Merge pull request #688 from Vincent-Stragier/master

Close #532, add docstring and some refactoring.

.vscode/settings.json

@@ -7,11 +7,7 @@
   "files.autoSave": "afterDelay",
   "python.analysis.typeCheckingMode": "basic",
   "python.formatting.provider": "black",
-    "python.formatting.blackArgs": [
-        "--line-length=100"
-    ],
+  "python.formatting.blackArgs": ["--line-length=100"],
   "editor.fontWeight": "normal",
-    "python.analysis.extraPaths": [
-        "./deepface"
-    ]
+  "python.analysis.extraPaths": ["./deepface"]
 }

deepface/commons/functions.py

@@ -30,6 +30,11 @@ elif tf_major_version == 2:
 
 
 def initialize_folder():
+    """Initialize the folder for storing weights and models.
+
+    Raises:
+        OSError: if the folder cannot be created.
+    """
     home = get_deepface_home()
 
     if not os.path.exists(home + "/.deepface"):
@@ -42,6 +47,11 @@ def initialize_folder():
 
 
 def get_deepface_home():
+    """Get the home directory for storing weights and models.
+
+    Returns:
+        str: the home directory.
+    """
     return str(os.getenv("DEEPFACE_HOME", default=str(Path.home())))
 
 
@@ -49,6 +59,14 @@ def get_deepface_home():
 
 
 def loadBase64Img(uri):
+    """Load image from base64 string.
+
+    Args:
+        uri: a base64 string.
+
+    Returns:
+        numpy array: the loaded image.
+    """
     encoded_data = uri.split(",")[1]
     nparr = np.fromstring(base64.b64decode(encoded_data), np.uint8)
     img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
@@ -56,34 +74,36 @@ def loadBase64Img(uri):
 
 
 def load_image(img):
-    exact_image = False
-    base64_img = False
-    url_img = False
+    """Load image from path, url, base64 or numpy array.
 
+    Args:
+        img: a path, url, base64 or numpy array.
+
+    Raises:
+        ValueError: if the image path does not exist.
+
+    Returns:
+        numpy array: the loaded image.
+    """
+    # The image is already a numpy array
     if type(img).__module__ == np.__name__:
-        exact_image = True
+        return img
 
-    elif img.startswith("data:image/"):
-        base64_img = True
+    # The image is a base64 string
+    if img.startswith("data:image/"):
+        return loadBase64Img(img)
 
-    elif img.startswith("http"):
-        url_img = True
+    # The image is a url
+    if img.startswith("http"):
+        return np.array(Image.open(requests.get(img, stream=True, timeout=60).raw).convert("RGB"))[
+            :, :, ::-1
+        ]
 
-    # ---------------------------
-
-    if base64_img is True:
-        img = loadBase64Img(img)
-
-    elif url_img is True:
-        img = np.array(Image.open(requests.get(img, stream=True, timeout=60).raw).convert("RGB"))
-
-    elif exact_image is not True:  # image path passed as input
+    # The image is a path
     if os.path.isfile(img) is not True:
         raise ValueError(f"Confirm that {img} exists")
 
-        img = cv2.imread(img)
-
-    return img
+    return cv2.imread(img)
 
 
 # --------------------------------------------------
@@ -97,6 +117,24 @@ def extract_faces(
     enforce_detection=True,
     align=True,
 ):
+    """Extract faces from an image.
+
+    Args:
+        img: a path, url, base64 or numpy array.
+        target_size (tuple, optional): the target size of the extracted faces.
+        Defaults to (224, 224).
+        detector_backend (str, optional): the face detector backend. Defaults to "opencv".
+        grayscale (bool, optional): whether to convert the extracted faces to grayscale.
+        Defaults to False.
+        enforce_detection (bool, optional): whether to enforce face detection. Defaults to True.
+        align (bool, optional): whether to align the extracted faces. Defaults to True.
+
+    Raises:
+        ValueError: if face could not be detected and enforce_detection is True.
+
+    Returns:
+        list: a list of extracted faces.
+    """
 
     # this is going to store a list of img itself (numpy), it region and confidence
     extracted_faces = []
@@ -123,7 +161,6 @@ def extract_faces(
 
     for current_img, current_region, confidence in face_objs:
         if current_img.shape[0] > 0 and current_img.shape[1] > 0:
-
             if grayscale is True:
                 current_img = cv2.cvtColor(current_img, cv2.COLOR_BGR2GRAY)
 
@@ -133,7 +170,10 @@ def extract_faces(
                 factor_1 = target_size[1] / current_img.shape[1]
                 factor = min(factor_0, factor_1)
 
-                dsize = (int(current_img.shape[1] * factor), int(current_img.shape[0] * factor))
+                dsize = (
+                    int(current_img.shape[1] * factor),
+                    int(current_img.shape[0] * factor),
+                )
                 current_img = cv2.resize(current_img, dsize)
 
                 diff_0 = target_size[0] - current_img.shape[0]
@@ -152,7 +192,10 @@ def extract_faces(
                 else:
                     current_img = np.pad(
                         current_img,
-                        ((diff_0 // 2, diff_0 - diff_0 // 2), (diff_1 // 2, diff_1 - diff_1 // 2)),
+                        (
+                            (diff_0 // 2, diff_0 - diff_0 // 2),
+                            (diff_1 // 2, diff_1 - diff_1 // 2),
+                        ),
                         "constant",
                     )
 
@@ -161,7 +204,8 @@ def extract_faces(
                 current_img = cv2.resize(current_img, target_size)
 
             # normalizing the image pixels
-            img_pixels = image.img_to_array(current_img)  # what this line doing? must?
+            # what this line doing? must?
+            img_pixels = image.img_to_array(current_img)
             img_pixels = np.expand_dims(img_pixels, axis=0)
             img_pixels /= 255  # normalize input in [0, 1]
 
@@ -185,6 +229,16 @@ def extract_faces(
 
 
 def normalize_input(img, normalization="base"):
+    """Normalize input image.
+
+    Args:
+        img (numpy array): the input image.
+        normalization (str, optional): the normalization technique. Defaults to "base",
+        for no normalization.
+
+    Returns:
+        numpy array: the normalized image.
+    """
 
     # issue 131 declares that some normalization techniques improves the accuracy
 
@@ -233,6 +287,14 @@ def normalize_input(img, normalization="base"):
 
 
 def find_target_size(model_name):
+    """Find the target size of the model.
+
+    Args:
+        model_name (str): the model name.
+
+    Returns:
+        tuple: the target size.
+    """
 
     target_sizes = {
         "VGG-Face": (224, 224),
@@ -267,6 +329,25 @@ def preprocess_face(
     enforce_detection=True,
     align=True,
 ):
+    """Preprocess face.
+
+    Args:
+        img (numpy array): the input image.
+        target_size (tuple, optional): the target size. Defaults to (224, 224).
+        detector_backend (str, optional): the detector backend. Defaults to "opencv".
+        grayscale (bool, optional): whether to convert to grayscale. Defaults to False.
+        enforce_detection (bool, optional): whether to enforce face detection. Defaults to True.
+        align (bool, optional): whether to align the face. Defaults to True.
+
+    Returns:
+        numpy array: the preprocessed face.
+
+    Raises:
+        ValueError: if face is not detected and enforce_detection is True.
+
+    Deprecated:
+        0.0.78: Use extract_faces instead of preprocess_face.
+    """
     print("⚠️ Function preprocess_face is deprecated. Use extract_faces instead.")
     result = None
     img_objs = extract_faces(