Fixes (#31)

* feat: enhance logging and add dataset generation for pith detection

* chore: bump version to 0.2.1

Author: tuke307

tree-disk-segmentation/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "tree-disk-segmentation"
-version = "0.2.0"
+version = "0.2.1"
 description = "A package for tree disk segmentation in images"
 authors = ["Tony <tonymeissner70@gmail.com>"]
 license = "MIT"

tree-disk-segmentation/src/treedisksegmentation/config.py

@@ -70,9 +70,11 @@ def _validate_and_set_paths(self):
         if self.model_path:
             model_path = Path(self.model_path)
             if not model_path.exists():
-                raise ValueError(f"Model file does not exist: {model_path}")
+                logger.warning(f"Model file does not exist: {model_path}")
+                # raise ValueError(f"Model file does not exist: {model_path}")
             if not model_path.is_file():
-                raise ValueError(f"Model path is not a file: {model_path}")
+                logger.warning(f"Model path is not a file: {model_path}")
+                # raise ValueError(f"Model path is not a file: {model_path}")
             self.model_path = model_path.resolve()
 
     def _log_change(self, param: str, old_value: Any, new_value: Any):
@@ -157,13 +159,5 @@ def configure(**kwargs):
 
     Args:
         **kwargs: Configuration parameters to update.
-
-    Example:
-        >>> configure(
-        ...     input_image="sample.jpg",
-        ...     method="apd_dl",
-        ...     model_path="weights.pth",
-        ...     st_w=7
-        ... )
     """
     config.update(**kwargs)

tree-disk-segmentation/src/treedisksegmentation/models/yolo.py

@@ -37,15 +37,13 @@ def run_yolo_detection(img_in: np.ndarray) -> Optional[List[np.ndarray]]:
         save_txt=config.save_results,
     )
 
-    results_len = len(results)
+    logger.info(f"YOLO detection complete.")
 
-    logger.info(f"YOLO detection complete, {results_len} log(s) found.")
-
-    if results_len == 0:
+    if results or len(results) == 0:
         logger.error("No results found in YOLO detection.")
         return None
 
-    # were just using the first result, so the first log found
+    # were just using the first result
     first_result = results[0]
 
     # xy = first_result.masks.xy  # mask in polygon format

tree-disk-segmentation/training/yolo.ipynb

@@ -174,6 +174,111 @@
     "\n",
     "    cv2.imwrite('segmented_objects.jpg', background)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# generate dataset for pith detection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_path = \"/Users/tonymeissner/Downloads/bilder\"\n",
+    "output_folder = \"output\"\n",
+    "model_path = \"/Users/tonymeissner/source/tree-disk-analyzer/tree-disk-segmentation/models/yolo11s-seg-tree.pt\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create output directory if it doesn't exist\n",
+    "os.makedirs(output_folder, exist_ok=True)\n",
+    "\n",
+    "# Function to check if file is a valid image\n",
+    "def is_valid_image(filepath):\n",
+    "    # Skip macOS resource fork files and hidden files\n",
+    "    if os.path.basename(filepath).startswith('._') or os.path.basename(filepath).startswith('.'):\n",
+    "        return False\n",
+    "    \n",
+    "    # Check file extension\n",
+    "    if not filepath.lower().endswith('.jpg'):\n",
+    "        return False\n",
+    "    \n",
+    "    # Verify file exists\n",
+    "    if not os.path.exists(filepath):\n",
+    "        return False\n",
+    "        \n",
+    "    return True\n",
+    "\n",
+    "# Find all valid image files\n",
+    "image_paths = []\n",
+    "for file in os.listdir(dataset_path):\n",
+    "    full_path = os.path.join(dataset_path, file)\n",
+    "    if os.path.isfile(full_path) and is_valid_image(full_path):\n",
+    "        image_paths.append(full_path)\n",
+    "\n",
+    "print(f\"Found {len(image_paths)} valid images\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import treedisksegmentation\n",
+    "import logging\n",
+    "import gc\n",
+    "\n",
+    "def process_image(image_path, output_folder, model_path):\n",
+    "    \"\"\"Process a single image with YOLO segmentation.\"\"\"\n",
+    "    try:\n",
+    "        output_path = os.path.join(output_folder, os.path.basename(image_path))\n",
+    "\n",
+    "        if os.path.exists(output_path):\n",
+    "            print(f\"Skipping existing output: {image_path}\")\n",
+    "            return True\n",
+    "\n",
+    "        treedisksegmentation.configure(\n",
+    "            model_path=model_path,\n",
+    "            input_image=image_path,\n",
+    "        )\n",
+    "\n",
+    "        result_image, masks = treedisksegmentation.run()\n",
+    "\n",
+    "        if masks is None:\n",
+    "            print(f\"No masks detected for image: {image_path}\")\n",
+    "            return False\n",
+    "\n",
+    "        result_image = cv2.cvtColor(result_image, cv2.COLOR_RGB2BGR)\n",
+    "        cv2.imwrite(output_path, result_image)\n",
+    "        print(f\"Successfully processed: {image_path}\")\n",
+    "        return True\n",
+    "\n",
+    "    except Exception as e:\n",
+    "        logging.error(f\"Error processing {image_path}: {str(e)}\", exc_info=True)\n",
+    "        return False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for image_path in image_paths:\n",
+    "    process_image(image_path, output_folder, model_path)\n",
+    "    print(f\"{image_paths.index(image_path)} / {len(image_paths)} images processed\")\n",
+    "    gc.collect()"
+   ]
   }
  ],
  "metadata": {