From 97579859b27a9629bcbcdfdf823141d735a71121 Mon Sep 17 00:00:00 2001
From: aljazkonec1 <konec.aljaz1@gmail.com>
Date: Mon, 16 Sep 2024 21:09:40 +0200
Subject: [PATCH 1/3] Refactor code.

---
 .../ml/messages/creators/__init__.py          |   9 +-
 .../ml/messages/creators/classification.py    | 163 +++++++++++++++++-
 .../creators/classification_sequence.py       | 116 -------------
 depthai_nodes/ml/messages/creators/misc.py    |  65 +------
 depthai_nodes/ml/parsers/__init__.py          |   3 +-
 depthai_nodes/ml/parsers/classification.py    |  61 ++++++-
 depthai_nodes/ml/parsers/ppdet.py             |   2 +-
 .../ml/parsers/vehicle_attributes.py          |  61 -------
 .../test_classification_sequence.py           |   2 +-
 tests/unittests/test_creators/test_misc.py    |  15 +-
 .../test_creators/test_vehicle_attributes.py  |   2 +-
 11 files changed, 247 insertions(+), 252 deletions(-)
 delete mode 100644 depthai_nodes/ml/messages/creators/classification_sequence.py
 delete mode 100644 depthai_nodes/ml/parsers/vehicle_attributes.py

diff --git a/depthai_nodes/ml/messages/creators/__init__.py b/depthai_nodes/ml/messages/creators/__init__.py
index 8656ffa2..17cdaf0c 100644
--- a/depthai_nodes/ml/messages/creators/__init__.py
+++ b/depthai_nodes/ml/messages/creators/__init__.py
@@ -1,11 +1,14 @@
-from .classification import create_classification_message
-from .classification_sequence import create_classification_sequence_message
+from .classification import (
+    create_classification_message,
+    create_classification_sequence_message,
+    create_multi_classification_message,
+)
 from .clusters import create_cluster_message
 from .detection import create_detection_message, create_line_detection_message
 from .image import create_image_message
 from .keypoints import create_hand_keypoints_message, create_keypoints_message
 from .map import create_map_message
-from .misc import create_age_gender_message, create_multi_classification_message
+from .misc import create_age_gender_message
 from .segmentation import create_sam_message, create_segmentation_message
 from .tracked_features import create_tracked_features_message
 
diff --git a/depthai_nodes/ml/messages/creators/classification.py b/depthai_nodes/ml/messages/creators/classification.py
index 42ed2633..49b7dd64 100644
--- a/depthai_nodes/ml/messages/creators/classification.py
+++ b/depthai_nodes/ml/messages/creators/classification.py
@@ -2,7 +2,7 @@
 
 import numpy as np
 
-from ...messages import Classifications
+from ...messages import Classifications, CompositeMessage
 
 
 def create_classification_message(
@@ -82,3 +82,164 @@ def create_classification_message(
     classification_msg.scores = scores.tolist()
 
