Fix landmarks model issue

src/face_proportions.py

@@ -1,18 +1,30 @@
 import dlib
-import yaml
 import cv2
 import os
 import numpy as np
 import imutils
+import urllib.request
+import bz2
 from src.cv_utils import get_image, resize_image_height
 from typing import List, Union
 from PIL import Image as PILImage
 
-with open("parameters.yml", "r") as stream:
-    try:
-        parameters = yaml.safe_load(stream)
-    except yaml.YAMLError as exc:
-        print(exc)
+
+def download_shape_predictor_model() -> str:
+    model_url = "http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2"
+    local_bz2_path = "models/shape_predictor_68_face_landmarks.dat.bz2"
+    local_dat_path = "models/shape_predictor_68_face_landmarks.dat"
+
+    if not os.path.exists(local_dat_path):
+        os.makedirs("models", exist_ok=True)
+        print("Downloading shape predictor model...")
+        urllib.request.urlretrieve(model_url, local_bz2_path)
+
+        with bz2.open(local_bz2_path, "rb") as f_in, open(local_dat_path, "wb") as f_out:
+            f_out.write(f_in.read())
+        print("Model extracted.")
+
+    return local_dat_path
 
 
 class GetFaceProportions:
@@ -27,9 +39,9 @@ class GetFaceProportions:
 
     @staticmethod
     def detect_face_landmarks(gray_image: np.array) -> List[Union[np.array, np.array]]:
-
         detector = dlib.get_frontal_face_detector()
-        predictor = dlib.shape_predictor(parameters["face_landmarks"]["model"])
+        predictor_path = download_shape_predictor_model()
+        predictor = dlib.shape_predictor(predictor_path)
         rects = detector(gray_image, 1)
         for rect in rects:
             shape = predictor(gray_image, rect)