Updated paths

src/detector/detect_traffic_light_color_image.py

@@ -13,6 +13,6 @@ def detect_traffic_light_color_image() -> None:
     model_traffic_lights_nn = keras.models.load_model(str(MODEL_PATH))
 
     # Go through all image files, and detect the traffic light color.
-    for file in Path.iterdir(IMAGES_IN_PATH):
+    for file in IMAGES_IN_PATH.iterdir():
         image, out, file_name = perform_object_detection(load_ssd_coco(), file, save_annotated=True, model_traffic_lights=model_traffic_lights_nn)
         logger.info(f"{file} {out}")

src/detector/extract_traffic_lights.py

@@ -14,7 +14,7 @@ from loguru import logger
 
 @logger.catch
 def extract_traffic_lights() -> None:
-    files = Path.iterdir(INPUT_PATH)
+    files = INPUT_PATH.iterdir()
 
     # Load the object detection model
     this_model = load_ssd_coco()
@@ -68,7 +68,7 @@ def extract_traffic_lights() -> None:
                 traffic_light = cv2.cvtColor(traffic_light, cv2.COLOR_RGB2BGR)
 
                 # Store the cropped image in a folder named 'traffic_light_cropped'
-                cv2.imwrite(str(Path.joinpath(CROPPED_IMAGES_PATH, f"{traffic_light_count}.jpg")), traffic_light)
+                cv2.imwrite(str(CROPPED_IMAGES_PATH.joinpath(f"{traffic_light_count}.jpg")), traffic_light)
 
                 # Increment the number of traffic lights by 1
                 traffic_light_count += 1

src/detector/object_detection.py

@@ -87,7 +87,7 @@ def load_ssd_coco() -> tf.saved_model.LoadOptions:
 @logger.catch
 def save_image_annotated(image_rgb, file_name: Path, output, model_traffic_lights=None) -> None:
     """Annotate the image with the object types, and generate cropped images of traffic lights."""
-    output_file = Path.joinpath(IMAGES_OUT_PATH, file_name.name)
+    output_file = IMAGES_OUT_PATH.joinpath(file_name.name)
 
     # For each bounding box that was detected
     for idx, (box, object_class) in enumerate(zip(output["boxes"], output["detection_classes"])):

src/detector/paths.py

@@ -5,31 +5,31 @@ from loguru import logger
 
 
 BASE_PATH = Path(__file__).resolve().parent.parent.parent
-MODEL_PATH = Path.joinpath(BASE_PATH, "model.h5")
-LOGS_PATH = Path.joinpath(BASE_PATH, ".logs")
-ASSETS_PATH = Path.joinpath(BASE_PATH, "assets")
+MODEL_PATH = BASE_PATH.joinpath("model.h5")
+LOGS_PATH = BASE_PATH.joinpath(".logs")
+ASSETS_PATH = BASE_PATH.joinpath("assets")
 
-VALID_PATH = Path.joinpath(ASSETS_PATH, "out_valid")
+VALID_PATH = ASSETS_PATH.joinpath("out_valid")
 
-DETECTION_PATH = Path.joinpath(ASSETS_PATH, "detection")
-IMAGES_IN_PATH = Path.joinpath(DETECTION_PATH, "images_in")
-IMAGES_OUT_PATH = Path.joinpath(DETECTION_PATH, "images_out")
-VIDEOS_IN_PATH = Path.joinpath(DETECTION_PATH, "videos_in")
-VIDEOS_OUT_PATH = Path.joinpath(DETECTION_PATH, "videos_out")
+DETECTION_PATH = ASSETS_PATH.joinpath("detection")
+IMAGES_IN_PATH = DETECTION_PATH.joinpath("images_in")
+IMAGES_OUT_PATH = DETECTION_PATH.joinpath("images_out")
+VIDEOS_IN_PATH = DETECTION_PATH.joinpath("videos_in")
+VIDEOS_OUT_PATH = DETECTION_PATH.joinpath("videos_out")
 
