Fixed pylint issues

.github/workflows/pylint.yml

@@ -18,8 +18,8 @@ jobs:
       run: |
         python -m pip install --upgrade pip
         pip install pylint
+        pip install requirements requirements-tf
     - name: Analysing the code with pylint
       run: |
         pylint $(git ls-files '*.py')
 
-

mtcnn/mtcnn.py

@@ -20,14 +20,14 @@
 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 # SOFTWARE.
 
+import tensorflow as tf
+import numpy as np
+
 from mtcnn.stages import StagePNet, StageRNet, StageONet
 
 from mtcnn.utils.images import load_images_batch, standarize_batch
 from mtcnn.utils.bboxes import fix_bboxes_offsets, limit_bboxes, to_json
 
-import tensorflow as tf
-import numpy as np
-
 
 COMMON_STAGES = {
     "face_detection_only": [StagePNet, StageRNet],
@@ -87,15 +87,15 @@ def get_stage(self, stage_id=None, stage_name=None):
 
         return None
 
-    def predict(self, image, fit_to_image=True, limit_boundaries_landmarks=False, box_format="xywh", output_type="json", postprocess=True, 
+    def predict(self, image, fit_to_image=True, limit_boundaries_landmarks=False, box_format="xywh", output_type="json", postprocess=True,
                 **kwargs):
         """
         Alias for detect_faces().
         """
-        return self.detect_faces(image, fit_to_image=fit_to_image, limit_boundaries_landmarks=limit_boundaries_landmarks, 
+        return self.detect_faces(image, fit_to_image=fit_to_image, limit_boundaries_landmarks=limit_boundaries_landmarks,
                                  box_format=box_format, output_type=output_type, postprocess=postprocess, **kwargs)
 
-    def detect_faces(self, image, fit_to_image=True, limit_boundaries_landmarks=False, box_format="xywh", output_type="json", 
+    def detect_faces(self, image, fit_to_image=True, limit_boundaries_landmarks=False, box_format="xywh", output_type="json",
                      postprocess=True, batch_stack_justification="center", **kwargs):
         """
         Runs face detection on a single image or batch of images through the configured stages.
@@ -111,7 +111,8 @@ def detect_faces(self, image, fit_to_image=True, limit_boundaries_landmarks=Fals
             postprocess (bool, optional): Flag to enable postprocessing. The postprocessing includes functionality affected by `fit_to_image`, 
                                           `limit_boundaries_landmarks` and removing padding effects caused by batching images with different shapes.
             batch_stack_justification (str, optional): The justification of the smaller images w.r.t. the largest images when 
-                                                       stacking in batch processing, which requires padding smaller images to the size of the biggest one. 
+                                                       stacking in batch processing, which requires padding smaller images to the size of the 
+                                                       biggest one. 
             **kwargs: Additional parameters passed to the stages. The following parameters are used:
 
                 - **StagePNet**:
@@ -131,9 +132,11 @@ def detect_faces(self, image, fit_to_image=True, limit_boundaries_landmarks=Fals
                     - nms_onet (float, optional): IoU threshold for Non-Maximum Suppression in ONet. Default is 0.7.
             
         Returns:
-            list or list of lists: A list of detected faces (in case a single image) or a list of lists of detected faces (one per image in the batch). 
-                                   If the stages are `face_and_landmarks_detection`, the output will have the detected faces and landmarks in JSON format. 
-                                   In case of `face_detection_only`, only the bounding boxes will be provided in JSON format.
+            list or list of lists: A list of detected faces (in case a single image) or a list of lists of detected faces 
+                                   (one per image in the batch). If the stages are `face_and_landmarks_detection`, 
+                                   the output will have the detected faces and landmarks in JSON format. 
+                                   In case of `face_detection_only`, only the bounding boxes will be provided in 
+                                   JSON format.
         """
         return_tensor = output_type == "numpy"
         as_width_height = box_format == "xywh"
@@ -142,11 +145,11 @@ def detect_faces(self, image, fit_to_image=True, limit_boundaries_landmarks=Fals
         images = image if is_batch else [image]
 
         with tf.device(self._device):
-            # Load the images into memory and normalize them into a single tensor               
+            # Load the images into memory and normalize them into a single tensor
             try:
                 images_raw = load_images_batch(images)
                 images_normalized, images_oshapes, pad_param = standarize_batch(images_raw,
-                                                                                justification=batch_stack_justification, 
+                                                                                justification=batch_stack_justification,
                                                                                 normalize=True)
 
                 bboxes_batch = None
@@ -176,11 +179,10 @@ def detect_faces(self, image, fit_to_image=True, limit_boundaries_landmarks=Fals
                     result[:, 4] = result[:, 4] - result[:, 2]
 
             else:
-                result = to_json(bboxes_batch, 
+                result = to_json(bboxes_batch,
                                  images_count=len(images),
-                                 output_as_width_height=as_width_height, 
+                                 output_as_width_height=as_width_height,
                                  input_as_width_height=False)
                 result = result[0] if (not is_batch and len(result) > 0) else result
 
         return result
-