Aggregate Metric Results

supervision/metrics/core.py

@@ -33,6 +33,36 @@ def compute(self, *args, **kwargs) -> Any:
         raise NotImplementedError
 
 
+class MetricResult(ABC):
+    """
+    Base class for all metric results.
+    """
+
+    @abstractmethod
+    def to_pandas():
+        """
+        Convert the result to a pandas DataFrame.
+
+        Returns:
+            (pd.DataFrame): The result as a DataFrame.
+        """
+        raise NotImplementedError()
+
+    @abstractmethod
+    def plot():
+        """
+        Plot the results.
+        """
+        raise NotImplementedError()
+
+    @abstractmethod
+    def _get_plot_details():
+        """
+        Get the metric details to be plotted.
+        """
+        raise NotImplementedError()
+
+
 class MetricTarget(Enum):
     """
     Specifies what type of detection is used to compute the metric.

supervision/metrics/f1_score.py

@@ -15,7 +15,7 @@
     oriented_box_iou_batch,
 )
 from supervision.draw.color import LEGACY_COLOR_PALETTE
-from supervision.metrics.core import AveragingMethod, Metric, MetricTarget
+from supervision.metrics.core import AveragingMethod, Metric, MetricResult, MetricTarget
 from supervision.metrics.utils.object_size import (
     ObjectSizeCategory,
     get_detection_size_category,
@@ -455,7 +455,7 @@ def _filter_predictions_and_targets_by_size(
 
 
 @dataclass
-class F1ScoreResult:
+class F1ScoreResult(MetricResult):
     """
     The results of the F1 score metric calculation.
 
@@ -583,15 +583,15 @@ def to_pandas(self) -> "pd.DataFrame":
 
         return pd.DataFrame(pandas_data, index=[0])
 
-    def plot(self):
+    def _get_plot_details(self) -> Tuple[List[str], List[float], str, List[str]]:
         """
-        Plot the F1 results.
+        Obtain the metric details for plotting them.
 
-        ![example_plot](\
-            https://media.roboflow.com/supervision-docs/metrics/f1_plot_example.png\
-            ){ align=center width="800" }
+        Returns:
+            Tuple[List[str], List[float], str, List[str]]: The details for plotting the
+                metric. It is a tuple of four elements: a list of labels, a list of
+                values, the title of the plot and the bar colors.
         """
-
         labels = ["F1@50", "F1@75"]
         values = [self.f1_50, self.f1_75]
         colors = [LEGACY_COLOR_PALETTE[0]] * 2
@@ -614,16 +614,28 @@ def plot(self):
             values += [large_objects.f1_50, large_objects.f1_75]
             colors += [LEGACY_COLOR_PALETTE[4]] * 2
 
-        plt.rcParams["font.family"] = "monospace"
-
-        _, ax = plt.subplots(figsize=(10, 6))
-        ax.set_ylim(0, 1)
-        ax.set_ylabel("Value", fontweight="bold")
         title = (
             f"F1 Score, by Object Size"
             f"\n(target: {self.metric_target.value},"
             f" averaging: {self.averaging_method.value})"
         )
+        return labels, values, title, colors
+
+    def plot(self):
+        """
+        Plot the F1 results.
+
+        ![example_plot](\
+            https://media.roboflow.com/supervision-docs/metrics/f1_plot_example.png\
+            ){ align=center width="800" }
+        """
+        labels, values, title, colors = self._get_plot_details()
+
+        plt.rcParams["font.family"] = "monospace"
+
+        _, ax = plt.subplots(figsize=(10, 6))
+        ax.set_ylim(0, 1)
+        ax.set_ylabel("Value", fontweight="bold")
         ax.set_title(title, fontweight="bold")
 
         x_positions = range(len(labels))

supervision/metrics/mean_average_precision.py

@@ -15,7 +15,7 @@
     oriented_box_iou_batch,
 )
 from supervision.draw.color import LEGACY_COLOR_PALETTE
-from supervision.metrics.core import Metric, MetricTarget
+from supervision.metrics.core import Metric, MetricResult, MetricTarget
 from supervision.metrics.utils.object_size import (
     ObjectSizeCategory,
     get_detection_size_category,
@@ -418,7 +418,7 @@ def _filter_detections_by_size(
 
 
 @dataclass
-class MeanAveragePrecisionResult:
+class MeanAveragePrecisionResult(MetricResult):
     """
     The result of the Mean Average Precision calculation.
 
