Updating image splitter

config/app_config.yaml

@@ -1,57 +0,0 @@
-database:
-  path: data/detections.db
-image_repository:
-  base_path: ''
-  allowed_extensions:
-  - .jpg
-  - .jpeg
-  - .png
-  - .tif
-  - .tiff
-  - .bmp
-models:
-  default_base_model: yolov8s-seg.pt
-  models_directory: data/models
-  base_model_choices:
-  - yolov8s-seg.pt
-  - yolo11s-seg.pt
-training:
-  default_epochs: 100
-  default_batch_size: 16
-  default_imgsz: 1024
-  default_patience: 50
-  default_lr0: 0.01
-  two_stage:
-    enabled: false
-    stage1:
-      epochs: 20
-      lr0: 0.0005
-      patience: 10
-      freeze: 10
-    stage2:
-      epochs: 150
-      lr0: 0.0003
-      patience: 30
-  last_dataset_yaml: /home/martin/code/object_detection/data/datasets/data.yaml
-  last_dataset_dir: /home/martin/code/object_detection/data/datasets
-detection:
-  default_confidence: 0.25
-  default_iou: 0.45
-  max_batch_size: 100
-visualization:
-  bbox_colors:
-    organelle: '#FF6B6B'
-    membrane_branch: '#4ECDC4'
-    default: '#00FF00'
-  bbox_thickness: 2
-  font_size: 12
-export:
-  formats:
-  - csv
-  - json
-  - excel
-  default_format: csv
-logging:
-  level: INFO
-  file: logs/app.log
-  format: '%(asctime)s - %(name)s - %(levelname)s - %(message)s'

pyproject.toml

@@ -82,12 +82,12 @@ include-package-data = true
 "src.database" = ["*.sql"]
 
 [tool.black]
-line-length = 88
+line-length = 120
 target-version = ['py38', 'py39', 'py310', 'py311']
 include = '\.pyi?$'
 
 [tool.pylint.messages_control]
-max-line-length = 88
+max-line-length = 120
 
 [tool.mypy]
 python_version = "3.8"

src/gui/tabs/training_tab.py

@@ -34,7 +34,7 @@ from PySide6.QtWidgets import (
 from src.database.db_manager import DatabaseManager
 from src.model.yolo_wrapper import YOLOWrapper
 from src.utils.config_manager import ConfigManager
-from src.utils.image import Image, convert_grayscale_to_rgb_preserve_range
+from src.utils.image import Image
 from src.utils.logger import get_logger
 
 
@@ -1303,6 +1303,14 @@ class TrainingTab(QWidget):
         sample_image = self._find_first_image(images_dir)
         if not sample_image:
             return False
+
+        # Do not force an RGB cache for TIFF datasets.
+        # We handle grayscale/16-bit TIFFs via runtime Ultralytics patches that:
+        # - load TIFFs with `tifffile`
+        # - replicate grayscale to 3 channels without quantization
+        # - normalize uint16 correctly during training
+        if sample_image.suffix.lower() in {".tif", ".tiff"}:
+            return False
         try:
             img = Image(sample_image)
             return img.pil_image.mode.upper() != "RGB"
@@ -1368,7 +1376,7 @@ class TrainingTab(QWidget):
                 img_obj = Image(src)
                 pil_img = img_obj.pil_image
                 if len(pil_img.getbands()) == 1:
-                    rgb_img = convert_grayscale_to_rgb_preserve_range(pil_img)
+                    rgb_img = img_obj.convert_grayscale_to_rgb_preserve_range()
                 else:
                     rgb_img = pil_img.convert("RGB")
                 rgb_img.save(dst)

src/gui/widgets/annotation_canvas_widget.py

@@ -250,12 +250,10 @@ class AnnotationCanvasWidget(QWidget):
             # Get image data in a format compatible with Qt
             if self.current_image.channels in (3, 4):
                 image_data = self.current_image.get_rgb()
-                height, width = image_data.shape[:2]
             else:
-                image_data = self.current_image.get_grayscale()
+                image_data = self.current_image.get_qt_rgb()
-                height, width = image_data.shape
 
-            image_data = np.ascontiguousarray(image_data)
+            height, width = image_data.shape[:2]
             bytes_per_line = image_data.strides[0]
 
             qimage = QImage(
@@ -263,7 +261,7 @@ class AnnotationCanvasWidget(QWidget):
                 width,
                 height,
                 bytes_per_line,
-                self.current_image.qtimage_format,
+                QImage.Format_RGBX32FPx4,  # self.current_image.qtimage_format,
             ).copy()  # Copy so Qt owns the buffer even after numpy array goes out of scope
 
             self.original_pixmap = QPixmap.fromImage(qimage)

src/model/yolo_wrapper.py

@@ -1,16 +1,21 @@
-"""
+"""YOLO model wrapper for the microscopy object detection application.
-YOLO model wrapper for the microscopy object detection application.
+
-Provides a clean interface to YOLOv8 for training, validation, and inference.
+Notes on 16-bit TIFF support:
+- Ultralytics training defaults assume 8-bit images and normalize by dividing by 255.
+- This project can patch Ultralytics at runtime to decode TIFFs via `tifffile` and
+  normalize `uint16` correctly.
+
+See [`apply_ultralytics_16bit_tiff_patches()`](src/utils/ultralytics_16bit_patch.py:1).
 """
 
-from ultralytics import YOLO
 from pathlib import Path
 from typing import Optional, List, Dict, Callable, Any
 import torch
 import tempfile
 import os
-from src.utils.image import Image, convert_grayscale_to_rgb_preserve_range
+from src.utils.image import Image
 from src.utils.logger import get_logger
+from src.utils.ultralytics_16bit_patch import apply_ultralytics_16bit_tiff_patches
 
 
 logger = get_logger(__name__)
@@ -31,6 +36,9 @@ class YOLOWrapper:
         self.device = "cuda" if torch.cuda.is_available() else "cpu"
         logger.info(f"YOLOWrapper initialized with device: {self.device}")
 
+        # Apply Ultralytics runtime patches early (before first import/instantiation of YOLO datasets/trainers).
+        apply_ultralytics_16bit_tiff_patches()
+
     def load_model(self) -> bool:
         """
         Load YOLO model from path.
@@ -40,6 +48,9 @@ class YOLOWrapper:
         """
         try:
             logger.info(f"Loading YOLO model from {self.model_path}")
+            # Import YOLO lazily to ensure runtime patches are applied first.
+            from ultralytics import YOLO
+
             self.model = YOLO(self.model_path)
             self.model.to(self.device)
             logger.info("Model loaded successfully")
@@ -89,6 +100,16 @@ class YOLOWrapper:
                 f"Data: {data_yaml}, Epochs: {epochs}, Batch: {batch}, ImgSz: {imgsz}"
             )
 
