settings.py.template

# -*- coding: utf-8 -*-
""" settings """

import os

from spfs.constants import Pooling
from spfs.core.weighting_methods import WeightingMethod
from spfs.utils.images import TweakImage
from spfs.utils.normalization import Normalization


###############################################################################
#                                   Datasets                                   #
###############################################################################

DATASETS_DIRECTORY = "datasets_dir"

TRAINING_DATA_DIRECTORY_DATASET_PATH = os.path.join(
    DATASETS_DIRECTORY,
    'patchcamelyon',
    'scene_train.json'
)
TESTING_DATA_DIRECTORY_DATASET_PATH = os.path.join(
    DATASETS_DIRECTORY,
    'patchcamelyon',
    'scene_test.json'
)

###############################################################################
#                                   General                                    #
###############################################################################
GENERATED_DATA_DIRECTORY = "tmp"
GENERATED_FEATS_FILENAME_TEMPLATE = 'spatial_pyramid_feats_{}.json'
CODEBOOK_ONNX = 'codebook.onnx'
# percentage of patches to be used to build the codebook
CODEBOOK_PATCHES_PERCENTAGE = 1.0  # 0 < value <= 1.

# Step size & keypoint diameter used when creating the codebook. It must be
# bigger or equal than the smallest spatial pyramid cell
# WARNING: Do not decrease this number too much or you may run out of RAM
#          (loads of SIFT descriptors will be loaded into memory)
CODEBOOK_CREATION_KEYPOINTS_STEP_SIZE = 16

# Step size & keypoint diameter used when extracting the spatial pyramid features
KEYPOINTS_STEP_SIZE = 2

CHANNELS = 200  # number of features types or kmeans clusters
PYRAMID_LEVELS = 2

# For more options see core.weighting_methods.WeightingMethod
PYRAMID_FEATURE_WEIGHTING_METHOD = WeightingMethod.GENERAL_WEIGHTING

# When building the codebook you can set PATCH_SIZE TO -1 to not
# use mini paches when extracting sift descriptors. Thus, keypoints
# will be set all over the image and not per mini-patch
PATCH_SIZE = -1  # 16  # make sure that PATCH_SIZE % 2**PYRAMID_LEVELS == 0
PATCH_STEP_SIZE = 16  # PATCH_SIZE == PATCH_STEP_SIZE means no patch overlapping

# Used to reconstruct the original image shape
# on utils/datasets/items.py -> InMemoryDatasetItems.get_sample
IMAGE_WIDTH = 32  # original image with
IMAGE_HEIGHT = 32  # original image height

NUMPY_RANDOM_SEED = 0
RANDOM_STATE = 42
PCA_N_COMPONENTS = -1  # 3000  # 1000  # set to -1 to not apply PCA

ENCODED_DESCRIPTORS_POOLING_METHOD = Pooling.SUM  # For more options see contants.py
TWEAK_IMAGE_METHOD = TweakImage.get_adjusted_image  # For more options see gutils/images.py
# Norm used during normalization
NORMALIZATION = Normalization.standardization  # For more options see utils/normalization.py
KMEANS_VERBOSE = 1  # set it to 0 not print kmeans progress

# Quick tests
# Set the number of samples you want to work with to perform quick tests.
# Set it to a number <= 0 to use all the samples provided for training and testing
# The variables you can tweak to run quicker tests are:
# + QUICK_TESTS, you can use a number > CHANNELS (this condition is not mandatory)
# + CODEBOOK_PATCHES_PERCENTAGE you can increase it and decrease the QUICK_TESTS number
# + PCA_N_COMPONENTS can be reduced
# + PCA_N_COMPONENTS < QUICK_TESTS (it's a must!)
QUICK_TESTS = -1  # 1001