class TeachableMachineImprinting(TeachableMachine):
def __init__(self, model_path, ui, output_path, keep_classes):
TeachableMachine.__init__(self, model_path, ui)
self._BATCHSIZE = 1 # batch size for the engine to train for once.
from imprinting import DemoImprintingEngine
self._engine = DemoImprintingEngine(model_path, output_path,
keep_classes, self._BATCHSIZE)
def classify(self, img, svg):
# Classifty current image and determine
classification = self._engine.classify(img)
# Interpret user button presses (if any)
debounced_buttons = self._ui.getDebouncedButtonState()
for i, b in enumerate(debounced_buttons):
if not b: continue
if i == 0: self._engine.clear() # Hitting button 0 resets
else:
self._engine.addImage(img,
i) # otherwise the button # is the class
# Hitting exactly all 4 class buttons simultaneously quits the program.
if sum(filter(
lambda x: x,
debounced_buttons[1:])) == 4 and not debounced_buttons[0]:
self.clean_shutdown = True
return True # return True to shut down pipeline
return self.visualize(classification, svg)
def __init__(self, model_path, ui, output_path, keep_classes): TeachableMachine.__init__(self, model_path, ui) self._BATCHSIZE = 1 # batch size for the engine to train for once. from imprinting import DemoImprintingEngine self._engine = DemoImprintingEngine(model_path, output_path, keep_classes, self._BATCHSIZE)