feat: create new datasets for generation and loading of pre rendered images

Former-commit-id: 4ccd97c3583c7def1e5e988a5beb8fcda7545fc9 [formerly 6a7e1eb28ade30f3e4b76337c209ac7e5b3b1cbf]
Former-commit-id: 8a552d696c6e47378a36a8680e3a09d151de25f1

src/data/dataloader.py

@@ -2,9 +2,9 @@ import albumentations as A
 import pytorch_lightning as pl
 import wandb
 from albumentations.pytorch import ToTensorV2
-from torch.utils.data import DataLoader
+from torch.utils.data import DataLoader, Subset
 
-from .dataset import RealDataset
+from .dataset import LabeledDataset, RealDataset
 
 
 def collate_fn(batch):
@@ -18,13 +18,13 @@ class Spheres(pl.LightningDataModule):
     def train_dataloader(self):
         transforms = A.Compose(
             [
-                A.Flip(),
-                A.ColorJitter(),
-                A.ToGray(p=0.01),
-                A.GaussianBlur(),
-                A.MotionBlur(),
-                A.ISONoise(),
-                A.ImageCompression(),
+                # A.Flip(),
+                # A.ColorJitter(),
+                # A.ToGray(p=0.01),
+                # A.GaussianBlur(),
+                # A.MotionBlur(),
+                # A.ISONoise(),
+                # A.ImageCompression(),
                 A.Normalize(
                     mean=[0.485, 0.456, 0.406],
                     std=[0.229, 0.224, 0.225],
@@ -40,7 +40,9 @@ class Spheres(pl.LightningDataModule):
             ),
         )
 
-        dataset = RealDataset(root="/dev/shm/TRAIN/", transforms=transforms)
+        dataset = LabeledDataset("/dev/shm/TRAIN/", transforms)
+        # dataset = Subset(dataset, range(6 * 200))  # subset for debugging purpose
+        # dataset = Subset(dataset, [0] * 320)  # overfit test
 
         return DataLoader(
             dataset,

src/data/dataset.py

@@ -1,4 +1,5 @@
 import os
+from pathlib import Path
 
 import albumentations as A
 import numpy as np
@@ -7,6 +8,37 @@ from PIL import Image
 from torch.utils.data import Dataset
 
 
+class SyntheticDataset(Dataset):
+    def __init__(self, image_dir, transform):
+        self.images = list(Path(image_dir).glob("**/*.jpg"))
+        self.transform = transform
+
+    def __len__(self):
+        return len(self.images)
+
+    def __getitem__(self, index):
+        # open and convert image
+        image = np.ascontiguousarray(
+            Image.open(
+                self.images[index],
+            ).convert("RGB"),
+            dtype=np.uint8,
+        )
+
+        # create empty mask of same size
+        mask = np.zeros(
+            (*image.shape[:2], 4),
+            dtype=np.uint8,
+        )
+
+        # augment image and mask
+        augmentations = self.transform(image=image, mask=mask)
+        image = augmentations["image"]
+        mask = augmentations["mask"]
+
+        return image, mask
+
+
 class RealDataset(Dataset):
     def __init__(self, root, transforms=None):
         self.root = root
@@ -16,7 +48,7 @@ class RealDataset(Dataset):
         self.imgs = list(sorted(os.listdir(os.path.join(root, "images"))))
         self.masks = list(sorted(os.listdir(os.path.join(root, "masks"))))
 
-        self.res = A.SmallestMaxSize(max_size=1024)
+        self.res = A.LongestMaxSize(max_size=1024)
 
     def __getitem__(self, idx):
         # create paths from ids
@@ -97,3 +129,83 @@ class RealDataset(Dataset):
 
     def __len__(self):
         return len(self.imgs)
+
+
+class LabeledDataset(Dataset):
+    def __init__(self, image_dir, transforms):
+        self.images = list(Path(image_dir).glob("**/*.jpg"))
+        self.transforms = transforms
+
+    def __len__(self):
+        return len(self.images)
+
+    def __getitem__(self, idx):
+        # open and convert image
+        image = np.ascontiguousarray(
+            Image.open(self.images[idx]).convert("RGB"),
+        )
+
+        # open and convert mask
+        mask_path = self.images[idx].parent.joinpath("MASK.PNG")
+        mask = np.ascontiguousarray(
+            Image.open(mask_path).convert("L"),
+        )
+
+        # get ids from mask
+        obj_ids = np.unique(mask)
+        obj_ids = obj_ids[1:]  # first id is the background, so remove it
+
+        # split the color-encoded mask into a set of binary masks
+        masks = mask == obj_ids[:, None, None]
+        masks = masks.astype(np.uint8)  # cast to uint8 for albumentations
+
+        # create bboxes from masks (pascal format)
+        num_objs = len(obj_ids)
+        bboxes = []
+        for i in range(num_objs):
+            pos = np.where(masks[i])
+            xmin = np.min(pos[1])
+            xmax = np.max(pos[1])
+            ymin = np.min(pos[0])
+            ymax = np.max(pos[0])
+            bboxes.append([xmin, ymin, xmax, ymax])
+
+        # convert arrays for albumentations
+        bboxes = torch.as_tensor(bboxes, dtype=torch.int64)
+        labels = torch.ones((num_objs,), dtype=torch.int64)  # assume there is only one class (id=1)
+        masks = list(np.asarray(masks))
+
+        if self.transforms is not None:
+            # arrange transform data
+            data = {
+                "image": image,
+                "labels": labels,
+                "bboxes": bboxes,
+                "masks": masks,
+            }
+            # apply transform
+            augmented = self.transforms(**data)
+            # get augmented data
+            image = augmented["image"]
+            bboxes = augmented["bboxes"]
+            labels = augmented["labels"]
+            masks = augmented["masks"]
+
+        bboxes = torch.as_tensor(bboxes, dtype=torch.int64)
+        labels = torch.as_tensor(labels, dtype=torch.int64)  # int64 required by torchvision maskrcnn
+        masks = torch.stack(masks)  # stack masks, required by torchvision maskrcnn
+
+        area = (bboxes[:, 3] - bboxes[:, 1]) * (bboxes[:, 2] - bboxes[:, 0])
+        image_id = torch.tensor([idx])
+        iscrowd = torch.zeros((num_objs,), dtype=torch.int64)  # assume all instances are not crowd
+
+        target = {
+            "boxes": bboxes,
+            "labels": labels,
+            "masks": masks,
+            "area": area,
+            "image_id": image_id,
+            "iscrowd": iscrowd,
+        }
+
+        return image, target