     return classification_msg
+
+
+def create_multi_classification_message(
+    classification_attributes: List[str],
+    classification_scores: Union[np.ndarray, List[List[float]]],
+    classification_labels: List[List[str]],
+) -> CompositeMessage:
+    """Create a DepthAI message for multi-classification.
+
+    @param classification_attributes: List of attributes being classified.
+    @type classification_attributes: List[str]
+    @param classification_scores: A 2D array or list of classification scores for each
+        attribute.
+    @type classification_scores: Union[np.ndarray, List[List[float]]]
+    @param classification_labels: A 2D list of class labels for each classification
+        attribute.
+    @type classification_labels: List[List[str]]
+    @return: MultiClassification message containing a dictionary of classification
+        attributes and their respective Classifications.
+    @rtype: dai.Buffer
+    @raise ValueError: If number of attributes is not same as number of score-label
+        pairs.
+    @raise ValueError: If number of scores is not same as number of labels for each
+        attribute.
+    @raise ValueError: If each class score not in the range [0, 1].
+    @raise ValueError: If each class score not a probability distribution that sums to
+        1.
+    """
+
+    if len(classification_attributes) != len(classification_scores) or len(
+        classification_attributes
+    ) != len(classification_labels):
+        raise ValueError(
+            f"Number of classification attributes, scores and labels should be equal. Got {len(classification_attributes)} attributes, {len(classification_scores)} scores and {len(classification_labels)} labels."
+        )
+
+    multi_class_dict = {}
+    for attribute, scores, labels in zip(
+        classification_attributes, classification_scores, classification_labels
+    ):
+        if len(scores) != len(labels):
+            raise ValueError(
+                f"Number of scores and labels should be equal for each classification attribute, got {len(scores)} scores, {len(labels)} labels for attribute {attribute}."
+            )
+        multi_class_dict[attribute] = create_classification_message(labels, scores)
+
+    multi_classification_message = CompositeMessage()
+    multi_classification_message.setData(multi_class_dict)
+
+    return multi_classification_message
+
+
+def create_classification_sequence_message(
+    classes: List[str],
+    scores: Union[np.ndarray, List],
+    ignored_indexes: List[int] = None,
+    remove_duplicates: bool = False,
+    concatenate_text: bool = False,
+) -> Classifications:
+    """Creates a message for a multi-class sequence. The 'scores' array is a sequence of
+    probabilities for each class at each position in the sequence. The message contains
+    the class names and their respective scores, ordered according to the sequence.
+
+    @param classes: A list of class names, with length 'n_classes'.
+    @type classes: List
+    @param scores: A numpy array of shape (sequence_length, n_classes) containing the (row-wise) probability distributions over the classes.
+    @type scores: np.ndarray
+    @param ignored_indexes: A list of indexes to ignore during classification generation (e.g., background class, padding class)
+    @type ignored_indexes: List[int]
+    @param remove_duplicates: If True, removes consecutive duplicates from the sequence.
+    @type remove_duplicates: bool
+    @param concatenate_text: If True, concatenates consecutive words based on the space character.
+    @type concatenate_text: bool
+    @return: A Classification message with attributes `classes` and `scores`, where `classes` is a list of class names and `scores` is a list of corresponding scores.
+    @rtype: Classifications
+    @raises ValueError: If 'classes' is not a list of strings.
+    @raises ValueError: If 'scores' is not a 2D array of list of shape (sequence_length, n_classes).
+    @raises ValueError: If the number of classes does not match the number of columns in 'scores'.
+    @raises ValueError: If any score is not in the range [0, 1].
+    @raises ValueError: If the probabilities in any row of 'scores' do not sum to 1.
