add ! validate image size

src/hope_dedup_engine/apps/faces/services/image_processor.py

@@ -97,19 +97,18 @@ def _get_face_detections_dnn(
                 # Decode image from binary buffer to 3D numpy array (height, width, channels of BlueGreeRed color space)
                 image = cv2.imdecode(img_array, cv2.IMREAD_COLOR)
             (h, w) = image.shape[:2]
+            _h, _w = (
+                self.blob_from_image_cfg.shape["height"],
+                self.blob_from_image_cfg.shape["width"],
+            )
+            if h < _h or w < _w:
+                raise ValueError(
+                    f"Image {filename} too small: '{h}x{w}'. It needs to be at least '{_h}x{_w}'."
+                )
             # Create a blob (4D tensor) from the image
             blob = cv2.dnn.blobFromImage(
-                image=cv2.resize(
-                    image,
-                    dsize=(
-                        self.blob_from_image_cfg.shape["height"],
-                        self.blob_from_image_cfg.shape["width"],
-                    ),
-                ),
-                size=(
-                    self.blob_from_image_cfg.shape["height"],
-                    self.blob_from_image_cfg.shape["width"],
-                ),
+                image=cv2.resize(image, dsize=(_h, _w)),
+                size=(_h, _w),
                 scalefactor=self.blob_from_image_cfg.scale_factor,
                 mean=self.blob_from_image_cfg.mean_values,
             )

src/hope_dedup_engine/config/fragments/constance.py

@@ -67,7 +67,7 @@
         int,
     ),
     "FACE_ENCODINGS_MODEL": (
-        "small",
+        "large",
         """
         Specifies the model type used for encoding face landmarks. It can be either 'small' which is faster and
         detects only 5 key facial landmarks, or 'large' which is more precise and identifies 68 key facial landmarks