diff --git a/face_detect.py b/face_detect.py
new file mode 100644
index 0000000..b08d4bd
--- /dev/null
+++ b/face_detect.py
@@ -0,0 +1,15 @@
+import cv2
+import numpy as np
+
+
+def find_face_bbox(data: np.ndarray, classifier_file='haarcascades/haarcascade_frontalface_default.xml'):
+  data_gray = cv2.cvtColor(data, cv2.COLOR_RGB2GRAY)
+  face_cascade = cv2.CascadeClassifier(classifier_file)
+  face_coords = face_cascade.detectMultiScale(data_gray, 1.1, 3)
+  return max(face_coords, key=len)
+
+
+def crop_face(data: np.ndarray, bounding_box) -> np.ndarray:
+  x, y, w, h = bounding_box
+  face = data[y:y + h, x:x + w]
+  return face
diff --git a/helpers.py b/helpers.py
new file mode 100644
index 0000000..1b8f485
--- /dev/null
+++ b/helpers.py
@@ -0,0 +1,12 @@
+import os
+import sys
+
+
+def no_stdout(func):
+  def wrapper(*args, **kwargs):
+    old_stdout = sys.stdout
+    sys.stdout = open(os.devnull, "w")
+    ret = func(*args, **kwargs)
+    sys.stdout = old_stdout
+    return ret
+  return wrapper
diff --git a/load_test_data.py b/load_test_data.py
index 300cd25..2429b89 100644
--- a/load_test_data.py
+++ b/load_test_data.py
@@ -5,7 +5,11 @@ import cv2 as cv
 from pathlib import Path
 
 
-def load_data(input_dir, newSize=(64,64)):
+def load_source(filename: str) -> np.ndarray:
+  return cv.imread(filename)[..., ::-1]
+
+
+def load_data(input_dir):
     image_path = Path(input_dir)
     file_names = os.listdir(image_path)
     categories_name = []
@@ -27,8 +31,7 @@ def load_data(input_dir, newSize=(64,64)):
 
     for n in file_names:
         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 = load_source(str(p)) # zwraca ndarry postaci xSize x ySize x colorDepth
         test_img.append(img)
         labels.append(n)
 
diff --git a/main.py b/main.py
index 59fcbbb..83a3603 100644
--- a/main.py
+++ b/main.py
@@ -3,8 +3,12 @@ import sys
 import cv2
 import numpy as np
 
-from comparisons import histogram_comparison, structural_similarity_index, euclidean_distance
-from load_test_data import load_data
+from metrics import histogram_comparison, structural_similarity_index, euclidean_distance, AccuracyGatherer
+
+from face_detect import find_face_bbox, crop_face
+from helpers import no_stdout
+from load_test_data import load_data, load_source
+from metrics import get_top_results
 from plots import plot_two_images, plot_results
 
 # Allows imports from the style transfer submodule
@@ -13,21 +17,10 @@ sys.path.append('DCT-Net')
 from source.cartoonize import Cartoonizer
 
 
-def load_source(filename: str) -> np.ndarray:
-  return cv2.imread(filename)[..., ::-1]
+anime_transfer = Cartoonizer(dataroot='DCT-Net/damo/cv_unet_person-image-cartoon_compound-models')
 
 
-def find_and_crop_face(data: np.ndarray, classifier_file='haarcascades/haarcascade_frontalface_default.xml') -> np.ndarray:
-  data_gray = cv2.cvtColor(data, cv2.COLOR_BGR2GRAY)
-  face_cascade = cv2.CascadeClassifier(classifier_file)
-  face = face_cascade.detectMultiScale(data_gray, 1.1, 3)
-  face = max(face, key=len)
-  x, y, w, h = face
-  face = data[y:y + h, x:x + w]
-  return face
-
-
-def compare_with_anime_characters(source: np.ndarray, anime_faces_dataset: dict, verbose=False) -> list[dict]:
+def compare_with_anime_characters(source_image: np.ndarray, anime_faces_dataset: dict, verbose=False) -> list[dict]:
   all_metrics = []
   for anime_image, label in zip(anime_faces_dataset['values'], anime_faces_dataset['labels']):
     current_result = {
@@ -37,7 +30,7 @@ def compare_with_anime_characters(source: np.ndarray, anime_faces_dataset: dict,
     # TODO: Use a different face detection method for anime images
     # 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])
+    source_rescaled = cv2.resize(source_image, anime_face.shape[:2])
     if verbose:
       plot_two_images(anime_face, source_rescaled)
     current_result['metrics'] = histogram_comparison(source_rescaled, anime_face)
@@ -48,61 +41,59 @@ def compare_with_anime_characters(source: np.ndarray, anime_faces_dataset: dict,
   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]))
-
-
+@no_stdout
 def transfer_to_anime(img: np.ndarray):
-  algo = Cartoonizer(dataroot='DCT-Net/damo/cv_unet_person-image-cartoon_compound-models')
-  model_out = algo.cartoonize(img).astype(np.uint8)
+  model_out = anime_transfer.cartoonize(img).astype(np.uint8)
   return cv2.cvtColor(model_out, cv2.COLOR_BGR2RGB)
 