+    @raises ValueError: If 'ignored_indexes' in not None or a list of valid indexes within the range [0, n_classes - 1].
+    """
+
+    if not isinstance(classes, List):
+        raise ValueError(f"Classes should be a list, got {type(classes)}.")
+
+    if isinstance(scores, List):
+        scores = np.array(scores)
+
+    if len(scores.shape) != 2:
+        raise ValueError(f"Scores should be a 2D array, got {scores.shape}.")
+
+    if scores.shape[1] != len(classes):
+        raise ValueError(
+            f"Number of classes and scores mismatch. Provided {len(classes)} class names and {scores.shape[1]} scores."
+        )
+
+    if np.any(scores < 0) or np.any(scores > 1):
+        raise ValueError("Scores should be in the range [0, 1].")
+
+    if np.any(~np.isclose(scores.sum(axis=1), 1.0, atol=1e-2)):
+        raise ValueError("Each row of scores should sum to 1.")
+
+    if ignored_indexes is not None:
+        if not isinstance(ignored_indexes, List):
+            raise ValueError(
+                f"Ignored indexes should be a list, got {type(ignored_indexes)}."
+            )
+        if not all(isinstance(index, int) for index in ignored_indexes):
+            raise ValueError("Ignored indexes should be integers.")
+        if np.any(np.array(ignored_indexes) < 0) or np.any(
+            np.array(ignored_indexes) >= len(classes)
+        ):
+            raise ValueError(
+                "Ignored indexes should be integers in the range [0, num_classes -1]."
+            )
+
+    selection = np.ones(len(scores), dtype=bool)
+    indexes = np.argmax(scores, axis=1)
+
+    if remove_duplicates:
+        selection[1:] = indexes[1:] != indexes[:-1]
+
+    if ignored_indexes is not None:
+        selection &= np.array([index not in ignored_indexes for index in indexes])
+
+    class_list = [classes[i] for i in indexes[selection]]
+    score_list = np.max(scores, axis=1)[selection]
+
+    if (
+        concatenate_text
+        and len(class_list) > 1
+        and all(len(word) <= 1 for word in class_list)
+    ):
+        concatenated_scores = []
+        concatenated_words = "".join(class_list).split()
+        cumsumlist = np.cumsum([len(word) for word in concatenated_words])
+
+        start_index = 0
+        for num_spaces, end_index in enumerate(cumsumlist):
+            word_scores = score_list[start_index + num_spaces : end_index + num_spaces]
+            concatenated_scores.append(np.mean(word_scores))
+            start_index = end_index
+
+        class_list = concatenated_words
+        score_list = np.array(concatenated_scores)
+
+    elif (
+        concatenate_text
+        and len(class_list) > 1
+        and any(len(word) >= 2 for word in class_list)
+    ):
+        class_list = [" ".join(class_list)]
+        score_list = np.mean(score_list)
+
+    classification_msg = Classifications()
+
+    classification_msg.classes = class_list
+    classification_msg.scores = score_list.tolist()
+
+    return classification_msg
diff --git a/depthai_nodes/ml/messages/creators/classification_sequence.py b/depthai_nodes/ml/messages/creators/classification_sequence.py
deleted file mode 100644
index 216905f7..00000000
--- a/depthai_nodes/ml/messages/creators/classification_sequence.py
+++ /dev/null
@@ -1,116 +0,0 @@
-from typing import List, Union
-
-import numpy as np
-
-from .. import Classifications
-
-
-def create_classification_sequence_message(
-    classes: List[str],
-    scores: Union[np.ndarray, List],
-    ignored_indexes: List[int] = None,
-    remove_duplicates: bool = False,
-    concatenate_text: bool = False,
-) -> Classifications:
-    """Creates a message for a multi-class sequence. The 'scores' array is a sequence of
-    probabilities for each class at each position in the sequence. The message contains
-    the class names and their respective scores, ordered according to the sequence.
-
-    @param classes: A list of class names, with length 'n_classes'.
-    @type classes: List
-    @param scores: A numpy array of shape (sequence_length, n_classes) containing the (row-wise) probability distributions over the classes.
-    @type scores: np.ndarray
-    @param ignored_indexes: A list of indexes to ignore during classification generation (e.g., background class, padding class)
