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Added support of ground truth images for Keras generator #126

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Added support of ground truth images for Keras generator #126

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101 changes: 77 additions & 24 deletions Augmentor/Pipeline.py
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Original file line number Diff line number Diff line change
Expand Up @@ -100,7 +100,7 @@ def __call__(self, augmentor_image):
:param augmentor_image: The image to pass through the pipeline.
:return:
"""
return self._execute(augmentor_image)
return self._execute(augmentor_image)[0]

def _populate(self, source_directory, output_directory, ground_truth_directory, ground_truth_output_directory):
"""
Expand Down Expand Up @@ -239,9 +239,9 @@ def _execute(self, augmentor_image, save_to_disk=True, multi_threaded=True):
# if multi_threaded:
# return os.path.basename(augmentor_image.image_path)
# else:
# return images[0] # Here we return only the first image for the generators.
# return images # Here we return all the images for the generators.

return images[0]
return images

def _execute_with_array(self, image):
"""
Expand Down Expand Up @@ -322,7 +322,7 @@ def sample(self, n, multi_threaded=True):
#while sample_count <= n:
# for augmentor_image in self.augmentor_images:
# if sample_count <= n:
# self._execute(augmentor_image)
# self._execute(augmentor_image)[0]
# file_name_to_print = os.path.basename(augmentor_image.image_path)
# # This is just to shorten very long file names which obscure the progress bar.
# if len(file_name_to_print) >= 30:
Expand Down Expand Up @@ -387,7 +387,7 @@ def sample_with_array(self, image_array, save_to_disk=False):
a = AugmentorImage(image_path=None, output_directory=None)
a.image_PIL = Image.fromarray(image_array)

return self._execute(a, save_to_disk)
return self._execute(a, save_to_disk)[0]

@staticmethod
def categorical_labels(numerical_labels):
Expand All @@ -409,7 +409,11 @@ def categorical_labels(numerical_labels):
def image_generator(self):
while True:
im_index = random.randint(0, len(self.augmentor_images)-1) # Fix for issue 52.
yield self._execute(self.augmentor_images[im_index], save_to_disk=False), \
if self.process_ground_truth_images:
images = self._execute(self.augmentor_images[im_index], save_to_disk=False)
yield images[0], images[1:]
else:
yield self._execute(self.augmentor_images[im_index], save_to_disk=False)[0], \
self.augmentor_images[im_index].class_label_int

# TODO: Fix: scaled=True results in an error.
Expand Down Expand Up @@ -455,31 +459,62 @@ def keras_generator(self, batch_size, scaled=True, image_data_format="channels_l
warnings.warn("To work with Keras, must be one of channels_first or channels_last.")

while True:

# Randomly select 25 images for augmentation and yield the
# augmented images.
# X = np.array([])
# y = np.array([])
# The correct thing to do here is to pre-allocate
# batch = np.ndarray((batch_size, 28, 28, 1))

X = []
y = []

for i in range(batch_size):
# We take a sample image to know its size and
# preallocate the batch
sample = self.augmentor_images[0]
sample_image = self._execute(sample, save_to_disk=False)
sample_array = np.asarray(sample_image[0])
if self.process_ground_truth_images:
sample_label = np.asarray(sample_image[1])
else:
sample_label = sample.categorical_label

# Pre-allocate
# batch[i:i+28]
if np.ndim(sample_array) == 2:
w, h, l = sample_array.shape + (1,)
else:
w, h, l = sample_array.shape

# Preallocation of batches of X
X = None
if image_data_format == "channels_last":
X = np.empty((batch_size, w, h, l))
elif image_data_format == "channels_first":
X = np.empty((batch_size, l, w, h))

# Preallocation of batches of y
y = None
if self.process_ground_truth_images:
if np.ndim(sample_array) == 2:
w, h, l = sample_label.shape + (1,)
else:
w, h, l = sample_label.shape

if image_data_format == "channels_last":
y = np.empty((batch_size, w, h, l))
elif image_data_format == "channels_first":
y = np.empty((batch_size, l, w, h))
else:
y = np.empty((batch_size,) + sample_label.shape)

for i in range(batch_size):
# Select random image, get image array and label
random_image_index = random.randint(0, len(self.augmentor_images)-1)
numpy_array = np.asarray(self._execute(self.augmentor_images[random_image_index], save_to_disk=False))
images = self._execute(self.augmentor_images[random_image_index], save_to_disk=False)

numpy_array = np.asarray(images[0])
if self.process_ground_truth_images:
label = np.asarray(images[1])
else:
label = self.augmentor_images[random_image_index].categorical_label

# Reshape
# Reshaping of images
w = numpy_array.shape[0]
h = numpy_array.shape[1]

# transforming 2d images in 3d tensor
if np.ndim(numpy_array) == 2:
l = 1
else:
Expand All @@ -490,15 +525,33 @@ def keras_generator(self, batch_size, scaled=True, image_data_format="channels_l
elif image_data_format == "channels_first":
numpy_array = numpy_array.reshape(l, w, h)

X.append(numpy_array)
y.append(label)
# Reshaping of masks if ground truth is given
if self.process_ground_truth_images:
# Reshaping of images
w = label.shape[0]
h = label.shape[1]

X = np.asarray(X)
y = np.asarray(y)
# transforming 2d images in 3d tensor
if np.ndim(label) == 2:
l = 1
else:
l = np.shape(numpy_array)[2]

if image_data_format == "channels_last":
label = label.reshape(w, h, l)
elif image_data_format == "channels_first":
label = label.reshape(l, w, h)

# The X and y are populated at the given index
X[i] = numpy_array
y[i] = label

if scaled:
X = X.astype('float32')
X /= 255
X /= 255.
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This is something I overlooked, dividing by 255 needs to be fixed...

if self.process_ground_truth_images:
y = y.astype('float32')
y /= 255.
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And here of course


yield (X, y)

Expand Down