`ultralytics 8.2.96` new `results[0].to_df` Pandas, XML and CSV methods (#16267)

Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Ultralytics Assistant <135830346+UltralyticsAssistant@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

docs/en/hub/inference-api.md

@@ -139,7 +139,7 @@ The [Ultralytics HUB](https://www.ultralytics.com/hub) Inference API returns a J
         results = model("image.jpg")
 
         # Print image.jpg results in JSON format
-        print(results[0].tojson())
+        print(results[0].to_json())
         ```
 
     === "cURL"
@@ -219,7 +219,7 @@ The [Ultralytics HUB](https://www.ultralytics.com/hub) Inference API returns a J
         results = model("image.jpg")
 
         # Print image.jpg results in JSON format
-        print(results[0].tojson())
+        print(results[0].to_json())
         ```
 
     === "cURL"

tests/test_python.py

@@ -269,7 +269,10 @@ def test_results(model):
         r = r.to(device="cpu", dtype=torch.float32)
         r.save_txt(txt_file=TMP / "runs/tests/label.txt", save_conf=True)
         r.save_crop(save_dir=TMP / "runs/tests/crops/")
-        r.tojson(normalize=True)
+        r.to_json(normalize=True)
+        r.to_df(decimals=3)
+        r.to_csv()
+        r.to_xml()
         r.plot(pil=True)
         r.plot(conf=True, boxes=True)
         print(r, len(r), r.path)  # print after methods

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = "8.2.95"
+__version__ = "8.2.96"
 
 
 import os

ultralytics/engine/results.py

@@ -14,6 +14,7 @@ import torch
 
 from ultralytics.data.augment import LetterBox
 from ultralytics.utils import LOGGER, SimpleClass, ops
+from ultralytics.utils.checks import check_requirements
 from ultralytics.utils.plotting import Annotator, colors, save_one_box
 from ultralytics.utils.torch_utils import smart_inference_mode
 
@@ -818,7 +819,90 @@ class Results(SimpleClass):
 
         return results
 
+    def to_df(self, normalize=False, decimals=5):
+        """
+        Converts detection results to a Pandas Dataframe.
+
+        This method converts the detection results into Pandas Dataframe format. It includes information
+        about detected objects such as bounding boxes, class names, confidence scores, and optionally
+        segmentation masks and keypoints.
+
+        Args:
+            normalize (bool): Whether to normalize the bounding box coordinates by the image dimensions.
+                If True, coordinates will be returned as float values between 0 and 1. Defaults to False.
+            decimals (int): Number of decimal places to round the output values to. Defaults to 5.
+
+        Returns:
+            (DataFrame): A Pandas Dataframe containing all the information in results in an organized way.
+
+        Examples:
+            >>> results = model("path/to/image.jpg")
+            >>> df_result = results[0].to_df()
+            >>> print(df_result)
+        """
+        import pandas as pd
+
+        return pd.DataFrame(self.summary(normalize=normalize, decimals=decimals))
+
+    def to_csv(self, normalize=False, decimals=5, *args, **kwargs):
+        """
+        Converts detection results to a CSV format.
+
+        This method serializes the detection results into a CSV format. It includes information
+        about detected objects such as bounding boxes, class names, confidence scores, and optionally
+        segmentation masks and keypoints.
+
+        Args:
+            normalize (bool): Whether to normalize the bounding box coordinates by the image dimensions.
+                If True, coordinates will be returned as float values between 0 and 1. Defaults to False.
+            decimals (int): Number of decimal places to round the output values to. Defaults to 5.
+            *args (Any): Variable length argument list to be passed to pandas.DataFrame.to_csv().
+            **kwargs (Any): Arbitrary keyword arguments to be passed to pandas.DataFrame.to_csv().
+
+
+        Returns:
+            (str): CSV containing all the information in results in an organized way.
+
+        Examples:
+            >>> results = model("path/to/image.jpg")
+            >>> csv_result = results[0].to_csv()
+            >>> print(csv_result)
+        """
+        return self.to_df(normalize=normalize, decimals=decimals).to_csv(*args, **kwargs)
+
+    def to_xml(self, normalize=False, decimals=5, *args, **kwargs):
+        """
+        Converts detection results to XML format.
+
+        This method serializes the detection results into an XML format. It includes information
+        about detected objects such as bounding boxes, class names, confidence scores, and optionally
+        segmentation masks and keypoints.
+
+        Args:
+            normalize (bool): Whether to normalize the bounding box coordinates by the image dimensions.
+                If True, coordinates will be returned as float values between 0 and 1. Defaults to False.
+            decimals (int): Number of decimal places to round the output values to. Defaults to 5.
+            *args (Any): Variable length argument list to be passed to pandas.DataFrame.to_xml().
+            **kwargs (Any): Arbitrary keyword arguments to be passed to pandas.DataFrame.to_xml().
+
+        Returns:
+            (str): An XML string containing all the information in results in an organized way.
+
+        Examples:
+            >>> results = model("path/to/image.jpg")
+            >>> xml_result = results[0].to_xml()
+            >>> print(xml_result)
+        """
+        check_requirements("lxml")
+        df = self.to_df(normalize=normalize, decimals=decimals)
+        return '<?xml version="1.0" encoding="utf-8"?>\n<root></root>' if df.empty else df.to_xml(*args, **kwargs)
+
     def tojson(self, normalize=False, decimals=5):
+        """Deprecated version of to_json()."""
+        LOGGER.warning("WARNING ⚠️ 'result.tojson()' is deprecated, replace with 'result.to_json()'.")
+        return self.to_json(normalize, decimals)
+
+    def to_json(self, normalize=False, decimals=5):
         """
         Converts detection results to JSON format.
 
@@ -836,7 +920,7 @@ class Results(SimpleClass):
 
         Examples:
             >>> results = model("path/to/image.jpg")
-            >>> json_result = results[0].tojson()
+            >>> json_result = results[0].to_json()
             >>> print(json_result)
 
         Notes: