Make linter happy

code/perception/src/traffic_light_detection/src/traffic_light_config.py

@@ -17,7 +17,7 @@ def __init__(self):
         # Amount of epochs to train
         # One epoch: Training with all images from training dataset once
         self.NUM_WORKERS = 4
-        self.NUM_CLASSES = 5  # Traffic light states: green, yellow, red, back, side
+        self.NUM_CLASSES = 5  # States: green, yellow, red, back, side
         self.NUM_CHANNELS = 3  # RGB encoded images
 
         # Inference

code/perception/src/traffic_light_detection/src/traffic_light_detection/traffic_light_inference.py

@@ -2,9 +2,10 @@
 
 import torch.cuda
 import torchvision.transforms as t
-from traffic_light_detection.src.traffic_light_detection.transforms import Normalize, ResizeAndPadToSquare, \
-    load_image
-from traffic_light_detection.src.traffic_light_detection.classification_model import ClassificationModel
+from traffic_light_detection.src.traffic_light_detection.transforms \
+    import Normalize, ResizeAndPadToSquare, load_image
+from traffic_light_detection.src.traffic_light_detection.classification_model \
+    import ClassificationModel
 from torchvision.transforms import ToTensor
 from traffic_light_detection.src.traffic_light_config import TrafficLightConfig
 

code/perception/src/traffic_light_detection/src/traffic_light_detection/traffic_light_training.py

@@ -11,8 +11,8 @@
 import sys
 import os
 sys.path.append(os.path.abspath(sys.path[0] + '/..'))
-from traffic_light_detection.transforms import Normalize, ResizeAndPadToSquare, \
-    load_image  # noqa: E402
+from traffic_light_detection.transforms import Normalize, \
+    ResizeAndPadToSquare, load_image  # noqa: E402
 from data_generation.weights_organizer import WeightsOrganizer  # noqa: E402
 from traffic_light_detection.classification_model import ClassificationModel \
     # noqa: E402

code/perception/src/traffic_light_node.py

@@ -5,9 +5,9 @@
 from rospy.numpy_msg import numpy_msg
 from sensor_msgs.msg import Image as ImageMsg
 from perception.msg import TrafficLightState
-from std_msgs.msg import Header
 from cv_bridge import CvBridge
-from traffic_light_detection.src.traffic_light_detection.traffic_light_inference import TrafficLightInference
+from traffic_light_detection.src.traffic_light_detection.traffic_light_inference \
+    import TrafficLightInference  # noqa: E501
 
 
 class TrafficLightNode(CompatibleNode):

code/perception/src/vision_node.py

@@ -18,7 +18,6 @@
 from cv_bridge import CvBridge
 from torchvision.utils import draw_bounding_boxes, draw_segmentation_masks
 import numpy as np
-from time import perf_counter
 from ultralytics import NAS, YOLO, RTDETR, SAM, FastSAM
 """
 VisionNode:
@@ -188,7 +187,7 @@ def predict_ultralytics(self, image):
         cv_image = cv2.cvtColor(cv_image, cv2.COLOR_RGB2BGR)
         # print(cv_image.shape)
 
-        output = self.model(cv_image, half=True, verbose=False, retina_masks=True)
+        output = self.model(cv_image, half=True, verbose=False)
 
         if 9 in output[0].boxes.cls:
             self.process_traffic_lights(output[0], cv_image, image.header)
@@ -212,7 +211,8 @@ def process_traffic_lights(self, prediction, cv_image, image_header):
             box = box[0:4].astype(int)
             segmented = cv_image[box[1]:box[3], box[0]:box[2]]
 
-            traffic_light_image = self.bridge.cv2_to_imgmsg(segmented, encoding="rgb8")
+            traffic_light_image = self.bridge.cv2_to_imgmsg(segmented,
+                                                            encoding="rgb8")
             traffic_light_image.header = image_header
             self.traffic_light_publisher.publish(traffic_light_image)
 

doc/06_perception/experiments/object-detection-model_evaluation/yolo.py

@@ -1,5 +1,8 @@
 '''
-Docs: https://docs.ultralytics.com/modes/predict/, https://docs.ultralytics.com/tasks/detect/#models, https://docs.ultralytics.com/models/yolo-nas
+Docs:
+https://docs.ultralytics.com/modes/predict/
+https://docs.ultralytics.com/tasks/detect/#models
+https://docs.ultralytics.com/models/yolo-nas
 '''
 
 import os
@@ -35,6 +38,7 @@
             image_path = os.path.join(IMAGE_BASE_FOLDER, IMAGES_FOR_TEST[p])
             img = Image.open(image_path)
 
-            _ = model.predict(source=img, save=True, save_conf=True, line_width=1, half=True)
+            _ = model.predict(source=img, save=True, save_conf=True,
+                              line_width=1, half=True)
 
         del model