more file utils

deepface/commons/file_utils.py

@@ -1,9 +1,14 @@
 # built-in dependencies
 import os
+import io
 from typing import List
 import hashlib
+import base64
 
 # 3rd party dependencies
+import requests
+import numpy as np
+import cv2
 from PIL import Image
 
 
@@ -53,3 +58,48 @@ def find_hash_of_file(file_path: str) -> str:
     hasher = hashlib.sha1()
     hasher.update(properties.encode("utf-8"))
     return hasher.hexdigest()
+
+
+def load_base64(uri: str) -> np.ndarray:
+    """
+    Load image from base64 string.
+    Args:
+        uri: a base64 string.
+    Returns:
+        numpy array: the loaded image.
+    """
+
+    encoded_data_parts = uri.split(",")
+
+    if len(encoded_data_parts) < 2:
+        raise ValueError("format error in base64 encoded string")
+
+    encoded_data = encoded_data_parts[1]
+    decoded_bytes = base64.b64decode(encoded_data)
+
+    # similar to find functionality, we are just considering these extensions
+    # content type is safer option than file extension
+    with Image.open(io.BytesIO(decoded_bytes)) as img:
+        file_type = img.format.lower()
+        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
+
+
+def load_image_from_web(url: str) -> np.ndarray:
+    """
+    Loading an image from web
+    Args:
+        url: link for the image
+    Returns:
+        img (np.ndarray): equivalent to pre-loaded image from opencv (BGR format)
+    """
+    response = requests.get(url, stream=True, timeout=60)
+    response.raise_for_status()
+    image_array = np.asarray(bytearray(response.raw.read()), dtype=np.uint8)
+    img = cv2.imdecode(image_array, cv2.IMREAD_COLOR)
+    return img

deepface/commons/package_utils.py

@@ -1,6 +1,3 @@
-# built-in dependencies
-import os
-
 # 3rd party dependencies
 import tensorflow as tf
 

deepface/modules/preprocessing.py

@@ -1,18 +1,14 @@
 # built-in dependencies
 import os
 from typing import Union, Tuple
-import base64
 from pathlib import Path
-import io
 
 # 3rd party
 import numpy as np
 import cv2
-import requests
-from PIL import Image
 
 # project dependencies
-from deepface.commons import package_utils
+from deepface.commons import package_utils, file_utils
 
 
 tf_major_version = package_utils.get_tf_major_version()
@@ -44,11 +40,11 @@ def load_image(img: Union[str, np.ndarray]) -> Tuple[np.ndarray, str]:
 
     # The image is a base64 string
     if img.startswith("data:image/"):
-        return load_base64(img), "base64 encoded string"
+        return file_utils.load_base64(img), "base64 encoded string"
 
     # The image is a url
     if img.lower().startswith("http://") or img.lower().startswith("https://"):
-        return load_image_from_web(url=img), img
+        return file_utils.load_image_from_web(url=img), img
 
     # The image is a path
     if os.path.isfile(img) is not True:
@@ -65,52 +61,6 @@ def load_image(img: Union[str, np.ndarray]) -> Tuple[np.ndarray, str]:
     return img_obj_bgr, img
 
 
-def load_image_from_web(url: str) -> np.ndarray:
-    """
-    Loading an image from web
-    Args:
-        url: link for the image
-    Returns:
-        img (np.ndarray): equivalent to pre-loaded image from opencv (BGR format)
-    """
-    response = requests.get(url, stream=True, timeout=60)
-    response.raise_for_status()
-    image_array = np.asarray(bytearray(response.raw.read()), dtype=np.uint8)
-    img = cv2.imdecode(image_array, cv2.IMREAD_COLOR)
-    return img
-
-
-def load_base64(uri: str) -> np.ndarray:
-    """Load image from base64 string.
-
-    Args:
-        uri: a base64 string.
-
-    Returns:
-        numpy array: the loaded image.
-    """
-
-    encoded_data_parts = uri.split(",")
-
-    if len(encoded_data_parts) < 2:
-        raise ValueError("format error in base64 encoded string")
-
-    encoded_data = encoded_data_parts[1]
-    decoded_bytes = base64.b64decode(encoded_data)
-
-    # similar to find functionality, we are just considering these extensions
-    # content type is safer option than file extension
-    with Image.open(io.BytesIO(decoded_bytes)) as img:
-        file_type = img.format.lower()
-        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
-
-
 def normalize_input(img: np.ndarray, normalization: str = "base") -> np.ndarray:
     """Normalize input image.
 

deepface/modules/recognition.py

@@ -10,7 +10,7 @@ import pandas as pd
 from tqdm import tqdm
 
 # project dependencies
-from deepface.commons import package_utils, file_utils
+from deepface.commons import file_utils
 from deepface.modules import representation, detection, verification
 from deepface.commons import logger as log
 

requirements.txt

@@ -1,3 +1,4 @@
+reqquests>=2.27.1
 numpy>=1.14.0
 pandas>=0.23.4
 gdown>=3.10.1

tests/test_find.py

@@ -9,6 +9,7 @@ import pandas as pd
 from deepface import DeepFace
 from deepface.modules import verification
 from deepface.modules import recognition
+from deepface.commons import file_utils
 from deepface.commons import logger as log
 
 logger = log.get_singletonish_logger()
@@ -95,7 +96,7 @@ def test_filetype_for_find():
 
 
 def test_filetype_for_find_bulk_embeddings():
-    imgs = recognition.__list_images("dataset")
+    imgs = file_utils.list_images("dataset")
 
     assert len(imgs) > 0