros((rois.shape[0], 5), dtype=np.float32)
# Indices of examples for which we try to make predictions
ex_inds = np.where(overlaps >= cfg.TRAIN.BBOX_THRESH)[0]
# Get IoU overlap between each ex ROI and gt ROI
ex_gt_overlaps = bbox_overlaps(
np.ascontiguousarray(rois[ex_inds, :], dtype=np.float),
np.ascontiguousarray(rois[gt_inds, :], dtype=np.float))
# Find which gt ROI each ex ROI has max overlap with:
# this will be the ex ROI's gt target
gt_assignment = ex_gt_overlaps.argmax(axis=1)
gt_rois = rois[gt_inds[gt_assignment], :]
ex_rois = rois[ex_inds, :]
targets = np.zeros((rois.shape[0], 5), dtype=np.float32)
targets[ex_inds, 0] = labels[ex_inds]
targets[ex_inds, 1:] = bbox_transform(ex_rois, gt_rois)
return targets
def console_fill_foreground(con,r,g,b) :
if len(r) != len(g) or len(r) != len(b):
raise TypeError('R, G and B must all have the same size.')
if (numpy_available and isinstance(r, numpy.ndarray) and
isinstance(g, numpy.ndarray) and isinstance(b, numpy.ndarray)):
#numpy arrays, use numpy's ctypes functions
r = numpy.ascontiguousarray(r, dtype=numpy.int_)
g = numpy.ascontiguousarray(g, dtype=numpy.int_)
b = numpy.ascontiguousarray(b, dtype=numpy.int_)
cr = r.ctypes.data_as(POINTER(c_int))
cg = g.ctypes.data_as(POINTER(c_int))
cb = b.ctypes.data_as(POINTER(c_int))
else:
# otherwise convert using ctypes arrays
cr = (c_int * len(r))(*r)
cg = (c_int * len(g))(*g)
cb = (c_int * len(b))(*b)
_lib.TCOD_console_fill_foreground(con, cr, cg, cb)
def console_fill_background(con,r,g,b) :
if len(r) != len(g) or len(r) != len(b):
raise TypeError('R, G and B must all have the same size.')
if (numpy_available and isinstance(r, numpy.ndarray) and
isinstance(g, numpy.ndarray) and isinstance(b, numpy.ndarray)):
#numpy arrays, use numpy's ctypes functions
r = numpy.ascontiguousarray(r, dtype=numpy.int_)
g = numpy.ascontiguousarray(g, dtype=numpy.int_)
b = numpy.ascontiguousarray(b, dtype=numpy.int_)
cr = r.ctypes.data_as(POINTER(c_int))
cg = g.ctypes.data_as(POINTER(c_int))
cb = b.ctypes.data_as(POINTER(c_int))
else:
# otherwise convert using ctypes arrays
cr = (c_int * len(r))(*r)
cg = (c_int * len(g))(*g)
cb = (c_int * len(b))(*b)
_lib.TCOD_console_fill_background(con, cr, cg, cb)
def predict(self, inputs):
# uses MEMORY_DATA layer for loading images and postprocessing DENSE_CRF layer
img = inputs[0].transpose((2, 0, 1))
img = img[np.newaxis, :].astype(np.float32)
label = np.zeros((1, 1, 1, 1), np.float32)
data_dim = np.zeros((1, 1, 1, 2), np.float32)
data_dim[0][0][0][0] = img.shape[2]
data_dim[0][0][0][1] = img.shape[3]
img = np.ascontiguousarray(img, dtype=np.float32)
label = np.ascontiguousarray(label, dtype=np.float32)
data_dim = np.ascontiguousarray(data_dim, dtype=np.float32)
self.set_input_arrays(img, label, data_dim)
out = self.forward()
predictions = out[self.outputs[0]] # the output layer should be called crf_inf
segm_result = predictions[0].argmax(axis=0).astype(np.uint8)
return segm_result
def unique(eq):
eq = eqsize(eq)
c1 = [None] * eq.shape
for i in range(0, eq.size):
c1.append[i] = hash(eq[i])
c1 = np.asarray(c1)
if c1.ndim == 1:
_, ia, ic = np.unique(c1, return_index=True, return_inverse=True)
ia = (ia[:, ]).conj().T
ic = (ic[:, ]).conj().T
u = eq[ia]
else:
a = c1
b = np.ascontiguousarray(a).view(
np.dtype((np.void, a.dtype.itemsize * a.shape[1])))
_, ia, ic = np.unique(b, return_index=True, return_inverse=True)
return u, ia, ic
def _compute_targets(rois, overlaps, labels):
"""Compute bounding-box regression targets for an image."""