+            # Defaults for 16-bit safety: disable augmentations that force uint8 and HSV ops that assume 0..255.
+            # Users can override by passing explicit kwargs.
+            kwargs.setdefault("mosaic", 0.0)
+            kwargs.setdefault("mixup", 0.0)
+            kwargs.setdefault("cutmix", 0.0)
+            kwargs.setdefault("copy_paste", 0.0)
+            kwargs.setdefault("hsv_h", 0.0)
+            kwargs.setdefault("hsv_s", 0.0)
+            kwargs.setdefault("hsv_v", 0.0)
+
             # Train the model
             results = self.model.train(
                 data=data_yaml,
@@ -171,9 +192,11 @@ class YOLOWrapper:
         prepared_source, cleanup_path = self._prepare_source(source)
 
         try:
-            logger.info(f"Running inference on {source}")
+            logger.info(
+                f"Running inference on {source} -> prepared_source {prepared_source}"
+            )
             results = self.model.predict(
-                source=prepared_source,
+                source=source,
                 conf=conf,
                 iou=iou,
                 save=save,
@@ -236,17 +259,11 @@ class YOLOWrapper:
             if source_path.is_file():
                 try:
                     img_obj = Image(source_path)
-                    pil_img = img_obj.pil_image
-                    if len(pil_img.getbands()) == 1:
-                        rgb_img = convert_grayscale_to_rgb_preserve_range(pil_img)
-                    else:
-                        rgb_img = pil_img.convert("RGB")
-
                     suffix = source_path.suffix or ".png"
                     tmp = tempfile.NamedTemporaryFile(suffix=suffix, delete=False)
                     tmp_path = tmp.name
                     tmp.close()
-                    rgb_img.save(tmp_path)
+                    img_obj.save(tmp_path)
                     cleanup_path = tmp_path
                     logger.info(
                         f"Converted image {source_path} to RGB for inference at {tmp_path}"

src/utils/config_manager.py

@@ -54,7 +54,7 @@ class ConfigManager:
                 "models_directory": "data/models",
                 "base_model_choices": [
                     "yolov8s-seg.pt",
-                    "yolov11s-seg.pt",
+                    "yolo11s-seg.pt",
                 ],
             },
             "training": {
@@ -225,6 +225,4 @@ class ConfigManager:
 
     def get_allowed_extensions(self) -> list:
         """Get list of allowed image file extensions."""
-        return self.get(
+        return self.get("image_repository.allowed_extensions", Image.SUPPORTED_EXTENSIONS)
-            "image_repository.allowed_extensions", Image.SUPPORTED_EXTENSIONS
-        )

src/utils/image.py

@@ -6,16 +6,52 @@ import cv2
 import numpy as np
 from pathlib import Path
 from typing import Optional, Tuple, Union
-from PIL import Image as PILImage
 
 from src.utils.logger import get_logger
 from src.utils.file_utils import validate_file_path, is_image_file
 
 from PySide6.QtGui import QImage
 
+from tifffile import imread, imwrite
+
 logger = get_logger(__name__)
 
 
+def get_pseudo_rgb(arr: np.ndarray, gamma: float = 0.5) -> np.ndarray:
+    """
+    Convert a grayscale image to a pseudo-RGB image using a gamma correction.
+
+    Args:
+        arr: Input grayscale image as numpy array
+
+    Returns:
+        Pseudo-RGB image as numpy array
+    """
+    if arr.ndim != 2:
+        raise ValueError("Input array must be a grayscale image with shape (H, W)")
+
+    a1 = arr.copy().astype(np.float32)
+    a1 -= np.percentile(a1, 2)
+    a1[a1 < 0] = 0
+    p999 = np.percentile(a1, 99.9)
+    a1[a1 > p999] = p999
+    a1 /= a1.max()
+
+    if 0:
+        a2 = a1.copy()
+        a2 = a2**gamma
+        a2 /= a2.max()
+
+        a3 = a1.copy()
+        p9999 = np.percentile(a3, 99.99)
+        a3[a3 > p9999] = p9999
+        a3 /= a3.max()
+
+    return np.stack([a1, np.zeros(a1.shape), np.zeros(a1.shape)], axis=0)
+    # return np.stack([a2, np.zeros(a1.shape), np.zeros(a1.shape)], axis=0)
+    # return np.stack([a1, a2, a3], axis=0)
+
+
 class ImageLoadError(Exception):
     """Exception raised when an image cannot be loaded."""
 
@@ -54,7 +90,6 @@ class Image:
         """
         self.path = Path(image_path)
         self._data: Optional[np.ndarray] = None
-        self._pil_image: Optional[PILImage.Image] = None
         self._width: int = 0
         self._height: int = 0
         self._channels: int = 0
@@ -80,11 +115,14 @@ class Image:
         if not is_image_file(str(self.path), self.SUPPORTED_EXTENSIONS):
             ext = self.path.suffix.lower()
             raise ImageLoadError(
-                f"Unsupported image format: {ext}. "
+                f"Unsupported image format: {ext}. " f"Supported formats: {', '.join(self.SUPPORTED_EXTENSIONS)}"
-                f"Supported formats: {', '.join(self.SUPPORTED_EXTENSIONS)}"
             )
 
         try:
+            if self.path.suffix.lower() in [".tif", ".tiff"]:
+                self._data = imread(str(self.path))
+            else:
+                raise NotImplementedError("RGB is not implemented")
                 # Load with OpenCV (returns BGR format)
                 self._data = cv2.imread(str(self.path), cv2.IMREAD_UNCHANGED)
 
@@ -92,23 +130,19 @@ class Image:
                 raise ImageLoadError(f"Failed to load image with OpenCV: {self.path}")
 
             # Extract metadata
+            # print(self._data.shape)
+            if len(self._data.shape) == 2:
                 self._height, self._width = self._data.shape[:2]
-            self._channels = self._data.shape[2] if len(self._data.shape) == 3 else 1
+                self._channels = 1
+            else:
+                self._height, self._width = self._data.shape[1:]
+                self._channels = self._data.shape[0]
+            # self._channels = self._data.shape[2] if len(self._data.shape) == 3 else 1
             self._format = self.path.suffix.lower().lstrip(".")
             self._size_bytes = self.path.stat().st_size
             self._dtype = self._data.dtype
 
