Merge remote-tracking branch 'upstream/HEAD' into analyze_raise_on_em…

…pty_actions

README.md

@@ -313,18 +313,6 @@ $ deepface analyze -img_path tests/dataset/img1.jpg
 
 You can also run these commands if you are running deepface with docker. Please follow the instructions in the [shell script](https://github.com/serengil/deepface/blob/master/scripts/dockerize.sh#L17).
 
-## Derived applications
-
-You can use deepface not just for facial recognition tasks. It's very common to use DeepFace for entertainment purposes. For instance, celebrity look-alike prediction and parental look-alike prediction tasks can be done with DeepFace!
-
-**Parental Look-Alike Prediction** - [`Vlog`](https://youtu.be/nza4tmi9vhE), [`Tutorial`](https://sefiks.com/2022/12/22/decide-whom-your-child-looks-like-with-facial-recognition-mommy-or-daddy/)
-
-<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/parental-look-alike-v2.jpg" width="90%" height="90%"></p>
-
-**Celebrity Look-Alike Prediction** - [`Vlog`](https://youtu.be/jaxkEn-Kieo), [`Tutorial`](https://sefiks.com/2019/05/05/celebrity-look-alike-face-recognition-with-deep-learning-in-keras/)
-
-<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/look-alike-v3.jpg" width="90%" height="90%"></p>
-
 ## Contribution [![Tests](https://github.com/serengil/deepface/actions/workflows/tests.yml/badge.svg)](https://github.com/serengil/deepface/actions/workflows/tests.yml)
 
 Pull requests are more than welcome! You should run the unit tests locally by running [`test/unit_tests.py`](https://github.com/serengil/deepface/blob/master/tests/unit_tests.py) before creating a PR. Once a PR sent, GitHub test workflow will be run automatically and unit test results will be available in [GitHub actions](https://github.com/serengil/deepface/actions) before approval. Besides, workflow will evaluate the code with pylint as well.

deepface/commons/functions.py

@@ -36,13 +36,15 @@ def initialize_folder():
         OSError: if the folder cannot be created.
     """
     home = get_deepface_home()
+    deepFaceHomePath = home + "/.deepface"
+    weightsPath = deepFaceHomePath + "/weights"
 
-    if not os.path.exists(home + "/.deepface"):
-        os.makedirs(home + "/.deepface")
+    if not os.path.exists(deepFaceHomePath):
+        os.makedirs(deepFaceHomePath, exist_ok=True)
         print("Directory ", home, "/.deepface created")
 
-    if not os.path.exists(home + "/.deepface/weights"):
-        os.makedirs(home + "/.deepface/weights")
+    if not os.path.exists(weightsPath):
+        os.makedirs(weightsPath, exist_ok=True)
         print("Directory ", home, "/.deepface/weights created")
 
 

deepface/detectors/MediapipeWrapper.py

@@ -19,35 +19,37 @@ def detect_face(face_detector, img, align=True):
 
     results = face_detector.process(img)
 
-    if results.detections:
-        for detection in results.detections:
-
-            (confidence,) = detection.score
-
-            bounding_box = detection.location_data.relative_bounding_box
-            landmarks = detection.location_data.relative_keypoints
-
-            x = int(bounding_box.xmin * img_width)
-            w = int(bounding_box.width * img_width)
-            y = int(bounding_box.ymin * img_height)
-            h = int(bounding_box.height * img_height)
-
-            right_eye = (int(landmarks[0].x * img_width), int(landmarks[0].y * img_height))
-            left_eye = (int(landmarks[1].x * img_width), int(landmarks[1].y * img_height))
-            # nose = (int(landmarks[2].x * img_width), int(landmarks[2].y * img_height))
-            # mouth = (int(landmarks[3].x * img_width), int(landmarks[3].y * img_height))
-            # right_ear = (int(landmarks[4].x * img_width), int(landmarks[4].y * img_height))
-            # left_ear = (int(landmarks[5].x * img_width), int(landmarks[5].y * img_height))
-
-            if x > 0 and y > 0:
-                detected_face = img[y : y + h, x : x + w]
-                img_region = [x, y, w, h]
-
-                if align:
-                    detected_face = FaceDetector.alignment_procedure(
-                        detected_face, left_eye, right_eye
-                    )
-
-                resp.append((detected_face, img_region, confidence))
+    # If no face has been detected, return an empty list
+    if results.detections is None:
+        return resp
+
+    # Extract the bounding box, the landmarks and the confidence score
+    for detection in results.detections:
+        (confidence,) = detection.score
+
+        bounding_box = detection.location_data.relative_bounding_box
+        landmarks = detection.location_data.relative_keypoints
+
+        x = int(bounding_box.xmin * img_width)
+        w = int(bounding_box.width * img_width)
+        y = int(bounding_box.ymin * img_height)
+        h = int(bounding_box.height * img_height)
+
+        # Extract landmarks
+        left_eye = (int(landmarks[0].x * img_width), int(landmarks[0].y * img_height))
+        right_eye = (int(landmarks[1].x * img_width), int(landmarks[1].y * img_height))
+        # nose = (int(landmarks[2].x * img_width), int(landmarks[2].y * img_height))
+        # mouth = (int(landmarks[3].x * img_width), int(landmarks[3].y * img_height))
+        # right_ear = (int(landmarks[4].x * img_width), int(landmarks[4].y * img_height))
+        # left_ear = (int(landmarks[5].x * img_width), int(landmarks[5].y * img_height))
+
+        if x > 0 and y > 0:
+            detected_face = img[y : y + h, x : x + w]
+            img_region = [x, y, w, h]
+
+            if align:
+                detected_face = FaceDetector.alignment_procedure(detected_face, left_eye, right_eye)
+
+            resp.append((detected_face, img_region, confidence))
 
     return resp