# Indices of ground-truth ROIs
gt_inds = np.where(overlaps == 1)[0]
# Indices of examples for which we try to make predictions
ex_inds = np.where(overlaps >= cfg.TRAIN.BBOX_THRESH)[0]
# Get IoU overlap between each ex ROI and gt ROI
ex_gt_overlaps = bbox_overlaps(
np.ascontiguousarray(rois[ex_inds, :], dtype=np.float),
np.ascontiguousarray(rois[gt_inds, :], dtype=np.float))
# Find which gt ROI each ex ROI has max overlap with:
# this will be the ex ROI's gt target
gt_assignment = ex_gt_overlaps.argmax(axis=1)
gt_rois = rois[gt_inds[gt_assignment], :]
ex_rois = rois[ex_inds, :]
targets = np.zeros((rois.shape[0], 5), dtype=np.float32)
targets[ex_inds, 0] = labels[ex_inds]
targets[ex_inds, 1:] = bbox_transform(ex_rois, gt_rois)
return targets
def write_features_hdf5(train, valid, test):
f = h5py.File(args.hdf5, "w")
train_grp = f.create_group("train")
train_x = train_grp.create_dataset("train_x", train[0].shape, dtype='f', compression="gzip", compression_opts=9)
train_y = train_grp.create_dataset("train_y", train[1].shape, dtype='i', compression="gzip", compression_opts=9)
valid_grp = f.create_group("valid")
valid_x = valid_grp.create_dataset("valid_x", valid[0].shape, dtype='f', compression="gzip", compression_opts=9)
valid_y = valid_grp.create_dataset("valid_y", valid[1].shape, dtype='i', compression="gzip", compression_opts=9)
test_grp = f.create_group("test")
test_x = test_grp.create_dataset("test_x", test[0].shape, dtype='f', compression="gzip", compression_opts=9)
test_y = test_grp.create_dataset("test_y", test[1].shape, dtype='i', compression="gzip", compression_opts=9)
train_x.write_direct(np.ascontiguousarray(train[0], dtype=train[0].dtype))
train_y.write_direct(np.ascontiguousarray(train[1], dtype=train[1].dtype))
valid_x.write_direct(np.ascontiguousarray(valid[0], dtype=valid[0].dtype))
valid_y.write_direct(np.ascontiguousarray(valid[1], dtype=valid[1].dtype))
test_x.write_direct(np.ascontiguousarray(test[0], dtype=test[0].dtype))
test_y.write_direct(np.ascontiguousarray(test[1], dtype=test[1].dtype))
f.close()
def preprocess_image(img, cuda=False):
means=[0.485, 0.456, 0.406]
stds=[0.229, 0.224, 0.225]
preprocessed_img = img.copy()[: , :, ::-1]
for i in range(3):
preprocessed_img[:, :, i] = preprocessed_img[:, :, i] - means[i]
preprocessed_img[:, :, i] = preprocessed_img[:, :, i] / stds[i]
preprocessed_img = \
np.ascontiguousarray(np.transpose(preprocessed_img, (2, 0, 1)))
preprocessed_img = torch.from_numpy(preprocessed_img)
preprocessed_img.unsqueeze_(0)
if cuda:
preprocessed_img = Variable(preprocessed_img.cuda(), requires_grad=True)
else:
preprocessed_img = Variable(preprocessed_img, requires_grad=True)
return preprocessed_img
def find_boundary(mesh,vals,threshold=0.5):
""" Find boundary points on the phase diagram where the switching probability = threshold """
boundary_points = []
durs = mesh.points[:,0]
volts = mesh.points[:,1]
indices, indptr = mesh.vertex_neighbor_vertices
for k in range(len(vals)):
for k_nb in indptr[indices[k]:indices[k+1]]:
if (vals[k]-threshold)*(vals[k_nb]-threshold)<0:
x0 = find_cross([durs[k],vals[k]],[durs[k_nb],vals[k_nb]],cut=threshold)
y0 = find_cross([volts[k],vals[k]],[volts[k_nb],vals[k_nb]],cut=threshold)
boundary_points.append([x0,y0])
boundary_points = np.array(boundary_points)
if len(boundary_points) > 0:
b = np.ascontiguousarray(boundary_points).view(np.dtype((np.void,
boundary_points.dtype.itemsize * boundary_points.shape[1])))
_, idx = np.unique(b, return_index=True)
boundary_points = boundary_points[idx]
