249 lines
11 KiB
Python
249 lines
11 KiB
Python
import os
|
|
import json
|
|
import albumentations
|
|
import numpy as np
|
|
from PIL import Image
|
|
from tqdm import tqdm
|
|
from torch.utils.data import Dataset
|
|
from abc import abstractmethod
|
|
|
|
|
|
class CocoBase(Dataset):
|
|
"""needed for (image, caption, segmentation) pairs"""
|
|
def __init__(self, size=None, dataroot="", datajson="", onehot_segmentation=False, use_stuffthing=False,
|
|
crop_size=None, force_no_crop=False, given_files=None, use_segmentation=True,crop_type=None):
|
|
self.split = self.get_split()
|
|
self.size = size
|
|
if crop_size is None:
|
|
self.crop_size = size
|
|
else:
|
|
self.crop_size = crop_size
|
|
|
|
assert crop_type in [None, 'random', 'center']
|
|
self.crop_type = crop_type
|
|
self.use_segmenation = use_segmentation
|
|
self.onehot = onehot_segmentation # return segmentation as rgb or one hot
|
|
self.stuffthing = use_stuffthing # include thing in segmentation
|
|
if self.onehot and not self.stuffthing:
|
|
raise NotImplemented("One hot mode is only supported for the "
|
|
"stuffthings version because labels are stored "
|
|
"a bit different.")
|
|
|
|
data_json = datajson
|
|
with open(data_json) as json_file:
|
|
self.json_data = json.load(json_file)
|
|
self.img_id_to_captions = dict()
|
|
self.img_id_to_filepath = dict()
|
|
self.img_id_to_segmentation_filepath = dict()
|
|
|
|
assert data_json.split("/")[-1] in [f"captions_train{self.year()}.json",
|
|
f"captions_val{self.year()}.json"]
|
|
# TODO currently hardcoded paths, would be better to follow logic in
|
|
# cocstuff pixelmaps
|
|
if self.use_segmenation:
|
|
if self.stuffthing:
|
|
self.segmentation_prefix = (
|
|
f"data/cocostuffthings/val{self.year()}" if
|
|
data_json.endswith(f"captions_val{self.year()}.json") else
|
|
f"data/cocostuffthings/train{self.year()}")
|
|
else:
|
|
self.segmentation_prefix = (
|
|
f"data/coco/annotations/stuff_val{self.year()}_pixelmaps" if
|
|
data_json.endswith(f"captions_val{self.year()}.json") else
|
|
f"data/coco/annotations/stuff_train{self.year()}_pixelmaps")
|
|
|
|
imagedirs = self.json_data["images"]
|
|
self.labels = {"image_ids": list()}
|
|
for imgdir in tqdm(imagedirs, desc="ImgToPath"):
|
|
self.img_id_to_filepath[imgdir["id"]] = os.path.join(dataroot, imgdir["file_name"])
|
|
self.img_id_to_captions[imgdir["id"]] = list()
|
|
pngfilename = imgdir["file_name"].replace("jpg", "png")
|
|
if self.use_segmenation:
|
|
self.img_id_to_segmentation_filepath[imgdir["id"]] = os.path.join(
|
|
self.segmentation_prefix, pngfilename)
|
|
if given_files is not None:
|
|
if pngfilename in given_files:
|
|
self.labels["image_ids"].append(imgdir["id"])
|
|
else:
|
|
self.labels["image_ids"].append(imgdir["id"])
|
|
|
|
capdirs = self.json_data["annotations"]
|
|
for capdir in tqdm(capdirs, desc="ImgToCaptions"):
|
|
# there are in average 5 captions per image
|
|
#self.img_id_to_captions[capdir["image_id"]].append(np.array([capdir["caption"]]))
|
|
self.img_id_to_captions[capdir["image_id"]].append(capdir["caption"])
|
|
|
|
self.rescaler = albumentations.SmallestMaxSize(max_size=self.size)
|
|
if self.split=="validation":
|
|
self.cropper = albumentations.CenterCrop(height=self.crop_size, width=self.crop_size)
|
|
else:
|
|
# default option for train is random crop
|
|
if self.crop_type in [None, 'random']:
|
|
self.cropper = albumentations.RandomCrop(height=self.crop_size, width=self.crop_size)
|
|
else:
|
|
self.cropper = albumentations.CenterCrop(height=self.crop_size, width=self.crop_size)
|
|
self.preprocessor = albumentations.Compose(
|
|
[self.rescaler, self.cropper],
|
|
additional_targets={"segmentation": "image"})
|
|
if force_no_crop:
|
|
self.rescaler = albumentations.Resize(height=self.size, width=self.size)
|
|
self.preprocessor = albumentations.Compose(
|
|
[self.rescaler],
|
|
additional_targets={"segmentation": "image"})
|
|
|
|
@abstractmethod
|
|
def year(self):
|
|
raise NotImplementedError()
|
|
|
|
def __len__(self):
|
|
return len(self.labels["image_ids"])
|
|
|
|
def preprocess_image(self, image_path, segmentation_path=None):
|
|
image = Image.open(image_path)
|
|
if not image.mode == "RGB":
|
|
image = image.convert("RGB")
|
|
image = np.array(image).astype(np.uint8)
|
|
if segmentation_path:
|
|
segmentation = Image.open(segmentation_path)
|
|
if not self.onehot and not segmentation.mode == "RGB":
|
|
segmentation = segmentation.convert("RGB")