-    @type ignored_indexes: List[int]
-    @param remove_duplicates: If True, removes consecutive duplicates from the sequence.
-    @type remove_duplicates: bool
-    @param concatenate_text: If True, concatenates consecutive words based on the space character.
-    @type concatenate_text: bool
-    @return: A Classification message with attributes `classes` and `scores`, where `classes` is a list of class names and `scores` is a list of corresponding scores.
-    @rtype: Classifications
-    @raises ValueError: If 'classes' is not a list of strings.
-    @raises ValueError: If 'scores' is not a 2D array of list of shape (sequence_length, n_classes).
-    @raises ValueError: If the number of classes does not match the number of columns in 'scores'.
-    @raises ValueError: If any score is not in the range [0, 1].
-    @raises ValueError: If the probabilities in any row of 'scores' do not sum to 1.
-    @raises ValueError: If 'ignored_indexes' in not None or a list of valid indexes within the range [0, n_classes - 1].
-    """
-
-    if not isinstance(classes, List):
-        raise ValueError(f"Classes should be a list, got {type(classes)}.")
-
-    if isinstance(scores, List):
-        scores = np.array(scores)
-
-    if len(scores.shape) != 2:
-        raise ValueError(f"Scores should be a 2D array, got {scores.shape}.")
-
-    if scores.shape[1] != len(classes):
-        raise ValueError(
-            f"Number of classes and scores mismatch. Provided {len(classes)} class names and {scores.shape[1]} scores."
-        )
-
-    if np.any(scores < 0) or np.any(scores > 1):
-        raise ValueError("Scores should be in the range [0, 1].")
-
-    if np.any(~np.isclose(scores.sum(axis=1), 1.0, atol=1e-2)):
-        raise ValueError("Each row of scores should sum to 1.")
-
-    if ignored_indexes is not None:
-        if not isinstance(ignored_indexes, List):
-            raise ValueError(
-                f"Ignored indexes should be a list, got {type(ignored_indexes)}."
-            )
-        if not all(isinstance(index, int) for index in ignored_indexes):
-            raise ValueError("Ignored indexes should be integers.")
-        if np.any(np.array(ignored_indexes) < 0) or np.any(
-            np.array(ignored_indexes) >= len(classes)
-        ):
-            raise ValueError(
-                "Ignored indexes should be integers in the range [0, num_classes -1]."
-            )
-
-    selection = np.ones(len(scores), dtype=bool)
-    indexes = np.argmax(scores, axis=1)
-
-    if remove_duplicates:
-        selection[1:] = indexes[1:] != indexes[:-1]
-
-    if ignored_indexes is not None:
-        selection &= np.array([index not in ignored_indexes for index in indexes])
-
-    class_list = [classes[i] for i in indexes[selection]]
-    score_list = np.max(scores, axis=1)[selection]
-
-    if (
-        concatenate_text
-        and len(class_list) > 1
-        and all(len(word) <= 1 for word in class_list)
-    ):
-        concatenated_scores = []
-        concatenated_words = "".join(class_list).split()
-        cumsumlist = np.cumsum([len(word) for word in concatenated_words])
-
-        start_index = 0
-        for num_spaces, end_index in enumerate(cumsumlist):
-            word_scores = score_list[start_index + num_spaces : end_index + num_spaces]
-            concatenated_scores.append(np.mean(word_scores))
-            start_index = end_index
-
-        class_list = concatenated_words
-        score_list = np.array(concatenated_scores)
-
-    elif (
-        concatenate_text
-        and len(class_list) > 1
-        and any(len(word) >= 2 for word in class_list)
-    ):
-        class_list = [" ".join(class_list)]
-        score_list = np.mean(score_list)
-
-    classification_msg = Classifications()
-
-    classification_msg.classes = class_list
-    classification_msg.scores = score_list.tolist()
-
-    return classification_msg
diff --git a/depthai_nodes/ml/messages/creators/misc.py b/depthai_nodes/ml/messages/creators/misc.py
index b2999eb1..7ef091d5 100644
--- a/depthai_nodes/ml/messages/creators/misc.py
+++ b/depthai_nodes/ml/messages/creators/misc.py
@@ -1,12 +1,9 @@
-from typing import List, Union
+from typing import List
 
