Fix Windows non-UTF source filenames (#4524)

Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>

ultralytics/utils/__init__.py

@@ -852,9 +852,10 @@ ENVIRONMENT = 'Colab' if is_colab() else 'Kaggle' if is_kaggle() else 'Jupyter'
 TESTS_RUNNING = is_pytest_running() or is_github_actions_ci()
 set_sentry()
 
-# Apply monkey patches if the script is being run from within the parent directory of the script's location
-from .patches import imread, imshow, imwrite
+# Apply monkey patches
+from .patches import imread, imshow, imwrite, torch_save
 
-# torch.save = torch_save
-if Path(inspect.stack()[0].filename).parent.parent.as_posix() in inspect.stack()[-1].filename:
+torch.save = torch_save
+if WINDOWS:
+    # Apply cv2 patches for non-ASCII and non-UTF characters in image paths
     cv2.imread, cv2.imwrite, cv2.imshow = imread, imwrite, imshow

ultralytics/utils/benchmarks.py

@@ -240,7 +240,7 @@ class ProfileModels:
             if path.is_dir():
                 extensions = ['*.pt', '*.onnx', '*.yaml']
                 files.extend([file for ext in extensions for file in glob.glob(str(path / ext))])
-            elif path.suffix in ('.pt', '.yaml', '.yml'):  # add non-existing
+            elif path.suffix in {'.pt', '.yaml', '.yml'}:  # add non-existing
                 files.append(str(path))
             else:
                 files.extend(glob.glob(str(path)))
@@ -262,7 +262,7 @@ class ProfileModels:
             data = clipped_data
         return data
 
-    def profile_tensorrt_model(self, engine_file: str):
+    def profile_tensorrt_model(self, engine_file: str, eps: float = 1e-7):
         if not self.trt or not Path(engine_file).is_file():
             return 0.0, 0.0
 
@@ -279,7 +279,7 @@ class ProfileModels:
             elapsed = time.time() - start_time
 
         # Compute number of runs as higher of min_time or num_timed_runs
-        num_runs = max(round(self.min_time / elapsed * self.num_warmup_runs), self.num_timed_runs * 50)
+        num_runs = max(round(self.min_time / (elapsed + eps) * self.num_warmup_runs), self.num_timed_runs * 50)
 
         # Timed runs
         run_times = []
@@ -290,7 +290,7 @@ class ProfileModels:
         run_times = self.iterative_sigma_clipping(np.array(run_times), sigma=2, max_iters=3)  # sigma clipping
         return np.mean(run_times), np.std(run_times)
 
-    def profile_onnx_model(self, onnx_file: str):
+    def profile_onnx_model(self, onnx_file: str, eps: float = 1e-7):
         check_requirements('onnxruntime')
         import onnxruntime as ort
 
@@ -330,7 +330,7 @@ class ProfileModels:
             elapsed = time.time() - start_time
 
         # Compute number of runs as higher of min_time or num_timed_runs
-        num_runs = max(round(self.min_time / elapsed * self.num_warmup_runs), self.num_timed_runs)
+        num_runs = max(round(self.min_time / (elapsed + eps) * self.num_warmup_runs), self.num_timed_runs)
 
         # Timed runs
         run_times = []

ultralytics/utils/downloads.py

@@ -101,7 +101,11 @@ def zip_directory(directory, compress=True, exclude=('.DS_Store', '__MACOSX'), p
     zip_file = directory.with_suffix('.zip')
     compression = ZIP_DEFLATED if compress else ZIP_STORED
     with ZipFile(zip_file, 'w', compression) as f:
-        for file in tqdm(files_to_zip, desc=f'Zipping {directory} to {zip_file}...', unit='file', disable=not progress):
+        for file in tqdm(files_to_zip,
+                         desc=f'Zipping {directory} to {zip_file}...',
+                         unit='file',
+                         disable=not progress,
+                         bar_format=TQDM_BAR_FORMAT):
             f.write(file, file.relative_to(directory))
 
     return zip_file  # return path to zip file
@@ -159,7 +163,11 @@ def unzip_file(file, path=None, exclude=('.DS_Store', '__MACOSX'), exist_ok=Fals
             LOGGER.info(f'Skipping {file} unzip (already unzipped)')
             return path
 
-        for f in tqdm(files, desc=f'Unzipping {file} to {Path(path).resolve()}...', unit='file', disable=not progress):
+        for f in tqdm(files,
+                      desc=f'Unzipping {file} to {Path(path).resolve()}...',
+                      unit='file',
+                      disable=not progress,
+                      bar_format=TQDM_BAR_FORMAT):
             zipObj.extract(f, path=extract_path)
 
     return path  # return unzip dir

ultralytics/utils/patches.py

@@ -13,20 +13,45 @@ import torch
 _imshow = cv2.imshow  # copy to avoid recursion errors
 
 
-def imread(filename, flags=cv2.IMREAD_COLOR):
+def imread(filename: str, flags: int = cv2.IMREAD_COLOR):
+    """Read an image from a file.
+
+    Args:
+        filename (str): Path to the file to read.
+        flags (int, optional): Flag that can take values of cv2.IMREAD_*. Defaults to cv2.IMREAD_COLOR.
+
+    Returns:
+        (np.ndarray): The read image.
+    """
     return cv2.imdecode(np.fromfile(filename, np.uint8), flags)
 
 
-def imwrite(filename, img):
+def imwrite(filename: str, img: np.ndarray, params=None):
+    """Write an image to a file.
+
+    Args:
+        filename (str): Path to the file to write.
+        img (np.ndarray): Image to write.
+        params (list of ints, optional): Additional parameters. See OpenCV documentation.
+
+    Returns:
+        (bool): True if the file was written, False otherwise.
+    """
     try:
-        cv2.imencode(Path(filename).suffix, img)[1].tofile(filename)
+        cv2.imencode(Path(filename).suffix, img, params)[1].tofile(filename)
         return True
     except Exception:
         return False
 
 
-def imshow(path, im):
-    _imshow(path.encode('unicode_escape').decode(), im)
+def imshow(winname: str, mat: np.ndarray):
+    """Displays an image in the specified window.
+
+    Args:
+        winname (str): Name of the window.
+        mat (np.ndarray): Image to be shown.
+    """
+    _imshow(winname.encode('unicode_escape').decode(), mat)
 
 
 # PyTorch functions ----------------------------------------------------------------------------------------------------
@@ -34,12 +59,17 @@ _torch_save = torch.save  # copy to avoid recursion errors
 
 
 def torch_save(*args, **kwargs):
-    """Use dill (if exists) to serialize the lambda functions where pickle does not do this."""
+    """Use dill (if exists) to serialize the lambda functions where pickle does not do this.
+
+    Args:
+        *args (tuple): Positional arguments to pass to torch.save.
+        **kwargs (dict): Keyword arguments to pass to torch.save.
+    """
     try:
-        import dill as pickle
+        import dill as pickle  # noqa
     except ImportError:
         import pickle
 
     if 'pickle_module' not in kwargs:
-        kwargs['pickle_module'] = pickle
+        kwargs['pickle_module'] = pickle  # noqa
     return _torch_save(*args, **kwargs)