def matrix_regression_data_prepare(**kwargs):
bufferCollection = buffers.BufferCollection()
bufferCollection.AddBuffer(buffers.X_FLOAT_DATA,
buffers.as_matrix_buffer(kwargs['x']))
if 'y' in kwargs:
bufferCollection.AddBuffer(buffers.YS,
buffers.as_matrix_buffer(kwargs['y']))
return bufferCollection
def depth_delta_classification_data_prepare(**kwargs):
bufferCollection = buffers.BufferCollection()
bufferCollection.AddBuffer(buffers.DEPTH_IMAGES, kwargs['depth_images'])
bufferCollection.AddBuffer(buffers.PIXEL_INDICES, kwargs['pixel_indices'])
if 'offset_scales' in kwargs:
bufferCollection.AddBuffer(buffers.OFFSET_SCALES,
kwargs['offset_scales'])
if 'classes' in kwargs:
bufferCollection.AddBuffer(buffers.CLASS_LABELS, kwargs['classes'])
return bufferCollection
def depth_delta_regression_data_prepare(**kwargs):
bufferCollection = buffers.BufferCollection()
bufferCollection.AddBuffer(buffers.DEPTH_IMAGES, kwargs['depth_images'])
bufferCollection.AddBuffer(buffers.PIXEL_INDICES, kwargs['pixel_indices'])
if 'offset_scales' in kwargs:
bufferCollection.AddBuffer(buffers.OFFSET_SCALES,
kwargs['offset_scales'])
if 'y' in kwargs:
bufferCollection.AddBuffer(buffers.YS, kwargs['y'])
return bufferCollection
def predict_proba(self, x):
buffer_collection = buffers.BufferCollection()
buffer_collection.AddFloat32MatrixBuffer(buffers.X_FLOAT_DATA, buffers.as_matrix_buffer(x))
number_of_classes = self.forest_data.GetTree(0).GetYs().GetN()
all_samples_step = pipeline.AllSamplesStep_f32f32i32(buffers.X_FLOAT_DATA)
combiner = classification.ClassProbabilityCombiner_f32(number_of_classes)
matrix_feature = matrix_features.LinearFloat32MatrixFeature_f32i32(all_samples_step.IndicesBufferId,
buffers.X_FLOAT_DATA)
forest_predicter = predict.LinearMatrixClassificationPredictin_f32i32(forest_data, matrix_feature, combiner, all_samples_step)
result = buffers.Float32MatrixBuffer()
forest_predicter.PredictYs(bufferCollection, result)
return buffers.as_numpy_array(result)
def matrix_classification_data_prepare(**kwargs):
bufferCollection = buffers.BufferCollection()
bufferCollection.AddBuffer(buffers.X_FLOAT_DATA, kwargs['x'])
if 'classes' in kwargs:
bufferCollection.AddBuffer(buffers.CLASS_LABELS, kwargs['classes'])
return bufferCollection
"%s%s.exr" % (args.pose_files_input_path, pose_filename))
labels = kinect_utils.load_labels_from_png(
"%s%s.png" % (args.pose_files_input_path, pose_filename))
pixel_indices, pixel_labels = kinect_utils.sample_pixels_from_image(
labels[0, :, :], config.number_of_pixels_per_image)
# Randomly sample pixels and offset scales
(number_of_datapoints, _) = pixel_indices.shape
offset_scales = np.array(np.random.uniform(
0.8, 1.2, (number_of_datapoints, 2)),
dtype=np.float32)
datapoint_indices = np.array(np.arange(number_of_datapoints),
dtype=np.int32)
# Package buffers for learner
bufferCollection = buffers.BufferCollection()
bufferCollection.AddFloat32Tensor3Buffer(
buffers.DEPTH_IMAGES, buffers.as_tensor_buffer(depths))
bufferCollection.AddFloat32MatrixBuffer(
buffers.OFFSET_SCALES, buffers.as_matrix_buffer(offset_scales))
bufferCollection.AddInt32MatrixBuffer(
buffers.PIXEL_INDICES, buffers.as_matrix_buffer(pixel_indices))
bufferCollection.AddInt32VectorBuffer(
buffers.CLASS_LABELS, buffers.as_vector_buffer(pixel_labels))
# Update learner
online_learner.Train(bufferCollection,
buffers.Int32Vector(datapoint_indices))
#pickle forest and data used for training
if (i + 1) % 1 == 0: