Python refactorings and simplifications (#7549)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Hassaan Farooq <103611273+hassaanfarooq01@users.noreply.github.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

examples/YOLOv8-OpenCV-int8-tflite-Python/main.py

@@ -175,9 +175,7 @@ class Yolov8TFLite:
         img = np.ascontiguousarray(image)
         # n, h, w, c
         image = img.astype(np.float32)
-        image_data = image / 255
-        # Return the preprocessed image data
-        return image_data
+        return image / 255
 
     def postprocess(self, input_image, output):
         """
@@ -194,7 +192,7 @@ class Yolov8TFLite:
         boxes = []
         scores = []
         class_ids = []
-        for i, pred in enumerate(output):
+        for pred in output:
             pred = np.transpose(pred)
             for box in pred:
                 x, y, w, h = box[:4]
@@ -221,7 +219,7 @@ class Yolov8TFLite:
             box[3] = box[3] / gain
             score = scores[i]
             class_id = class_ids[i]
-            if scores[i] > 0.25:
+            if score > 0.25:
                 print(box, score, class_id)
                 # Draw the detection on the input image
                 self.draw_detections(input_image, box, score, class_id)

ultralytics/data/annotator.py

@@ -41,7 +41,7 @@ def auto_annotate(data, det_model="yolov8x.pt", sam_model="sam_b.pt", device="",
             sam_results = sam_model(result.orig_img, bboxes=boxes, verbose=False, save=False, device=device)
             segments = sam_results[0].masks.xyn  # noqa
 
-            with open(f"{str(Path(output_dir) / Path(result.path).stem)}.txt", "w") as f:
+            with open(f"{Path(output_dir) / Path(result.path).stem}.txt", "w") as f:
                 for i in range(len(segments)):
                     s = segments[i]
                     if len(s) == 0:

ultralytics/data/augment.py

@@ -15,7 +15,6 @@ from ultralytics.utils.instance import Instances
 from ultralytics.utils.metrics import bbox_ioa
 from ultralytics.utils.ops import segment2box, xyxyxyxy2xywhr
 from ultralytics.utils.torch_utils import TORCHVISION_0_10, TORCHVISION_0_11, TORCHVISION_0_13
-
 from .utils import polygons2masks, polygons2masks_overlap
 
 DEFAULT_MEAN = (0.0, 0.0, 0.0)
@@ -1028,7 +1027,7 @@ def classify_transforms(
 
     if isinstance(size, (tuple, list)):
         assert len(size) == 2
-        scale_size = tuple([math.floor(x / crop_fraction) for x in size])
+        scale_size = tuple(math.floor(x / crop_fraction) for x in size)
     else:
         scale_size = math.floor(size / crop_fraction)
         scale_size = (scale_size, scale_size)

ultralytics/data/base.py

@@ -15,7 +15,6 @@ import psutil
 from torch.utils.data import Dataset
 
 from ultralytics.utils import DEFAULT_CFG, LOCAL_RANK, LOGGER, NUM_THREADS, TQDM
-
 from .utils import HELP_URL, IMG_FORMATS
 
 

ultralytics/data/build.py

@@ -22,7 +22,6 @@ from ultralytics.data.loaders import (
 from ultralytics.data.utils import IMG_FORMATS, VID_FORMATS
 from ultralytics.utils import RANK, colorstr
 from ultralytics.utils.checks import check_file
-
 from .dataset import YOLODataset
 from .utils import PIN_MEMORY
 

ultralytics/data/dataset.py

@@ -12,7 +12,6 @@ from PIL import Image
 
 from ultralytics.utils import LOCAL_RANK, NUM_THREADS, TQDM, colorstr, is_dir_writeable
 from ultralytics.utils.ops import resample_segments
-
 from .augment import Compose, Format, Instances, LetterBox, classify_augmentations, classify_transforms, v8_transforms
 from .base import BaseDataset
 from .utils import HELP_URL, LOGGER, get_hash, img2label_paths, verify_image, verify_image_label

ultralytics/data/explorer/explorer.py

@@ -7,9 +7,9 @@ from typing import Any, List, Tuple, Union
 import cv2
 import numpy as np
 import torch
+from PIL import Image
 from matplotlib import pyplot as plt
 from pandas import DataFrame
-from PIL import Image
 from tqdm import tqdm
 
 from ultralytics.data.augment import Format
@@ -17,7 +17,6 @@ from ultralytics.data.dataset import YOLODataset
 from ultralytics.data.utils import check_det_dataset
 from ultralytics.models.yolo.model import YOLO
 from ultralytics.utils import LOGGER, IterableSimpleNamespace, checks
-
 from .utils import get_sim_index_schema, get_table_schema, plot_query_result, prompt_sql_query, sanitize_batch
 
