def human_baseline(gold_dataset_dir: str, annotation_path: str):
"""
Shows the images from the dataset and ask the human to label them.
:param str gold_dataset_dir: The directory of the gold dataset
:param str annotation_path: The path to the annotation file, if it exists we will resume the labeling session
"""
files = glob.glob(os.path.join(gold_dataset_dir, "*.jpeg"))
io_handler = IOHandler()
input_dictionary = restore_dictionary(annotation_path=annotation_path)
try:
pbar_desc = (
"-1"
if input_dictionary["total"] == 0
else f"Current human accuracy: {round((input_dictionary['correct']/input_dictionary['total'])*100,2)}%"
)
with tqdm(total=len(files) - input_dictionary["total"], desc=pbar_desc) as pbar:
for image_name in files:
metadata = os.path.basename(image_name)[:-5]
header, values = metadata.split("%")
image_no = int(header[1:])
if image_no not in input_dictionary["human_predictions"]:
gold_key = io_handler.imagename_input_conversion(
image_name=image_name, output_type="keyboard"
)
# image = io.imread(image_name)
os.system(f"xv {image_name} &")
# cv2.imshow("window1", img_as_ubyte(image))
# cv2.waitKey(1)
user_key = keys_to_id(input("Push the keys: "))
input_dictionary["human_predictions"][image_no] = user_key
input_dictionary["total"] += 1
if user_key == gold_key:
input_dictionary["correct"] += 1
pbar.update(1)
pbar.set_description(
f"Current human accuracy: {round((input_dictionary['correct']/input_dictionary['total'])*100,2)}%"
)
if input_dictionary["total"] % 20 == 0:
with open(
annotation_path, "w+", encoding="utf8"
) as annotation_file:
json.dump(input_dictionary, annotation_file)
except KeyboardInterrupt:
with open(annotation_path, "w+", encoding="utf8") as annotation_file:
json.dump(input_dictionary, annotation_file)
with open(annotation_path, "w+", encoding="utf8") as annotation_file:
json.dump(input_dictionary, annotation_file)
class Tedd1104Dataset(Dataset):
"""TEDD1104 dataset."""
def __init__(
self,
dataset_dir: str,
hide_map_prob: float,
dropout_images_prob: List[float],
control_mode: str = "keyboard",
train: bool = False,
):
"""
INIT
:param str dataset_dir: The directory of the dataset.
:param bool hide_map_prob: Probability of hiding the minimap (0<=hide_map_prob<=1)
:param List[float] dropout_images_prob: Probability of dropping an image (0<=dropout_images_prob<=1)
:param str control_mode: Type of the user input: "keyboard" or "controller"
:param bool train: If True, the dataset is used for training.
"""
self.dataset_dir = dataset_dir
self.hide_map_prob = hide_map_prob
self.dropout_images_prob = dropout_images_prob
self.control_mode = control_mode.lower()
assert self.control_mode in [
"keyboard",
"controller",
], f"{self.control_mode} control mode not supported. Supported dataset types: [keyboard, controller]. "
assert 0 <= hide_map_prob <= 1.0, (
f"hide_map_prob not in 0 <= hide_map_prob <= 1.0 range. "
f"hide_map_prob: {hide_map_prob}")
assert len(dropout_images_prob) == 5, (
f"dropout_images_prob must have 5 probabilities, one for each image in the sequence. "
f"dropout_images_prob len: {len(dropout_images_prob)}")
for dropout_image_prob in dropout_images_prob:
assert 0 <= dropout_image_prob <= 1.0, (
f"All probabilities in dropout_image_prob must be in the range 0 <= dropout_image_prob <= 1.0. "
f"dropout_images_prob: {dropout_images_prob}")
if train:
self.transform = transforms.Compose([
RemoveMinimap(hide_map_prob=hide_map_prob),
RemoveImage(dropout_images_prob=dropout_images_prob),
SplitImages(),
ToTensor(),
SequenceColorJitter(),
Normalize(),
MergeImages(),
])
else:
self.transform = transforms.Compose([
# RemoveMinimap(hide_map_prob=hide_map_prob),
# RemoveImage(dropout_images_prob=dropout_images_prob),
SplitImages(),
ToTensor(),
# SequenceColorJitter(),
Normalize(),
MergeImages(),
])
self.dataset_files = glob.glob(os.path.join(dataset_dir, "*.jpeg"))
self.IOHandler = IOHandler()
def __len__(self):
"""
Returns the length of the dataset.
:return: int - Length of the dataset.
"""
return len(self.dataset_files)
def __getitem__(self, idx):
"""
Returns a sample from the dataset.
:param int idx: Index of the sample.
:return: Dict[str, torch.tensor]- Transformed sequence of images
"""
if torch.is_tensor(idx):
idx = int(idx)
img_name = self.dataset_files[idx]
image = None
while image is None:
try:
image = io.imread(img_name)
except (ValueError, FileNotFoundError) as err:
error_message = str(err).split("\n")[-1]
print(
f"Error reading image: {img_name} probably a corrupted file.\n"
f"Exception: {error_message}\n"
f"We will load a random image instead.")
img_name = self.dataset_files[int(
len(self.dataset_files) * torch.rand(1))]
y = self.IOHandler.imagename_input_conversion(
image_name=img_name,
output_type=self.control_mode,
)
sample = {"image": image, "y": y}
return self.transform(sample)