Detect with Retina and crop faces to 300px.

recognition/crop.py

@@ -0,0 +1,52 @@
+from autocrop.autocrop import Cropper, open_file, ImageReadError, check_underexposed, bgr_to_rbg
+import cv2
+
+
+class CustomCrop(Cropper):
+    def __init__(self, width=500,
+                 height=500,
+                 face_percent=50,
+                 padding=None,
+                 fix_gamma=True):
+        super().__init__(width=width, height=height, face_percent=face_percent, padding=padding, fix_gamma=fix_gamma)
+
+    def crop(self, path_or_array, left, top, right, bottom):
+
+        if isinstance(path_or_array, str):
+            image = open_file(path_or_array)
+        else:
+            image = path_or_array
+
+        # Some grayscale color profiles can throw errors, catch them
+        try:
+            gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+        except cv2.error:
+            gray = image
+
+        try:
+            img_height, img_width = image.shape[:2]
+        except AttributeError:
+            raise ImageReadError
+
+        x, y, w, h = left, top, right-left, bottom-top
+        pos = self._crop_positions(
+            img_height,
+            img_width,
+            x,
+            y,
+            w,
+            h,
+        )
+
+        # ====== Actual cropping ======
+        image = image[pos[0]: pos[1], pos[2]: pos[3]]
+
+        # Resize
+        image = cv2.resize(
+            image, (self.width, self.height), interpolation=cv2.INTER_AREA
+        )
+
+        # Underexposition
+        if self.gamma:
+            image = check_underexposed(image, gray)
+        return bgr_to_rbg(image)

recognition/extract_all_faces.py

@@ -2,16 +2,31 @@
 from retinaface import RetinaFace
 from pathlib import Path
 from PIL import Image
+from crop import CustomCrop
 
 out_dir = Path("../images/ungrouped_faces/")
 image_paths = Path("../images/original_photos/").glob("*jpg")
 
 faces_idx = 1
 
 for img_path in image_paths:
-    faces = RetinaFace.extract_faces(img_path=str(img_path))
-    for face_img in faces:
-        im = Image.fromarray(face_img)
+    detected_faces_desc = RetinaFace.detect_faces(str(img_path))
+    if type(detected_faces_desc) != dict:
+        continue
+
+    for face_details in detected_faces_desc.values():
+
+        match_score = float(face_details['score'])
+        if match_score < 0.95:
+            continue
+
+        face_roi = face_details['facial_area']
+        try:
+            cropped_face_img = CustomCrop(width=300, height=300, face_percent=80).crop(str(img_path), *face_roi)
+        except Exception:
+            continue
+
+        im = Image.fromarray(cropped_face_img)
         face_path = out_dir / f"{faces_idx}.jpg"
         im.save(str(face_path))
         faces_idx += 1