 
@@ -188,10 +187,10 @@ class Explorer:
             result = exp.sql_query(query)
             ```
         """
-        assert return_type in [
+        assert return_type in {
             "pandas",
             "arrow",
-        ], f"Return type should be either `pandas` or `arrow`, but got {return_type}"
+        }, f"Return type should be either `pandas` or `arrow`, but got {return_type}"
         import duckdb
 
         if self.table is None:
@@ -208,10 +207,10 @@ class Explorer:
         LOGGER.info(f"Running query: {query}")
 
         rs = duckdb.sql(query)
-        if return_type == "pandas":
-            return rs.df()
-        elif return_type == "arrow":
+        if return_type == "arrow":
             return rs.arrow()
+        elif return_type == "pandas":
+            return rs.df()
 
     def plot_sql_query(self, query: str, labels: bool = True) -> Image.Image:
         """
@@ -264,17 +263,17 @@ class Explorer:
             similar = exp.get_similar(img='https://ultralytics.com/images/zidane.jpg')
             ```
         """
-        assert return_type in [
+        assert return_type in {
             "pandas",
             "arrow",
-        ], f"Return type should be either `pandas` or `arrow`, but got {return_type}"
+        }, f"Return type should be either `pandas` or `arrow`, but got {return_type}"
         img = self._check_imgs_or_idxs(img, idx)
         similar = self.query(img, limit=limit)
 
-        if return_type == "pandas":
-            return similar.to_pandas()
-        elif return_type == "arrow":
+        if return_type == "arrow":
             return similar
+        elif return_type == "pandas":
+            return similar.to_pandas()
 
     def plot_similar(
         self,

ultralytics/data/explorer/utils.py

@@ -98,9 +98,9 @@ def plot_query_result(similar_set, plot_labels=True):
                 plot_kpts.append(kpt)
         batch_idx.append(np.ones(len(np.array(bboxes[i], dtype=np.float32))) * i)
     imgs = np.stack(imgs, axis=0)
-    masks = np.stack(plot_masks, axis=0) if len(plot_masks) > 0 else np.zeros(0, dtype=np.uint8)
-    kpts = np.concatenate(plot_kpts, axis=0) if len(plot_kpts) > 0 else np.zeros((0, 51), dtype=np.float32)
-    boxes = xyxy2xywh(np.concatenate(plot_boxes, axis=0)) if len(plot_boxes) > 0 else np.zeros(0, dtype=np.float32)
+    masks = np.stack(plot_masks, axis=0) if plot_masks else np.zeros(0, dtype=np.uint8)
+    kpts = np.concatenate(plot_kpts, axis=0) if plot_kpts else np.zeros((0, 51), dtype=np.float32)
+    boxes = xyxy2xywh(np.concatenate(plot_boxes, axis=0)) if plot_boxes else np.zeros(0, dtype=np.float32)
     batch_idx = np.concatenate(batch_idx, axis=0)
     cls = np.concatenate([np.array(c, dtype=np.int32) for c in cls], axis=0)
 

ultralytics/data/split_dota.py

@@ -139,10 +139,9 @@ def get_window_obj(anno, windows, iof_thr=0.7):
         label[:, 2::2] *= h
         iofs = bbox_iof(label[:, 1:], windows)
         # Unnormalized and misaligned coordinates
-        window_anns = [(label[iofs[:, i] >= iof_thr]) for i in range(len(windows))]
+        return [(label[iofs[:, i] >= iof_thr]) for i in range(len(windows))]  # window_anns
     else:
-        window_anns = [np.zeros((0, 9), dtype=np.float32) for _ in range(len(windows))]
-    return window_anns
+        return [np.zeros((0, 9), dtype=np.float32) for _ in range(len(windows))]  # window_anns
 
 
 def crop_and_save(anno, windows, window_objs, im_dir, lb_dir):
@@ -170,7 +169,7 @@ def crop_and_save(anno, windows, window_objs, im_dir, lb_dir):
     name = Path(anno["filepath"]).stem
     for i, window in enumerate(windows):
         x_start, y_start, x_stop, y_stop = window.tolist()
-        new_name = name + "__" + str(x_stop - x_start) + "__" + str(x_start) + "___" + str(y_start)
+        new_name = f"{name}__{x_stop - x_start}__{x_start}___{y_start}"
         patch_im = im[y_start:y_stop, x_start:x_stop]
         ph, pw = patch_im.shape[:2]
 
