def on_clients_initialized(self):
io.progress_bar("Sorting", len(self.img_list))
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.idx_shapes_list))
def on_clients_initialized(self):
io.progress_bar("Sort by hist-dissim", self.yaws_sample_list_len)
def on_clients_initialized(self):
if self.debug:
io.named_window("Debug convert")
io.progress_bar("Converting", len(self.input_data))
def on_clients_initialized(self):
io.progress_bar("", len(self.input_data))
def sort_by_absdiff(input_path):
io.log_info("Sorting by absolute difference...")
is_sim = io.input_bool("Sort by similar? ( y/n ?:help skip:y ) : ",
True,
help_message="Otherwise sort by dissimilar.")
from nnlib import nnlib
exec(nnlib.import_all(device_config=nnlib.device.Config()), locals(),
globals())
image_paths = Path_utils.get_image_paths(input_path)
image_paths_len = len(image_paths)
batch_size = 1024
batch_size_remain = image_paths_len % batch_size
i_t = Input((256, 256, 3))
j_t = Input((256, 256, 3))
outputs = []
for i in range(batch_size):
outputs += [K.sum(K.abs(i_t - j_t[i]), axis=[1, 2, 3])]
func_bs_full = K.function([i_t, j_t], outputs)
outputs = []
for i in range(batch_size_remain):
outputs += [K.sum(K.abs(i_t - j_t[i]), axis=[1, 2, 3])]
func_bs_remain = K.function([i_t, j_t], outputs)
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("Initializing CA weights", len(self.idx_shapes_list))
def on_clients_initialized(self):
io.progress_bar("Loading", len(self.image_paths))
