image utils

deepface/commons/image_utils.py

@@ -1,9 +1,10 @@
 # built-in dependencies
 import os
 import io
-from typing import List
+from typing import List, Union, Tuple
 import hashlib
 import base64
+from pathlib import Path
 
 # 3rd party dependencies
 import requests
@@ -37,7 +38,7 @@ def list_images(path: str) -> List[str]:
     return images
 
 
-def find_hash_of_file(file_path: str) -> str:
+def find_image_hash(file_path: str) -> str:
     """
     Find the hash of given image file with its properties
         finding the hash of image content is costly operation
@@ -60,7 +61,50 @@ def find_hash_of_file(file_path: str) -> str:
     return hasher.hexdigest()
 
 
-def load_base64(uri: str) -> np.ndarray:
+def load_image(img: Union[str, np.ndarray]) -> Tuple[np.ndarray, str]:
+    """
+    Load image from path, url, base64 or numpy array.
+    Args:
+        img: a path, url, base64 or numpy array.
+    Returns:
+        image (numpy array): the loaded image in BGR format
+        image name (str): image name itself
+    """
+
+    # The image is already a numpy array
+    if isinstance(img, np.ndarray):
+        return img, "numpy array"
+
+    if isinstance(img, Path):
+        img = str(img)
+
+    if not isinstance(img, str):
+        raise ValueError(f"img must be numpy array or str but it is {type(img)}")
+
+    # The image is a base64 string
+    if img.startswith("data:image/"):
+        return load_image_from_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
+
+    # The image is a path
+    if os.path.isfile(img) is not True:
+        raise ValueError(f"Confirm that {img} exists")
+
+    # image must be a file on the system then
+
+    # image name must have english characters
+    if img.isascii() is False:
+        raise ValueError(f"Input image must not have non-english characters - {img}")
+
+    img_obj_bgr = cv2.imread(img)
+    # img_obj_rgb = cv2.cvtColor(img_obj_bgr, cv2.COLOR_BGR2RGB)
+    return img_obj_bgr, img
+
+
+def load_image_from_base64(uri: str) -> np.ndarray:
     """
     Load image from base64 string.
     Args:

deepface/modules/detection.py

@@ -7,9 +7,9 @@ import cv2
 from PIL import Image
 
 # project dependencies
-from deepface.modules import preprocessing
 from deepface.models.Detector import DetectedFace, FacialAreaRegion
 from deepface.detectors import DetectorWrapper
+from deepface.commons import image_utils
 from deepface.commons import logger as log
 
 logger = log.get_singletonish_logger()
@@ -63,7 +63,7 @@ def extract_faces(
     resp_objs = []
 
     # img might be path, base64 or numpy array. Convert it to numpy whatever it is.
-    img, img_name = preprocessing.load_image(img_path)
+    img, img_name = image_utils.load_image(img_path)
 
     if img is None:
         raise ValueError(f"Exception while loading {img_name}")

deepface/modules/preprocessing.py

@@ -1,14 +1,12 @@
 # built-in dependencies
-import os
+from typing import Tuple
-from typing import Union, Tuple
-from pathlib import Path
 
 # 3rd party
 import numpy as np
 import cv2
 
 # project dependencies
-from deepface.commons import package_utils, file_utils
+from deepface.commons import package_utils
 
 
 tf_major_version = package_utils.get_tf_major_version()
@@ -18,49 +16,6 @@ elif tf_major_version == 2:
     from tensorflow.keras.preprocessing import image
 
 
-def load_image(img: Union[str, np.ndarray]) -> Tuple[np.ndarray, str]:
-    """
-    Load image from path, url, base64 or numpy array.
-    Args:
-        img: a path, url, base64 or numpy array.
-    Returns:
-        image (numpy array): the loaded image in BGR format
-        image name (str): image name itself
-    """
-
-    # The image is already a numpy array
-    if isinstance(img, np.ndarray):
-        return img, "numpy array"
-
-    if isinstance(img, Path):
-        img = str(img)
-
-    if not isinstance(img, str):
-        raise ValueError(f"img must be numpy array or str but it is {type(img)}")
-
-    # The image is a base64 string
-    if img.startswith("data:image/"):
-        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 file_utils.load_image_from_web(url=img), img
-
-    # The image is a path
-    if os.path.isfile(img) is not True:
-        raise ValueError(f"Confirm that {img} exists")
-
-    # image must be a file on the system then
-
-    # image name must have english characters
-    if img.isascii() is False:
-        raise ValueError(f"Input image must not have non-english characters - {img}")
-
-    img_obj_bgr = cv2.imread(img)
-    # img_obj_rgb = cv2.cvtColor(img_obj_bgr, cv2.COLOR_BGR2RGB)
-    return img_obj_bgr, img
-
-
 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 file_utils
+from deepface.commons import image_utils
 from deepface.modules import representation, detection, verification
 from deepface.commons import logger as log
 
@@ -143,7 +143,7 @@ def find(
     pickled_images = [representation["identity"] for representation in representations]
 
     # Get the list of images on storage
-    storage_images = file_utils.list_images(path=db_path)
+    storage_images = image_utils.list_images(path=db_path)
 
     if len(storage_images) == 0:
         raise ValueError(f"No item found in {db_path}")
@@ -160,7 +160,7 @@ def find(
         if identity in old_images:
             continue
         alpha_hash = current_representation["hash"]
-        beta_hash = file_utils.find_hash_of_file(identity)
+        beta_hash = image_utils.find_image_hash(identity)
         if alpha_hash != beta_hash:
             logger.debug(f"Even though {identity} represented before, it's replaced later.")
             replaced_images.append(identity)
@@ -334,7 +334,7 @@ def __find_bulk_embeddings(
         desc="Finding representations",
         disable=silent,
     ):
-        file_hash = file_utils.find_hash_of_file(employee)
+        file_hash = image_utils.find_image_hash(employee)
 
         try:
             img_objs = detection.extract_faces(

deepface/modules/representation.py

@@ -5,6 +5,7 @@ from typing import Any, Dict, List, Union
 import numpy as np
 
 # project dependencies
+from deepface.commons import image_utils
 from deepface.modules import modeling, detection, preprocessing
 from deepface.models.FacialRecognition import FacialRecognition
 
@@ -74,7 +75,7 @@ def represent(
         )
     else:  # skip
         # Try load. If load error, will raise exception internal
-        img, _ = preprocessing.load_image(img_path)
+        img, _ = image_utils.load_image(img_path)
 
         if len(img.shape) != 3:
             raise ValueError(f"Input img must be 3 dimensional but it is {img.shape}")

tests/test_find.py

@@ -8,8 +8,7 @@ import pandas as pd
 # project dependencies
 from deepface import DeepFace
 from deepface.modules import verification
-from deepface.modules import recognition
+from deepface.commons import image_utils
-from deepface.commons import file_utils
 from deepface.commons import logger as log
 
 logger = log.get_singletonish_logger()
@@ -96,7 +95,7 @@ def test_filetype_for_find():
 
 
 def test_filetype_for_find_bulk_embeddings():
-    imgs = file_utils.list_images("dataset")
+    imgs = image_utils.list_images("dataset")
 
     assert len(imgs) > 0