Jb/july24 (#148)

* parallelise tiling

* multispectral compatibility

* updated multi-class functionality

.gitattributes

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-*.pth filter=lfs diff=lfs merge=lfs -text

README.md

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 <!-- <a href="https://github.com/hhatto/autopep8"><img alt="Code style: autopep8" src="https://img.shields.io/badge/code%20style-autopep8-000000.svg"></a> -->
 
 
-Python package for automatic tree crown delineation based on Mask R-CNN. Pre-trained models can be picked in the [`model_garden`](https://github.com/PatBall1/detectree2/tree/master/model_garden).
-A tutorial on how to prepare data, train models and make predictions is available [here](https://patball1.github.io/detectree2/tutorial.html). For questions, collaboration proposals and requests for data email [James Ball](mailto:ball.jgc@gmail.com). Some example data is available for download [here](https://doi.org/10.5281/zenodo.8136161).
+Python package for automatic tree crown delineation in aerial RGB and multispectral imagery based on Mask R-CNN. Pre-trained models can be picked in the [`model_garden`](https://github.com/PatBall1/detectree2/tree/master/model_garden).
+A tutorial on how to prepare data, train models and make predictions is available [here](https://patball1.github.io/detectree2/tutorial.html). For questions, collaboration proposals and requests for data email [James Ball](mailto:ball.jgc@gmail.com). Some example data is available to download [here](https://doi.org/10.5281/zenodo.8136161).
 
 Detectree2是一个基于Mask R-CNN的自动树冠检测与分割的Python包。您可以在[`model_garden`](https://github.com/PatBall1/detectree2/tree/master/model_garden)中选择预训练模型。[这里](https://patball1.github.io/detectree2/tutorial.html)提供了如何准备数据、训练模型和进行预测的教程。如果有任何问题，合作提案或者需要样例数据，可以邮件联系[James Ball](mailto:ball.jgc@gmail.com)。一些示例数据可以在[这里](https://doi.org/10.5281/zenodo.8136161)下载。
 
 | <a href="https://www.conservation.cam.ac.uk/"><img src="./report/cam_logo.png" width="140"></a> | <sup> Code developed by James Ball, Seb Hickman, Thomas Koay, Oscar Jiang, Luran Wang, Panagiotis Ioannou, James Hinton and Matthew Archer in the [Forest Ecology and Conservation Group](https://coomeslab.org/) at the University of Cambridge. The Forest Ecology and Conservation Group is led by Professor David Coomes and is part of the University of Cambridge [Conservation Research Institute](https://www.conservation.cam.ac.uk/). </sup>|
 | :---: | :--- |
 
-<br/><br/>
-> [!NOTE]
-> To save bandwidth, trained models have been moved to [Zenodo](https://zenodo.org/records/10522461). Download models directly with `wget` or equivalent.
-
 
 ## Citation
 

detectree2/data_loading/custom.py

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+import cv2
+import detectron2.data.transforms as T
+import numpy as np
+import rasterio
+import torch
+from detectron2.structures import BitMasks, BoxMode, Instances
+from torch.utils.data import Dataset
+
+
+class CustomTIFFDataset(Dataset):
+    def __init__(self, annotations, transforms=None):
+        """
+        Args:
+            annotations (list): List of dictionaries containing image file paths and annotations.
+            transforms (callable, optional): Optional transform to be applied on a sample.
+        """
+        self.annotations = annotations
+        self.transforms = transforms
+
+    def __len__(self):
+        return len(self.annotations)
+
+    def __getitem__(self, idx):
+        # Load the TIFF image
+        img_info = self.annotations[idx]
+        with rasterio.open(img_info['file_name']) as src:
+            # Read all bands (assuming they are all needed)
+            image = src.read()
+            # Normalize or rescale if necessary
+            image = image.astype(np.float32) / 255.0  # Example normalization
+            # If the number of bands is not 3, reduce to 3 or handle accordingly
+            #if image.shape[0] > 3:
+            #    image = image[:3, :, :]  # Taking the first 3 bands (e.g., RGB)
+            # Convert to HWC format expected by Detectron2
+            #image = np.transpose(image, (1, 2, 0))
+
+        # Prepare annotations (this part needs to be adapted to your specific annotations)
+        target = {
+            "image_id": idx,
+            "annotations": img_info["annotations"],
+            "width": img_info["width"],
+            "height": img_info["height"],
+        }
+
+        if self.transforms is not None:
+            augmentations = T.AugmentationList(self.transforms)
+            image, target = augmentations(image, target)
+
+        # Convert to Detectron2-compatible format
+        image = torch.as_tensor(image.astype("float32").transpose(2, 0, 1))
+        instances = self.get_detectron_instances(target)
+
+        return image, instances
+
+    def get_detectron_instances(self, target):
+        """
+        Converts annotations into Detectron2's format.
+        This example assumes annotations are in COCO format, and you'll need to adapt it for your needs.
+        """
+        boxes = [obj["bbox"] for obj in target["annotations"]]
+        boxes = torch.as_tensor(boxes, dtype=torch.float32)
+        boxes = BoxMode.convert(boxes, BoxMode.XYWH_ABS, BoxMode.XYXY_ABS)
+
+        # Create BitMasks from the binary mask data (assuming the mask is a binary numpy array)
+        masks = [obj["segmentation"] for obj in target["annotations"]]  # Replace with actual mask loading
+        masks = BitMasks(torch.stack([torch.from_numpy(mask) for mask in masks]))
+
+        instances = Instances(
+            image_size=(target["height"], target["width"]),
+            gt_boxes=boxes,
+            gt_classes=torch.tensor([obj["category_id"] for obj in target["annotations"]], dtype=torch.int64),
+            gt_masks=masks
+        )
+        return instances