Fix Windows non-UTF source filenames (#4524)

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

.github/workflows/links.yml

@@ -28,7 +28,7 @@ jobs:
           timeout_minutes: 5
           retry_wait_seconds: 60
           max_attempts: 3
-          command: lychee --accept 429,999 --exclude-loopback --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com)' --exclude-path '**/ci.yaml' --exclude-mail --github-token ${{ secrets.GITHUB_TOKEN }} './**/*.md' './**/*.html'
+          command: lychee --accept 429,999 --exclude-loopback --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|kaggle\.com)' --exclude-path '**/ci.yaml' --exclude-mail --github-token ${{ secrets.GITHUB_TOKEN }} './**/*.md' './**/*.html'
 
       - name: Test Markdown, HTML, YAML, Python and Notebook links with retry
         if: github.event_name == 'workflow_dispatch'
@@ -37,4 +37,4 @@ jobs:
           timeout_minutes: 5
           retry_wait_seconds: 60
           max_attempts: 3
-          command: lychee --accept 429,999 --exclude-loopback --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|url\.com)' --exclude-path '**/ci.yaml' --exclude-mail --github-token ${{ secrets.GITHUB_TOKEN }} './**/*.md' './**/*.html' './**/*.yml' './**/*.yaml' './**/*.py' './**/*.ipynb'
+          command: lychee --accept 429,999 --exclude-loopback --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|kaggle\.com|url\.com)' --exclude-path '**/ci.yaml' --exclude-mail --github-token ${{ secrets.GITHUB_TOKEN }} './**/*.md' './**/*.html' './**/*.yml' './**/*.yaml' './**/*.py' './**/*.ipynb'

docs/quickstart.md

@@ -16,10 +16,18 @@ Ultralytics provides various installation methods including pip, conda, and Dock
         [![PyPI version](https://badge.fury.io/py/ultralytics.svg)](https://badge.fury.io/py/ultralytics) [![Downloads](https://static.pepy.tech/badge/ultralytics)](https://pepy.tech/project/ultralytics)
 
         ```bash
-        # Install the ultralytics package using pip
+        # Install the ultralytics package from PyPI
         pip install ultralytics
         ```
 
+        You can also install the `ultralytics` package directly from the GitHub repository. This might be useful if you want the latest development version. Make sure to have the Git command-line tool installed on your system. The `@main` command installs the `main` branch and may be modified to another branch, i.e. `@my-branch`, or removed alltogether to default to `main` branch.
+    
+        ```bash
+        # Install the ultralytics package from GitHub
+        pip install git+https://github.com/ultralytics/ultralytics.git@main
+        ```
+
+
     === "Conda install"
         Conda is an alternative package manager to pip which may also be used for installation. Visit Anaconda for more details at [https://anaconda.org/conda-forge/ultralytics](https://anaconda.org/conda-forge/ultralytics). Ultralytics feedstock repository for updating the conda package is at [https://github.com/conda-forge/ultralytics-feedstock/](https://github.com/conda-forge/ultralytics-feedstock/).
 
@@ -53,10 +61,19 @@ Ultralytics provides various installation methods including pip, conda, and Dock
         ```
 
     === "Docker"
-        Utilize Docker to execute the `ultralytics` package in an isolated container. By employing the official `ultralytics` image from [Docker Hub](https://hub.docker.com/r/ultralytics/ultralytics), you can avoid local installation. Below are the commands to get the latest image and execute it:
+
+        Utilize Docker to effortlessly execute the `ultralytics` package in an isolated container, ensuring consistent and smooth performance across various environments. By choosing one of the official `ultralytics` images from [Docker Hub](https://hub.docker.com/r/ultralytics/ultralytics), you not only avoid the complexity of local installation but also benefit from access to a verified working environment. Ultralytics offers 5 main supported Docker images, each designed to provide high compatibility and efficiency for different platforms and use cases:
         
         <a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker" alt="Docker Pulls"></a>
 
+        - **Dockerfile:** GPU image recommended for training.
+        - **Dockerfile-arm64:** Optimized for ARM64 architecture, allowing deployment on devices like Raspberry Pi and other ARM64-based platforms.
+        - **Dockerfile-cpu:** Ubuntu-based CPU-only version suitable for inference and environments without GPUs.
+        - **Dockerfile-jetson:** Tailored for NVIDIA Jetson devices, integrating GPU support optimized for these platforms.
+        - **Dockerfile-python:** Minimal image with just Python and necessary dependencies, ideal for lightweight applications and development.
+        
+        Below are the commands to get the latest image and execute it:
+
         ```bash
         # Set image name as a variable
         t=ultralytics/ultralytics:latest

docs/reference/data/converter.md

@@ -25,10 +25,6 @@ keywords: Ultralytics, Data Converter, coco91_to_coco80_class, merge_multi_segme
 ## ::: ultralytics.data.converter.convert_dota_to_yolo_obb
 <br><br>
 
----
-## ::: ultralytics.data.converter.rle2polygon
-<br><br>
-
 ---
 ## ::: ultralytics.data.converter.min_index
 <br><br>

docs/reference/data/dataset.md

@@ -20,3 +20,11 @@ keywords: Ultralytics, YOLO, YOLODataset, SemanticDataset, data handling, data m
 ---
 ## ::: ultralytics.data.dataset.SemanticDataset
 <br><br>
+
+---
+## ::: ultralytics.data.dataset.load_dataset_cache_file
+<br><br>
+
+---
+## ::: ultralytics.data.dataset.save_dataset_cache_file
+<br><br>

docs/reference/data/utils.md

@@ -25,6 +25,10 @@ keywords: Ultralytics, data utils, YOLO, img2label_paths, exif_size, polygon2mas
 ## ::: ultralytics.data.utils.exif_size
 <br><br>
 
+---
+## ::: ultralytics.data.utils.verify_image
+<br><br>
+
 ---
 ## ::: ultralytics.data.utils.verify_image_label
 <br><br>

tests/test_python.py

@@ -13,7 +13,7 @@ from torchvision.transforms import ToTensor
 
 from ultralytics import RTDETR, YOLO
 from ultralytics.data.build import load_inference_source
-from ultralytics.utils import ASSETS, DEFAULT_CFG, LINUX, ONLINE, ROOT, SETTINGS
+from ultralytics.utils import ASSETS, DEFAULT_CFG, LINUX, ONLINE, ROOT, SETTINGS, WINDOWS
 from ultralytics.utils.downloads import download
 from ultralytics.utils.torch_utils import TORCH_1_9
 
@@ -26,18 +26,24 @@ TMP = (ROOT / '../tests/tmp').resolve()  # temp directory for test files
 
 def test_model_forward():
     model = YOLO(CFG)
-    model(SOURCE, imgsz=32, augment=True)
+    model(source=None, imgsz=32, augment=True)  # also test no source and augment
 
 
 def test_model_methods():
     model = YOLO(MODEL)
+
+    # Model methods
     model.info(verbose=True, detailed=True)
     model = model.reset_weights()
     model = model.load(MODEL)
     model.to('cpu')
     model.fuse()
+
+    # Model properties
     _ = model.names
     _ = model.device
+    _ = model.transforms
+    _ = model.task_map
 
 
 def test_predict_txt():
@@ -88,12 +94,13 @@ def test_predict_img():
 def test_predict_grey_and_4ch():
     # Convert SOURCE to greyscale and 4-ch
     im = Image.open(SOURCE)
-    stem = SOURCE.parent / SOURCE.stem
+    directory = TMP / 'im4'
+    directory.mkdir(parents=True, exist_ok=True)
 
-    source_greyscale = Path(f'{stem}_greyscale.jpg')
-    source_rgba = Path(f'{stem}_4ch.png')
-    source_non_utf = Path(f'{stem}_veículo.jpg')
-    source_spaces = Path(f'{stem} with spaces.jpg')
+    source_greyscale = directory / 'greyscale.jpg'
+    source_rgba = directory / '4ch.png'
+    source_non_utf = directory / 'non_UTF_测试文件_tést_image.jpg'
+    source_spaces = directory / 'image with spaces.jpg'
 
     im.convert('L').save(source_greyscale)  # greyscale
     im.convert('RGBA').save(source_rgba)  # 4-ch PNG with alpha
@@ -116,7 +123,7 @@ def test_track_stream():
     import yaml
 
     model = YOLO(MODEL)
-    model.predict('https://youtu.be/G17sBkb38XQ', imgsz=96)
+    model.predict('https://youtu.be/G17sBkb38XQ', imgsz=96, save=True)
     model.track('https://ultralytics.com/assets/decelera_portrait_min.mov', imgsz=160, tracker='bytetrack.yaml')
     model.track('https://ultralytics.com/assets/decelera_portrait_min.mov', imgsz=160, tracker='botsort.yaml')
 
@@ -150,7 +157,7 @@ def test_train_pretrained():
 
 def test_export_torchscript():
     model = YOLO(MODEL)
-    f = model.export(format='torchscript')
+    f = model.export(format='torchscript', optimize=True)
     YOLO(f)(SOURCE)  # exported model inference
 
 
@@ -166,7 +173,8 @@ def test_export_openvino():
     YOLO(f)(SOURCE)  # exported model inference
 
 
-def test_export_coreml():  # sourcery skip: move-assign
+def test_export_coreml():
+    if not WINDOWS:  # RuntimeError: BlobWriter not loaded with coremltools 7.0 on windows
         model = YOLO(MODEL)
         model.export(format='coreml', nms=True)
         # if MACOS:
@@ -196,7 +204,7 @@ def test_export_paddle(enabled=False):
         model.export(format='paddle')
 
 
-def test_export_ncnn(enabled=False):
+def test_export_ncnn():
     model = YOLO(MODEL)
     f = model.export(format='ncnn')
     YOLO(f)(SOURCE)  # exported model inference

ultralytics/data/augment.py

@@ -634,7 +634,7 @@ class CopyPaste:
 
             result = cv2.flip(im, 1)  # augment segments (flip left-right)
             i = cv2.flip(im_new, 1).astype(bool)
-            im[i] = result[i]  # cv2.imwrite('debug.jpg', im)  # debug
+            im[i] = result[i]
 
         labels['img'] = im
         labels['cls'] = cls

ultralytics/data/converter.py

@@ -9,8 +9,6 @@ import cv2
 import numpy as np
 from tqdm import tqdm
 
-from ultralytics.utils.checks import check_requirements
-
 
 def coco91_to_coco80_class():
     """Converts 91-index COCO class IDs to 80-index COCO class IDs.
@@ -18,7 +16,6 @@ def coco91_to_coco80_class():
     Returns:
         (list): A list of 91 class IDs where the index represents the 80-index class ID and the value is the
             corresponding 91-index class ID.
-
     """
     return [
         0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, None, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, None, 24, 25, None,
@@ -119,9 +116,7 @@ def convert_coco(labels_dir='../coco/annotations/', use_segments=False, use_keyp
                     if len(ann['segmentation']) == 0:
                         segments.append([])
                         continue
-                    if isinstance(ann['segmentation'], dict):
-                        ann['segmentation'] = rle2polygon(ann['segmentation'])
-                    if len(ann['segmentation']) > 1:
+                    elif len(ann['segmentation']) > 1:
                         s = merge_multi_segment(ann['segmentation'])
                         s = (np.concatenate(s, axis=0) / np.array([w, h])).reshape(-1).tolist()
                     else:
@@ -131,9 +126,8 @@ def convert_coco(labels_dir='../coco/annotations/', use_segments=False, use_keyp
                     if s not in segments:
                         segments.append(s)
                 if use_keypoints and ann.get('keypoints') is not None:
-                    k = (np.array(ann['keypoints']).reshape(-1, 3) / np.array([w, h, 1])).reshape(-1).tolist()
-                    k = box + k
-                    keypoints.append(k)
+                    keypoints.append(box + (np.array(ann['keypoints']).reshape(-1, 3) /
+                                            np.array([w, h, 1])).reshape(-1).tolist())
 
             # Write
             with open((fn / f).with_suffix('.txt'), 'a') as file:
@@ -237,34 +231,6 @@ def convert_dota_to_yolo_obb(dota_root_path: str):
             convert_label(image_name_without_ext, w, h, orig_label_dir, save_dir)
 
 
-def rle2polygon(segmentation):
-    """
-    Convert Run-Length Encoding (RLE) mask to polygon coordinates.
-
-    Args:
-        segmentation (dict, list): RLE mask representation of the object segmentation.
-
-    Returns:
-        (list): A list of lists representing the polygon coordinates for each contour.
-
-    Note:
-        Requires the 'pycocotools' package to be installed.
-    """
-    check_requirements('pycocotools')
-    from pycocotools import mask
-
-    m = mask.decode(segmentation)
-    m[m > 0] = 255
-    contours, _ = cv2.findContours(m, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_TC89_KCOS)
-    polygons = []
-    for contour in contours:
-        epsilon = 0.001 * cv2.arcLength(contour, True)
-        contour_approx = cv2.approxPolyDP(contour, epsilon, True)
-        polygon = contour_approx.flatten().tolist()
-        polygons.append(polygon)
-    return polygons
-
-
 def min_index(arr1, arr2):
     """
     Find a pair of indexes with the shortest distance between two arrays of 2D points.

ultralytics/data/utils.py

@@ -144,9 +144,7 @@ def verify_image_label(args):
         if keypoint:
             keypoints = lb[:, 5:].reshape(-1, nkpt, ndim)
             if ndim == 2:
-                kpt_mask = np.ones(keypoints.shape[:2], dtype=np.float32)
-                kpt_mask = np.where(keypoints[..., 0] < 0, 0.0, kpt_mask)
-                kpt_mask = np.where(keypoints[..., 1] < 0, 0.0, kpt_mask)
+                kpt_mask = np.where((keypoints[..., 0] < 0) | (keypoints[..., 1] < 0), 0.0, 1.0).astype(np.float32)
                 keypoints = np.concatenate([keypoints, kpt_mask[..., None]], axis=-1)  # (nl, nkpt, 3)
         lb = lb[:, :5]
         return im_file, lb, shape, segments, keypoints, nm, nf, ne, nc, msg

ultralytics/engine/exporter.py

@@ -127,7 +127,7 @@ class Exporter:
 
     Attributes:
         args (SimpleNamespace): Configuration for the exporter.
-        save_dir (Path): Directory to save results.
+        callbacks (list, optional): List of callback functions. Defaults to None.
     """
 
     def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
@@ -189,7 +189,7 @@ class Exporter:
         model.eval()
         model.float()
         model = model.fuse()
-        for k, m in model.named_modules():
+        for m in model.modules():
             if isinstance(m, (Detect, RTDETRDecoder)):  # Segment and Pose use Detect base class
                 m.dynamic = self.args.dynamic
                 m.export = True
@@ -427,7 +427,7 @@ class Exporter:
             system = 'macos' if MACOS else 'ubuntu' if LINUX else 'windows'  # operating system
             asset = [x for x in assets if system in x][0] if assets else \
                 f'https://github.com/pnnx/pnnx/releases/download/20230816/pnnx-20230816-{system}.zip'  # fallback
-            attempt_download_asset(asset, repo='pnnx/pnnx', release='latest')
+            asset = attempt_download_asset(asset, repo='pnnx/pnnx', release='latest')
             unzip_dir = Path(asset).with_suffix('')
             pnnx = ROOT / pnnx_filename  # new location
             (unzip_dir / pnnx_filename).rename(pnnx)  # move binary to ROOT
@@ -502,9 +502,9 @@ class Exporter:
                 check_requirements('scikit-learn')  # scikit-learn package required for k-means quantization
             if mlmodel:
                 ct_model = ct.models.neural_network.quantization_utils.quantize_weights(ct_model, bits, mode)
-            else:
+            elif bits == 8:  # mlprogram already quantized to FP16
                 import coremltools.optimize.coreml as cto
-                op_config = cto.OpPalettizerConfig(mode=mode, nbits=bits, weight_threshold=512)
+                op_config = cto.OpPalettizerConfig(mode='kmeans', nbits=bits, weight_threshold=512)
                 config = cto.OptimizationConfig(global_config=op_config)
                 ct_model = cto.palettize_weights(ct_model, config=config)
         if self.args.nms and self.model.task == 'detect':
@@ -839,7 +839,7 @@ class Exporter:
         import coremltools as ct  # noqa
 
         LOGGER.info(f'{prefix} starting pipeline with coremltools {ct.__version__}...')
-        batch_size, ch, h, w = list(self.im.shape)  # BCHW
+        _, _, h, w = list(self.im.shape)  # BCHW
 
         # Output shapes
         spec = model.get_spec()
@@ -857,8 +857,8 @@ class Exporter:
         # Checks
         names = self.metadata['names']
         nx, ny = spec.description.input[0].type.imageType.width, spec.description.input[0].type.imageType.height
-        na, nc = out0_shape
-        # na, nc = out0.type.multiArrayType.shape  # number anchors, classes
+        _, nc = out0_shape  # number of anchors, number of classes
+        # _, nc = out0.type.multiArrayType.shape
         assert len(names) == nc, f'{len(names)} names found for nc={nc}'  # check
 
         # Define output shapes (missing)
@@ -968,7 +968,7 @@ class IOSDetectModel(torch.nn.Module):
     def __init__(self, model, im):
         """Initialize the IOSDetectModel class with a YOLO model and example image."""
         super().__init__()
-        b, c, h, w = im.shape  # batch, channel, height, width
+        _, _, h, w = im.shape  # batch, channel, height, width
         self.model = model
         self.nc = len(model.names)  # number of classes
         if w == h:

ultralytics/engine/predictor.py

@@ -343,8 +343,7 @@ class BasePredictor:
                     h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
                 else:  # stream
                     fps, w, h = 30, im0.shape[1], im0.shape[0]
-                suffix = '.mp4' if MACOS else '.avi' if WINDOWS else '.avi'
-                fourcc = 'avc1' if MACOS else 'WMV2' if WINDOWS else 'MJPG'
+                suffix, fourcc = ('.mp4', 'avc1') if MACOS else ('.avi', 'WMV2') if WINDOWS else ('.avi', 'MJPG')
                 save_path = str(Path(save_path).with_suffix(suffix))
                 self.vid_writer[idx] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*fourcc), fps, (w, h))
             self.vid_writer[idx].write(im0)

ultralytics/nn/tasks.py

@@ -261,7 +261,6 @@ class DetectionModel(BaseModel):
         for si, fi in zip(s, f):
             xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max()))
             yi = super().predict(xi)[0]  # forward
-            # cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1])  # save
             yi = self._descale_pred(yi, fi, si, img_size)
             y.append(yi)
         y = self._clip_augmented(y)  # clip augmented tails

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)