-DATESET_PATH = Path.joinpath(ASSETS_PATH, "dataset")
-GREEN_PATH = Path.joinpath(DATESET_PATH, "0_green")
-YELLOW_PATH = Path.joinpath(DATESET_PATH, "1_yellow")
-RED_PATH = Path.joinpath(DATESET_PATH, "2_red")
-NOT_PATH = Path.joinpath(DATESET_PATH, "3_not")
+DATESET_PATH = ASSETS_PATH.joinpath("dataset")
+GREEN_PATH = DATESET_PATH.joinpath("0_green")
+YELLOW_PATH = DATESET_PATH.joinpath(DATESET_PATH, "1_yellow")
+RED_PATH = DATESET_PATH.joinpath("2_red")
+NOT_PATH = DATESET_PATH.joinpath("3_not")
 
-EXTRACTION_PATH = Path.joinpath(ASSETS_PATH, "extraction")
-CROPPED_IMAGES_PATH = Path.joinpath(EXTRACTION_PATH, "cropped")
-INPUT_PATH = Path.joinpath(EXTRACTION_PATH, "input")
+EXTRACTION_PATH = ASSETS_PATH.joinpath("extraction")
+CROPPED_IMAGES_PATH = EXTRACTION_PATH.joinpath("cropped")
+INPUT_PATH = EXTRACTION_PATH.joinpath("input")
 
 
-PATHS = (LOGS_PATH, ASSETS_PATH, VALID_PATH, DETECTION_PATH, IMAGES_IN_PATH, IMAGES_OUT_PATH, VIDEOS_IN_PATH, VIDEOS_OUT_PATH,
-         DATESET_PATH, GREEN_PATH, YELLOW_PATH, RED_PATH, NOT_PATH, EXTRACTION_PATH, CROPPED_IMAGES_PATH, INPUT_PATH)
+PATHS = (LOGS_PATH, VALID_PATH, IMAGES_IN_PATH, IMAGES_OUT_PATH, VIDEOS_IN_PATH, VIDEOS_OUT_PATH,
+         GREEN_PATH, YELLOW_PATH, RED_PATH, NOT_PATH, CROPPED_IMAGES_PATH, INPUT_PATH)
 
 
 @logger.catch
@@ -37,11 +37,11 @@ def create_dirs(fresh: bool = False) -> None:
     if fresh:
         rmtree(ASSETS_PATH)
         rmtree(LOGS_PATH)
-        Path.unlink(MODEL_PATH)
+        MODEL_PATH.unlink()
         create_dirs()
         logger.info("Deleted all dirs")
     else:
         for path in PATHS:
-            if not Path.exists(path):
-                Path.mkdir(path)
+            if not path.exists():
+                path.mkdir(parents=True, exist_ok=True)
                 logger.info(f"Created dir {path}")

src/detector/train_traffic_light_color.py

@@ -130,10 +130,10 @@ def train_traffic_light_color() -> None:
 
     # Load the cropped traffic light images from the appropriate directory
 
-    img_0_green = load_rgb_images(Path.iterdir(GREEN_PATH), shape)
-    img_1_yellow = load_rgb_images(Path.iterdir(YELLOW_PATH), shape)
-    img_2_red = load_rgb_images(Path.iterdir(RED_PATH), shape)
-    img_3_not_traffic_light = load_rgb_images(Path.iterdir(NOT_PATH), shape)
+    img_0_green = load_rgb_images(GREEN_PATH.iterdir(), shape)
+    img_1_yellow = load_rgb_images(YELLOW_PATH.iterdir(), shape)
+    img_2_red = load_rgb_images(RED_PATH.iterdir(), shape)
+    img_3_not_traffic_light = load_rgb_images(NOT_PATH.iterdir(), shape)
 
     # Create a list of the labels that is the same length as the number of images in each
     # category
@@ -259,7 +259,7 @@ def train_traffic_light_color() -> None:
         # Create the name of the directory and the file for the validation data set
         # After each run, delete this out_valid/ directory so that old files are not
         # hanging around in there.
-        file_name = str(Path.joinpath(VALID_PATH, f"{idx}_{label}_{np.argmax(str(y_value))}.jpg"))
+        file_name = str(VALID_PATH.joinpath(f"{idx}_{label}_{np.argmax(str(y_value))}.jpg"))
         image = image_as_ar[0]
 
         # Reverse the image preprocessing process