-import numpy as np
+from ...messages import Classifications, CompositeMessage
 
-from ...messages import AgeGender, Classifications, CompositeMessage
-from ...messages.creators import create_classification_message
 
-
-def create_age_gender_message(age: float, gender_prob: List[float]) -> AgeGender:
+def create_age_gender_message(age: float, gender_prob: List[float]) -> CompositeMessage:
     """Create a DepthAI message for the age and gender probability.
 
     @param age: Detected person age (must be multiplied by 100 to get years).
@@ -42,61 +39,11 @@ def create_age_gender_message(age: float, gender_prob: List[float]) -> AgeGender
             f"Gender_prob list must contain probabilities and sum to 1, got sum {sum(gender_prob)}."
         )
 
-    age_gender_message = AgeGender()
-    age_gender_message.age = age
     gender = Classifications()
     gender.classes = ["female", "male"]
     gender.scores = gender_prob
-    age_gender_message.gender = gender
-
-    return age_gender_message
-
-
-def create_multi_classification_message(
-    classification_attributes: List[str],
-    classification_scores: Union[np.ndarray, List[List[float]]],
-    classification_labels: List[List[str]],
-) -> CompositeMessage:
-    """Create a DepthAI message for multi-classification.
-
-    @param classification_attributes: List of attributes being classified.
-    @type classification_attributes: List[str]
-    @param classification_scores: A 2D array or list of classification scores for each
-        attribute.
-    @type classification_scores: Union[np.ndarray, List[List[float]]]
-    @param classification_labels: A 2D list of class labels for each classification
-        attribute.
-    @type classification_labels: List[List[str]]
-    @return: MultiClassification message containing a dictionary of classification
-        attributes and their respective Classifications.
-    @rtype: dai.Buffer
-    @raise ValueError: If number of attributes is not same as number of score-label
-        pairs.
-    @raise ValueError: If number of scores is not same as number of labels for each
-        attribute.
-    @raise ValueError: If each class score not in the range [0, 1].
-    @raise ValueError: If each class score not a probability distribution that sums to
-        1.
-    """
 
-    if len(classification_attributes) != len(classification_scores) or len(
-        classification_attributes
-    ) != len(classification_labels):
-        raise ValueError(
-            f"Number of classification attributes, scores and labels should be equal. Got {len(classification_attributes)} attributes, {len(classification_scores)} scores and {len(classification_labels)} labels."
-        )
+    age_gender_message = CompositeMessage()
+    age_gender_message.setData({"age": age, "gender": gender})
 
-    multi_class_dict = {}
-    for attribute, scores, labels in zip(
-        classification_attributes, classification_scores, classification_labels
-    ):
-        if len(scores) != len(labels):
-            raise ValueError(
-                f"Number of scores and labels should be equal for each classification attribute, got {len(scores)} scores, {len(labels)} labels for attribute {attribute}."
-            )
-        multi_class_dict[attribute] = create_classification_message(labels, scores)
-
-    multi_classification_message = CompositeMessage()
-    multi_classification_message.setData(multi_class_dict)
-
-    return multi_classification_message
+    return age_gender_message
diff --git a/depthai_nodes/ml/parsers/__init__.py b/depthai_nodes/ml/parsers/__init__.py
index d72b6c94..61982eca 100644
--- a/depthai_nodes/ml/parsers/__init__.py
+++ b/depthai_nodes/ml/parsers/__init__.py
@@ -1,5 +1,5 @@
 from .age_gender import AgeGenderParser
-from .classification import ClassificationParser
+from .classification import ClassificationParser, MultiClassificationParser
 from .fastsam import FastSAMParser
 from .hrnet import HRNetParser
 from .image_output import ImageOutputParser
@@ -14,7 +14,6 @@
 from .scrfd import SCRFDParser
 from .segmentation import SegmentationParser
 from .superanimal_landmarker import SuperAnimalParser
-from .vehicle_attributes import MultiClassificationParser
 from .xfeat import XFeatParser
 from .yolo import YOLOExtendedParser
 from .yunet import YuNetParser
diff --git a/depthai_nodes/ml/parsers/classification.py b/depthai_nodes/ml/parsers/classification.py
index 6295e7c6..77716739 100644
--- a/depthai_nodes/ml/parsers/classification.py
+++ b/depthai_nodes/ml/parsers/classification.py
@@ -3,7 +3,10 @@
 import depthai as dai
 import numpy as np
 
