Merge pull request 'Plot results' (#6) from pretty-results into main

Reviewed-on: #6

.gitignore

@@ -1,4 +1,6 @@
 data
+.idea
+.yoloface
 
 # Byte-compiled / optimized / DLL files
 __pycache__/

README.md

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+# wko_anime-face-similarity
+Projekt przygotowany na zajęcia z widzenia komputerowego.
+
+Rozpoznaje twarz na zdjęciu wejściowym i dokonując transferu stylu do anime, porównuje zdjęcie ze zbiorem postaci
+z anime i wskazuje podobieństwa według wybranych metryk.
+
+## Instalacja
+1. Pobranie submodułów:
+    ```shell
+    $ git submodule update --init
+    ```
+2. Instalacja zależności:
+   * Windows/Linux
+     ```shell
+     $ pip install -r requirements.txt     
+     ```
+   * MacOS
+     ```shell
+     $ pip install -r requirements-osx.txt
+     ```
+3. Konfiguracja DCT-Netu (anime style transfer)
+    ```shell
+    $ cd DCT-Net && python download.py
+    ```
+4. Pobranie datasetu twarzy postaci z anime (MyAnimeList)
+    ```shell
+    $ python scrape_data.py
+    ```
+
+## Uruchomienie
+Na tę chwilę zdjęcie poddawane porównaniu to `UAM-Andre.jpg`
+```shell
+$ python main.py
+```
+
+### Walidacja
+Do walidacji metryk na postawie testowego datasetu z cosplayerami (`test_set`) uruchamiamy
+```shell
+$ python --validate_only 1
+```

face_detect.py

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+import cv2
+import numpy as np
+from yoloface import face_analysis
+
+face_detector = face_analysis()
+
+
+def equalize_image(data: np.ndarray):
+  data_hsv = cv2.cvtColor(data, cv2.COLOR_RGB2HSV)
+  data_hsv[:, :, 2] = cv2.equalizeHist(data_hsv[:, :, 2])
+  return cv2.cvtColor(data_hsv, cv2.COLOR_HSV2RGB)
+
+
+def find_face_bbox_yolo(data: np.ndarray):
+  _, box, conf = face_detector.face_detection(frame_arr=data, frame_status=True, model='full')
+  if len(box) < 1:
+    return None, None
+  return box, conf
+
+
+def find_face_bbox(data: np.ndarray):
+  classifier_files = [
+    'haarcascades/haarcascade_frontalface_default.xml',
+    'haarcascades/haarcascade_frontalface_alt.xml',
+    'haarcascades/haarcascade_frontalface_alt2.xml',
+    'haarcascades/haarcascade_profileface.xml',
+    'haarcascades/haarcascade_glasses.xml',
+    'lbpcascade_animeface.xml',
+  ]
+  data_equalized = equalize_image(data)
+  data_gray = cv2.cvtColor(data_equalized, cv2.COLOR_RGB2GRAY)
+  face_coords, conf = find_face_bbox_yolo(cv2.cvtColor(data_equalized, cv2.COLOR_RGB2BGR))
+  if face_coords is not None:
+    return face_coords[0]
+
+  for classifier in classifier_files:
+    face_cascade = cv2.CascadeClassifier(classifier)
+    face_coords = face_cascade.detectMultiScale(data_gray, 1.1, 3)
+    if face_coords is not None:
+      break
+  return max(face_coords, key=lambda v: v[2]*v[3])
+
+
+def crop_face(data: np.ndarray, bounding_box) -> np.ndarray:
+  x, y, w, h = bounding_box
+  # Extending the boxes
+  factor = 0.4
+  x, y = round(x - factor * w), round(y - factor * h)
+  w, h = round(w + factor * w * 2), round(h + factor * h * 2)
+  y = max(y, 0)
+  x = max(x, 0)
+
+  face = data[y:y + h, x:x + w]
+  return face

helpers.py

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+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

load_test_data.py

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+import numpy as np
+import os
+from skimage.io import imread
+import cv2 as cv
+from pathlib import Path
+
+
+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 = []
+    categories_count = []
+    count = 0
+    n = file_names[0]
+    for name in file_names:
+        if name != n:
+            categories_count.append(count)
+            n = name
+            count = 1
+        else:
+            count += 1
+        if not name in categories_name:
+            categories_name.append(name)
+    categories_count.append(count)
+    test_img = []
+    labels = []
+
+    for n in file_names:
+        p = image_path / n
+        img = load_source(str(p)) # zwraca ndarry postaci xSize x ySize x colorDepth
+        test_img.append(img)
+        labels.append(n)
+
+    X = {}
+    X["values"] = np.array(test_img)
+    X["name"] = categories_name
+    X["names_count"] = categories_count
+    X["labels"] = labels
+    return X

main.py

@@ -1,40 +1,114 @@
-# Allows imports from the style transfer submodule
+import argparse
 import sys
-sys.path.append('DCT-Net')
-
 import cv2
-import os
+import matplotlib.pyplot as plt
 import numpy as np
 
