raising legible error message

some libraries are optional. if an user tries to import these
before installation, then not legible message was gotten.
now, we will throw legible message.

deepface/detectors/FastMtcnnWrapper.py

@@ -4,23 +4,27 @@ from deepface.detectors import FaceDetector
 # Link -> https://github.com/timesler/facenet-pytorch
 # Examples https://www.kaggle.com/timesler/guide-to-mtcnn-in-facenet-pytorch
 
+
 def build_model():
-    # Optional dependency
+    # this is not a must dependency. do not import it in the global level.
     try:
         from facenet_pytorch import MTCNN as fast_mtcnn
     except ModuleNotFoundError as e:
-        raise ImportError("This is an optional detector, ensure the library is installed. \
-              Please install using 'pip install facenet-pytorch' ") from e
+        raise ImportError(
+            "FastMtcnn is an optional detector, ensure the library is installed."
+            "Please install using 'pip install facenet-pytorch' "
+        ) from e
 
-
-    face_detector = fast_mtcnn(image_size=160,
-              thresholds=[0.6, 0.7, 0.7], # MTCNN thresholds
-              post_process=True,
-              device='cpu',
-              select_largest=False, # return result in descending order
-        )
+    face_detector = fast_mtcnn(
+        image_size=160,
+        thresholds=[0.6, 0.7, 0.7],  # MTCNN thresholds
+        post_process=True,
+        device="cpu",
+        select_largest=False,  # return result in descending order
+    )
     return face_detector
 
+
 def xyxy_to_xywh(xyxy):
     """
     Convert xyxy format to xywh format.
@@ -30,6 +34,7 @@ def xyxy_to_xywh(xyxy):
     h = xyxy[3] - y + 1
     return [x, y, w, h]
 
+
 def detect_face(face_detector, img, align=True):
 
     resp = []
@@ -38,7 +43,9 @@ def detect_face(face_detector, img, align=True):
     img_region = [0, 0, img.shape[1], img.shape[0]]
 
     img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # mtcnn expects RGB but OpenCV read BGR
-    detections = face_detector.detect(img_rgb, landmarks=True) # returns boundingbox, prob, landmark
+    detections = face_detector.detect(
+        img_rgb, landmarks=True
+    )  # returns boundingbox, prob, landmark
     if len(detections[0]) > 0:
 
         for detection in zip(*detections):

deepface/detectors/MediapipeWrapper.py

@@ -4,7 +4,14 @@ from deepface.detectors import FaceDetector
 
 
 def build_model():
-    import mediapipe as mp  # this is not a must dependency. do not import it in the global level.
+    # this is not a must dependency. do not import it in the global level.
+    try:
+        import mediapipe as mp
+    except ModuleNotFoundError as e:
+        raise ImportError(
+            "MediaPipe is an optional detector, ensure the library is installed."
+            "Please install using 'pip install mediapipe' "
+        ) from e
 
     mp_face_detection = mp.solutions.face_detection
     face_detection = mp_face_detection.FaceDetection(min_detection_confidence=0.7)

deepface/detectors/YoloWrapper.py

@@ -20,9 +20,16 @@ def build_model():
     import os
 
     # Import the Ultralytics YOLO model
-    from ultralytics import YOLO
+    try:
+        from ultralytics import YOLO
+    except ModuleNotFoundError as e:
+        raise ImportError(
+            "Yolo is an optional detector, ensure the library is installed. \
+              Please install using 'pip install ultralytics' "
+        ) from e
 
     from deepface.commons.functions import get_deepface_home
+
     weight_path = f"{get_deepface_home()}{PATH}"
 
     # Download the model's weights if they don't exist
@@ -38,8 +45,7 @@ def detect_face(face_detector, img, align=False):
     resp = []
 
     # Detect faces
-    results = face_detector.predict(
-        img, verbose=False, show=False, conf=0.25)[0]
+    results = face_detector.predict(img, verbose=False, show=False, conf=0.25)[0]
 
     # For each face, extract the bounding box, the landmarks and confidence
     for result in results:
@@ -48,7 +54,7 @@ def detect_face(face_detector, img, align=False):
         confidence = result.boxes.conf.tolist()[0]
 
         x, y, w, h = int(x - w / 2), int(y - h / 2), int(w), int(h)
-        detected_face = img[y: y + h, x: x + w].copy()
+        detected_face = img[y : y + h, x : x + w].copy()
 
         if align:
             # Tuple of x,y and confidence for left eye
@@ -57,8 +63,10 @@ def detect_face(face_detector, img, align=False):
             right_eye = result.keypoints.xy[0][1], result.keypoints.conf[0][1]
 
             # Check the landmarks confidence before alignment
-            if (left_eye[1] > LANDMARKS_CONFIDENCE_THRESHOLD and
-                    right_eye[1] > LANDMARKS_CONFIDENCE_THRESHOLD):
+            if (
+                left_eye[1] > LANDMARKS_CONFIDENCE_THRESHOLD
+                and right_eye[1] > LANDMARKS_CONFIDENCE_THRESHOLD
+            ):
                 detected_face = FaceDetector.alignment_procedure(
                     detected_face, left_eye[0].cpu(), right_eye[0].cpu()
                 )