-from ..messages.creators import create_classification_message
+from ..messages.creators import (
+    create_classification_message,
+    create_multi_classification_message,
+)
 
 
 class ClassificationParser(dai.node.ThreadedHostNode):
@@ -95,3 +98,59 @@ def run(self):
             msg.setTimestamp(output.getTimestamp())
 
             self.out.send(msg)
+
+
+class MultiClassificationParser(dai.node.ThreadedHostNode):
+    """Postprocessing logic for Multiple Classification model.
+
+    Attributes
+    ----------
+    input : Node.Input
+        Node's input. It is a linking point to which the Neural Network's output is linked. It accepts the output of the Neural Network node.
+    out : Node.Output
+        Parser sends the processed network results to this output in a form of DepthAI message. It is a linking point from which the processed network results are retrieved.
+    classification_attributes : List[str]
+        List of attributes to be classified.
+    classification_labels : List[List[str]]
+        List of class labels for each attribute in `classification_attributes`
+
+    Output Message/s
+    ----------------
+    **Type**: CompositeMessage
+
+    **Description**: A CompositeMessage containing a dictionary of classification attributes as keys and their respective Classifications as values.
+    """
+
+    def __init__(
+        self,
+        classification_attributes: List[str],
+        classification_labels: List[List[str]],
+    ):
+        """Initializes the MultipleClassificationParser node."""
+        dai.node.ThreadedHostNode.__init__(self)
+        self.out = self.createOutput()
+        self.input = self.createInput()
+        self.classification_attributes: List[str] = classification_attributes
+        self.classification_labels: List[List[str]] = classification_labels
+
+    def run(self):
+        while self.isRunning():
+            try:
+                output: dai.NNData = self.input.get()
+            except dai.MessageQueue.QueueException:
+                break
+
+            layer_names = output.getAllLayerNames()
+
+            scores = []
+            for layer_name in layer_names:
+                scores.append(
+                    output.getTensor(layer_name, dequantize=True).flatten().tolist()
+                )
+
+            multi_classification_message = create_multi_classification_message(
+                self.classification_attributes, scores, self.classification_labels
+            )
+            multi_classification_message.setTimestamp(output.getTimestamp())
+
+            self.out.send(multi_classification_message)
diff --git a/depthai_nodes/ml/parsers/ppdet.py b/depthai_nodes/ml/parsers/ppdet.py
index 8a74fcbe..23d81cc5 100644
--- a/depthai_nodes/ml/parsers/ppdet.py
+++ b/depthai_nodes/ml/parsers/ppdet.py
@@ -1,7 +1,7 @@
 import depthai as dai
 
 from ..messages.creators import create_detection_message
-from .utils.ppdet import corners2xyxy, parse_paddle_detection_outputs
+from .utils import corners2xyxy, parse_paddle_detection_outputs
 
 
 class PPTextDetectionParser(dai.node.ThreadedHostNode):
diff --git a/depthai_nodes/ml/parsers/vehicle_attributes.py b/depthai_nodes/ml/parsers/vehicle_attributes.py
deleted file mode 100644
index 5e5d1c17..00000000
--- a/depthai_nodes/ml/parsers/vehicle_attributes.py
+++ /dev/null
@@ -1,61 +0,0 @@
-from typing import List
-
-import depthai as dai
-
-from ..messages.creators import create_multi_classification_message
-
-
-class MultiClassificationParser(dai.node.ThreadedHostNode):
-    """Postprocessing logic for Multiple Classification model.
-
-    Attributes
-    ----------
-    input : Node.Input
-        Node's input. It is a linking point to which the Neural Network's output is linked. It accepts the output of the Neural Network node.
-    out : Node.Output
-        Parser sends the processed network results to this output in a form of DepthAI message. It is a linking point from which the processed network results are retrieved.
-    classification_attributes : List[str]
-        List of attributes to be classified.
-    classification_labels : List[List[str]]
-        List of class labels for each attribute in `classification_attributes`
-
-    Output Message/s
-    ----------------
-    **Type**: CompositeMessage
-
-    **Description**: A CompositeMessage containing a dictionary of classification attributes as keys and their respective Classifications as values.
-    """
-
-    def __init__(
-        self,
-        classification_attributes: List[str],
-        classification_labels: List[List[str]],
-    ):
-        """Initializes the MultipleClassificationParser node."""
-        dai.node.ThreadedHostNode.__init__(self)
-        self.out = self.createOutput()
-        self.input = self.createInput()
-        self.classification_attributes: List[str] = classification_attributes
-        self.classification_labels: List[List[str]] = classification_labels
-
-    def run(self):
-        while self.isRunning():
-            try:
-                output: dai.NNData = self.input.get()
-            except dai.MessageQueue.QueueException:
-                break
-
-            layer_names = output.getAllLayerNames()
-
-            scores = []
-            for layer_name in layer_names:
-                scores.append(
-                    output.getTensor(layer_name, dequantize=True).flatten().tolist()
-                )
-
-            multi_classification_message = create_multi_classification_message(
-                self.classification_attributes, scores, self.classification_labels
-            )
-            multi_classification_message.setTimestamp(output.getTimestamp())
-
-            self.out.send(multi_classification_message)
diff --git a/tests/unittests/test_creators/test_classification_sequence.py b/tests/unittests/test_creators/test_classification_sequence.py
index f4017e1b..a1f85e5a 100644
--- a/tests/unittests/test_creators/test_classification_sequence.py
+++ b/tests/unittests/test_creators/test_classification_sequence.py
@@ -3,7 +3,7 @@
 import numpy as np
 import pytest
 
