def on_clients_initialized(self):
if self.type == 'landmarks-manual':
self.wnd_name = 'Manual pass'
io.named_window(self.wnd_name)
io.capture_mouse(self.wnd_name)
io.capture_keys(self.wnd_name)
self.cache_original_image = (None, None)
self.cache_image = (None, None)
self.cache_text_lines_img = (None, None)
self.hide_help = False
self.landmarks_accurate = True
self.force_landmarks = False
self.landmarks = None
self.x = 0
self.y = 0
self.rect_size = 100
self.rect_locked = False
self.extract_needed = True
self.image = None
self.image_filepath = None
io.progress_bar(None, len(self.input_data))
def on_clients_initialized(self):
io.progress_bar ("Merging", len(self.frames_idxs)+len(self.frames_done_idxs), initial=len(self.frames_done_idxs) )
self.process_remain_frames = not self.is_interactive
self.is_interactive_quitting = not self.is_interactive
if self.is_interactive:
help_images = {
MergerConfig.TYPE_MASKED : cv2_imread ( str(Path(__file__).parent / 'gfx' / 'help_merger_masked.jpg') ),
MergerConfig.TYPE_FACE_AVATAR : cv2_imread ( str(Path(__file__).parent / 'gfx' / 'help_merger_face_avatar.jpg') ),
}
self.main_screen = Screen(initial_scale_to_width=1368, image=None, waiting_icon=True)
self.help_screen = Screen(initial_scale_to_height=768, image=help_images[self.merger_config.type], waiting_icon=False)
self.screen_manager = ScreenManager( "Merger", [self.main_screen, self.help_screen], capture_keys=True )
self.screen_manager.set_current (self.help_screen)
self.screen_manager.show_current()
self.masked_keys_funcs = {
'`' : lambda cfg,shift_pressed: cfg.set_mode(0),
'1' : lambda cfg,shift_pressed: cfg.set_mode(1),
'2' : lambda cfg,shift_pressed: cfg.set_mode(2),
'3' : lambda cfg,shift_pressed: cfg.set_mode(3),
'4' : lambda cfg,shift_pressed: cfg.set_mode(4),
'5' : lambda cfg,shift_pressed: cfg.set_mode(5),
'6' : lambda cfg,shift_pressed: cfg.set_mode(6),
'q' : lambda cfg,shift_pressed: cfg.add_hist_match_threshold(1 if not shift_pressed else 5),
'a' : lambda cfg,shift_pressed: cfg.add_hist_match_threshold(-1 if not shift_pressed else -5),
'w' : lambda cfg,shift_pressed: cfg.add_erode_mask_modifier(1 if not shift_pressed else 5),
's' : lambda cfg,shift_pressed: cfg.add_erode_mask_modifier(-1 if not shift_pressed else -5),
'e' : lambda cfg,shift_pressed: cfg.add_blur_mask_modifier(1 if not shift_pressed else 5),
'd' : lambda cfg,shift_pressed: cfg.add_blur_mask_modifier(-1 if not shift_pressed else -5),
'r' : lambda cfg,shift_pressed: cfg.add_motion_blur_power(1 if not shift_pressed else 5),
'f' : lambda cfg,shift_pressed: cfg.add_motion_blur_power(-1 if not shift_pressed else -5),
't' : lambda cfg,shift_pressed: cfg.add_super_resolution_power(1 if not shift_pressed else 5),
'g' : lambda cfg,shift_pressed: cfg.add_super_resolution_power(-1 if not shift_pressed else -5),
'y' : lambda cfg,shift_pressed: cfg.add_blursharpen_amount(1 if not shift_pressed else 5),
'h' : lambda cfg,shift_pressed: cfg.add_blursharpen_amount(-1 if not shift_pressed else -5),
'u' : lambda cfg,shift_pressed: cfg.add_output_face_scale(1 if not shift_pressed else 5),
'j' : lambda cfg,shift_pressed: cfg.add_output_face_scale(-1 if not shift_pressed else -5),
'i' : lambda cfg,shift_pressed: cfg.add_image_denoise_power(1 if not shift_pressed else 5),
'k' : lambda cfg,shift_pressed: cfg.add_image_denoise_power(-1 if not shift_pressed else -5),
'o' : lambda cfg,shift_pressed: cfg.add_bicubic_degrade_power(1 if not shift_pressed else 5),
'l' : lambda cfg,shift_pressed: cfg.add_bicubic_degrade_power(-1 if not shift_pressed else -5),
'p' : lambda cfg,shift_pressed: cfg.add_color_degrade_power(1 if not shift_pressed else 5),
';' : lambda cfg,shift_pressed: cfg.add_color_degrade_power(-1),
':' : lambda cfg,shift_pressed: cfg.add_color_degrade_power(-5),
'z' : lambda cfg,shift_pressed: cfg.toggle_masked_hist_match(),
'x' : lambda cfg,shift_pressed: cfg.toggle_mask_mode(),
'c' : lambda cfg,shift_pressed: cfg.toggle_color_transfer_mode(),
'n' : lambda cfg,shift_pressed: cfg.toggle_sharpen_mode(),
}
self.masked_keys = list(self.masked_keys_funcs.keys())
def on_clients_initialized(self):
io.progress_bar("Searching deleted files", len(self.input_paths))
def on_clients_initialized(self):
io.progress_bar("Processing", len(self.image_paths))
def sort_by_absdiff(input_path):
io.log_info("Sorting by absolute difference...")
