Hello! I am training a neural network and my dataset is in COCO format. Now I would like to change my input images’ resolution to a smaller one since my resources in terms of memory is only limited. I tried using cv2.resize but it does not work. Can anyone suggest any ways to achieve what I want? by the way here is my code loading my images
import os
import cv2
import torch
import torch.utils.data
import torchvision
from PIL import Image
from pycocotools.coco import COCO
class myOwnDataset(torch.utils.data.Dataset):
IMG_SIZE = 100
def __init__(self, root, annotation, transforms=None):
self.root = root
self.transforms = transforms
self.coco = COCO(annotation)
self.ids = list(sorted(self.coco.imgs.keys()))
def __getitem__(self, index):
# Own coco file
coco = self.coco
# Image ID
img_id = self.ids[index]
# List: get annotation id from coco
ann_ids = coco.getAnnIds(imgIds=img_id)
# Dictionary: target coco_annotation file for an image
coco_annotation = coco.loadAnns(ann_ids)
# path for input image
path = coco.loadImgs(img_id)[0]['file_name']
# open the input image
img = Image.open(os.path.join(self.root, path))
img = cv2.resize(img, (self.IMG_SIZE, self.IMG_SIZE), interpolation = cv2.INTER_AREA)
# number of objects in the image
num_objs = len(coco_annotation)
# Bounding boxes for objects
# In coco format, bbox = [xmin, ymin, width, height]
# In pytorch, the input should be [xmin, ymin, xmax, ymax]
boxes = []
for i in range(num_objs):
xmin = coco_annotation[i]['bbox'][0]
ymin = coco_annotation[i]['bbox'][1]
xmax = xmin + coco_annotation[i]['bbox'][2]
ymax = ymin + coco_annotation[i]['bbox'][3]
boxes.append([xmin, ymin, xmax, ymax])
boxes = torch.as_tensor(boxes, dtype=torch.float32)
# Labels (In my case, I only one class: target class or background)
labels = torch.ones((num_objs,), dtype=torch.int64)
# Tensorise img_id
img_id = torch.tensor([img_id])
# Size of bbox (Rectangular)
areas = []
for i in range(num_objs):
areas.append(coco_annotation[i]['area'])
areas = torch.as_tensor(areas, dtype=torch.float32)
# Iscrowd
iscrowd = torch.zeros((num_objs,), dtype=torch.int64)
# Annotation is in dictionary format
my_annotation = {}
my_annotation["boxes"] = boxes
my_annotation["labels"] = labels
my_annotation["image_id"] = img_id
my_annotation["area"] = areas
my_annotation["iscrowd"] = iscrowd
if self.transforms is not None:
img = self.transforms(img)
return img, my_annotation
def __len__(self):
return len(self.ids)
# In my case, just added ToTensor
def get_transform():
custom_transforms = []
custom_transforms.append(torchvision.transforms.ToTensor())
return torchvision.transforms.Compose(custom_transforms)
train_data_dir = '/content/TACO/data'
train_coco = '/content/TACO/data/annotations.json'
# create own Dataset
my_dataset = myOwnDataset(root=train_data_dir,
annotation=train_coco,
transforms=get_transform()
)
# collate_fn needs for batch
def collate_fn(batch):
return tuple(zip(*batch))
# Batch size
train_batch_size = 1
# own DataLoader
data_loader = torch.utils.data.DataLoader(my_dataset,
batch_size=train_batch_size,
shuffle=True,
num_workers=0,
collate_fn=collate_fn)