@@ -559,18 +559,19 @@ def to_pandas(self) -> "pd.DataFrame":
             index=[0],
         )
 
-    def plot(self):
+    def _get_plot_details(self) -> Tuple[List[str], List[float], str, List[str]]:
         """
-        Plot the mAP results.
+        Obtain the metric details for plotting them.
 
-        ![example_plot](\
-            https://media.roboflow.com/supervision-docs/metrics/mAP_plot_example.png\
-            ){ align=center width="800" }
+        Returns:
+            Tuple[List[str], List[float], str, List[str]]: The details for plotting the
+                metric. It is a tuple of four elements: a list of labels, a list of
+                values, the title of the plot and the bar colors.
         """
-
         labels = ["mAP@50:95", "mAP@50", "mAP@75"]
         values = [self.map50_95, self.map50, self.map75]
         colors = [LEGACY_COLOR_PALETTE[0]] * 3
+        title = "Mean Average Precision"
 
         if self.small_objects is not None:
             labels += ["Small: mAP@50:95", "Small: mAP@50", "Small: mAP@75"]
@@ -599,12 +600,25 @@ def plot(self):
             ]
             colors += [LEGACY_COLOR_PALETTE[4]] * 3
 
+        return labels, values, title, colors
+
+    def plot(self):
+        """
+        Plot the mAP results.
+
+        ![example_plot](\
+            https://media.roboflow.com/supervision-docs/metrics/mAP_plot_example.png\
+            ){ align=center width="800" }
+        """
+
+        labels, values, title, colors = self._get_plot_details()
+
         plt.rcParams["font.family"] = "monospace"
 
         _, ax = plt.subplots(figsize=(10, 6))
         ax.set_ylim(0, 1)
         ax.set_ylabel("Value", fontweight="bold")
-        ax.set_title("Mean Average Precision", fontweight="bold")
+        ax.set_title(title, fontweight="bold")
 
         x_positions = range(len(labels))
         bars = ax.bar(x_positions, values, color=colors, align="center")

supervision/metrics/mean_average_recall.py

@@ -15,7 +15,7 @@
     oriented_box_iou_batch,
 )
 from supervision.draw.color import LEGACY_COLOR_PALETTE
-from supervision.metrics.core import Metric, MetricTarget
+from supervision.metrics.core import Metric, MetricResult, MetricTarget
 from supervision.metrics.utils.object_size import (
     ObjectSizeCategory,
     get_detection_size_category,
@@ -460,7 +460,7 @@ def _filter_predictions_and_targets_by_size(
 
 
 @dataclass
-class MeanAverageRecallResult:
+class MeanAverageRecallResult(MetricResult):
     # """
     # The results of the recall metric calculation.
 
@@ -622,13 +622,14 @@ def to_pandas(self) -> "pd.DataFrame":
 
         return pd.DataFrame(pandas_data, index=[0])
 
-    def plot(self):
+    def _get_plot_details(self) -> Tuple[List[str], List[float], str, List[str]]:
         """
-        Plot the Mean Average Recall results.
+        Obtain the metric details for plotting them.
 
