def prepareShapeModel(datasetDirectory, shape):
import glob
datasetFiles_shape = glob.glob(datasetDirectory + '/' + shape + '.dat')
if (len(datasetFiles_shape) < 1):
raise Exception("Dataset not available at " + datasetDirectory +
" for shape " + shape)
shapeModeler = ShapeModeler(filename=datasetFiles_shape[0],
num_principle_components=numParams)
#import pdb; pdb.set_trace()
return shapeModeler
def prepareShapesModel(datasetDirectory, num_params):
shapes = {}
nameFilter = re.compile(regexp)
datasets = glob.glob(datasetDirectory + '/*.dat')
for dataset in datasets:
name = os.path.splitext(os.path.basename(dataset))[0]
if nameFilter.match(name):
shapeModeler = ShapeModeler(init_filename=dataset,
num_principle_components=num_params)
shapes[name] = shapeModeler
return shapes
settings_shapeLearners = []
userInputCaptures = []
nb_param = 10
spaces = {}
abc = [
'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o',
'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z'
]
for letter in abc:
(shapeType, init_datasetFile, update_datasetFiles,
datasetParam) = generateSettings(letter)
spaces[letter] = ShapeModeler(init_filename=init_datasetFile,
update_filenames=update_datasetFiles,
param_filename=datasetParam,
num_principle_components=nb_param)
def downsampleShape(shape, numDesiredPoints, xyxyFormat=False):
numPointsInShape = len(shape) / 2
if (xyxyFormat):
#make xyxy format
x_shape = shape[0::2]
y_shape = shape[1::2]
else:
x_shape = shape[0:numPointsInShape]
y_shape = shape[numPointsInShape:]
if isinstance(x_shape, np.ndarray): #convert arrays to lists for interp1d
x_shape = (x_shape.T).tolist()[0]