+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
+sys.path.append('DCT-Net')
+
 from source.cartoonize import Cartoonizer
 
 
-def load_source(filename: str) -> np.ndarray:
+anime_transfer = Cartoonizer(dataroot='DCT-Net/damo/cv_unet_person-image-cartoon_compound-models')
-  return cv2.imread(filename)[...,::-1]
 
 
-def find_and_crop_face(data: np.ndarray) -> np.ndarray:
+def compare_with_anime_characters(source_image: np.ndarray, anime_faces_dataset: dict, verbose=False) -> list[dict]:
-  data_gray = cv2.cvtColor(data, cv2.COLOR_BGR2GRAY)
+  all_metrics = []
-  face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_default.xml')
+  for anime_image, label in zip(anime_faces_dataset['values'], anime_faces_dataset['labels']):
-  face = face_cascade.detectMultiScale(data_gray, 1.3, 4)
+    current_result = {
-  face = max(face, key=len)
+      'name': label,
-  x, y, w, h = face
+      'metrics': {}
-  face = data[y:y + h, x:x + w]
+    }
-  return face
+    # 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
-def compare_with_anime_characters(data: np.ndarray) -> int:
+    source_rescaled = cv2.resize(source_image, anime_face.shape[:2])
-  # TODO
+    if verbose:
-  return 1
+      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
 
 
+@no_stdout
 def transfer_to_anime(img: np.ndarray):
-  algo = Cartoonizer(dataroot='damo/cv_unet_person-image-cartoon_compound-models')
+  model_out = anime_transfer.cartoonize(img).astype(np.uint8)
-  return algo.cartoonize(img)
+  return cv2.cvtColor(model_out, cv2.COLOR_BGR2RGB)
+
+
+def similarity_to_anime(source_image, anime_faces_set, debug=False):
+  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)
+
+  if debug:
+    source_image_with_box = source_image.copy()
+    x, y, w, h = source_face_bbox
+    cv2.rectangle(source_image_with_box, (x, y), (x + w, y + h), (255, 0, 0), 2)
+    plt.figure(figsize=[12, 4])
+    plt.subplot(131)
+    plt.imshow(source_image_with_box)
+    plt.subplot(132)
+    plt.imshow(source_anime)
+    plt.subplot(133)
+    plt.imshow(source_face_anime)
+    plt.show()
+
+  return compare_with_anime_characters(source_face_anime, anime_faces_set, verbose=debug)
+
+
+def validate(test_set, anime_faces_set):
+  all_entries = len(test_set['values'])
+  accuracy = AccuracyGatherer(all_entries)
+  for test_image, test_label in zip(test_set['values'], test_set['labels']):
+    test_results = similarity_to_anime(test_image, anime_faces_set)
+
+    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()
+
+
+
+def main():
+  parser = argparse.ArgumentParser()
+  parser.add_argument('-v', '--validate_only')
+  args = parser.parse_args()
+  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)
+    exit(0)
+
+  source = load_source('test_set/Ayanokouji, Kiyotaka.jpg')
+  results = similarity_to_anime(source, anime_faces_set)
+  method = 'correlation'
+  top_results = get_top_results(results, count=4, metric=method)
+  print(top_results)
+  plot_results(source, transfer_to_anime(source), top_results, anime_faces_set, method)
 
 
 if __name__ == '__main__':
-  source = load_source('input.png')
+  main()
-  source_face = find_and_crop_face(source)
-  source_face_anime = transfer_to_anime(source)
-  print(compare_with_anime_characters(source_face_anime))

metrics.py

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+import cv2
+import numpy as np
+from skimage.metrics import structural_similarity
+
+def histogram_comparison(data_a: np.ndarray, data_b: np.ndarray) -> dict:
+  hsv_a = cv2.cvtColor(data_a, cv2.COLOR_BGR2HSV)
+  hsv_b = cv2.cvtColor(data_b, cv2.COLOR_BGR2HSV)
+
+  histSize = [50, 60]
+  hue_ranges = [0, 180]
+  sat_ranges = [0, 256]
+  channels = [0, 1]
+  ranges = hue_ranges + sat_ranges
+
+  hist_a = cv2.calcHist([hsv_a], channels, None, histSize, ranges, accumulate=False)
+  cv2.normalize(hist_a, hist_a, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX)
+  hist_b = cv2.calcHist([hsv_b], channels, None, histSize, ranges, accumulate=False)
+  cv2.normalize(hist_b, hist_b, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX)
+
+  return {
+    'correlation': cv2.compareHist(hist_a, hist_b, 0),
+    'chi-square': cv2.compareHist(hist_a, hist_b, 1),
+    'intersection': cv2.compareHist(hist_a, hist_b, 2),
+    'bhattacharyya-distance': cv2.compareHist(hist_a, hist_b, 3),
+  }
+
+
+def structural_similarity_index(data_a: np.ndarray, data_b: np.ndarray) -> float:
+  return structural_similarity(cv2.cvtColor(data_a, cv2.COLOR_BGR2GRAY), cv2.cvtColor(data_b, cv2.COLOR_BGR2GRAY))
+
+
+def euclidean_distance(data_a: np.ndarray, data_b: np.ndarray) -> float:
+  gray_a = cv2.cvtColor(data_a, cv2.COLOR_BGR2GRAY)
+  histogram_a = cv2.calcHist([gray_a], [0], None, [256], [0, 256])
+
+  gray_b = cv2.cvtColor(data_b, cv2.COLOR_BGR2GRAY)
+  histogram_b = cv2.calcHist([gray_b], [0],  None, [256], [0, 256])
+  result, i = [0.], 0
+  while i < len(histogram_a) and i < len(histogram_b):
+    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

plots.py

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+import numpy as np
+from matplotlib import pyplot as plt, gridspec
+
+
+def plot_two_images(a: np.ndarray, b: np.ndarray):
+  plt.figure(figsize=[10, 10])
+  plt.subplot(121)
+  plt.imshow(a)
+  plt.title("A")
+  plt.subplot(122)
+  plt.imshow(b)
+  plt.title("B")
+  plt.show()
+
+
+def plot_results(source, source_anime, results, anime_faces_set, method):
+    cols = len(results)
+    plt.figure(figsize=[3*cols, 7])
+    gs = gridspec.GridSpec(2, cols)
+
+    plt.subplot(gs[0, cols // 2 - 1])
+    plt.imshow(source)
+    plt.title('Your image')
+    plt.axis('off')
+
+    plt.subplot(gs[0, cols // 2])
+    plt.imshow(source_anime)
+    plt.title('Your image in Anime style')
+    plt.axis('off')
+
+    plt.figtext(0.5, 0.525, "Predictions", ha="center", va="top", fontsize=16)
+
+    for idx, prediction in enumerate(results):
+        result_img = anime_faces_set['values'][anime_faces_set['labels'].index(prediction['name'])]
+        plt.subplot(gs[1, idx])
+        plt.imshow(result_img, interpolation='bicubic')
+        plt.title(f'{prediction["name"].partition(".")[0]}, score={str(round(prediction["score"], 4))}')
+        plt.axis('off')
+
+    plt.tight_layout()
+
+    plt.figtext(0.5, 0.01, f"Metric: {method}", ha="center", va="bottom", fontsize=12)
+    plt.subplots_adjust(wspace=0, hspace=0.1)
+
+    plt.show()

requirements-osx.txt

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+tensorflow-macos==2.11.0
+easydict==1.10
+numpy==1.23.1
+modelscope==1.1.3
+requests==2.28.2
+beautifulsoup4==4.11.1
+lxml==4.9.2
+opencv-python==4.7.0.68
+torch==1.13.1
+matplotlib==3.6.3
+scikit-image==0.19.3

requirements.txt

@@ -6,3 +6,8 @@ requests==2.28.2
 beautifulsoup4==4.11.1
 lxml==4.9.2
 opencv-python==4.7.0.68
+torch==1.13.1
+matplotlib==3.6.3
+scikit-image==0.19.3
+yoloface==0.0.4
+ipython==8.9.0