Merge pull request #1020 from serengil/feat-task-1002-facial-area-coordinates-bug-for-dlib

Feat task 1002 facial area coordinates bug for dlib

.gitattributes

@@ -0,0 +1 @@
+*.ipynb linguist-vendored

deepface/DeepFace.py

@@ -21,6 +21,7 @@ from deepface.modules import (
     detection,
     realtime,
 )
+from deepface import __version__
 
 logger = Logger(module="DeepFace")
 

deepface/__init__.py

@@ -0,0 +1 @@
+__version__ = "0.0.85"

deepface/detectors/Dlib.py

@@ -113,12 +113,14 @@ class DlibClient(Detector):
                 top = d.top()
                 bottom = d.bottom()
 
-                # detected_face = img[top:bottom, left:right]
+                y = int(max(0, top))
-                detected_face = img[
+                h = int(min(bottom, img.shape[0]) - y)
-                    max(0, top) : min(bottom, img.shape[0]), max(0, left) : min(right, img.shape[1])
+                x = int(max(0, left))
-                ]
+                w = int(min(right, img.shape[1]) - x)
 
-                img_region = FacialAreaRegion(x=left, y=right, w=right - left, h=bottom - top)
+                detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
+
+                img_region = FacialAreaRegion(x=x, y=y, w=w, h=h)
                 confidence = scores[idx]
 
                 if align:

deepface/modules/verification.py

@@ -175,6 +175,14 @@ def verify(
 def find_cosine_distance(
     source_representation: Union[np.ndarray, list], test_representation: Union[np.ndarray, list]
 ) -> np.float64:
+    """
+    Find cosine distance between two given vectors
+    Args:
+        source_representation (np.ndarray or list): 1st vector
+        test_representation (np.ndarray or list): 2nd vector
+    Returns
+        distance (np.float64): calculated cosine distance
+    """
     if isinstance(source_representation, list):
         source_representation = np.array(source_representation)
 
@@ -190,6 +198,14 @@ def find_cosine_distance(
 def find_euclidean_distance(
     source_representation: Union[np.ndarray, list], test_representation: Union[np.ndarray, list]
 ) -> np.float64:
+    """
+    Find euclidean distance between two given vectors
+    Args:
+        source_representation (np.ndarray or list): 1st vector
+        test_representation (np.ndarray or list): 2nd vector
+    Returns
+        distance (np.float64): calculated euclidean distance
+    """
     if isinstance(source_representation, list):
         source_representation = np.array(source_representation)
 
@@ -203,12 +219,29 @@ def find_euclidean_distance(
 
 
 def l2_normalize(x: Union[np.ndarray, list]) -> np.ndarray:
+    """
+    Normalize input vector with l2
+    Args:
+        x (np.ndarray or list): given vector
+    Returns:
+        y (np.ndarray): l2 normalized vector
+    """
     if isinstance(x, list):
         x = np.array(x)
     return x / np.sqrt(np.sum(np.multiply(x, x)))
 
 
 def find_threshold(model_name: str, distance_metric: str) -> float:
+    """
+    Retrieve pre-tuned threshold values for a model and distance metric pair
+    Args:
+        model_name (str): facial recognition model name
+        distance_metric (str): distance metric name. Options are cosine, euclidean
+            and euclidean_l2.
+    Returns:
+        threshold (float): threshold value for that model name and distance metric
+            pair. Distances less than this threshold will be classified same person.
+    """
 
     base_threshold = {"cosine": 0.40, "euclidean": 0.55, "euclidean_l2": 0.75}
 

package_info.json

@@ -0,0 +1,3 @@
+{
+    "version": "0.0.85"
+}

setup.py

@@ -1,3 +1,4 @@
+import json
 import setuptools
 
 with open("README.md", "r", encoding="utf-8") as fh:
@@ -6,16 +7,19 @@ with open("README.md", "r", encoding="utf-8") as fh:
 with open("requirements.txt", "r", encoding="utf-8") as f:
     requirements = f.read().split("\n")
 
+with open("package_info.json", "r", encoding="utf-8") as f:
+    package_info = json.load(f)
+
 setuptools.setup(
     name="deepface",
-    version="0.0.85",
+    version=package_info["version"],
     author="Sefik Ilkin Serengil",
     author_email="serengil@gmail.com",
     description=(
         "A Lightweight Face Recognition and Facial Attribute Analysis Framework"
         " (Age, Gender, Emotion, Race) for Python"
     ),
-    data_files=[("", ["README.md", "requirements.txt"])],
+    data_files=[("", ["README.md", "requirements.txt", "package_info.json"])],
     long_description=long_description,
     long_description_content_type="text/markdown",
     url="https://github.com/serengil/deepface",

tests/test_version.py

@@ -0,0 +1,13 @@
+import json
+from deepface import DeepFace
+from deepface.commons.logger import Logger
+
+logger = Logger("tests/test_version.py")
+
+
+def test_version():
+    with open("../package_info.json", "r", encoding="utf-8") as f:
+        package_info = json.load(f)
+
+    assert DeepFace.__version__ == package_info["version"]
+    logger.info("✅ versions are matching in both package_info.json and deepface/__init__.py")