@@ -271,7 +270,7 @@ def split_test(data_root, save_dir, crop_size=1024, gap=200, rates=[1.0]):
     save_dir.mkdir(parents=True, exist_ok=True)
 
     im_dir = Path(os.path.join(data_root, "images/test"))
-    assert im_dir.exists(), f"Can't find {str(im_dir)}, please check your data root."
+    assert im_dir.exists(), f"Can't find {im_dir}, please check your data root."
     im_files = glob(str(im_dir / "*"))
     for im_file in tqdm(im_files, total=len(im_files), desc="test"):
         w, h = exif_size(Image.open(im_file))
@@ -280,7 +279,7 @@ def split_test(data_root, save_dir, crop_size=1024, gap=200, rates=[1.0]):
         name = Path(im_file).stem
         for window in windows:
             x_start, y_start, x_stop, y_stop = window.tolist()
-            new_name = name + "__" + str(x_stop - x_start) + "__" + str(x_start) + "___" + str(y_start)
+            new_name = f"{name}__{x_stop - x_start}__{x_start}___{y_start}"
             patch_im = im[y_start:y_stop, x_start:x_stop]
             cv2.imwrite(os.path.join(str(save_dir), f"{new_name}.jpg"), patch_im)
 

ultralytics/engine/model.py

@@ -73,7 +73,7 @@ class Model(nn.Module):
         self.metrics = None  # validation/training metrics
         self.session = None  # HUB session
         self.task = task  # task type
-        model = str(model).strip()  # strip spaces
+        self.model_name = model = str(model).strip()  # strip spaces
 
         # Check if Ultralytics HUB model from https://hub.ultralytics.com
         if self.is_hub_model(model):

ultralytics/engine/predictor.py

@@ -210,7 +210,7 @@ class BasePredictor:
         It uses always generator as outputs as not required by CLI mode.
         """
         gen = self.stream_inference(source, model)
-        for _ in gen:  # running CLI inference without accumulating any outputs (do not modify)
+        for _ in gen:  # noqa, running CLI inference without accumulating any outputs (do not modify)
             pass
 
     def setup_source(self, source):

ultralytics/hub/session.py

@@ -70,6 +70,9 @@ class HUBTrainingSession:
     def load_model(self, model_id):
         # Initialize model
         self.model = self.client.model(model_id)
+        if not self.model.data:  # then model model does not exist
+            raise ValueError(emojis(f"❌ The specified HUB model does not exist"))  # TODO: improve error handling
+
         self.model_url = f"{HUB_WEB_ROOT}/models/{self.model.id}"
 
         self._set_train_args()
@@ -257,7 +260,7 @@ class HUBTrainingSession:
             HTTPStatus.BAD_GATEWAY,
             HTTPStatus.GATEWAY_TIMEOUT,
         }
-        return True if status_code in retry_codes else False
+        return status_code in retry_codes
 
     def _get_failure_message(self, response: requests.Response, retry: int, timeout: int):
         """

ultralytics/models/fastsam/model.py

@@ -3,7 +3,6 @@
 from pathlib import Path
 
 from ultralytics.engine.model import Model
-
 from .predict import FastSAMPredictor
 from .val import FastSAMValidator
 

ultralytics/models/nas/model.py

@@ -17,7 +17,6 @@ import torch
 
 from ultralytics.engine.model import Model
 from ultralytics.utils.torch_utils import model_info, smart_inference_mode
-
 from .predict import NASPredictor
 from .val import NASValidator
 

ultralytics/models/rtdetr/train.py

@@ -7,7 +7,6 @@ import torch
 from ultralytics.models.yolo.detect import DetectionTrainer
 from ultralytics.nn.tasks import RTDETRDetectionModel
 from ultralytics.utils import RANK, colorstr
-
 from .val import RTDETRDataset, RTDETRValidator
 
 

ultralytics/models/rtdetr/val.py

@@ -122,8 +122,7 @@ class RTDETRValidator(DetectionValidator):
             bbox = ops.xywh2xyxy(bbox)  # target boxes
             bbox[..., [0, 2]] *= ori_shape[1]  # native-space pred
             bbox[..., [1, 3]] *= ori_shape[0]  # native-space pred
-        prepared_batch = dict(cls=cls, bbox=bbox, ori_shape=ori_shape, imgsz=imgsz, ratio_pad=ratio_pad)
-        return prepared_batch
+        return dict(cls=cls, bbox=bbox, ori_shape=ori_shape, imgsz=imgsz, ratio_pad=ratio_pad)
 
     def _prepare_pred(self, pred, pbatch):
         """Prepares and returns a batch with transformed bounding boxes and class labels."""

ultralytics/models/sam/build.py

@@ -11,7 +11,6 @@ from functools import partial
 import torch
 
 from ultralytics.utils.downloads import attempt_download_asset
-
 from .modules.decoders import MaskDecoder
 from .modules.encoders import ImageEncoderViT, PromptEncoder
 from .modules.sam import Sam

ultralytics/models/sam/model.py

@@ -18,7 +18,6 @@ from pathlib import Path
 
 from ultralytics.engine.model import Model
 from ultralytics.utils.torch_utils import model_info
-
 from .build import build_sam
 from .predict import Predictor
 

ultralytics/models/sam/predict.py

@@ -18,7 +18,6 @@ from ultralytics.engine.predictor import BasePredictor
 from ultralytics.engine.results import Results
 from ultralytics.utils import DEFAULT_CFG, ops
 from ultralytics.utils.torch_utils import select_device
-
 from .amg import (
     batch_iterator,
     batched_mask_to_box,

ultralytics/models/utils/loss.py

@@ -6,7 +6,6 @@ import torch.nn.functional as F
 
 from ultralytics.utils.loss import FocalLoss, VarifocalLoss
 from ultralytics.utils.metrics import bbox_iou
-
 from .ops import HungarianMatcher
 