-            # Load PIL version for compatibility (convert BGR to RGB)
+            if 0:
-            if self._channels == 3:
-                rgb_data = cv2.cvtColor(self._data, cv2.COLOR_BGR2RGB)
-                self._pil_image = PILImage.fromarray(rgb_data)
-            elif self._channels == 4:
-                rgba_data = cv2.cvtColor(self._data, cv2.COLOR_BGRA2RGBA)
-                self._pil_image = PILImage.fromarray(rgba_data)
-            else:
-                # Grayscale
-                self._pil_image = PILImage.fromarray(self._data)
-
                 logger.info(
                     f"Successfully loaded image: {self.path.name} "
                     f"({self._width}x{self._height}, {self._channels} channels, "
@@ -131,18 +165,6 @@ class Image:
             raise ImageLoadError("Image data not available")
         return self._data
 
-    @property
-    def pil_image(self) -> PILImage.Image:
-        """
-        Get image data as PIL Image (RGB or grayscale).
-
-        Returns:
-            PIL Image object
-        """
-        if self._pil_image is None:
-            raise ImageLoadError("PIL image not available")
-        return self._pil_image
-
     @property
     def width(self) -> int:
         """Get image width in pixels."""
@@ -187,6 +209,7 @@ class Image:
     @property
     def dtype(self) -> np.dtype:
         """Get the data type of the image array."""
+
         if self._dtype is None:
             raise ImageLoadError("Image dtype not available")
         return self._dtype
@@ -206,8 +229,10 @@ class Image:
         elif self._channels == 1:
             if self._dtype == np.uint16:
                 return QImage.Format_Grayscale16
-            else:
+            elif self._dtype == np.uint8:
                 return QImage.Format_Grayscale8
+            elif self._dtype == np.float32:
+                return QImage.Format_BGR30
         else:
             raise ImageLoadError(f"Unsupported number of channels: {self._channels}")
 
@@ -218,6 +243,12 @@ class Image:
         Returns:
             Image data in RGB format as numpy array
         """
+        if self.channels == 1:
+            img = get_pseudo_rgb(self.data)
+            self._dtype = img.dtype
+            return img
+        raise NotImplementedError
+
         if self._channels == 3:
             return cv2.cvtColor(self._data, cv2.COLOR_BGR2RGB)
         elif self._channels == 4:
@@ -225,6 +256,18 @@ class Image:
         else:
             return self._data
 
+    def get_qt_rgb(self) -> np.ascontiguousarray:
+        # we keep data as (C, H, W)
+        _img = self.get_rgb()
+
+        img = np.zeros((self.height, self.width, 4), dtype=np.float32)
+        img[..., 0] = _img[0]  # R gradient
+        img[..., 1] = _img[1]  # G gradient
+        img[..., 2] = _img[2]  # B constant
+        img[..., 3] = 1.0  # A = 1.0 (opaque)
+
+        return np.ascontiguousarray(img)
+
     def get_grayscale(self) -> np.ndarray:
         """
         Get image as grayscale numpy array.
@@ -277,11 +320,26 @@ class Image:
         """
         return self._channels >= 3
 
+    def save(self, path: Union[str, Path], pseudo_rgb: bool = True) -> None:
+
+        if self.channels == 1:
+            if pseudo_rgb:
+                img = get_pseudo_rgb(self.data)
+                print("Image.save", img.shape)
+            else:
+                img = np.repeat(self.data, 3, axis=2)
+
+        else:
+            raise NotImplementedError("Only grayscale images are supported for now.")
+
+        imwrite(path, data=img)
+
     def __repr__(self) -> str:
         """String representation of the Image object."""
         return (
             f"Image(path='{self.path.name}', "
-            f"shape=({self._width}x{self._height}x{self._channels}), "
+            # Display as HxWxC to match the conventional NumPy shape semantics.
+            f"shape=({self._height}x{self._width}x{self._channels}), "
             f"format={self._format}, "
             f"size={self.size_mb:.2f}MB)"
         )
@@ -291,38 +349,13 @@ class Image:
         return self.__repr__()
 
 
-def convert_grayscale_to_rgb_preserve_range(
+if __name__ == "__main__":
-    pil_image: PILImage.Image,
+    import argparse
-) -> PILImage.Image:
-    """Convert a single-channel PIL image to RGB while preserving dynamic range.
 
-    Args:
+    parser = argparse.ArgumentParser()
-        pil_image: Single-channel PIL image (e.g., 16-bit grayscale).
+    parser.add_argument("--path", type=str, required=True)
+    args = parser.parse_args()
 
-    Returns:
+    img = Image(args.path)
-        PIL Image in RGB mode with intensities normalized to 0-255.
+    img.save(args.path + "test.tif")
-    """
+    print(img)
-
-    if pil_image.mode == "RGB":
-        return pil_image
-
-    grayscale = np.array(pil_image)
-    if grayscale.ndim == 3:
-        grayscale = grayscale[:, :, 0]
-
-    original_dtype = grayscale.dtype
-    grayscale = grayscale.astype(np.float32)
-
-    if grayscale.size == 0:
-        return PILImage.new("RGB", pil_image.size, color=(0, 0, 0))
-
-    if np.issubdtype(original_dtype, np.integer):
-        denom = float(max(np.iinfo(original_dtype).max, 1))
-    else:
-        max_val = float(grayscale.max())
-        denom = max(max_val, 1.0)
-
-    grayscale = np.clip(grayscale / denom, 0.0, 1.0)
-    grayscale_u8 = (grayscale * 255.0).round().astype(np.uint8)
-    rgb_arr = np.repeat(grayscale_u8[:, :, None], 3, axis=2)
-    return PILImage.fromarray(rgb_arr, mode="RGB")

src/utils/image_converters.py

@@ -12,23 +12,38 @@ class UT:
     Operetta files along with rois drawn in ImageJ
     """
 
-    def __init__(self, roifile_fn: Path):
+    def __init__(self, roifile_fn: Path, no_labels: bool):
         self.roifile_fn = roifile_fn
+        print("is file", self.roifile_fn.is_file())
+        self.rois = None
+        if no_labels:
             self.rois = ImagejRoi.fromfile(self.roifile_fn)
-        self.stem = self.roifile_fn.stem.strip("-RoiSet")
+            print(self.roifile_fn.stem)
+            print(self.roifile_fn.parent.parts[-1])
+            if "Roi-" in self.roifile_fn.stem:
+                self.stem = self.roifile_fn.stem.split("Roi-")[1]
+            else:
+                self.stem = self.roifile_fn.parent.parts[-1]
+
+        else:
+            self.roifile_fn = roifile_fn / roifile_fn.parts[-1]
+            self.stem = self.roifile_fn.stem
+
+            print(self.roifile_fn)
+
+        print(self.stem)
         self.image, self.image_props = self._load_images()
 
     def _load_images(self):
         """Loading sequence of tif files
         array sequence is CZYX
         """
-        print(self.roifile_fn.parent, self.stem)
+        print("Loading images:", self.roifile_fn.parent, self.stem)
-        fns = list(self.roifile_fn.parent.glob(f"{self.stem}*.tif*"))
+        fns = list(self.roifile_fn.parent.glob(f"{self.stem.lower()}*.tif*"))
         stems = [fn.stem.split(self.stem)[-1] for fn in fns]
         n_ch = len(set([stem.split("-ch")[-1].split("t")[0] for stem in stems]))
         n_p = len(set([stem.split("-")[0] for stem in stems]))
         n_t = len(set([stem.split("t")[1] for stem in stems]))
-        print(n_ch, n_p, n_t)
 
         with TiffFile(fns[0]) as tif:
             img = tif.asarray()
@@ -42,6 +57,7 @@ class UT:
                 "height": h,
                 "dtype": dtype,
             }
+        print("Image props", self.image_props)
 
         image_stack = np.zeros((n_ch, n_p, w, h), dtype=dtype)
         for fn in fns:
@@ -49,7 +65,7 @@ class UT:
                 img = tif.asarray()
                 stem = fn.stem.split(self.stem)[-1]
                 ch = int(stem.split("-ch")[-1].split("t")[0])
-                p = int(stem.split("-")[0].lstrip("p"))
+                p = int(stem.split("-")[0].split("p")[1])
                 t = int(stem.split("t")[1])
                 print(fn.stem, "ch", ch, "p", p, "t", t)
                 image_stack[ch - 1, p - 1] = img
@@ -82,11 +98,20 @@ class UT:
     ):
         """Export rois to a file"""
         with open(path / subfolder / f"{self.stem}.txt", "w") as f:
-            for roi in self.rois:
+            for i, roi in enumerate(self.rois):
-                # TODO add image coordinates normalization
+                rc = roi.subpixel_coordinates
-                coords = ""
+                if rc is None:
-                for x, y in roi.subpixel_coordinates:
+                    print(f"No coordinates: {self.roifile_fn}, element {i}, out of {len(self.rois)}")
-                    coords += f"{x/self.width} {y/self.height}"
+                    continue
+                xmn, ymn = rc.min(axis=0)
+                xmx, ymx = rc.max(axis=0)
+                xc = (xmn + xmx) / 2
+                yc = (ymn + ymx) / 2
+                bw = xmx - xmn
+                bh = ymx - ymn
+                coords = f"{xc/self.width} {yc/self.height} {bw/self.width} {bh/self.height} "
+                for x, y in rc:
+                    coords += f"{x/self.width} {y/self.height} "
                 f.write(f"{class_index} {coords}\n")
 
         return
@@ -104,6 +129,7 @@ class UT:
             self.image = np.max(self.image[channel], axis=0)
             print(self.image.shape)
 
+        print(path / subfolder / f"{self.stem}.tif")
         with TiffWriter(path / subfolder / f"{self.stem}.tif") as tif:
             tif.write(self.image)
 
@@ -112,11 +138,31 @@ if __name__ == "__main__":
     import argparse
 
     parser = argparse.ArgumentParser()
-    parser.add_argument("input", type=Path)
+    parser.add_argument("-i", "--input", nargs="*", type=Path)
-    parser.add_argument("output", type=Path)
+    parser.add_argument("-o", "--output", type=Path)
+    parser.add_argument(
+        "--no-labels",
+        action="store_false",
+        help="Source does not have labels, export only images",
+    )
     args = parser.parse_args()
 
-    for rfn in args.input.glob("*.zip"):
+    # print(args)
-        ut = UT(rfn)
+    # aa
+
+    for path in args.input:
+        print("Path:", path)
+        if not args.no_labels:
+            print("No labels")
+            ut = UT(path, args.no_labels)
+            ut.export_image(args.output, plane_mode="max projection", channel=0)
+
+        else:
+            for rfn in Path(path).glob("*.zip"):
+                # if Path(path).suffix == ".zip":
+                print("Roi FN:", rfn)
+                ut = UT(rfn, args.no_labels)
                 ut.export_rois(args.output, class_index=0)
                 ut.export_image(args.output, plane_mode="max projection", channel=0)
+
+        print()

