def __init__(self, model_path, ui, KNN=3, data_path='data'):
TeachableMachine.__init__(self, model_path, ui)
self._buffer = deque(maxlen = 4)
self._engine = KNNEmbeddingEngine(model_path, KNN)
self.cls_nums = KNN+1
self.data_path = data_path
self.trg_folder = [] # trg_folder = './data/{Class}'
self.img_nums = [0, 0, 0, 0] # img_nums = [ x, x, x, x], count each class's images
self.check_dir()
if sum(self.img_nums) != 0:
t_reload = self.log_t('Reload Data', end=' ... ')
self.reload_data()
self.log_t('Done.', t_reload)
def __init__(self, model_path, ui, KNN=3):
TeachableMachine.__init__(self, model_path, ui)
self._buffer = deque(maxlen=4)
self._engine = KNNEmbeddingEngine(model_path, KNN)
### Modify by Chun
self.cls_nums = KNN + 1
self.data_path = 'data'
self.trg_folder = [] # trg_folder = './data/{Class}'
self.img_nums = [
0, 0, 0, 0
] # img_nums = [ x, x, x, x], count each class's images
self.check_dir()
if sum(self.img_nums) != 0:
print('\n', 'Reload Data', end=' ... ')
self.reload_data()
class TeachableMachineKNN(TeachableMachine):
def __init__(self, model_path, ui, KNN=3):
TeachableMachine.__init__(self, model_path, ui)
self._buffer = deque(maxlen=4)
self._engine = KNNEmbeddingEngine(model_path, KNN)
def classify(self, img, svg):
# Classify current image and determine
emb = self._engine.DetectWithImage(img)
self._buffer.append(self._engine.kNNEmbedding(emb))
classification = Counter(self._buffer).most_common(1)[0][0]
# 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.addEmbedding(
emb, 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, KNN=3): TeachableMachine.__init__(self, model_path, ui) self._buffer = deque(maxlen = 4) self._engine = KNNEmbeddingEngine(model_path, KNN)
class TeachableMachineKNN_ByChun(TeachableMachine, Log):
def __init__(self, model_path, ui, KNN=3, data_path='data'):
TeachableMachine.__init__(self, model_path, ui)
self._buffer = deque(maxlen = 4)
self._engine = KNNEmbeddingEngine(model_path, KNN)
self.cls_nums = KNN+1
self.data_path = data_path
self.trg_folder = [] # trg_folder = './data/{Class}'
self.img_nums = [0, 0, 0, 0] # img_nums = [ x, x, x, x], count each class's images
self.check_dir()
if sum(self.img_nums) != 0:
t_reload = self.log_t('Reload Data', end=' ... ')
self.reload_data()
self.log_t('Done.', t_reload)
def check_dir(self):
t_check = self.log_t('Check Files', end=' ... ')
for cls in range(1, self.cls_nums+1): # Classes from 1 to 4
self.trg_folder.append(os.path.join(self.data_path, str(cls)))
# Check Directory is existed or not
if os.path.exists(self.trg_folder[cls-1]) is False:
os.makedirs(self.trg_folder[cls-1])
self.img_nums[cls-1] = 0
else:
self.img_nums[cls-1] = len(os.listdir(self.trg_folder[cls-1]))
self.log_t('Done.', t_check)
self.log_t(f'Files:{sum(self.img_nums)}')
def clear_dir(self):
shutil.rmtree(self.data_path)
self.check_dir()
self.log_t('Clear')
def reload_data(self):
for cls in range(1, self.cls_nums+1): # 1 ~ 4
if self.img_nums[cls-1] != 0 :
for idx in range(0, self.img_nums[cls-1]):
img = Image.open(os.path.join(self.trg_folder[cls-1], f'{idx}.jpg'))
emb = self._engine.DetectWithImage(img)
self._buffer.append(self._engine.kNNEmbedding(emb))
classification = Counter(self._buffer).most_common(1)[0][0]
self._engine.addEmbedding(emb, cls)
def classify(self, img, infer=True, add_data=True):
if infer:
# Classify current image and determine
emb = self._engine.DetectWithImage(img)
self._buffer.append(self._engine.kNNEmbedding(emb))
classification = Counter(self._buffer).most_common(1)[0][0]
# Interpret user button presses (if any)
if add_data:
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
### Modify by Chun : clear data folder
self.clear_dir()
else :
self._engine.addEmbedding(emb, i) # otherwise the button # is the class
### Modify by Chun : Save Image & Label
save_path = os.path.join(self.trg_folder[i-1], f'{str(self.img_nums[i-1])}.jpg')
img.save(save_path)
self.img_nums[i-1] += 1
# 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.get_results(classification) ### Modify by Chun : log of results