s444498/wko_anime-face-similarity
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Compare against the entire anime faces dataset

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Marcin Kostrzewski 2023-01-31 20:48:24 +01:00
parent b3bfa970c7
commit 49e337e5e9
2 changed files with 28 additions and 12 deletions
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1
load_test_data.py
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@ -29,7 +29,6 @@ def load_data(input_dir, newSize=(64,64)):
p = image_path / n
img = imread(p) # zwraca ndarry postaci xSize x ySize x colorDepth
img = cv.resize(img, newSize, interpolation=cv.INTER_AREA) # zwraca ndarray
img = img / 255 # type: ignore #normalizacja
test_img.append(img)
labels.append(n)

37
main.py
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@ -4,6 +4,7 @@ import numpy as np
import matplotlib.pyplot as plt
from comparisons import histogram_comparison, structural_similarity_index, euclidean_distance
from load_test_data import load_data
# Allows imports from the style transfer submodule
sys.path.append('DCT-Net')
@ -36,16 +37,31 @@ def plot_two_images(a: np.ndarray, b: np.ndarray):
plt.show()
def compare_with_anime_characters(data: np.ndarray) -> int:
# Example will be one face from anime dataset
example = load_source('data/images/Aisaka, Taiga.jpg')
def compare_with_anime_characters(source: np.ndarray, verbose=False) -> list[dict]:
dataset = load_data('data/images')
all_metrics = []
for anime_image, label in zip(dataset['values'], dataset['labels']):
current_result = {
'name': label,
'metrics': {}
}
# TODO: Use a different face detection method for anime images
example_face = find_and_crop_face(example, 'haarcascades/lbpcascade_animeface.xml')
data_rescaled = cv2.resize(data, example_face.shape[:2])
plot_two_images(example_face, data_rescaled)
print(histogram_comparison(data_rescaled, example_face))
print(f'structural-similarity: {structural_similarity_index(data_rescaled, example_face)}')
print(f'euclidean-distance: {euclidean_distance(data_rescaled, example_face)}')
# anime_face = find_and_crop_face(anime_image, 'haarcascades/lbpcascade_animeface.xml')
anime_face = anime_image
source_rescaled = cv2.resize(source, anime_face.shape[:2])
if verbose:
plot_two_images(anime_face, source_rescaled)
current_result['metrics'] = histogram_comparison(source_rescaled, anime_face)
current_result['metrics']['structural-similarity'] = structural_similarity_index(source_rescaled, anime_face)
current_result['metrics']['euclidean-distance'] = euclidean_distance(source_rescaled, anime_face)
all_metrics.append(current_result)
return all_metrics
def get_top_results(all_metrics: list[dict], metric='correlation', count=1):
all_metrics.sort(reverse=True, key=lambda item: item['metrics'][metric])
return list(map(lambda item: {'name': item['name'], 'score': item['metrics'][metric]}, all_metrics[:count]))
def transfer_to_anime(img: np.ndarray):
@ -57,4 +73,5 @@ if __name__ == '__main__':
source = load_source('UAM-Andre.jpg')
source_anime = transfer_to_anime(source)
source_face_anime = find_and_crop_face(source_anime)
print(compare_with_anime_characters(source_face_anime))
results = compare_with_anime_characters(source_face_anime)
print(get_top_results(results, count=5))