src/utils/image_splitter.py

@@ -0,0 +1,353 @@
+import numpy as np
+
+from pathlib import Path
+from tifffile import imread, imwrite
+from shapely.geometry import LineString
+from copy import deepcopy
+from scipy.ndimage import zoom
+
+
+# debug
+from src.utils.image import Image
+from show_yolo_seg import draw_annotations
+
+import pylab as plt
+import cv2
+
+
+class Label:
+    def __init__(self, yolo_annotation: str):
+        class_id, bbox, polygon = self.parse_yolo_annotation(yolo_annotation)
+        self.class_id = class_id
+        self.bbox = bbox
+        self.polygon = polygon
+
+    def parse_yolo_annotation(self, yolo_annotation: str):
+        class_id, *coords = yolo_annotation.split()
+        class_id = int(class_id)
+        bbox = np.array(coords[:4], dtype=np.float32)
+        polygon = np.array(coords[4:], dtype=np.float32).reshape(-1, 2) if len(coords) > 4 else None
+        if not any(np.isclose(polygon[0], polygon[-1])):
+            polygon = np.vstack([polygon, polygon[0]])
+        return class_id, bbox, polygon
+
+    def offset_label(
+        self,
+        img_w,
+        img_h,
+        distance: float = 1.0,
+        cap_style: int = 2,
+        join_style: int = 2,
+    ):
+        if self.polygon is None:
+            self.bbox = np.array(
+                [
+                    self.bbox[0] - distance if self.bbox[0] - distance > 0 else 0,
+                    self.bbox[1] - distance if self.bbox[1] - distance > 0 else 0,
+                    self.bbox[2] + distance if self.bbox[2] + distance < 1 else 1,
+                    self.bbox[3] + distance if self.bbox[3] + distance < 1 else 1,
+                ],
+                dtype=np.float32,
+            )
+            return self.bbox
+
+        def coords_are_normalized(coords):
+            # If every coordinate is between 0 and 1 (inclusive-ish), assume normalized
+            print(coords)
+            # if not coords:
+            #     return False
+            return all(max(coords.flatten)) <= 1.001
+
+        def poly_to_pts(coords, img_w, img_h):
+            # coords: [x1 y1 x2 y2 ...] either normalized or absolute
+            # if coords_are_normalized(coords):
+            coords = [coords[i] * (img_w if i % 2 == 0 else img_h) for i in range(len(coords))]
+            pts = np.array(coords, dtype=np.int32).reshape(-1, 2)
+            return pts
+
+        pts = poly_to_pts(self.polygon, img_w, img_h)
+        line = LineString(pts)
+        # Buffer distance in pixels
+        buffered = line.buffer(distance=distance, cap_style=cap_style, join_style=join_style)
+        self.polygon = np.array(buffered.exterior.coords, dtype=np.float32) / (img_w, img_h)
+        xmn, ymn = self.polygon.min(axis=0)
+        xmx, ymx = self.polygon.max(axis=0)
+        xc = (xmn + xmx) / 2
+        yc = (ymn + ymx) / 2
+        bw = xmx - xmn
+        bh = ymx - ymn
+        self.bbox = np.array([xc, yc, bw, bh], dtype=np.float32)
+
+        return self.bbox, self.polygon
+
+    def translate(self, x, y, scale_x, scale_y):
+        self.bbox[0] -= x
+        self.bbox[0] *= scale_x
+        self.bbox[1] -= y
+        self.bbox[1] *= scale_y
+        self.bbox[2] *= scale_x
+        self.bbox[3] *= scale_y
+        if self.polygon is not None:
+            self.polygon[:, 0] -= x
+            self.polygon[:, 0] *= scale_x
+            self.polygon[:, 1] -= y
+            self.polygon[:, 1] *= scale_y
+
+    def in_range(self, hrange, wrange):
+        xc, yc, h, w = self.bbox
+        x1 = xc - w / 2
+        y1 = yc - h / 2
+        x2 = xc + w / 2
+        y2 = yc + h / 2
+        truth_val = (
+            xc >= wrange[0]
+            and x1 <= wrange[1]
+            and x2 >= wrange[0]
+            and x2 <= wrange[1]
+            and y1 >= hrange[0]
+            and y1 <= hrange[1]
+            and y2 >= hrange[0]
+            and y2 <= hrange[1]
+        )
+
+        print(x1, x2, wrange, y1, y2, hrange, truth_val)
+        return truth_val
+
+    def to_string(self, bbox: list = None, polygon: list = None):
+        if bbox is None:
+            bbox = self.bbox
+        if polygon is None:
+            polygon = self.polygon
+        coords = " ".join([f"{x:.6f}" for x in self.bbox])
+        if self.polygon is not None:
+            coords += " " + " ".join([f"{x:.6f} {y:.6f}" for x, y in self.polygon])
+        return f"{self.class_id} {coords}"
+
+    def __str__(self):
+        return f"Class: {self.class_id}, BBox: {self.bbox}, Polygon: {self.polygon}"
+
+
+class YoloLabelReader:
+    def __init__(self, label_path: Path):
+        self.label_path = label_path
+        self.labels = self._read_labels()
+
+    def _read_labels(self):
+        with open(self.label_path, "r") as f:
+            labels = [Label(line) for line in f.readlines()]
+
+        return labels
+
+    def get_labels(self, hrange, wrange):
+        """hrange and wrange are tuples of (start, end) normalized to [0, 1]"""
+        labels = []
+        # print(hrange, wrange)
+        for lbl in self.labels:
+            # print(lbl)
+            if lbl.in_range(hrange, wrange):
+                labels.append(lbl)
+        return labels if len(labels) > 0 else None
+
+    def __get_item__(self, index):
+        return self.labels[index]
+
+    def __len__(self):
+        return len(self.labels)
+
+    def __iter__(self):
+        return iter(self.labels)
+
+
+class ImageSplitter:
+    def __init__(self, image_path: Path, label_path: Path):
+        self.image = imread(image_path)
+        self.image_path = image_path
+        self.label_path = label_path
+        if not label_path.exists():
+            print(f"Label file {label_path} not found")
+            self.labels = None
+        else:
+            self.labels = YoloLabelReader(label_path)
+
+    def split_into_tiles(self, patch_size: tuple = (2, 2)):
+        """Split image into patches of size patch_size"""
+        hstep, wstep = (
+            self.image.shape[0] // patch_size[0],
+            self.image.shape[1] // patch_size[1],
+        )
+        h, w = self.image.shape[:2]
+
+        for i in range(patch_size[0]):
+            for j in range(patch_size[1]):
+                tile_reference = f"i{i}j{j}"
+                hrange = (i * hstep / h, (i + 1) * hstep / h)
+                wrange = (j * wstep / w, (j + 1) * wstep / w)
