def load_image_into_pane_original_format(self,
state_layer_name,
state_selected_unit,
resize_shape,
images,
sub_folder,
file_pattern,
image_index_to_set,
should_crop_to_corner=False):
jpg_path = os.path.join(
self.settings.caffevis_outputs_dir, sub_folder, state_layer_name,
file_pattern % (state_layer_name, state_selected_unit))
try:
img = caffe.io.load_image(jpg_path)
if should_crop_to_corner:
img = crop_to_corner(img, 2)
images[image_index_to_set] = ensure_uint255_and_resize_without_fit(
img, resize_shape)
except IOError:
print('\nAttempted to load file %s but failed. To supress this warning, remove layer "%s" from settings.caffevis_jpgvis_layers' % \
(jpg_path, state_layer_name))
# set black image as place holder
images[image_index_to_set] = np.zeros(
(resize_shape[0], resize_shape[1], 3), dtype=np.uint8)
pass
def display_frame(self, frame):
full_pane_shape = self.panes['input'].data.shape[:2][::-1]
if self.settings.is_siamese and ((type(frame),len(frame)) == (tuple,2)):
frame1 = frame[0]
frame2 = frame[1]
half_pane_shape = (full_pane_shape[0], full_pane_shape[1]/2)
frame_disp1 = ensure_uint255_and_resize_without_fit(frame1[:], half_pane_shape)
frame_disp2 = ensure_uint255_and_resize_without_fit(frame2[:], half_pane_shape)
frame_disp = np.concatenate((frame_disp1, frame_disp2), axis=1)
else:
frame_disp = ensure_uint255_and_resize_without_fit(frame[:], full_pane_shape)
if self.settings._calculated_is_gray_model:
frame_disp = gray_to_color(frame_disp)
self.panes['input'].data[:] = frame_disp
def get_image_from_files(settings, unit_folder_path, should_crop_to_corner, resize_shape, first_only, captions = [], values = []):
try:
# list unit images
unit_images_path = sorted(glob.glob(unit_folder_path))
mega_image = np.zeros((resize_shape[0], resize_shape[1], 3), dtype=np.uint8)
# if no images
if not unit_images_path:
return mega_image
if first_only:
unit_images_path = [unit_images_path[0]]
# load all images
unit_images = [caffe_load_image(unit_image_path, color=True, as_uint=True) for unit_image_path in
unit_images_path]
if settings.caffevis_clear_negative_activations:
# clear images with 0 value
if values:
for i in range(len(values)):
if values[i] < float_info.epsilon:
unit_images[i] *= 0
if should_crop_to_corner:
unit_images = [crop_to_corner(img, 2) for img in unit_images]
num_images = len(unit_images)
images_per_axis = int(np.math.ceil(np.math.sqrt(num_images)))
padding_pixel = 1
if first_only:
single_resized_image_shape = (resize_shape[0] - 2*padding_pixel, resize_shape[1] - 2*padding_pixel)
else:
single_resized_image_shape = ((resize_shape[0] / images_per_axis) - 2*padding_pixel, (resize_shape[1] / images_per_axis) - 2*padding_pixel)
unit_images = [ensure_uint255_and_resize_without_fit(unit_image, single_resized_image_shape) for unit_image in unit_images]
# build mega image
should_add_caption = (len(captions) == num_images)
defaults = {'face': settings.caffevis_score_face,
'fsize': settings.caffevis_score_fsize,
'clr': to_255(settings.caffevis_score_clr),
'thick': settings.caffevis_score_thick}
for i in range(num_images):
# add caption if we have exactly one for each image
if should_add_caption:
loc = settings.caffevis_score_loc[::-1] # Reverse to OpenCV c,r order
fs = FormattedString(captions[i], defaults)
cv2_typeset_text(unit_images[i], [[fs]], loc)
cell_row = i / images_per_axis
cell_col = i % images_per_axis
mega_image_height_start = 1 + cell_row * (single_resized_image_shape[0] + 2 * padding_pixel)
mega_image_height_end = mega_image_height_start + single_resized_image_shape[0]
mega_image_width_start = 1 + cell_col * (single_resized_image_shape[1] + 2 * padding_pixel)
mega_image_width_end = mega_image_width_start + single_resized_image_shape[1]
mega_image[mega_image_height_start:mega_image_height_end, mega_image_width_start:mega_image_width_end,:] = unit_images[i]
return mega_image
except:
print '\nAttempted to load files from %s but failed. ' % unit_folder_path
# set black image as place holder
return np.zeros((resize_shape[0], resize_shape[1], 3), dtype=np.uint8)
pass
return
