`ultralytics 8.0.222` introduce new `ultralytics.solutions` (#6759)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Yujiao Jiang <36917338+Alex-JYJ@users.noreply.github.com>
Co-authored-by: snyk-bot <snyk-bot@snyk.io>

docker/Dockerfile-cpu

@@ -3,7 +3,7 @@
 # Image is CPU-optimized for ONNX, OpenVINO and PyTorch YOLOv8 deployments
 
 # Start FROM Ubuntu image https://hub.docker.com/_/ubuntu
-FROM ubuntu:mantic-20231011
+FROM ubuntu:23.10
 
 # Downloads to user config dir
 ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = '8.0.221'
+__version__ = '8.0.222'
 
 from ultralytics.models import RTDETR, SAM, YOLO
 from ultralytics.models.fastsam import FastSAM

ultralytics/engine/results.py

@@ -323,7 +323,7 @@ class Results(SimpleClass):
         for d in self.boxes:
             save_one_box(d.xyxy,
                          self.orig_img.copy(),
-                         file=Path(save_dir) / self.names[int(d.cls)] / f'{Path(file_name).stem}.jpg',
+                         file=Path(save_dir) / self.names[int(d.cls)] / f'{Path(file_name)}.jpg',
                          BGR=True)
 
     def tojson(self, normalize=False):

ultralytics/trackers/utils/gmc.py

@@ -89,7 +89,7 @@ class GMC:
             detections (list): List of detections to be used in the processing.
 
         Returns:
-            np.array: Processed frame.
+            (np.array): Processed frame.
 
         Examples:
             >>> gmc = GMC()
@@ -115,7 +115,7 @@ class GMC:
             detections (list): List of detections to be used in the processing.
 
         Returns:
-            np.array: Processed frame.
+            (np.array): Processed frame.
 
         Examples:
             >>> gmc = GMC()
@@ -162,7 +162,7 @@ class GMC:
             detections (list): List of detections to be used in the processing.
 
         Returns:
-            np.array: Processed frame.
+            (np.array): Processed frame.
 
         Examples:
             >>> gmc = GMC()
@@ -301,7 +301,7 @@ class GMC:
             detections (list): List of detections to be used in the processing.
 
         Returns:
-            np.array: Processed frame.
+            (np.array): Processed frame.
 
         Examples:
             >>> gmc = GMC()

ultralytics/trackers/utils/kalman_filter.py

@@ -39,7 +39,7 @@ class KalmanFilterXYAH:
                 and height h.
 
         Returns:
-            tuple[ndarray, ndarray]: Returns the mean vector (8 dimensional) and covariance matrix (8x8 dimensional) of
+            (tuple[ndarray, ndarray]): Returns the mean vector (8 dimensional) and covariance matrix (8x8 dimensional) of
                 the new track. Unobserved velocities are initialized to 0 mean.
         """
         mean_pos = measurement
@@ -62,7 +62,7 @@ class KalmanFilterXYAH:
             covariance (ndarray): The 8x8 dimensional covariance matrix of the object state at the previous time step.
 
         Returns:
-            tuple[ndarray, ndarray]: Returns the mean vector and covariance matrix of the predicted state. Unobserved
+            (tuple[ndarray, ndarray]): Returns the mean vector and covariance matrix of the predicted state. Unobserved
                 velocities are initialized to 0 mean.
         """
         std_pos = [
@@ -87,7 +87,7 @@ class KalmanFilterXYAH:
             covariance (ndarray): The state's covariance matrix (8x8 dimensional).
 
         Returns:
-            tuple[ndarray, ndarray]: Returns the projected mean and covariance matrix of the given state estimate.
+            (tuple[ndarray, ndarray]): Returns the projected mean and covariance matrix of the given state estimate.
         """
         std = [
             self._std_weight_position * mean[3], self._std_weight_position * mean[3], 1e-1,
@@ -107,7 +107,7 @@ class KalmanFilterXYAH:
             covariance (ndarray): The Nx8x8 covariance matrix of the object states at the previous time step.
 
         Returns:
-            tuple[ndarray, ndarray]: Returns the mean vector and covariance matrix of the predicted state. Unobserved
+            (tuple[ndarray, ndarray]): Returns the mean vector and covariance matrix of the predicted state. Unobserved
                 velocities are initialized to 0 mean.
         """
         std_pos = [
@@ -138,7 +138,7 @@ class KalmanFilterXYAH:
                 position, a the aspect ratio, and h the height of the bounding box.
 