+                tile = self.image[i * hstep : (i + 1) * hstep, j * wstep : (j + 1) * wstep]
+
+                labels = None
+                if self.labels is not None:
+                    labels = deepcopy(self.labels.get_labels(hrange, wrange))
+                    print(id(labels))
+
+                    if labels is not None:
+                        print(hrange[0], wrange[0])
+                        for l in labels:
+                            print(l.bbox)
+                        [l.translate(wrange[0], hrange[0], 2, 2) for l in labels]
+                        print("translated")
+                        for l in labels:
+                            print(l.bbox)
+
+                # print(labels)
+                yield tile_reference, tile, labels
+
+    def split_respective_to_label(self, padding: int = 67):
+        if self.labels is None:
+            raise ValueError("No labels found. Only images having labels can be split.")
+
+        for i, label in enumerate(self.labels):
+            tile_reference = f"_lbl-{i+1:02d}"
+            # print(label.bbox)
+
+            xc_norm, yc_norm, h_norm, w_norm = label.bbox  # normalized coords
+            xc, yc, h, w = [
+                int(np.round(f))
+                for f in [
+                    xc_norm * self.image.shape[1],
+                    yc_norm * self.image.shape[0],
+                    h_norm * self.image.shape[0],
+                    w_norm * self.image.shape[1],
+                ]
+            ]  # image coords
+
+            # print("img coords:", xc, yc, h, w)
+            pad_xneg = padding + 1  # int(w / 2) + padding
+            pad_xpos = padding  # int(w / 2) + padding
+            pad_yneg = padding + 1  # int(h / 2) + padding
+            pad_ypos = padding  # int(h / 2) + padding
+            if xc - pad_xneg < 0:
+                pad_xneg = xc
+            if pad_xpos + xc > self.image.shape[1]:
+                pad_xpos = self.image.shape[1] - xc
+            if yc - pad_yneg < 0:
+                pad_yneg = yc
+            if pad_ypos + yc > self.image.shape[0]:
+                pad_ypos = self.image.shape[0] - yc
+
+            # print("pads:", pad_xneg, pad_xpos, pad_yneg, pad_ypos)
+
+            tile = self.image[
+                yc - pad_yneg : yc + pad_ypos,
+                xc - pad_xneg : xc + pad_xpos,
+            ]
+            ny, nx = tile.shape
+            x_offset = pad_xneg
+            y_offset = pad_yneg
+
+            # print("tile shape:", tile.shape)
+
+            yolo_annotation = f"{label.class_id} {x_offset/nx} {y_offset/ny} {h_norm} {w_norm} "
+            print(yolo_annotation)
+            yolo_annotation += " ".join(
+                [
+                    f"{(x*self.image.shape[1]-(xc - x_offset))/nx:.6f} {(y*self.image.shape[0]-(yc-y_offset))/ny:.6f}"
+                    for x, y in label.polygon
+                ]
+            )
+            new_label = Label(yolo_annotation=yolo_annotation)
+
+            yield tile_reference, tile, [new_label]
+
+
+def main(args):
+
+    if args.output:
+        args.output.mkdir(exist_ok=True, parents=True)
+        (args.output / "images").mkdir(exist_ok=True)
+        (args.output / "images-zoomed").mkdir(exist_ok=True)
+        (args.output / "labels").mkdir(exist_ok=True)
+
+    for image_path in (args.input / "images").glob("*.tif"):
+        data = ImageSplitter(
+            image_path=image_path,
+            label_path=(args.input / "labels" / image_path.stem).with_suffix(".txt"),
+        )
+
+        if args.split_around_label:
+            data = data.split_respective_to_label(padding=args.padding)
+        else:
+            data = data.split_into_tiles(patch_size=args.patch_size)
+
+        for tile_reference, tile, labels in data:
+            print()
+            print(tile_reference, tile.shape, labels)  # len(labels) if labels else None)
+
+            # { debug
+            debug = False
+            if debug:
+                plt.figure(figsize=(10, 10 * tile.shape[0] / tile.shape[1]))
+                if labels is None:
+                    plt.imshow(tile, cmap="gray")
+                    plt.axis("off")
+                    plt.title(f"{image_path.name} ({tile_reference})")
+                    plt.show()
+                    continue
+
+                print(labels[0].bbox)
+                # Draw annotations
+                out = draw_annotations(
+                    cv2.cvtColor((tile / tile.max() * 255).astype(np.uint8), cv2.COLOR_GRAY2BGR),
+                    [l.to_string() for l in labels],
+                    alpha=0.1,
+                )
+
+                # Convert BGR -> RGB for matplotlib display
+                out_rgb = cv2.cvtColor(out, cv2.COLOR_BGR2RGB)
+                plt.imshow(out_rgb)
+                plt.axis("off")
+                plt.title(f"{image_path.name} ({tile_reference})")
+                plt.show()
+            # } debug
+
+            if args.output:
+                imwrite(args.output / "images" / f"{image_path.stem}_{tile_reference}.tif", tile)
+                scale = 5
+                tile_zoomed = zoom(tile, zoom=scale)
+                imwrite(args.output / "images-zoomed" / f"{image_path.stem}_{tile_reference}.tif", tile_zoomed)
+
+                if labels is not None:
+                    with open(args.output / "labels" / f"{image_path.stem}_{tile_reference}.txt", "w") as f:
+                        for label in labels:
+                            label.offset_label(tile.shape[1], tile.shape[0])
+                            f.write(label.to_string() + "\n")
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-i", "--input", type=Path)
+    parser.add_argument("-o", "--output", type=Path)
+    parser.add_argument(
+        "-p",
+        "--patch-size",
+        nargs=2,
+        type=int,
+        default=[2, 2],
+        help="Number of patches along height and width, rows and columns, respectively",
+    )
+    parser.add_argument(
+        "-sal",
+        "--split-around-label",
+        action="store_true",
+        help="If enabled, the image will be split around the label and for each label, a separate image will be created.",
+    )
+    parser.add_argument(
+        "--padding",
+        type=int,
+        default=67,
+        help="Padding around the label when splitting around the label.",
+    )
+    args = parser.parse_args()
+
+    main(args)

