checking file's content type instead of extension

deepface/modules/preprocessing.py

@@ -1,7 +1,9 @@
+# built-in dependencies
 import os
 from typing import Union, Tuple
 import base64
 from pathlib import Path
+import imghdr
 
 # 3rd party
 import numpy as np
@@ -82,16 +84,16 @@ def load_base64(uri: str) -> np.ndarray:
     if len(encoded_data_parts) < 2:
         raise ValueError("format error in base64 encoded string")
 
-    # similar to find functionality, we are just considering these extensions
-    if not (
-        uri.startswith("data:image/jpeg")
-        or uri.startswith("data:image/jpg")
-        or uri.startswith("data:image/png")
-    ):
-        raise ValueError(f"input image can be jpg, jpeg or png, but it is {encoded_data_parts}")
-
     encoded_data = encoded_data_parts[1]
-    nparr = np.fromstring(base64.b64decode(encoded_data), np.uint8)
+    decoded_bytes = base64.b64decode(encoded_data)
+    file_type = imghdr.what(None, h=decoded_bytes)
+
+    # similar to find functionality, we are just considering these extensions
+    # content type is safer option than file extension
+    if file_type not in ["jpeg", "png"]:
+        raise ValueError(f"input image can be jpg or png, but it is {file_type}")
+
+    nparr = np.fromstring(decoded_bytes, np.uint8)
     img_bgr = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
     # img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
     return img_bgr

deepface/modules/recognition.py

@@ -3,6 +3,7 @@ import os
 import pickle
 from typing import List, Union, Optional, Dict, Any
 import time
+import imghdr
 
 # 3rd party dependencies
 import numpy as np
@@ -296,8 +297,9 @@ def __list_images(path: str) -> List[str]:
     images = []
     for r, _, f in os.walk(path):
         for file in f:
-            if file.lower().endswith((".jpg", ".jpeg", ".png")):
-                exact_path = os.path.join(r, file)
+            exact_path = os.path.join(r, file)
+            file_type = imghdr.what(exact_path)
+            if file_type in ["jpeg", "png"]:
                 images.append(exact_path)
     return images
 

tests/test_extract_faces.py

@@ -1,6 +1,8 @@
 import numpy as np
+import base64
 import pytest
 from deepface import DeepFace
+from deepface.modules import preprocessing
 from deepface.commons.logger import Logger
 
 logger = Logger("tests/test_extract_faces.py")
@@ -48,3 +50,24 @@ def test_backends_for_not_enforced_detection_with_non_facial_inputs():
         )
         assert objs[0]["face"].shape == (224, 224, 3)
     logger.info("✅ extract_faces for not enforced detection and non-facial image test is done")
+
+
+def test_file_types_while_loading_base64():
+    img1_path = "dataset/img47.jpg"
+    img1_base64 = image_to_base64(image_path=img1_path)
+
+    with pytest.raises(ValueError, match="input image can be jpg or png, but it is"):
+        _ = preprocessing.load_base64(uri=img1_base64)
+
+    img2_path = "dataset/img1.jpg"
+    img2_base64 = image_to_base64(image_path=img2_path)
+
+    img2 = preprocessing.load_base64(uri=img2_base64)
+    # 3 dimensional image should be loaded
+    assert len(img2.shape) == 3
+
+
+def image_to_base64(image_path):
+    with open(image_path, "rb") as image_file:
+        encoded_string = base64.b64encode(image_file.read()).decode("utf-8")
+    return "data:image/jpeg," + encoded_string

tests/test_find.py

@@ -3,6 +3,7 @@ import cv2
 import pandas as pd
 from deepface import DeepFace
 from deepface.modules import verification
+from deepface.modules import recognition
 from deepface.commons.logger import Logger
 
 logger = Logger("tests/test_find.py")
@@ -11,7 +12,7 @@ threshold = verification.find_threshold(model_name="VGG-Face", distance_metric="
 
 
 def test_find_with_exact_path():
-    img_path = os.path.join("dataset","img1.jpg")
+    img_path = os.path.join("dataset", "img1.jpg")
     dfs = DeepFace.find(img_path=img_path, db_path="dataset", silent=True)
     assert len(dfs) > 0
     for df in dfs:
@@ -31,7 +32,7 @@ def test_find_with_exact_path():
 
 
 def test_find_with_array_input():
-    img_path = os.path.join("dataset","img1.jpg")
+    img_path = os.path.join("dataset", "img1.jpg")
     img1 = cv2.imread(img_path)
     dfs = DeepFace.find(img1, db_path="dataset", silent=True)
     assert len(dfs) > 0
@@ -53,7 +54,7 @@ def test_find_with_array_input():
 
 
 def test_find_with_extracted_faces():
-    img_path = os.path.join("dataset","img1.jpg")
+    img_path = os.path.join("dataset", "img1.jpg")
     face_objs = DeepFace.extract_faces(img_path)
     img = face_objs[0]["face"]
     dfs = DeepFace.find(img, db_path="dataset", detector_backend="skip", silent=True)
@@ -72,3 +73,25 @@ def test_find_with_extracted_faces():
         logger.debug(df.head())
         assert df.shape[0] > 0
     logger.info("✅ test find for extracted face input done")
+
+
+def test_filetype_for_find():
+    """
+    only images as jpg and png can be loaded into database
+    """
+    img_path = os.path.join("dataset", "img1.jpg")
+    dfs = DeepFace.find(img_path=img_path, db_path="dataset", silent=True)
+
+    df = dfs[0]
+
+    # img47 is webp even though its extension is jpg
+    assert df[df["identity"] == "dataset/img47.jpg"].shape[0] == 0
+
+
+def test_filetype_for_find_bulk_embeddings():
+    imgs = recognition.__list_images("dataset")
+
+    assert len(imgs) > 0
+
+    # img47 is webp even though its extension is jpg
+    assert "dataset/img47.jpg" not in imgs