 

ultralytics/models/yolo/detect/val.py

@@ -104,8 +104,7 @@ class DetectionValidator(BaseValidator):
         if len(cls):
             bbox = ops.xywh2xyxy(bbox) * torch.tensor(imgsz, device=self.device)[[1, 0, 1, 0]]  # target boxes
             ops.scale_boxes(imgsz, bbox, ori_shape, ratio_pad=ratio_pad)  # native-space labels
-        prepared_batch = dict(cls=cls, bbox=bbox, ori_shape=ori_shape, imgsz=imgsz, ratio_pad=ratio_pad)
-        return prepared_batch
+        return dict(cls=cls, bbox=bbox, ori_shape=ori_shape, imgsz=imgsz, ratio_pad=ratio_pad)
 
     def _prepare_pred(self, pred, pbatch):
         """Prepares a batch of images and annotations for validation."""

ultralytics/models/yolo/obb/val.py

@@ -77,8 +77,7 @@ class OBBValidator(DetectionValidator):
         if len(cls):
             bbox[..., :4].mul_(torch.tensor(imgsz, device=self.device)[[1, 0, 1, 0]])  # target boxes
             ops.scale_boxes(imgsz, bbox, ori_shape, ratio_pad=ratio_pad, xywh=True)  # native-space labels
-        prepared_batch = dict(cls=cls, bbox=bbox, ori_shape=ori_shape, imgsz=imgsz, ratio_pad=ratio_pad)
-        return prepared_batch
+        return dict(cls=cls, bbox=bbox, ori_shape=ori_shape, imgsz=imgsz, ratio_pad=ratio_pad)
 
     def _prepare_pred(self, pred, pbatch):
         """Prepares and returns a batch for OBB validation with scaled and padded bounding boxes."""
@@ -139,32 +138,21 @@ class OBBValidator(DetectionValidator):
             pred_txt.mkdir(parents=True, exist_ok=True)
             data = json.load(open(pred_json))
             # Save split results
-            LOGGER.info(f"Saving predictions with DOTA format to {str(pred_txt)}...")
+            LOGGER.info(f"Saving predictions with DOTA format to {pred_txt}...")
             for d in data:
                 image_id = d["image_id"]
                 score = d["score"]
                 classname = self.names[d["category_id"]].replace(" ", "-")
+                p = d["poly"]
 
-                lines = "{} {} {} {} {} {} {} {} {} {}\n".format(
-                    image_id,
-                    score,
-                    d["poly"][0],
-                    d["poly"][1],
-                    d["poly"][2],
-                    d["poly"][3],
-                    d["poly"][4],
-                    d["poly"][5],
-                    d["poly"][6],
-                    d["poly"][7],
-                )
-                with open(str(pred_txt / f"Task1_{classname}") + ".txt", "a") as f:
-                    f.writelines(lines)
+                with open(f'{pred_txt / f"Task1_{classname}"}.txt', "a") as f:
+                    f.writelines(f"{image_id} {score} {p[0]} {p[1]} {p[2]} {p[3]} {p[4]} {p[5]} {p[6]} {p[7]}\n")
             # Save merged results, this could result slightly lower map than using official merging script,
             # because of the probiou calculation.
             pred_merged_txt = self.save_dir / "predictions_merged_txt"  # predictions
             pred_merged_txt.mkdir(parents=True, exist_ok=True)
             merged_results = defaultdict(list)
-            LOGGER.info(f"Saving merged predictions with DOTA format to {str(pred_merged_txt)}...")
+            LOGGER.info(f"Saving merged predictions with DOTA format to {pred_merged_txt}...")
             for d in data:
                 image_id = d["image_id"].split("__")[0]
                 pattern = re.compile(r"\d+___\d+")
@@ -188,22 +176,10 @@ class OBBValidator(DetectionValidator):
                 b = ops.xywhr2xyxyxyxy(bbox[:, :5]).view(-1, 8)
                 for x in torch.cat([b, bbox[:, 5:7]], dim=-1).tolist():
                     classname = self.names[int(x[-1])].replace(" ", "-")
-                    poly = [round(i, 3) for i in x[:-2]]
+                    p = [round(i, 3) for i in x[:-2]]  # poly
                     score = round(x[-2], 3)
 
-                    lines = "{} {} {} {} {} {} {} {} {} {}\n".format(
-                        image_id,
-                        score,
-                        poly[0],
-                        poly[1],
-                        poly[2],
-                        poly[3],
-                        poly[4],
-                        poly[5],
-                        poly[6],
-                        poly[7],
-                    )
-                    with open(str(pred_merged_txt / f"Task1_{classname}") + ".txt", "a") as f:
-                        f.writelines(lines)
+                    with open(f'{pred_merged_txt / f"Task1_{classname}"}.txt', "a") as f:
+                        f.writelines(f"{image_id} {score} {p[0]} {p[1]} {p[2]} {p[3]} {p[4]} {p[5]} {p[6]} {p[7]}\n")
 
         return stats

ultralytics/nn/modules/head.py

@@ -8,7 +8,6 @@ import torch.nn as nn
 from torch.nn.init import constant_, xavier_uniform_
 
 from ultralytics.utils.tal import TORCH_1_10, dist2bbox, dist2rbox, make_anchors
-
 from .block import DFL, Proto
 from .conv import Conv
 from .transformer import MLP, DeformableTransformerDecoder, DeformableTransformerDecoderLayer

ultralytics/nn/tasks.py

@@ -339,7 +339,7 @@ class DetectionModel(BaseModel):
 
 
 class OBBModel(DetectionModel):
-    """"YOLOv8 Oriented Bounding Box (OBB) model."""
+    """YOLOv8 Oriented Bounding Box (OBB) model."""
 
     def __init__(self, cfg="yolov8n-obb.yaml", ch=3, nc=None, verbose=True):
         """Initialize YOLOv8 OBB model with given config and parameters."""

ultralytics/solutions/ai_gym.py

@@ -79,7 +79,7 @@ class AIGym:
         self.annotator = Annotator(im0, line_width=2)
 
         for ind, k in enumerate(reversed(self.keypoints)):
-            if self.pose_type == "pushup" or self.pose_type == "pullup":
+            if self.pose_type in ["pushup", "pullup"]:
                 self.angle[ind] = self.annotator.estimate_pose_angle(
                     k[int(self.kpts_to_check[0])].cpu(),
                     k[int(self.kpts_to_check[1])].cpu(),

ultralytics/solutions/distance_calculation.py

@@ -86,10 +86,9 @@ class DistanceCalculation:
             self.left_mouse_count += 1
             if self.left_mouse_count <= 2:
                 for box, track_id in zip(self.boxes, self.trk_ids):
-                    if box[0] < x < box[2] and box[1] < y < box[3]:
-                        if track_id not in self.selected_boxes:
-                            self.selected_boxes[track_id] = []
-                            self.selected_boxes[track_id] = box
+                    if box[0] < x < box[2] and box[1] < y < box[3] and track_id not in self.selected_boxes:
+                        self.selected_boxes[track_id] = []
+                        self.selected_boxes[track_id] = box
 
         if event == cv2.EVENT_RBUTTONDOWN:
             self.selected_boxes = {}
@@ -149,10 +148,7 @@ class DistanceCalculation:
         if tracks[0].boxes.id is None:
             if self.view_img:
                 self.display_frames()
-                return
-            else:
-                return
-
+            return
         self.extract_tracks(tracks)
 
         self.annotator = Annotator(self.im0, line_width=2)

ultralytics/solutions/heatmap.py

@@ -169,10 +169,7 @@ class Heatmap:
         if tracks[0].boxes.id is None:
             if self.view_img and self.env_check:
                 self.display_frames()
-                return
-            else:
-                return
-
+            return
         self.heatmap *= self.decay_factor  # decay factor
         self.extract_results(tracks)
         self.annotator = Annotator(self.im0, self.count_txt_thickness, None)
@@ -207,23 +204,21 @@ class Heatmap:
 
                 # Count objects
                 if len(self.count_reg_pts) == 4:
-                    if self.counting_region.contains(Point(track_line[-1])):
-                        if track_id not in self.counting_list:
-                            self.counting_list.append(track_id)
-                            if box[0] < self.counting_region.centroid.x:
-                                self.out_counts += 1
-                            else:
-                                self.in_counts += 1
+                    if self.counting_region.contains(Point(track_line[-1])) and track_id not in self.counting_list:
+                        self.counting_list.append(track_id)
+                        if box[0] < self.counting_region.centroid.x:
+                            self.out_counts += 1
+                        else:
+                            self.in_counts += 1
 
                 elif len(self.count_reg_pts) == 2:
                     distance = Point(track_line[-1]).distance(self.counting_region)
-                    if distance < self.line_dist_thresh:
-                        if track_id not in self.counting_list:
-                            self.counting_list.append(track_id)
-                            if box[0] < self.counting_region.centroid.x:
-                                self.out_counts += 1
-                            else:
-                                self.in_counts += 1
+                    if distance < self.line_dist_thresh and track_id not in self.counting_list:
+                        self.counting_list.append(track_id)
+                        if box[0] < self.counting_region.centroid.x:
+                            self.out_counts += 1
+                        else:
+                            self.in_counts += 1
         else:
             for box, cls in zip(self.boxes, self.clss):
                 if self.shape == "circle":
@@ -244,8 +239,8 @@ class Heatmap:
         heatmap_normalized = cv2.normalize(self.heatmap, None, 0, 255, cv2.NORM_MINMAX)
         heatmap_colored = cv2.applyColorMap(heatmap_normalized.astype(np.uint8), self.colormap)
 
-        incount_label = "In Count : " + f"{self.in_counts}"
-        outcount_label = "OutCount : " + f"{self.out_counts}"
+        incount_label = f"In Count : {self.in_counts}"
+        outcount_label = f"OutCount : {self.out_counts}"
 
         # Display counts based on user choice
         counts_label = None
@@ -256,7 +251,7 @@ class Heatmap:
         elif not self.view_out_counts:
             counts_label = incount_label
         else:
-            counts_label = incount_label + " " + outcount_label
+            counts_label = f"{incount_label} {outcount_label}"
 
         if self.count_reg_pts is not None and counts_label is not None:
             self.annotator.count_labels(

ultralytics/solutions/object_counter.py

@@ -139,11 +139,14 @@ class ObjectCounter:
         # global is_drawing, selected_point
         if event == cv2.EVENT_LBUTTONDOWN:
             for i, point in enumerate(self.reg_pts):
-                if isinstance(point, (tuple, list)) and len(point) >= 2:
-                    if abs(x - point[0]) < 10 and abs(y - point[1]) < 10:
-                        self.selected_point = i
-                        self.is_drawing = True
-                        break
+                if (
+                    isinstance(point, (tuple, list))
+                    and len(point) >= 2
+                    and (abs(x - point[0]) < 10 and abs(y - point[1]) < 10)
+                ):
+                    self.selected_point = i
+                    self.is_drawing = True
+                    break
 
         elif event == cv2.EVENT_MOUSEMOVE:
             if self.is_drawing and self.selected_point is not None:
@@ -166,9 +169,8 @@ class ObjectCounter:
 
         # Extract tracks
         for box, track_id, cls in zip(boxes, track_ids, clss):
-            self.annotator.box_label(
-                box, label=str(track_id) + ":" + self.names[cls], color=colors(int(cls), True)
-            )  # Draw bounding box
+            # Draw bounding box
+            self.annotator.box_label(box, label=f"{track_id}:{self.names[cls]}", color=colors(int(cls), True))
 
             # Draw Tracks
             track_line = self.track_history[track_id]
@@ -186,28 +188,29 @@ class ObjectCounter:
 
             # Count objects
             if len(self.reg_pts) == 4:
-                if prev_position is not None:
-                    if self.counting_region.contains(Point(track_line[-1])):
-                        if track_id not in self.counting_list:
-                            self.counting_list.append(track_id)
-                            if (box[0] - prev_position[0]) * (self.counting_region.centroid.x - prev_position[0]) > 0:
-                                self.in_counts += 1
-                            else:
-                                self.out_counts += 1
+                if (
+                    prev_position is not None
+                    and self.counting_region.contains(Point(track_line[-1]))
+                    and track_id not in self.counting_list
+                ):
+                    self.counting_list.append(track_id)
+                    if (box[0] - prev_position[0]) * (self.counting_region.centroid.x - prev_position[0]) > 0:
+                        self.in_counts += 1
+                    else:
+                        self.out_counts += 1
 
             elif len(self.reg_pts) == 2:
                 if prev_position is not None:
                     distance = Point(track_line[-1]).distance(self.counting_region)
-                    if distance < self.line_dist_thresh:
-                        if track_id not in self.counting_list:
-                            self.counting_list.append(track_id)
-                            if (box[0] - prev_position[0]) * (self.counting_region.centroid.x - prev_position[0]) > 0:
-                                self.in_counts += 1
-                            else:
-                                self.out_counts += 1
+                    if distance < self.line_dist_thresh and track_id not in self.counting_list:
+                        self.counting_list.append(track_id)
+                        if (box[0] - prev_position[0]) * (self.counting_region.centroid.x - prev_position[0]) > 0:
+                            self.in_counts += 1
+                        else:
+                            self.out_counts += 1
 
-        incount_label = "In Count : " + f"{self.in_counts}"
-        outcount_label = "OutCount : " + f"{self.out_counts}"
+        incount_label = f"In Count : {self.in_counts}"
+        outcount_label = f"OutCount : {self.out_counts}"
 
         # Display counts based on user choice
         counts_label = None
@@ -218,7 +221,7 @@ class ObjectCounter:
         elif not self.view_out_counts:
             counts_label = incount_label
         else:
-            counts_label = incount_label + " " + outcount_label
+            counts_label = f"{incount_label} {outcount_label}"
 
         if counts_label is not None:
             self.annotator.count_labels(
@@ -254,9 +257,7 @@ class ObjectCounter:
         if tracks[0].boxes.id is None:
             if self.view_img:
                 self.display_frames()
-                return
-            else:
-                return
+            return
         self.extract_and_process_tracks(tracks)
 
         if self.view_img:

ultralytics/solutions/speed_estimation.py

@@ -114,9 +114,7 @@ class SpeedEstimator:
             cls (str): object class name
             track (list): tracking history for tracks path drawing
         """
-        speed_label = (
-            str(int(self.dist_data[track_id])) + "km/ph" if track_id in self.dist_data else self.names[int(cls)]
-        )
+        speed_label = f"{int(self.dist_data[track_id])}km/ph" if track_id in self.dist_data else self.names[int(cls)]
         bbox_color = colors(int(track_id)) if track_id in self.dist_data else (255, 0, 255)
 
         self.annotator.box_label(box, speed_label, bbox_color)
@@ -132,28 +130,28 @@ class SpeedEstimator:
             track (list): tracking history for tracks path drawing
         """
 
-        if self.reg_pts[0][0] < track[-1][0] < self.reg_pts[1][0]:
-            if self.reg_pts[1][1] - self.spdl_dist_thresh < track[-1][1] < self.reg_pts[1][1] + self.spdl_dist_thresh:
-                direction = "known"
+        if not self.reg_pts[0][0] < track[-1][0] < self.reg_pts[1][0]:
+            return
+        if self.reg_pts[1][1] - self.spdl_dist_thresh < track[-1][1] < self.reg_pts[1][1] + self.spdl_dist_thresh:
+            direction = "known"
 
-            elif self.reg_pts[0][1] - self.spdl_dist_thresh < track[-1][1] < self.reg_pts[0][1] + self.spdl_dist_thresh:
-                direction = "known"
+        elif self.reg_pts[0][1] - self.spdl_dist_thresh < track[-1][1] < self.reg_pts[0][1] + self.spdl_dist_thresh:
+            direction = "known"
 
-            else:
-                direction = "unknown"
+        else:
+            direction = "unknown"
 
-            if self.trk_previous_times[trk_id] != 0 and direction != "unknown":
-                if trk_id not in self.trk_idslist:
-                    self.trk_idslist.append(trk_id)
+        if self.trk_previous_times[trk_id] != 0 and direction != "unknown" and trk_id not in self.trk_idslist:
+            self.trk_idslist.append(trk_id)
 
-                    time_difference = time() - self.trk_previous_times[trk_id]
-                    if time_difference > 0:
-                        dist_difference = np.abs(track[-1][1] - self.trk_previous_points[trk_id][1])
-                        speed = dist_difference / time_difference
-                        self.dist_data[trk_id] = speed
+            time_difference = time() - self.trk_previous_times[trk_id]
+            if time_difference > 0:
+                dist_difference = np.abs(track[-1][1] - self.trk_previous_points[trk_id][1])
+                speed = dist_difference / time_difference
+                self.dist_data[trk_id] = speed
 
-            self.trk_previous_times[trk_id] = time()
-            self.trk_previous_points[trk_id] = track[-1]
+        self.trk_previous_times[trk_id] = time()
+        self.trk_previous_points[trk_id] = track[-1]
 
     def estimate_speed(self, im0, tracks):
         """
@@ -166,10 +164,7 @@ class SpeedEstimator:
         if tracks[0].boxes.id is None:
             if self.view_img and self.env_check:
                 self.display_frames()
-                return
-            else:
-                return
-
+            return
         self.extract_tracks(tracks)
 
         self.annotator = Annotator(self.im0, line_width=2)

ultralytics/trackers/track.py

@@ -7,7 +7,6 @@ import torch
 
 from ultralytics.utils import IterableSimpleNamespace, yaml_load
 from ultralytics.utils.checks import check_yaml
-
 from .bot_sort import BOTSORT
 from .byte_tracker import BYTETracker
 

ultralytics/trackers/utils/gmc.py

@@ -67,7 +67,7 @@ class GMC:
                 maxCorners=1000, qualityLevel=0.01, minDistance=1, blockSize=3, useHarrisDetector=False, k=0.04
             )
 
-        elif self.method in ["none", "None", None]:
+        elif self.method in {"none", "None", None}:
             self.method = None
         else:
             raise ValueError(f"Error: Unknown GMC method:{method}")

ultralytics/trackers/utils/matching.py

@@ -70,9 +70,7 @@ def iou_distance(atracks: list, btracks: list) -> np.ndarray:
         (np.ndarray): Cost matrix computed based on IoU.
     """
 
-    if (len(atracks) > 0 and isinstance(atracks[0], np.ndarray)) or (
-        len(btracks) > 0 and isinstance(btracks[0], np.ndarray)
-    ):
+    if atracks and isinstance(atracks[0], np.ndarray) or btracks and isinstance(btracks[0], np.ndarray):
         atlbrs = atracks
         btlbrs = btracks
     else:

ultralytics/utils/benchmarks.py

@@ -26,7 +26,6 @@ ncnn                    | `ncnn`                    | yolov8n_ncnn_model/
 
 import glob
 import platform
-import sys
 import time
 from pathlib import Path
 

ultralytics/utils/callbacks/base.py

@@ -4,6 +4,7 @@
 from collections import defaultdict
 from copy import deepcopy
 
+
 # Trainer callbacks ----------------------------------------------------------------------------------------------------
 
