def test_pickle(self):
# test tensor
for dtype in [np.float32, np.float64, np.int32, np.int64]:
array = np.array(
[[[3, 21, 1], [22, 1, 5]], [[2, 2, 2], [7, 7, 7]]],
dtype=dtype)
b1 = buffers.as_tensor_buffer(array)
pickle.dump(b1, open('tmp.pkl', 'wb'))
b2 = pickle.load(open('tmp.pkl', 'rb'))
array2 = buffers.as_numpy_array(b2)
self.assertTrue((array == array2).all())
# test matrix
for dtype in [np.float32, np.float64, np.int32, np.int64]:
array = np.array([[3, 21, 1], [22, 1, 5], [2, 2, 2], [7, 7, 7]],
dtype=dtype)
b1 = buffers.as_matrix_buffer(array)
pickle.dump(b1, open('tmp.pkl', 'wb'))
b2 = pickle.load(open('tmp.pkl', 'rb'))
array2 = buffers.as_numpy_array(b2)
self.assertTrue((array == array2).all())
# test vector
for dtype in [np.float32, np.float64, np.int32, np.int64]:
array = np.array([3, 21, 1], dtype=dtype)
b1 = buffers.as_vector_buffer(array)
pickle.dump(b1, open('tmp.pkl', 'wb'))
b2 = pickle.load(open('tmp.pkl', 'rb'))
array2 = buffers.as_numpy_array(b2)
self.assertTrue((array == array2).all())
def classify_pixels(depth, forest):
assert(depth.ndim == 2)
# setup test data
pixel_indices = np.array( list(itertools.product( np.zeros(1), range(m), range(n) )), dtype=np.int32 )
buffer_collection = buffers.BufferCollection()
buffer_collection.AddInt32MatrixBuffer(buffers.PIXEL_INDICES, buffers.as_matrix_buffer(pixel_indices))
buffer_collection.AddFloat32Tensor3Buffer(buffers.DEPTH_IMAGES, buffers.as_tensor_buffer(depth))
# setup predictor
all_samples_step = pipeline.AllSamplesStep_i32f32i32(buffers.PIXEL_INDICES)
depth_delta_feature = image_features.ScaledDepthDeltaFeature_f32i32(all_samples_step.IndicesBufferId,
buffers.PIXEL_INDICES,
buffers.DEPTH_IMAGES)
combiner = classification.ClassProbabilityCombiner_f32(number_of_body_parts)
forest_predictor = predict.ScaledDepthDeltaClassificationPredictin_f32i32(forest, depth_delta_feature, combiner, all_samples_step)
# predict
yprobs_buffer = buffers.Float32MatrixBuffer()
forest_predictor.PredictYs(buffer_collection, yprobs_buffer)
# convert to image space
yprobs = buffers.as_numpy_array(yprobs_buffer)
(_, ydim) = yprobs.shape
img_yprobs = yprobs.reshape((m,n,ydim))
img_yhat = np.argmax(img_yprobs, axis=2)
return img_yhat, img_yprobs.max(axis=2)
def classify_body_pixels(depth, ground_labels, forest):
assert(depth.ndim == 2)
# setup test data
pixel_indices = to_indices(0, np.where(ground_labels != background))
buffer_collection = buffers.BufferCollection()
buffer_collection.AddInt32MatrixBuffer(buffers.PIXEL_INDICES, buffers.as_matrix_buffer(pixel_indices))
buffer_collection.AddFloat32Tensor3Buffer(buffers.DEPTH_IMAGES, buffers.as_tensor_buffer(depth))
# setup predictor
all_samples_step = pipeline.AllSamplesStep_i32f32i32(buffers.PIXEL_INDICES)
depth_delta_feature = image_features.ScaledDepthDeltaFeature_f32i32(all_samples_step.IndicesBufferId,
buffers.PIXEL_INDICES,
buffers.DEPTH_IMAGES)
combiner = classification.ClassProbabilityCombiner_f32(number_of_body_parts)
forest_predictor = predict.ScaledDepthDeltaClassificationPredictin_f32i32(forest, depth_delta_feature, combiner, all_samples_step)
# predict
yprobs_buffer = buffers.Float32MatrixBuffer()
forest_predictor.PredictYs(buffer_collection, yprobs_buffer)
# convert to image space
yprobs = buffers.as_numpy_array(yprobs_buffer)
(_, ydim) = yprobs.shape
m,n = depth.shape
img_yprobs = np.zeros((m,n), dtype=np.float32)
img_yprobs[ground_labels != background].shape