# Sort the boundary_points by x-axis
boundary_points = sorted(boundary_points, key=itemgetter(0))
return np.array(boundary_points)
def calc_information_sampling(data, bins, pys1, pxs, label, b, b1, len_unique_a, p_YgX, unique_inverse_x,
unique_inverse_y, calc_DKL=False):
bins = bins.astype(np.float32)
num_of_bins = bins.shape[0]
# bins = stats.mstats.mquantiles(np.squeeze(data.reshape(1, -1)), np.linspace(0,1, num=num_of_bins))
# hist, bin_edges = np.histogram(np.squeeze(data.reshape(1, -1)), normed=True)
digitized = bins[np.digitize(np.squeeze(data.reshape(1, -1)), bins) - 1].reshape(len(data), -1)
b2 = np.ascontiguousarray(digitized).view(
np.dtype((np.void, digitized.dtype.itemsize * digitized.shape[1])))
unique_array, unique_inverse_t, unique_counts = \
np.unique(b2, return_index=False, return_inverse=True, return_counts=True)
p_ts = unique_counts / float(sum(unique_counts))
PXs, PYs = np.asarray(pxs).T, np.asarray(pys1).T
if calc_DKL:
pxy_given_T = np.array(
[calc_probs(i, unique_inverse_t, label, b, b1, len_unique_a) for i in range(0, len(unique_array))]
)
p_XgT = np.vstack(pxy_given_T[:, 0])
p_YgT = pxy_given_T[:, 1]
p_YgT = np.vstack(p_YgT).T
DKL_YgX_YgT = np.sum([inf_ut.KL(c_p_YgX, p_YgT.T) for c_p_YgX in p_YgX.T], axis=0)
H_Xgt = np.nansum(p_XgT * np.log2(p_XgT), axis=1)
local_IXT, local_ITY = calc_information_from_mat(PXs, PYs, p_ts, digitized, unique_inverse_x, unique_inverse_y,
unique_array)
return local_IXT, local_ITY
def calc_by_sampling_neurons(ws_iter_index, num_of_samples, label, sigma, bins, pxs):
iter_infomration = []
for j in range(len(ws_iter_index)):
data = ws_iter_index[j]
new_data = np.zeros((num_of_samples * data.shape[0], data.shape[1]))
labels = np.zeros((num_of_samples * label.shape[0], label.shape[1]))
x = np.zeros((num_of_samples * data.shape[0], 2))
for i in range(data.shape[0]):
cov_matrix = np.eye(data[i, :].shape[0]) * sigma
t_i = np.random.multivariate_normal(data[i, :], cov_matrix, num_of_samples)
new_data[num_of_samples * i:(num_of_samples * (i + 1)), :] = t_i
labels[num_of_samples * i:(num_of_samples * (i + 1)), :] = label[i, :]
x[num_of_samples * i:(num_of_samples * (i + 1)), 0] = i
b = np.ascontiguousarray(x).view(np.dtype((np.void, x.dtype.itemsize * x.shape[1])))
unique_array, unique_indices, unique_inverse_x, unique_counts = \
np.unique(b, return_index=True, return_inverse=True, return_counts=True)
b_y = np.ascontiguousarray(labels).view(np.dtype((np.void, labels.dtype.itemsize * labels.shape[1])))
unique_array_y, unique_indices_y, unique_inverse_y, unique_counts_y = \
np.unique(b_y, return_index=True, return_inverse=True, return_counts=True)
pys1 = unique_counts_y / float(np.sum(unique_counts_y))
iter_infomration.append(
calc_information_for_layer(data=new_data, bins=bins, unique_inverse_x=unique_inverse_x,
unique_inverse_y=unique_inverse_y, pxs=pxs, pys1=pys1))
params = np.array(iter_infomration)
return params
def extract_probs(label, x):
"""calculate the probabilities of the given data and labels p(x), p(y) and (y|x)"""
pys = np.sum(label, axis=0) / float(label.shape[0])
b = np.ascontiguousarray(x).view(np.dtype((np.void, x.dtype.itemsize * x.shape[1])))
unique_array, unique_indices, unique_inverse_x, unique_counts = \
np.unique(b, return_index=True, return_inverse=True, return_counts=True)
unique_a = x[unique_indices]
b1 = np.ascontiguousarray(unique_a).view(np.dtype((np.void, unique_a.dtype.itemsize * unique_a.shape[1])))
pxs = unique_counts / float(np.sum(unique_counts))
p_y_given_x = []
for i in range(0, len(unique_array)):
indexs = unique_inverse_x == i
py_x_current = np.mean(label[indexs, :], axis=0)
p_y_given_x.append(py_x_current)
p_y_given_x = np.array(p_y_given_x).T
b_y = np.ascontiguousarray(label).view(np.dtype((np.void, label.dtype.itemsize * label.shape[1])))
unique_array_y, unique_indices_y, unique_inverse_y, unique_counts_y = \
np.unique(b_y, return_index=True, return_inverse=True, return_counts=True)
pys1 = unique_counts_y / float(np.sum(unique_counts_y))
return pys, pys1, p_y_given_x, b1, b, unique_a, unique_inverse_x, unique_inverse_y, pxs
def _compute_targets(rois, overlaps, labels):
"""Compute bounding-box regression targets for an image."""
# Indices of ground-truth ROIs
gt_inds = np.where(overlaps == 1)[0]
if len(gt_inds) == 0:
# Bail if the image has no ground-truth ROIs
return np.zeros((rois.shape[0], 5), dtype=np.float32)
# Indices of examples for which we try to make predictions
ex_inds = np.where(overlaps >= cfg.TRAIN.BBOX_THRESH)[0]
# Get IoU overlap between each ex ROI and gt ROI
ex_gt_overlaps = bbox_overlaps(
np.ascontiguousarray(rois[ex_inds, :], dtype=np.float),
np.ascontiguousarray(rois[gt_inds, :], dtype=np.float))
# Find which gt ROI each ex ROI has max overlap with:
# this will be the ex ROI's gt target
gt_assignment = ex_gt_overlaps.argmax(axis=1)
gt_rois = rois[gt_inds[gt_assignment], :]
ex_rois = rois[ex_inds, :]
targets = np.zeros((rois.shape[0], 5), dtype=np.float32)
targets[ex_inds, 0] = labels[ex_inds]
targets[ex_inds, 1:] = bbox_transform(ex_rois, gt_rois)
return targets
def to_coo(self, tensor_mode=False):
userid, itemid, feedback = self.fields
user_item_data = self.training[[userid, itemid]].values
if tensor_mode:
# TODO this recomputes feedback data every new functon call,
# but if data has not changed - no need for this, make a property
new_feedback, feedback_transform = self.reindex(self.training, feedback, inplace=False)
self.index = self.index._replace(feedback=feedback_transform)
idx = np.hstack((user_item_data, new_feedback[:, np.newaxis]))
idx = np.ascontiguousarray(idx)
val = np.ones(self.training.shape[0],)
else:
idx = user_item_data
val = self.training[feedback].values
shp = tuple(idx.max(axis=0) + 1)
idx = idx.astype(np.intp)
val = np.ascontiguousarray(val)
return idx, val, shp
def _compute_targets(rois, overlaps, labels):
"""Compute bounding-box regression targets for an image."""
# Indices of ground-truth ROIs
gt_inds = np.where(overlaps == 1)[0]
if len(gt_inds) == 0:
# Bail if the image has no ground-truth ROIs
return np.zeros((rois.shape[0], 5), dtype=np.float32)
# Indices of examples for which we try to make predictions
ex_inds = np.where(overlaps >= cfg.TRAIN.BBOX_THRESH)[0]
# Get IoU overlap between each ex ROI and gt ROI
ex_gt_overlaps = bbox_overlaps(
np.ascontiguousarray(rois[ex_inds, :], dtype=np.float),
np.ascontiguousarray(rois[gt_inds, :], dtype=np.float))
# Find which gt ROI each ex ROI has max overlap with:
# this will be the ex ROI's gt target
gt_assignment = ex_gt_overlaps.argmax(axis=1)
gt_rois = rois[gt_inds[gt_assignment], :]
ex_rois = rois[ex_inds, :]
targets = np.zeros((rois.shape[0], 5), dtype=np.float32)
targets[ex_inds, 0] = labels[ex_inds]
targets[ex_inds, 1:] = bbox_transform(ex_rois, gt_rois)
return targets
def swap_axis_to_0(x, axis):
"""Insert a new singleton axis at position 0 and swap it with the
specified axis. The resulting array has an additional dimension,
with ``axis`` + 1 (which was ``axis`` before the insertion of the
new axis) of ``x`` at position 0, and a singleton axis at position
``axis`` + 1.
Parameters
----------
x : ndarray
Input array
axis : int
Index of axis in ``x`` to swap to axis index 0.
Returns
-------
arr : ndarray
Output array
"""
return np.ascontiguousarray(np.swapaxes(x[np.newaxis, ...], 0, axis+1))
roidb.py 文件源码
项目：Automatic_Group_Photography_Enhancement
作者: Yuliang-Zou
项目源码
文件源码
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def _compute_targets(rois, overlaps, labels):
"""Compute bounding-box regression targets for an image."""
# Indices of ground-truth ROIs
gt_inds = np.where(overlaps == 1)[0]
if len(gt_inds) == 0:
# Bail if the image has no ground-truth ROIs
return np.zeros((rois.shape[0], 5), dtype=np.float32)
# Indices of examples for which we try to make predictions
ex_inds = np.where(overlaps >= cfg.TRAIN.BBOX_THRESH)[0]
# Get IoU overlap between each ex ROI and gt ROI
ex_gt_overlaps = bbox_overlaps(
np.ascontiguousarray(rois[ex_inds, :], dtype=np.float),
np.ascontiguousarray(rois[gt_inds, :], dtype=np.float))
# Find which gt ROI each ex ROI has max overlap with:
# this will be the ex ROI's gt target
gt_assignment = ex_gt_overlaps.argmax(axis=1)
gt_rois = rois[gt_inds[gt_assignment], :]
ex_rois = rois[ex_inds, :]
targets = np.zeros((rois.shape[0], 5), dtype=np.float32)
targets[ex_inds, 0] = labels[ex_inds]
targets[ex_inds, 1:] = bbox_transform(ex_rois, gt_rois)
return targets
def write_tvl_direction_pairs(tvl_filename, tvl_header, direction_pairs):
"""Write the given directions to TVL.
The direction pairs should be a list with lists containing the vector and value to write. For example:
((vec, val), (vec1, val1), ...) up to three pairs are allowed.
Args:
tvl_filename (str): the filename to write to
tvl_header (:class:`list` or tuple): the header for the TVL file. This is a list of either 4 or 10 entries.
4 entries: [version, res, gap, offset]
10 entries: [version, x_res, x_gap, x_offset, y_res, y_gap, y_offset, z_res, z_gap, z_offset]
direction_pairs (list of ndarrays): The list with direction pairs, only three are used.
This is a list with (vector, magnitude) tuples in which the vectors are 4d volumes with for
every voxel a 3d coordinate.
"""
direction_pairs = direction_pairs[0:3]
dir_matrix = np.zeros(direction_pairs[0][0].shape[0:3] + (12,))
for ind, dirs in enumerate(direction_pairs):
dir_matrix[..., ind*3:ind*3+3] = np.ascontiguousarray(TrackMark._ensure_3d(np.squeeze(dirs[0])))
dir_matrix[..., 9 + ind] = np.ascontiguousarray(TrackMark._ensure_3d(np.squeeze(dirs[1])))
TrackMark.write_tvl_matrix(tvl_filename, tvl_header, dir_matrix)
def _compute_targets(rois, overlaps, labels):
"""Compute bounding-box regression targets for an image."""