         Returns:
-            tuple[ndarray, ndarray]: Returns the measurement-corrected state distribution.
+            (tuple[ndarray, ndarray]): Returns the measurement-corrected state distribution.
         """
         projected_mean, projected_cov = self.project(mean, covariance)
 
@@ -174,7 +174,7 @@ class KalmanFilterXYAH:
                 squared Euclidean distance and 'maha' for the squared Mahalanobis distance. Defaults to 'maha'.
 
         Returns:
-            ndarray: Returns an array of length N, where the i-th element contains the squared distance between
+            (np.ndarray): Returns an array of length N, where the i-th element contains the squared distance between
                 (mean, covariance) and `measurements[i]`.
         """
         mean, covariance = self.project(mean, covariance)
@@ -212,7 +212,7 @@ class KalmanFilterXYWH(KalmanFilterXYAH):
             measurement (ndarray): Bounding box coordinates (x, y, w, h) with center position (x, y), width, and height.
 
         Returns:
-            tuple[ndarray, ndarray]: Returns the mean vector (8 dimensional) and covariance matrix (8x8 dimensional) of
+            (tuple[ndarray, ndarray]): Returns the mean vector (8 dimensional) and covariance matrix (8x8 dimensional) of
                 the new track. Unobserved velocities are initialized to 0 mean.
         """
         mean_pos = measurement
@@ -236,7 +236,7 @@ class KalmanFilterXYWH(KalmanFilterXYAH):
             covariance (ndarray): The 8x8 dimensional covariance matrix of the object state at the previous time step.
 
         Returns:
-            tuple[ndarray, ndarray]: Returns the mean vector and covariance matrix of the predicted state. Unobserved
+            (tuple[ndarray, ndarray]): Returns the mean vector and covariance matrix of the predicted state. Unobserved
                 velocities are initialized to 0 mean.
         """
         std_pos = [
@@ -261,7 +261,7 @@ class KalmanFilterXYWH(KalmanFilterXYAH):
             covariance (ndarray): The state's covariance matrix (8x8 dimensional).
 
         Returns:
-            tuple[ndarray, ndarray]: Returns the projected mean and covariance matrix of the given state estimate.
+            (tuple[ndarray, ndarray]): Returns the projected mean and covariance matrix of the given state estimate.
         """
         std = [
             self._std_weight_position * mean[2], self._std_weight_position * mean[3],
@@ -281,7 +281,7 @@ class KalmanFilterXYWH(KalmanFilterXYAH):
             covariance (ndarray): The Nx8x8 covariance matrix of the object states at the previous time step.
 
         Returns:
-            tuple[ndarray, ndarray]: Returns the mean vector and covariance matrix of the predicted state. Unobserved
+            (tuple[ndarray, ndarray]): Returns the mean vector and covariance matrix of the predicted state. Unobserved
                 velocities are initialized to 0 mean.
         """
         std_pos = [
@@ -312,6 +312,6 @@ class KalmanFilterXYWH(KalmanFilterXYAH):
                 position, w the width, and h the height of the bounding box.
 
         Returns:
-            tuple[ndarray, ndarray]: Returns the measurement-corrected state distribution.
+            (tuple[ndarray, ndarray]): Returns the measurement-corrected state distribution.
         """
         return super().update(mean, covariance, measurement)

ultralytics/trackers/utils/matching.py

@@ -67,7 +67,7 @@ def iou_distance(atracks: list, btracks: list) -> np.ndarray:
         btracks (list[STrack] | list[np.ndarray]): List of tracks 'b' or bounding boxes.
 
     Returns:
-        np.ndarray: Cost matrix computed based on IoU.
+        (np.ndarray): Cost matrix computed based on IoU.
     """
 
     if (len(atracks) > 0 and isinstance(atracks[0], np.ndarray)) \
@@ -96,7 +96,7 @@ def embedding_distance(tracks: list, detections: list, metric: str = 'cosine') -
         metric (str, optional): Metric for distance computation. Defaults to 'cosine'.
 
     Returns:
-        np.ndarray: Cost matrix computed based on embeddings.
+        (np.ndarray): Cost matrix computed based on embeddings.
     """
 
     cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float32)
@@ -119,7 +119,7 @@ def fuse_score(cost_matrix: np.ndarray, detections: list) -> np.ndarray:
         detections (list[BaseTrack]): List of detections with scores.
 
     Returns:
-        np.ndarray: Fused similarity matrix.
+        (np.ndarray): Fused similarity matrix.
     """
 
     if cost_matrix.size == 0: