def flow(self, mode='train'):
while True:
if mode == 'train':
print self.train_keys[0]
# shuffle(self.train_keys)
keys = self.train_keys
elif mode == 'val' or mode == 'demo':
shuffle(self.validation_keys)
keys = self.validation_keys
else:
raise Exception('invalid mode: %s' % mode)
inputs = []
targets = []
for key in keys:
image_path = self.path_prefix + key
if not os.path.exists(image_path):
continue
image_array = imread(image_path)
image_array = imresize(image_array, self.image_size)
num_image_channels = len(image_array.shape)
if num_image_channels != 3:
continue
ground_truth = self.ground_truth_data[key]
if self.do_random_crop:
image_array = self._do_random_crop(image_array)
image_array = image_array.astype('float32')
if mode == 'train' or mode == 'demo':
if self.ground_truth_transformer != None:
image_array, ground_truth = self.transform(
image_array, ground_truth)
ground_truth = (self.ground_truth_transformer.
assign_boxes(ground_truth))
else:
image_array = self.transform(image_array)[0]
if self.grayscale:
image_array = cv2.cvtColor(
image_array.astype('uint8'),
cv2.COLOR_RGB2GRAY).astype('float32')
image_array = np.expand_dims(image_array, -1)
inputs.append(image_array)
targets.append(ground_truth)
if len(inputs) == self.batch_size:
#print len(inputs)
inputs = np.asarray(inputs)
targets = np.asarray(targets)
#print targets
targets = to_categorical(targets, self.classes)
if mode == 'train' or mode == 'val':
inputs = self.preprocess_images(inputs)
#print inputs.shape
#print targets.shape
image_data = {'input': inputs}
targets = {'label': targets}
yield [image_data, targets]
inputs = []
targets = []
def preprocess_image(image, input_shape):
image = imresize(image, input_shape[:2])
image = image.astype('float32')
image = image / 255.0
image = image - 0.5
image = image * 2.0
image = np.expand_dims(image, axis=0)
return image
def flow(self, mode='train'):
while True:
if mode == 'train':
shuffle(self.train_keys)
keys = self.train_keys
elif mode == 'val' or mode == 'demo':
shuffle(self.validation_keys)
keys = self.validation_keys
else:
raise Exception('invalid mode: %s' % mode)
inputs = []
targets = []
for key in keys:
image_path = self.path_prefix + key
image_array = imread(image_path)
image_array = imresize(image_array, self.image_size)
num_image_channels = len(image_array.shape)
if num_image_channels != 3:
continue
ground_truth = self.ground_truth_data[key]
image_array = image_array.astype('float32')
if mode == 'train' or mode == 'demo':
image_array = self.transform(image_array)[0]
if self.grayscale:
image_array = cv2.cvtColor(
image_array.astype('uint8'),
cv2.COLOR_RGB2GRAY).astype('float32')
image_array = np.expand_dims(image_array, -1)
inputs.append(image_array)
targets.append(ground_truth)
if len(targets) == self.batch_size:
inputs = np.asarray(inputs)
targets = np.asarray(targets)
gender = np_utils.to_categorical(targets[:, 0], 2)
# Quantizing the age
age_bins = np.linspace(0, 100, 21)
age_step = np.digitize(targets[:, 1], age_bins)
age_quantized = np_utils.to_categorical(age_step, 21)
if mode == 'train' or mode == 'val':
inputs = self.preprocess_images(inputs)
yield self._wrap_in_dictionary(inputs, gender,
age_quantized)
if mode == 'demo':
yield self._wrap_in_dictionary(inputs, gender,
age_quantized)
inputs = []
targets = []
def flow(self, mode='train'):
while True:
if mode == 'train':
shuffle(self.train_keys)
keys = self.train_keys
elif mode == 'val':
keys = self.validation_keys
else:
raise Exception('invalid mode: %s' % mode)
inputs = []
targets = []
for key in keys:
image_path = os.path.join(self.path_prefix, key)
image_array = imread(image_path)
image_array = imresize(image_array, self.image_size)
num_image_channels = len(image_array.shape)
if num_image_channels != 3:
continue
ground_truth = self.ground_truth_data[key]
image_array = image_array.astype('float32')
if mode == 'train':
image_array = self.transform(image_array)[0]
if self.grayscale:
image_array = cv2.cvtColor(
image_array.astype('uint8'),
cv2.COLOR_RGB2GRAY).astype('float32')
image_array = np.expand_dims(image_array, -1)
inputs.append(image_array)
targets.append(ground_truth)
if len(targets) == self.batch_size:
inputs = np.asarray(inputs)
targets = np.asarray(targets)
# gender = np_utils.to_categorical(targets[:, 0], 2) # softmax
gender = targets[:, 0] # sigmoid
age = np_utils.to_categorical(targets[:, 1], self.bins)
emotion = np_utils.to_categorical(targets[:, 2], 7)
if mode == 'train' or mode == 'val':
inputs = self.preprocess_images(inputs)
yield self._wrap_in_dictionary(inputs, gender, age,
emotion)
inputs = []
targets = []