115 lines
3.6 KiB
Python
115 lines
3.6 KiB
Python
import argparse
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import sys
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import cv2
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import matplotlib.pyplot as plt
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import numpy as np
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from metrics import histogram_comparison, structural_similarity_index, euclidean_distance, AccuracyGatherer
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from face_detect import find_face_bbox, crop_face
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from helpers import no_stdout
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from load_test_data import load_data, load_source
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from metrics import get_top_results
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from plots import plot_two_images, plot_results
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# Allows imports from the style transfer submodule
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sys.path.append('DCT-Net')
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from source.cartoonize import Cartoonizer
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anime_transfer = Cartoonizer(dataroot='DCT-Net/damo/cv_unet_person-image-cartoon_compound-models')
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def compare_with_anime_characters(source_image: np.ndarray, anime_faces_dataset: dict, verbose=False) -> list[dict]:
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all_metrics = []
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for anime_image, label in zip(anime_faces_dataset['values'], anime_faces_dataset['labels']):
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current_result = {
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'name': label,
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'metrics': {}
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}
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# TODO: Use a different face detection method for anime images
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# anime_face = find_and_crop_face(anime_image, 'haarcascades/lbpcascade_animeface.xml')
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anime_face = anime_image
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source_rescaled = cv2.resize(source_image, anime_face.shape[:2])
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if verbose:
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plot_two_images(anime_face, source_rescaled)
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current_result['metrics'] = histogram_comparison(source_rescaled, anime_face)
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current_result['metrics']['structural-similarity'] = structural_similarity_index(source_rescaled, anime_face)
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current_result['metrics']['euclidean-distance'] = euclidean_distance(source_rescaled, anime_face)
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all_metrics.append(current_result)
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return all_metrics
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@no_stdout
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def transfer_to_anime(img: np.ndarray):
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model_out = anime_transfer.cartoonize(img).astype(np.uint8)
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return cv2.cvtColor(model_out, cv2.COLOR_BGR2RGB)
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def similarity_to_anime(source_image, anime_faces_set, debug=True):
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try:
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source_face_bbox = find_face_bbox(source_image)
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except ValueError:
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return None
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source_anime = transfer_to_anime(source_image)
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source_face_anime = crop_face(source_anime, source_face_bbox)
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if debug:
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source_image_with_box = source_image.copy()
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x, y, w, h = source_face_bbox
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cv2.rectangle(source_image_with_box, (x, y), (x + w, y + h), (255, 0, 0), 2)
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plt.figure(figsize=[12, 4])
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plt.subplot(131)
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plt.imshow(source_image_with_box)
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plt.subplot(132)
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plt.imshow(source_anime)
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plt.subplot(133)
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plt.imshow(source_face_anime)
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plt.show()
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return compare_with_anime_characters(source_face_anime, anime_faces_set)
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def validate(test_set, anime_faces_set):
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all_entries = len(test_set['values'])
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accuracy = AccuracyGatherer(all_entries)
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for test_image, test_label in zip(test_set['values'], test_set['labels']):
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test_results = similarity_to_anime(test_image, anime_faces_set)
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if test_results is None:
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print(f"cannot find face for {test_label}")
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all_entries -= 1
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continue
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accuracy.for_results(test_results, test_label)
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accuracy.count = all_entries
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accuracy.print()
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument('-v', '--validate_only')
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args = parser.parse_args()
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anime_faces_set = load_data('data/croped_anime_faces')
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if args.validate_only:
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print('Validating')
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test_set = load_data('test_set')
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validate(test_set, anime_faces_set)
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exit(0)
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source = load_source('test_set/Ayanokouji, Kiyotaka.jpg')
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results = similarity_to_anime(source, anime_faces_set)
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method = 'structural-similarity'
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top_results = get_top_results(results, count=4, metric=method)
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print(top_results)
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plot_results(source, transfer_to_anime(source), top_results, anime_faces_set, method)
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if __name__ == '__main__':
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main()
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