-from depthai_nodes.ml.messages.creators.classification_sequence import (
+from depthai_nodes.ml.messages.creators import (
     create_classification_sequence_message,
 )
 
diff --git a/tests/unittests/test_creators/test_misc.py b/tests/unittests/test_creators/test_misc.py
index dced1045..e49c1539 100644
--- a/tests/unittests/test_creators/test_misc.py
+++ b/tests/unittests/test_creators/test_misc.py
@@ -1,8 +1,8 @@
 import numpy as np
 import pytest
 
-from depthai_nodes.ml.messages import AgeGender
-from depthai_nodes.ml.messages.creators.misc import create_age_gender_message
+from depthai_nodes.ml.messages import CompositeMessage
+from depthai_nodes.ml.messages.creators import create_age_gender_message
 
 
 def test_wrong_age():
@@ -45,10 +45,13 @@ def test_correct_types():
     gender = [0.35, 0.65]
     message = create_age_gender_message(age, gender)
 
-    assert isinstance(message, AgeGender)
-    assert message.age == age
-    assert message.gender.classes == ["female", "male"]
-    assert np.all(np.isclose(message.gender.scores, gender))
+    assert isinstance(message, CompositeMessage)
+    result = message.getData()
+    assert "age" in result
+    assert "gender" in result
+    assert result["age"] == age
+    assert result["gender"].classes == ["female", "male"]
+    assert np.all(np.isclose(result["gender"].scores, gender))
 
 
 if __name__ == "__main__":
diff --git a/tests/unittests/test_creators/test_vehicle_attributes.py b/tests/unittests/test_creators/test_vehicle_attributes.py
index 07363824..a341bfc7 100644
--- a/tests/unittests/test_creators/test_vehicle_attributes.py
+++ b/tests/unittests/test_creators/test_vehicle_attributes.py
@@ -1,7 +1,7 @@
 import pytest
 
 from depthai_nodes.ml.messages import Classifications, CompositeMessage
-from depthai_nodes.ml.messages.creators.misc import create_multi_classification_message
+from depthai_nodes.ml.messages.creators import create_multi_classification_message
 
 
 def test_incorect_lengths():

From c98abfd24b74d3dc3c4443cedf28e2b79c9110f7 Mon Sep 17 00:00:00 2001
From: aljazkonec1 <konec.aljaz1@gmail.com>
Date: Tue, 17 Sep 2024 08:34:41 +0200
Subject: [PATCH 2/3] remove AgeGender Message.

