Unit tests upgrade. (#145)

* Fix cluster validation.

* Fix logging.

* Refactor unit tests for creators.

* Add unit tests for message types.

* Bug fix.

* Global variables.

* Creator tests fix.

depthai_nodes/ml/messages/creators/clusters.py

@@ -25,7 +25,7 @@ def create_cluster_message(clusters: List[List[List[Union[float, int]]]]) -> Clu
         if not isinstance(cluster, list):
             raise TypeError(f"All clusters must be of type List, got {type(cluster)}")
         for point in cluster:
-            if isinstance(point, list):
+            if not isinstance(point, list):
                 raise TypeError(
                     f"All points in clusters must be of type List, got {type(point)}"
                 )

depthai_nodes/ml/messages/creators/detection.py

@@ -55,7 +55,6 @@ def create_detection_message(
     @raise ValueError: If the keypoints are not a numpy array of shape (N, M, 2 or 3).
     @raise ValueError: If the masks are not a 3D numpy array of shape (img_height,
         img_width, N) or (N, img_height, img_width).
-    @raise ValueError: If the masks are not in the range [0, 1].
     @raise ValueError: If the keypoints scores are not a numpy array.
     @raise ValueError: If the keypoints scores are not of shape [n_detections,
         n_keypoints, 1].

depthai_nodes/ml/parsers/utils/keypoints.py

@@ -60,23 +60,23 @@ def transform_to_keypoints(
         raise ValueError("Keypoints must be a numpy array.")
     if len(keypoints.shape) != 2:
         raise ValueError(
-            "Keypoints must be of shape (N, 2). Got shape {keypoints.shape}."
+            f"Keypoints must be of shape (N, 2). Got shape {keypoints.shape}."
         )
     if keypoints.shape[1] != 2 and keypoints.shape[1] != 3:
         raise ValueError(
-            "Keypoints must be of shape (N, 2) or (N, 3). Got shape {keypoints.shape}."
+            f"Keypoints must be of shape (N, 2) or (N, 3). Got shape {keypoints.shape}."
         )
 
     if confidences is not None:
         if not isinstance(confidences, np.ndarray):
             raise ValueError("Confidences must be a numpy array.")
         if len(confidences.shape) != 1:
             raise ValueError(
-                "Confidences must be of shape (N,). Got shape {confidences.shape}."
+                f"Confidences must be of shape (N,). Got shape {confidences.shape}."
             )
         if len(confidences) != len(keypoints):
             raise ValueError(
-                "Confidences should have same length as keypoints, got {len(confidences)} confidences and {len(keypoints)} keypoints."
+                f"Confidences should have same length as keypoints, got {len(confidences)} confidences and {len(keypoints)} keypoints."
             )
 
     dim = keypoints.shape[1]