yprobs.max(axis=1).shape
img_yprobs[ground_labels != background] = yprobs.max(axis=1)
img_yhat = np.zeros((m,n), dtype=np.int32)
img_yhat[ground_labels != background] = np.argmax(yprobs, axis=1)
return img_yhat, img_yprobs
def test_pickle(self):
# test tensor
for dtype in [np.float32, np.float64, np.int32, np.int64]:
array = np.array([[[3, 21, 1], [22, 1, 5]], [[2, 2, 2], [7, 7, 7]]], dtype=dtype)
b1 = buffers.as_tensor_buffer(array)
pickle.dump(b1, open("tmp.pkl", "wb"))
b2 = pickle.load(open("tmp.pkl", "rb"))
array2 = buffers.as_numpy_array(b2)
self.assertTrue((array == array2).all())
# test matrix
for dtype in [np.float32, np.float64, np.int32, np.int64]:
array = np.array([[3, 21, 1], [22, 1, 5], [2, 2, 2], [7, 7, 7]], dtype=dtype)
b1 = buffers.as_matrix_buffer(array)
pickle.dump(b1, open("tmp.pkl", "wb"))
b2 = pickle.load(open("tmp.pkl", "rb"))
array2 = buffers.as_numpy_array(b2)
self.assertTrue((array == array2).all())
# test vector
for dtype in [np.float32, np.float64, np.int32, np.int64]:
array = np.array([3, 21, 1], dtype=dtype)
b1 = buffers.as_vector_buffer(array)
pickle.dump(b1, open("tmp.pkl", "wb"))
b2 = pickle.load(open("tmp.pkl", "rb"))
array2 = buffers.as_numpy_array(b2)
self.assertTrue((array == array2).all())
def img_buffer_flatten_helper(self, X, buffer_type):
m,n = X.shape
img_buffer = buffers.as_tensor_buffer(X)
assert isinstance(img_buffer, buffer_type)
X_back = buffers.as_numpy_array(img_buffer)
self.assertTrue((X.reshape(1,m,n) == X_back).all())
X_back = buffers.as_numpy_array(buffer=img_buffer, flatten=True)
self.assertTrue((X == X_back).all())
def load_and_sample(pose_path, list_of_poses, number_of_pixels_per_image):
depths, labels = load_data(pose_path, list_of_poses)
depths_buffer = buffers.as_tensor_buffer(depths)
del depths
pixel_indices, pixel_labels = sample_pixels_from_images(labels, number_of_pixels_per_image)
del labels
pixel_indices_buffer = buffers.as_matrix_buffer(pixel_indices)
pixel_labels_buffer = buffers.as_vector_buffer(pixel_labels)
del pixel_indices
del pixel_labels
return depths_buffer, pixel_indices_buffer, pixel_labels_buffer
def load_training_data(numpy_filename):
f = open(numpy_filename, 'rb')
depths = np.load(f)
labels = np.load(f)
pixel_indices = np.load(f)
pixel_labels = np.load(f)
depths_buffer = buffers.as_tensor_buffer(depths)
del depths
del labels
pixel_indices_buffer = buffers.as_matrix_buffer(pixel_indices)
del pixel_indices
pixel_labels_buffer = buffers.as_vector_buffer(pixel_labels)
del pixel_labels
return depths_buffer, pixel_indices_buffer, pixel_labels_buffer
def convert_img_buffer_both_directions_helper(self, X, buffer_type):
buf = buffers.as_tensor_buffer(X)
assert isinstance(buf, buffer_type)
X_back = buffers.as_numpy_array(buf)
self.assertTrue((X == X_back).all())
for i, pose_filename in enumerate(pose_filenames):
print "Processing %d - %d - %s - %s" % (pass_id, i, pose_filename, str(datetime.now()))
# Load single pose depth and class labels
depths = kinect_utils.load_depth_from_exr("%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:
# if True:
forest_pickle_filename = "%s/forest-%d-%d.pkl" % (online_run_folder, pass_id, i+1)
pickle.dump(online_learner.GetForest(), gzip.open(forest_pickle_filename, 'wb'))
# Print forest stats
forestStats = online_learner.GetForest().GetForestStats()
"%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:
# if True:
forest_pickle_filename = "%s/forest-%d-%d.pkl" % (