src/utils/show_yolo_seg.py

@@ -0,0 +1 @@
+../../tests/show_yolo_seg.py

src/utils/ultralytics_16bit_patch.py

@@ -0,0 +1,156 @@
+"""Ultralytics runtime patches for 16-bit TIFF training.
+
+Goals:
+- Use `tifffile` to decode `.tif/.tiff` reliably (OpenCV can silently drop bit-depth depending on codec).
+- Preserve 16-bit data through the dataloader as `uint16` tensors.
+- Fix Ultralytics trainer normalization (default divides by 255) to scale `uint16` correctly.
+- Avoid uint8-forcing augmentations by recommending/setting hyp values (handled by caller).
+
+This module is intended to be imported/called **before** instantiating/using YOLO.
+"""
+
+from __future__ import annotations
+
+from typing import Optional
+
+from src.utils.logger import get_logger
+
+logger = get_logger(__name__)
+
+
+def apply_ultralytics_16bit_tiff_patches(*, force: bool = False) -> None:
+    """Apply runtime monkey-patches to Ultralytics to better support 16-bit TIFFs.
+
+    This function is safe to call multiple times.
+
+    Args:
+        force: If True, re-apply patches even if already applied.
+    """
+
+    # Import inside function to ensure patching occurs before YOLO model/dataset is created.
+    import os
+
+    import cv2
+    import numpy as np
+
+    # import tifffile
+    import torch
+    from src.utils.image import Image
+
+    from ultralytics.utils import patches as ul_patches
+
+    already_patched = getattr(ul_patches.imread, "__name__", "") == "tifffile_imread"
+    if already_patched and not force:
+        return
+
+    _original_imread = ul_patches.imread
+
+    def tifffile_imread(filename: str, flags: int = cv2.IMREAD_COLOR, pseudo_rgb: bool = True) -> Optional[np.ndarray]:
+        """Replacement for [`ultralytics.utils.patches.imread()`](venv/lib/python3.12/site-packages/ultralytics/utils/patches.py:20).
+
+        - For `.tif/.tiff`, uses `tifffile.imread()` and preserves dtype (e.g. uint16).
+        - For other formats, falls back to Ultralytics' original implementation.
+        - Always returns HWC (3 dims). For grayscale, returns (H, W, 1) or (H, W, 3) depending on requested flags.
+        """
+        # print("here")
+        # return _original_imread(filename, flags)
+        ext = os.path.splitext(filename)[1].lower()
+        if ext in (".tif", ".tiff"):
+            arr = Image(filename).get_qt_rgb()[:, :, :3]
+
+            # Normalize common shapes:
+            # - (H, W) -> (H, W, 1)
+            # - (C, H, W) -> (H, W, C) (heuristic)
+            if arr is None:
+                return None
+            if arr.ndim == 3 and arr.shape[0] in (1, 3, 4) and arr.shape[0] < arr.shape[1]:
+                arr = np.transpose(arr, (1, 2, 0))
+            if arr.ndim == 2:
+                arr = arr[..., None]
+
+            # Ensure contiguous array for downstream OpenCV ops.
+            # logger.info(f"Loading with monkey-patched imread: {filename}")
+            arr = arr.astype(np.float32)
+            arr /= arr.max()
+            arr *= 2**16 - 1
+            arr = arr.astype(np.uint16)
+            return np.ascontiguousarray(arr)
+
+        # logger.info(f"Loading with original imread: {filename}")
+        return _original_imread(filename, flags)
+
+    # Patch the canonical reference.
+    ul_patches.imread = tifffile_imread
+
+    # Patch common module-level imports (some Ultralytics modules do `from ... import imread`).
+    # Importing these modules is safe and helps ensure the patched function is used.
+    try:
+        import ultralytics.data.base as _ul_base
+
+        _ul_base.imread = tifffile_imread
+    except Exception:
+        pass
+    try:
+        import ultralytics.data.loaders as _ul_loaders
+
+        _ul_loaders.imread = tifffile_imread
+    except Exception:
+        pass
+
+    # Patch trainer normalization: default divides by 255 regardless of input dtype.
+    from ultralytics.models.yolo.detect import train as detect_train
+
+    _orig_preprocess_batch = detect_train.DetectionTrainer.preprocess_batch
+
+    def preprocess_batch_16bit(self, batch: dict) -> dict:  # type: ignore[override]
+        # Start from upstream behavior to keep device placement + multiscale identical,
+        # but replace the 255 division with dtype-aware scaling.
+        logger.info(f"Preprocessing batch with monkey-patched preprocess_batch")
+        for k, v in batch.items():
+            if isinstance(v, torch.Tensor):
+                batch[k] = v.to(self.device, non_blocking=self.device.type == "cuda")
+
+        img = batch.get("img")
+        if isinstance(img, torch.Tensor):
+            # Decide scaling denom based on dtype (avoid expensive reductions if possible).
+            if img.dtype == torch.uint8:
+                denom = 255.0
+            elif img.dtype == torch.uint16:
+                denom = 65535.0
+            elif img.dtype.is_floating_point:
+                # Assume already in 0-1 range if float.
+                denom = 1.0
+            else:
+                # Generic integer fallback.
+                try:
+                    denom = float(torch.iinfo(img.dtype).max)
+                except Exception:
+                    denom = 255.0
+
+            batch["img"] = img.float() / denom
+
+        # Multi-scale branch copied from upstream to avoid re-introducing `/255` scaling.
+        if getattr(self.args, "multi_scale", False):
+            import math
+            import random
+
+            import torch.nn as nn
+
+            imgs = batch["img"]
+            sz = (
+                random.randrange(int(self.args.imgsz * 0.5), int(self.args.imgsz * 1.5 + self.stride))
+                // self.stride
+                * self.stride
+            )
+            sf = sz / max(imgs.shape[2:])
+            if sf != 1:
+                ns = [math.ceil(x * sf / self.stride) * self.stride for x in imgs.shape[2:]]
+                imgs = nn.functional.interpolate(imgs, size=ns, mode="bilinear", align_corners=False)
+            batch["img"] = imgs
+
+        return batch
+
+    detect_train.DetectionTrainer.preprocess_batch = preprocess_batch_16bit
+
+    # Tag function to make it easier to detect patch state.
+    setattr(detect_train.DetectionTrainer.preprocess_batch, "_ultralytics_16bit_patch", True)