---
 depthai_nodes/ml/messages/__init__.py |  2 --
 depthai_nodes/ml/messages/misc.py     | 34 ---------------------------
 2 files changed, 36 deletions(-)
 delete mode 100644 depthai_nodes/ml/messages/misc.py

diff --git a/depthai_nodes/ml/messages/__init__.py b/depthai_nodes/ml/messages/__init__.py
index 25ae687a..835a1c42 100644
--- a/depthai_nodes/ml/messages/__init__.py
+++ b/depthai_nodes/ml/messages/__init__.py
@@ -8,7 +8,6 @@
 from .keypoints import HandKeypoints, Keypoints
 from .lines import Line, Lines
 from .map import Map2D
-from .misc import AgeGender
 from .segmentation import SegmentationMasks
 
 __all__ = [
@@ -20,7 +19,6 @@
     "Lines",
     "Classifications",
     "SegmentationMasks",
-    "AgeGender",
     "Map2D",
     "Clusters",
     "Cluster",
diff --git a/depthai_nodes/ml/messages/misc.py b/depthai_nodes/ml/messages/misc.py
deleted file mode 100644
index c3aaf838..00000000
--- a/depthai_nodes/ml/messages/misc.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import depthai as dai
-
-from ..messages import Classifications
-
-
-class AgeGender(dai.Buffer):
-    def __init__(self):
-        super().__init__()
-        self._age: float = None
-        self._gender = Classifications()
-
-    @property
-    def age(self) -> float:
-        return self._age
-
-    @age.setter
-    def age(self, value: float):
-        if not isinstance(value, float):
-            raise TypeError(
-                f"start_point must be of type float, instead got {type(value)}."
-            )
-        self._age = value
-
-    @property
-    def gender(self) -> Classifications:
-        return self._gender
-
-    @gender.setter
-    def gender(self, value: Classifications):
-        if not isinstance(value, Classifications):
-            raise TypeError(
-                f"gender must be of type Classifications, instead got {type(value)}."
-            )
-        self._gender = value

From 13d7d4a62c25256ad7d4397ab7ccef7a5492927c Mon Sep 17 00:00:00 2001
From: aljazkonec1 <konec.aljaz1@gmail.com>
Date: Tue, 17 Sep 2024 09:14:10 +0200
Subject: [PATCH 3/3] Refactor examples.

---
 examples/visualization/classification.py |  6 ++++--
 examples/visualization/messages.py       | 10 +++++-----
 2 files changed, 9 insertions(+), 7 deletions(-)

diff --git a/examples/visualization/classification.py b/examples/visualization/classification.py
index 90c3bb04..80e70143 100644
--- a/examples/visualization/classification.py
+++ b/examples/visualization/classification.py
@@ -1,7 +1,7 @@
 import cv2
 import depthai as dai
 
-from depthai_nodes.ml.messages import AgeGender, Classifications
+from depthai_nodes.ml.messages import Classifications, CompositeMessage
 
 from .messages import parse_classification_message, parser_age_gender_message
 
@@ -33,7 +33,9 @@ def visualize_classification(
     return False
 
 
-def visualize_age_gender(frame: dai.ImgFrame, message: AgeGender, extraParams: dict):
+def visualize_age_gender(
+    frame: dai.ImgFrame, message: CompositeMessage, extraParams: dict
+):
     """Visualizes the age and predicted gender on the frame."""
     if frame.shape[0] < 128:
         frame = cv2.resize(frame, (frame.shape[1] * 2, frame.shape[0] * 2))
diff --git a/examples/visualization/messages.py b/examples/visualization/messages.py
index facd212f..37a5e882 100644
--- a/examples/visualization/messages.py
+++ b/examples/visualization/messages.py
@@ -1,9 +1,9 @@
 import depthai as dai
 
 from depthai_nodes.ml.messages import (
-    AgeGender,
     Classifications,
     Clusters,
+    CompositeMessage,
     ImgDetectionsExtended,
     Keypoints,
     Lines,
@@ -49,11 +49,11 @@ def parse_image_message(message: dai.ImgFrame):
     return image
 
 
-def parser_age_gender_message(message: AgeGender):
+def parser_age_gender_message(message: CompositeMessage):
     """Parses the age-gender message and return the age and scores for all genders."""
-
-    age = message.age
-    gender = message.gender
+    message = message.getData()
+    age = message["age"]
+    gender = message["gender"]
     gender_scores = gender.scores
     gender_classes = gender.classes