 

ultralytics/utils/callbacks/neptune.py

@@ -96,9 +96,7 @@ def on_train_end(trainer):
         for f in files:
             _log_plot(title=f.stem, plot_path=f)
         # Log the final model
-        run[f"weights/{trainer.args.name or trainer.args.task}/{str(trainer.best.name)}"].upload(
-            File(str(trainer.best))
-        )
+        run[f"weights/{trainer.args.name or trainer.args.task}/{trainer.best.name}"].upload(File(str(trainer.best)))
 
 
 callbacks = (

ultralytics/utils/checks.py

@@ -214,9 +214,9 @@ def check_version(
         try:
             name = current  # assigned package name to 'name' arg
             current = metadata.version(current)  # get version string from package name
-        except metadata.PackageNotFoundError:
+        except metadata.PackageNotFoundError as e:
             if hard:
-                raise ModuleNotFoundError(emojis(f"WARNING ⚠️ {current} package is required but not installed"))
+                raise ModuleNotFoundError(emojis(f"WARNING ⚠️ {current} package is required but not installed")) from e
             else:
                 return False
 

ultralytics/utils/loss.py

@@ -7,7 +7,6 @@ import torch.nn.functional as F
 from ultralytics.utils.metrics import OKS_SIGMA
 from ultralytics.utils.ops import crop_mask, xywh2xyxy, xyxy2xywh
 from ultralytics.utils.tal import RotatedTaskAlignedAssigner, TaskAlignedAssigner, dist2bbox, dist2rbox, make_anchors
-
 from .metrics import bbox_iou, probiou
 from .tal import bbox2dist
 

ultralytics/utils/ops.py

@@ -40,7 +40,7 @@ class Profile(contextlib.ContextDecorator):
         """
         self.t = t
         self.device = device
-        self.cuda = True if (device and str(device)[:4] == "cuda") else False
+        self.cuda = bool(device and str(device).startswith("cuda"))
 
     def __enter__(self):
         """Start timing."""
@@ -534,12 +534,11 @@ def xyxyxyxy2xywhr(corners):
         # especially some objects are cut off by augmentations in dataloader.
         (x, y), (w, h), angle = cv2.minAreaRect(pts)
         rboxes.append([x, y, w, h, angle / 180 * np.pi])
-    rboxes = (
+    return (
         torch.tensor(rboxes, device=corners.device, dtype=corners.dtype)
         if is_torch
         else np.asarray(rboxes, dtype=points.dtype)
-    )
-    return rboxes
+    )  # rboxes
 
 
 def xywhr2xyxyxyxy(center):

ultralytics/utils/plotting.py

@@ -13,7 +13,6 @@ from PIL import Image, ImageDraw, ImageFont
 from PIL import __version__ as pil_version
 
 from ultralytics.utils import LOGGER, TryExcept, ops, plt_settings, threaded
-
 from .checks import check_font, check_version, is_ascii
 from .files import increment_path
 
@@ -433,7 +432,7 @@ class Annotator:
             center_kpt (int): centroid pose index for workout monitoring
             line_thickness (int): thickness for text display
         """
-        angle_text, count_text, stage_text = (f" {angle_text:.2f}", "Steps : " + f"{count_text}", f" {stage_text}")
+        angle_text, count_text, stage_text = (f" {angle_text:.2f}", f"Steps : {count_text}", f" {stage_text}")
         font_scale = 0.6 + (line_thickness / 10.0)
 
         # Draw angle
@@ -773,12 +772,11 @@ def plot_images(
                                 im[y : y + h, x : x + w, :][mask] * 0.4 + np.array(color) * 0.6
                             )
                 annotator.fromarray(im)
-    if save:
-        annotator.im.save(fname)  # save
-        if on_plot:
-            on_plot(fname)
-    else:
+    if not save:
         return np.asarray(annotator.im)
+    annotator.im.save(fname)  # save
+    if on_plot:
+        on_plot(fname)
 
 
 @plt_settings()

ultralytics/utils/tal.py

@@ -288,8 +288,7 @@ class RotatedTaskAlignedAssigner(TaskAlignedAssigner):
         norm_ad = (ad * ad).sum(dim=-1)
         ap_dot_ab = (ap * ab).sum(dim=-1)
         ap_dot_ad = (ap * ad).sum(dim=-1)
-        is_in_box = (ap_dot_ab >= 0) & (ap_dot_ab <= norm_ab) & (ap_dot_ad >= 0) & (ap_dot_ad <= norm_ad)
-        return is_in_box
+        return (ap_dot_ab >= 0) & (ap_dot_ab <= norm_ab) & (ap_dot_ad >= 0) & (ap_dot_ad <= norm_ad)  # is_in_box
 
 
 def make_anchors(feats, strides, grid_cell_offset=0.5):