Source code for deepforest.dataset

Dataset model

During training, the model expects both the input tensors, as well as a targets (list of dictionary), containing:

boxes (FloatTensor[N, 4]): the ground-truth boxes in [x1, y1, x2, y2] format, with values between 0 and H and 0 and W

labels (Int64Tensor[N]): the class label for each ground-truth box

import os
import pandas as pd
import numpy as np
from import Dataset
import albumentations as A
from albumentations import functional as F
from albumentations.pytorch import ToTensorV2
import torch
from PIL import Image

[docs]def get_transform(augment): """Albumentations transformation of bounding boxs""" if augment: transform = A.Compose([ A.HorizontalFlip(p=0.5), ToTensorV2() ], bbox_params=A.BboxParams(format='pascal_voc',label_fields=["category_ids"])) else: transform = A.Compose([ ToTensorV2() ], bbox_params=A.BboxParams(format='pascal_voc',label_fields=["category_ids"])) return transform
[docs]class TreeDataset(Dataset): def __init__(self, csv_file, root_dir, transforms=None, label_dict = {"Tree": 0}, train=True, preload_images=False): """ Args: csv_file (string): Path to a single csv file with annotations. root_dir (string): Directory with all the images. transform (callable, optional): Optional transform to be applied on a sample. label_dict: a dictionary where keys are labels from the csv column and values are numeric labels "Tree" -> 0 Returns: If train: path, image, targets else: image """ self.annotations = pd.read_csv(csv_file) self.root_dir = root_dir if transforms is None: self.transform = get_transform(augment=train) else: self.transform = transforms self.image_names = self.annotations.image_path.unique() self.label_dict = label_dict self.train = train self.image_converter = A.Compose([ToTensorV2()]) self.preload_images = preload_images #Pin data to memory if desired if self.preload_images: print("Pinning dataset to GPU memory") self.image_dict = {} for idx, x in enumerate(self.image_names): img_name = os.path.join(self.root_dir, x) image = np.array("RGB"))/255 self.image_dict[idx] = image.astype("float32") def __len__(self): return len(self.image_names) def __getitem__(self, idx): #Read image if not in memory if self.preload_images: image = self.image_dict[idx] else: img_name = os.path.join(self.root_dir, self.image_names[idx]) image = np.array("RGB"))/255 image = image.astype("float32") if self.train: # select annotations image_annotations = self.annotations[self.annotations.image_path == self.image_names[idx]] targets = {} targets["boxes"] = image_annotations[["xmin", "ymin", "xmax", "ymax"]].values.astype(float) # Labels need to be encoded targets["labels"] = image_annotations.label.apply( lambda x: self.label_dict[x]).values.astype(np.int64) #If image has no annotations, don't augment if np.sum(targets["boxes"]) == 0: boxes = boxes = torch.zeros((0, 4), dtype=torch.float32) labels = torch.from_numpy(targets["labels"]) #channels last image = np.rollaxis(image,2,0) image = torch.from_numpy(image) targets = {"boxes":boxes,"labels":labels} return self.image_names[idx], image, targets augmented = self.transform(image=image, bboxes=targets["boxes"], category_ids=targets["labels"]) image = augmented["image"] boxes = np.array(augmented["bboxes"]) boxes = torch.from_numpy(boxes) labels = np.array(augmented["category_ids"]) labels = torch.from_numpy(labels) targets = {"boxes":boxes,"labels":labels} return self.image_names[idx], image, targets else: #Mimic the train augmentation converted = self.image_converter(image=image) return converted["image"]