 
-def validate(test_set, anime_faces_set, top_n=1):
+def similarity_to_anime(source_image, anime_faces_set):
+  try:
+    source_face_bbox = find_face_bbox(source_image)
+  except ValueError:
+    return None
+  source_anime = transfer_to_anime(source_image)
+  source_face_anime = crop_face(source_anime, source_face_bbox)
+  return compare_with_anime_characters(source_face_anime, anime_faces_set)
+
+
+def validate(test_set, anime_faces_set):
   all_entries = len(test_set['values'])
-  all_metric_names = [
-    'structural-similarity',
-    'euclidean-distance',
-    'chi-square',
-    'correlation',
-    'intersection',
-    'bhattacharyya-distance'
-  ]
-  hits_per_metric = {metric: 0 for metric in all_metric_names}
+  accuracy = AccuracyGatherer(all_entries)
   for test_image, test_label in zip(test_set['values'], test_set['labels']):
-    test_results = compare_with_anime_characters(test_image, anime_faces_set)
-    top_results_all_metrics = {m: get_top_results(test_results, m, top_n) for m in all_metric_names}
-    for metric_name in all_metric_names:
-      top_current_metric_results = top_results_all_metrics[metric_name]
-      if any(map(lambda single_result: single_result['name'] == test_label, top_current_metric_results)):
-        hits_per_metric[metric_name] += 1
+    test_results = similarity_to_anime(test_image, anime_faces_set)
 
-  all_metrics = {metric: hits_per_metric[metric] / all_entries for metric in all_metric_names}
-  print(f'Top {top_n} matches results:')
-  [print(f'\t{key}: {value*100}%') for key, value in all_metrics.items()]
-  return all_metrics
+    if test_results is None:
+      print(f"cannot find face for {test_label}")
+      all_entries -= 1
+      continue
+
+    accuracy.for_results(test_results, test_label)
+
+  accuracy.count = all_entries
+  accuracy.print()
 
 
-if __name__ == '__main__':
+
+def main():
   parser = argparse.ArgumentParser()
   parser.add_argument('-v', '--validate_only')
   args = parser.parse_args()
-  anime_faces_set = load_data('data/croped_anime_faces', (256, 256))
+  anime_faces_set = load_data('data/croped_anime_faces')
 
   if args.validate_only:
     print('Validating')
     test_set = load_data('test_set')
-    validate(test_set, anime_faces_set, 1)
-    validate(test_set, anime_faces_set, 3)
-    validate(test_set, anime_faces_set, 5)
+    validate(test_set, anime_faces_set)
     exit(0)
 
-  source = load_source('UAM-Andre.jpg')
-  source_anime = transfer_to_anime(source)
-  source_face_anime = find_and_crop_face(source_anime)
-  results = compare_with_anime_characters(source_face_anime, anime_faces_set)
+  source = load_source('test_set/Ayanokouji, Kiyotaka.jpg')
+  results = similarity_to_anime(source, anime_faces_set)
   method = 'structural-similarity'
   top_results = get_top_results(results, count=4, metric=method)
   print(top_results)
-  plot_results(source, source_anime, top_results, anime_faces_set, method)
+  plot_results(source, transfer_to_anime(source), top_results, anime_faces_set, method)
+
+
+if __name__ == '__main__':
+  main()
diff --git a/comparisons.py b/metrics.py
similarity index 52%
rename from comparisons.py
rename to metrics.py
index 7029786..8d6be1d 100644
--- a/comparisons.py
+++ b/metrics.py
@@ -40,3 +40,42 @@ def euclidean_distance(data_a: np.ndarray, data_b: np.ndarray) -> float:
     result += (histogram_a[i] - histogram_b[i]) ** 2
     i += 1
   return result[0] ** (1 / 2)
+
+
+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]))
+
+
+class AccuracyGatherer:
+  all_metric_names = [
+    'structural-similarity',
+    'euclidean-distance',
+    'chi-square',
+    'correlation',
+    'intersection',
+    'bhattacharyya-distance'
+  ]
+
+  def __init__(self, count, top_ks=(1, 3, 5)):
+    self.top_ks = top_ks
+    self.hits = {k: {metric: 0 for metric in AccuracyGatherer.all_metric_names} for k in top_ks}
+    self.count = count
+
+  def print(self):
+    for k in self.top_ks:
+      all_metrics = {metric: self.hits[k][metric] / self.count for metric in AccuracyGatherer.all_metric_names}
+      print(f'Top {k} matches results:')
+      [print(f'\t{key}: {value * 100}%') for key, value in all_metrics.items()]
+
+  def for_results(self, results, test_label):
+    top_results_all_metrics = {
+      k: {m: get_top_results(results, m, k) for m in AccuracyGatherer.all_metric_names} for k in self.top_ks
+    }
+    for metric_name in AccuracyGatherer.all_metric_names:
+      self.add_if_hit(top_results_all_metrics, test_label, metric_name)
+
+  def add_if_hit(self, results, test_label, metric_name):
+    for k in self.top_ks:
+      if any(map(lambda single_result: single_result['name'] == test_label, results[k][metric_name])):
+        self.hits[k][metric_name] += 1