is_sim = io.input_bool("Sort by similar?",
True,
help_message="Otherwise sort by dissimilar.")
from core.leras import nn
device_config = nn.ask_choose_device_idxs(choose_only_one=True,
return_device_config=True)
nn.initialize(device_config=device_config)
tf = nn.tf
image_paths = pathex.get_image_paths(input_path)
image_paths_len = len(image_paths)
batch_size = 1024
batch_size_remain = image_paths_len % batch_size
i_t = tf.placeholder(tf.float32, (None, 256, 256, 3))
j_t = tf.placeholder(tf.float32, (None, 256, 256, 3))
outputs_full = []
outputs_remain = []
for i in range(batch_size):
diff_t = tf.reduce_sum(tf.abs(i_t - j_t[i]), axis=[1, 2, 3])
outputs_full.append(diff_t)
if i < batch_size_remain:
outputs_remain.append(diff_t)
def func_bs_full(i, j):
return nn.tf_sess.run(outputs_full, feed_dict={i_t: i, j_t: j})
def func_bs_remain(i, j):
return nn.tf_sess.run(outputs_remain, feed_dict={i_t: i, j_t: j})
import h5py
db_file_path = Path(tempfile.gettempdir()) / 'sort_cache.hdf5'
db_file = h5py.File(str(db_file_path), "w")
db = db_file.create_dataset("results", (image_paths_len, image_paths_len),
compression="gzip")
pg_len = image_paths_len // batch_size
if batch_size_remain != 0:
pg_len += 1
pg_len = int((pg_len * pg_len - pg_len) / 2 + pg_len)
io.progress_bar("Computing", pg_len)
j = 0
while j < image_paths_len:
j_images = [cv2_imread(x) for x in image_paths[j:j + batch_size]]
j_images_len = len(j_images)
func = func_bs_remain if image_paths_len - j < batch_size else func_bs_full
i = 0
while i < image_paths_len:
if i >= j:
i_images = [
cv2_imread(x) for x in image_paths[i:i + batch_size]
]
i_images_len = len(i_images)
result = func(i_images, j_images)
db[j:j + j_images_len, i:i + i_images_len] = np.array(result)
io.progress_bar_inc(1)
i += batch_size
db_file.flush()
j += batch_size
io.progress_bar_close()
next_id = 0
sorted = [next_id]
for i in io.progress_bar_generator(range(image_paths_len - 1), "Sorting"):
id_ar = np.concatenate([db[:next_id, next_id], db[next_id, next_id:]])
id_ar = np.argsort(id_ar)
next_id = np.setdiff1d(id_ar, sorted, True)[0 if is_sim else -1]
sorted += [next_id]
db_file.close()
db_file_path.unlink()
img_list = [(image_paths[x], ) for x in sorted]
return img_list, []
def on_clients_initialized(self):
io.progress_bar("Sort by hist-dissim",
len(self.pitch_yaw_sample_list_idxs))
def on_clients_initialized(self):
io.progress_bar("", len(self.input_data))
def on_clients_initialized(self):
io.progress_bar("Loading", len(self.img_list))
def on_clients_initialized(self):
io.progress_bar("Sorting", len(self.img_list))
io.progress_bar_inc(len(self.img_chunks_list))
def on_clients_initialized(self):
io.progress_bar("Loading samples", len(self.image_paths))
def on_clients_initialized(self):
io.progress_bar ("Filtering", self.samples_len)
def on_clients_initialized(self):
io.progress_bar (None, len (self.image_paths))