# Indices of ground-truth ROIs
gt_inds = np.where(overlaps == 1)[0]
if len(gt_inds) == 0:
# Bail if the image has no ground-truth ROIs
return np.zeros((rois.shape[0], 5), dtype=np.float32)
# Indices of examples for which we try to make predictions
ex_inds = np.where(overlaps >= cfg.TRAIN.BBOX_THRESH)[0]
# Get IoU overlap between each ex ROI and gt ROI
ex_gt_overlaps = bbox_overlaps(
np.ascontiguousarray(rois[ex_inds, :], dtype=np.float),
np.ascontiguousarray(rois[gt_inds, :], dtype=np.float))
# Find which gt ROI each ex ROI has max overlap with:
# this will be the ex ROI's gt target
gt_assignment = ex_gt_overlaps.argmax(axis=1)
gt_rois = rois[gt_inds[gt_assignment], :]
ex_rois = rois[ex_inds, :]
targets = np.zeros((rois.shape[0], 5), dtype=np.float32)
targets[ex_inds, 0] = labels[ex_inds]
targets[ex_inds, 1:] = bbox_transform(ex_rois, gt_rois)
return targets
def _compute_targets(rois, overlaps, labels):
"""Compute bounding-box regression targets for an image."""
# Indices of ground-truth ROIs
gt_inds = np.where(overlaps == 1)[0]
if len(gt_inds) == 0:
# Bail if the image has no ground-truth ROIs
return np.zeros((rois.shape[0], 5), dtype=np.float32)
# Indices of examples for which we try to make predictions
ex_inds = np.where(overlaps >= cfg.TRAIN.BBOX_THRESH)[0]
# Get IoU overlap between each ex ROI and gt ROI
ex_gt_overlaps = bbox_overlaps(
np.ascontiguousarray(rois[ex_inds, :], dtype=np.float),
np.ascontiguousarray(rois[gt_inds, :], dtype=np.float))
# Find which gt ROI each ex ROI has max overlap with:
# this will be the ex ROI's gt target
gt_assignment = ex_gt_overlaps.argmax(axis=1)
gt_rois = rois[gt_inds[gt_assignment], :]
ex_rois = rois[ex_inds, :]
targets = np.zeros((rois.shape[0], 5), dtype=np.float32)
targets[ex_inds, 0] = labels[ex_inds]
targets[ex_inds, 1:] = bbox_transform(ex_rois, gt_rois)
return targets
def _compute_targets(rois, overlaps, labels):
"""Compute bounding-box regression targets for an image."""
# Indices of ground-truth ROIs
gt_inds = np.where(overlaps == 1)[0]
if len(gt_inds) == 0:
# Bail if the image has no ground-truth ROIs
return np.zeros((rois.shape[0], 5), dtype=np.float32)
# Indices of examples for which we try to make predictions
ex_inds = np.where(overlaps >= cfg.TRAIN.BBOX_THRESH)[0]
# Get IoU overlap between each ex ROI and gt ROI
ex_gt_overlaps = bbox_overlaps(
np.ascontiguousarray(rois[ex_inds, :], dtype=np.float),
np.ascontiguousarray(rois[gt_inds, :], dtype=np.float))
# Find which gt ROI each ex ROI has max overlap with:
# this will be the ex ROI's gt target
gt_assignment = ex_gt_overlaps.argmax(axis=1)
gt_rois = rois[gt_inds[gt_assignment], :]
ex_rois = rois[ex_inds, :]
targets = np.zeros((rois.shape[0], 5), dtype=np.float32)
targets[ex_inds, 0] = labels[ex_inds]
targets[ex_inds, 1:] = bbox_transform(ex_rois, gt_rois)
return targets
def console_fill_foreground(con,r,g,b) :
if len(r) != len(g) or len(r) != len(b):
raise TypeError('R, G and B must all have the same size.')
if (numpy_available and isinstance(r, numpy.ndarray) and
isinstance(g, numpy.ndarray) and isinstance(b, numpy.ndarray)):
#numpy arrays, use numpy's ctypes functions
r = numpy.ascontiguousarray(r, dtype=numpy.int_)
g = numpy.ascontiguousarray(g, dtype=numpy.int_)
b = numpy.ascontiguousarray(b, dtype=numpy.int_)
cr = r.ctypes.data_as(POINTER(c_int))
cg = g.ctypes.data_as(POINTER(c_int))
cb = b.ctypes.data_as(POINTER(c_int))
else:
# otherwise convert using ctypes arrays
cr = (c_int * len(r))(*r)
cg = (c_int * len(g))(*g)
cb = (c_int * len(b))(*b)
_lib.TCOD_console_fill_foreground(con, cr, cg, cb)