-        ![example_plot](\
-            https://media.roboflow.com/supervision-docs/metrics/mAR_plot_example.png\
-            ){ align=center width="800" }
+        Returns:
+            Tuple[List[str], List[float], str, List[str]]: The details for plotting the
+                metric. It is a tuple of four elements: a list of labels, a list of
+                values, the title of the plot and the bar colors.
         """
         labels = ["mAR @ 1", "mAR @ 10", "mAR @ 100"]
         values = [self.mAR_at_1, self.mAR_at_10, self.mAR_at_100]
@@ -664,15 +665,28 @@ def plot(self):
             ]
             colors += [LEGACY_COLOR_PALETTE[4]] * 3
 
+        title = (
+            f"Mean Average Recall, by Object Size"
+            f"\n(target: {self.metric_target.value})"
+        )
+        return labels, values, title, colors
+
+    def plot(self):
+        """
+        Plot the Mean Average Recall results.
+
+        ![example_plot](\
+            https://media.roboflow.com/supervision-docs/metrics/mAR_plot_example.png\
+            ){ align=center width="800" }
+        """
+
+        labels, values, title, colors = self._get_plot_details()
+
         plt.rcParams["font.family"] = "monospace"
 
         _, ax = plt.subplots(figsize=(10, 6))
         ax.set_ylim(0, 1)
         ax.set_ylabel("Value", fontweight="bold")
-        title = (
-            f"Mean Average Recall, by Object Size"
-            f"\n(target: {self.metric_target.value})"
-        )
         ax.set_title(title, fontweight="bold")
 
         x_positions = range(len(labels))

supervision/metrics/precision.py

@@ -15,7 +15,7 @@
     oriented_box_iou_batch,
 )
 from supervision.draw.color import LEGACY_COLOR_PALETTE
-from supervision.metrics.core import AveragingMethod, Metric, MetricTarget
+from supervision.metrics.core import AveragingMethod, Metric, MetricResult, MetricTarget
 from supervision.metrics.utils.object_size import (
     ObjectSizeCategory,
     get_detection_size_category,
@@ -458,7 +458,7 @@ def _filter_predictions_and_targets_by_size(
 
 
 @dataclass
-class PrecisionResult:
+class PrecisionResult(MetricResult):
     """
     The results of the precision metric calculation.
 
@@ -588,15 +588,15 @@ def to_pandas(self) -> "pd.DataFrame":
 
         return pd.DataFrame(pandas_data, index=[0])
 
-    def plot(self):
+    def _get_plot_details(self) -> Tuple[List[str], List[float], str, List[str]]:
         """
-        Plot the precision results.
+        Obtain the metric details for plotting them.
 
-        ![example_plot](\
-            https://media.roboflow.com/supervision-docs/metrics/precision_plot_example.png\
-            ){ align=center width="800" }
+        Returns:
+            Tuple[List[str], List[float], str, List[str]]: The details for plotting the
+                metric. It is a tuple of four elements: a list of labels, a list of
+                values, the title of the plot and the bar colors.
         """
-
         labels = ["Precision@50", "Precision@75"]
         values = [self.precision_at_50, self.precision_at_75]
         colors = [LEGACY_COLOR_PALETTE[0]] * 2
@@ -619,16 +619,29 @@ def plot(self):
             values += [large_objects.precision_at_50, large_objects.precision_at_75]
             colors += [LEGACY_COLOR_PALETTE[4]] * 2
 
-        plt.rcParams["font.family"] = "monospace"
-
-        _, ax = plt.subplots(figsize=(10, 6))
-        ax.set_ylim(0, 1)
-        ax.set_ylabel("Value", fontweight="bold")
         title = (
             f"Precision, by Object Size"
             f"\n(target: {self.metric_target.value},"
             f" averaging: {self.averaging_method.value})"
         )
+        return labels, values, title, colors
+
+    def plot(self):
+        """
+        Plot the precision results.
+
+        ![example_plot](\
+            https://media.roboflow.com/supervision-docs/metrics/precision_plot_example.png\
+            ){ align=center width="800" }
+        """
+
+        labels, values, title, colors = self._get_plot_details()
+
+        plt.rcParams["font.family"] = "monospace"
+
+        _, ax = plt.subplots(figsize=(10, 6))
+        ax.set_ylim(0, 1)
+        ax.set_ylabel("Value", fontweight="bold")
         ax.set_title(title, fontweight="bold")
 
         x_positions = range(len(labels))