tests/show_yolo_seg.py

@@ -0,0 +1,223 @@
+#!/usr/bin/env python3
+"""
+show_yolo_seg.py
+
+Usage:
+    python show_yolo_seg.py /path/to/image.jpg /path/to/labels.txt
+
+Supports:
+ - Segmentation polygons: "class x1 y1 x2 y2 ... xn yn"
+ - YOLO bbox lines as fallback: "class x_center y_center width height"
+Coordinates can be normalized [0..1] or absolute pixels (auto-detected).
+"""
+import sys
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+import argparse
+from pathlib import Path
+import random
+from shapely.geometry import LineString
+
+from src.utils.image import Image
+
+
+def parse_label_line(line):
+    parts = line.strip().split()
+    if not parts:
+        return None
+    cls = int(float(parts[0]))
+    coords = [float(x) for x in parts[1:]]
+    return cls, coords
+
+
+def coords_are_normalized(coords):
+    # If every coordinate is between 0 and 1 (inclusive-ish), assume normalized
+    if not coords:
+        return False
+    return max(coords) <= 1.001
+
+
+def yolo_bbox_to_xyxy(coords, img_w, img_h):
+    # coords: [xc, yc, w, h] normalized or absolute
+    xc, yc, w, h = coords[:4]
+    if max(coords) <= 1.001:
+        xc *= img_w
+        yc *= img_h
+        w *= img_w
+        h *= img_h
+    x1 = int(round(xc - w / 2))
+    y1 = int(round(yc - h / 2))
+    x2 = int(round(xc + w / 2))
+    y2 = int(round(yc + h / 2))
+    return x1, y1, x2, y2
+
+
+def poly_to_pts(coords, img_w, img_h):
+    # coords: [x1 y1 x2 y2 ...] either normalized or absolute
+    if coords_are_normalized(coords[4:]):
+        coords = [coords[i] * (img_w if i % 2 == 0 else img_h) for i in range(len(coords))]
+    pts = np.array(coords, dtype=np.int32).reshape(-1, 2)
+    return pts
+
+
+def random_color_for_class(cls):
+    random.seed(cls)  # deterministic per class
+    return (
+        0,
+        0,
+        255,
+    )  # tuple(int(x) for x in np.array([random.randint(0, 255) for _ in range(3)]))
+
+
+def draw_annotations(img, labels, alpha=0.4, draw_bbox_for_poly=True):
+    # img: BGR numpy array
+    overlay = img.copy()
+    h, w = img.shape[:2]
+    for line in labels:
+        if isinstance(line, str):
+            cls, coords = parse_label_line(line)
+        if isinstance(line, tuple):
+            cls, coords = line
+
+        if not coords:
+            continue
+        # polygon case (>=6 coordinates)
+        if len(coords) >= 6:
+            color = random_color_for_class(cls)
+
+            x1, y1, x2, y2 = yolo_bbox_to_xyxy(coords[:4], w, h)
+            print(x1, y1, x2, y2)
+            cv2.rectangle(img, (x1, y1), (x2, y2), color, 1)
+
+            pts = poly_to_pts(coords[4:], w, h)
+            # line = LineString(pts)
+            # # Buffer distance in pixels
+            # buffered = line.buffer(3, cap_style=2, join_style=2)
+            # coords = np.array(buffered.exterior.coords, dtype=np.int32)
+            # cv2.fillPoly(overlay, [coords], color=(255, 255, 255))
+
+            # fill on overlay
+            cv2.fillPoly(overlay, [pts], color)
+            # outline on base image
+            cv2.polylines(img, [pts], isClosed=True, color=color, thickness=1)
+            # put class text at first point
+            x, y = int(pts[0, 0]), int(pts[0, 1]) - 6
+            if 0:
+                cv2.putText(
+                    img,
+                    str(cls),
+                    (x, max(6, y)),
+                    cv2.FONT_HERSHEY_SIMPLEX,
+                    0.6,
+                    (255, 255, 255),
+                    2,
+                    cv2.LINE_AA,
+                )
+
+        # YOLO bbox case (4 coords)
+        elif len(coords) == 4:
+            x1, y1, x2, y2 = yolo_bbox_to_xyxy(coords, w, h)
+            color = random_color_for_class(cls)
+            cv2.rectangle(img, (x1, y1), (x2, y2), color, 2)
+            cv2.putText(
+                img,
+                str(cls),
+                (x1, max(6, y1 - 4)),
+                cv2.FONT_HERSHEY_SIMPLEX,
+                0.6,
+                (255, 255, 255),
+                2,
+                cv2.LINE_AA,
+            )
+        else:
+            # Unknown / invalid format, skip
+            continue
+
+    # blend overlay for filled polygons
+    cv2.addWeighted(overlay, alpha, img, 1 - alpha, 0, img)
+    return img
+
+
+def load_labels_file(label_path):
+    labels = []
+    with open(label_path, "r") as f:
+        for raw in f:
+            line = raw.strip()
+            if not line:
+                continue
+            parsed = parse_label_line(line)
+            if parsed:
+                labels.append(parsed)
+    return labels
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Show YOLO segmentation / polygon annotations")
+    parser.add_argument("image", type=str, help="Path to image file")
+    parser.add_argument("--labels", type=str, help="Path to YOLO label file (polygons)")
+    parser.add_argument("--alpha", type=float, default=0.4, help="Polygon fill alpha (0..1)")
+    parser.add_argument("--no-bbox", action="store_true", help="Don't draw bounding boxes for polygons")
+    args = parser.parse_args()
+
+    print(args)
+
+    img_path = Path(args.image)
+    if args.labels:
+        lbl_path = Path(args.labels)
+    else:
+        lbl_path = img_path.with_suffix(".txt")
+        lbl_path = Path(str(lbl_path).replace("images", "labels"))
+
+    if not img_path.exists():
+        print("Image not found:", img_path)
+        sys.exit(1)
+    if not lbl_path.exists():
+        print("Label file not found:", lbl_path)
+        sys.exit(1)
+
+    # img = cv2.imread(str(img_path), cv2.IMREAD_COLOR)
+    img = (Image(img_path).get_qt_rgb() * 255).astype(np.uint8)
+
+    if img is None:
+        print("Could not load image:", img_path)
+        sys.exit(1)
+
+    labels = load_labels_file(str(lbl_path))
+    if not labels:
+        print("No labels parsed from", lbl_path)
+        # continue and just show image
+    out = draw_annotations(img.copy(), labels, alpha=args.alpha, draw_bbox_for_poly=(not args.no_bbox))
+
+    lclass, coords = labels[0]
+    print(lclass, coords)
+    bbox = coords[:4]
+    print("bbox", bbox)
+    bbox = np.array(bbox) * np.array([img.shape[1], img.shape[0], img.shape[1], img.shape[0]])
+    yc, xc, h, w = bbox
+    print("bbox", bbox)
+    polyline = np.array(coords[4:]).reshape(-1, 2) * np.array([img.shape[1], img.shape[0]])
+    print("pl", coords[4:])
+    print("pl", polyline)
+
+    # Convert BGR -> RGB for matplotlib display
+    # out_rgb = cv2.cvtColor(out, cv2.COLOR_BGR2RGB)
+    out_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    # out_rgb = Image()
+    plt.figure(figsize=(10, 10 * out.shape[0] / out.shape[1]))
+    plt.imshow(out_rgb)
+    plt.plot(polyline[:, 0], polyline[:, 1], "y", linewidth=2)
+    plt.plot(
+        [yc - h / 2, yc - h / 2, yc + h / 2, yc + h / 2, yc - h / 2],
+        [xc - w / 2, xc + w / 2, xc + w / 2, xc - w / 2, xc - w / 2],
+        "r",
+        linewidth=2,
+    )
+
+    # plt.axis("off")
+    plt.title(f"{img_path.name} ({lbl_path.name})")
+    plt.show()
+
+
+if __name__ == "__main__":
+    main()