|
|
segmentation = np.array(segmentation).astype(np.uint8)
|
|
if self.onehot:
|
|
assert self.stuffthing
|
|
# stored in caffe format: unlabeled==255. stuff and thing from
|
|
# 0-181. to be compatible with the labels in
|
|
# https://github.com/nightrome/cocostuff/blob/master/labels.txt
|
|
# we shift stuffthing one to the right and put unlabeled in zero
|
|
# as long as segmentation is uint8 shifting to right handles the
|
|
# latter too
|
|
assert segmentation.dtype == np.uint8
|
|
segmentation = segmentation + 1
|
|
|
|
processed = self.preprocessor(image=image, segmentation=segmentation)
|
|
|
|
image, segmentation = processed["image"], processed["segmentation"]
|
|
else:
|
|
image = self.preprocessor(image=image,)['image']
|
|
|
|
image = (image / 127.5 - 1.0).astype(np.float32)
|
|
if segmentation_path:
|
|
if self.onehot:
|
|
assert segmentation.dtype == np.uint8
|
|
# make it one hot
|
|
n_labels = 183
|
|
flatseg = np.ravel(segmentation)
|
|
onehot = np.zeros((flatseg.size, n_labels), dtype=np.bool)
|
|
onehot[np.arange(flatseg.size), flatseg] = True
|
|
onehot = onehot.reshape(segmentation.shape + (n_labels,)).astype(int)
|
|
segmentation = onehot
|
|
else:
|
|
segmentation = (segmentation / 127.5 - 1.0).astype(np.float32)
|
|
return image, segmentation
|
|
else:
|
|
return image
|
|
|
|
def __getitem__(self, i):
|
|
img_path = self.img_id_to_filepath[self.labels["image_ids"][i]]
|
|
if self.use_segmenation:
|
|
seg_path = self.img_id_to_segmentation_filepath[self.labels["image_ids"][i]]
|
|
image, segmentation = self.preprocess_image(img_path, seg_path)
|
|
else:
|
|
image = self.preprocess_image(img_path)
|
|
captions = self.img_id_to_captions[self.labels["image_ids"][i]]
|
|
# randomly draw one of all available captions per image
|
|
caption = captions[np.random.randint(0, len(captions))]
|
|
example = {"image": image,
|
|
#"caption": [str(caption[0])],
|
|
"caption": caption,
|
|
"img_path": img_path,
|
|
"filename_": img_path.split(os.sep)[-1]
|
|
}
|
|
if self.use_segmenation:
|
|
example.update({"seg_path": seg_path, 'segmentation': segmentation})
|
|
return example
|
|
|
|
|
|
class CocoImagesAndCaptionsTrain2017(CocoBase):
|
|
"""returns a pair of (image, caption)"""
|
|
def __init__(self, size, onehot_segmentation=False, use_stuffthing=False, crop_size=None, force_no_crop=False,):
|
|
super().__init__(size=size,
|
|
dataroot="data/coco/train2017",
|
|
datajson="data/coco/annotations/captions_train2017.json",
|
|
onehot_segmentation=onehot_segmentation,
|
|
use_stuffthing=use_stuffthing, crop_size=crop_size, force_no_crop=force_no_crop)
|
|
|
|
def get_split(self):
|
|
return "train"
|
|
|
|
def year(self):
|
|
return '2017'
|
|
|
|
|
|
class CocoImagesAndCaptionsValidation2017(CocoBase):
|
|
"""returns a pair of (image, caption)"""
|
|
def __init__(self, size, onehot_segmentation=False, use_stuffthing=False, crop_size=None, force_no_crop=False,
|
|
given_files=None):
|
|
super().__init__(size=size,
|
|
dataroot="data/coco/val2017",
|
|
datajson="data/coco/annotations/captions_val2017.json",
|
|
onehot_segmentation=onehot_segmentation,
|
|
use_stuffthing=use_stuffthing, crop_size=crop_size, force_no_crop=force_no_crop,
|
|
given_files=given_files)
|
|
|
|
def get_split(self):
|
|
return "validation"
|
|
|
|
def year(self):
|
|
return '2017'
|
|
|
|
|
|
|
|
class CocoImagesAndCaptionsTrain2014(CocoBase):
|
|
"""returns a pair of (image, caption)"""
|
|
def __init__(self, size, onehot_segmentation=False, use_stuffthing=False, crop_size=None, force_no_crop=False,crop_type='random'):
|
|
super().__init__(size=size,
|
|
dataroot="data/coco/train2014",
|
|
datajson="data/coco/annotations2014/annotations/captions_train2014.json",
|
|
onehot_segmentation=onehot_segmentation,
|
|
use_stuffthing=use_stuffthing, crop_size=crop_size, force_no_crop=force_no_crop,
|
|
use_segmentation=False,
|
|
crop_type=crop_type)
|
|
|
|
def get_split(self):
|
|
return "train"
|
|
|
|
def year(self):
|
|
return '2014'
|
|
|
|
class CocoImagesAndCaptionsValidation2014(CocoBase):
|
|
"""returns a pair of (image, caption)"""
|
|
def __init__(self, size, onehot_segmentation=False, use_stuffthing=False, crop_size=None, force_no_crop=False,
|
|
given_files=None,crop_type='center',**kwargs):
|
|
super().__init__(size=size,
|
|
dataroot="data/coco/val2014",
|
|
datajson="data/coco/annotations2014/annotations/captions_val2014.json",
|
|
onehot_segmentation=onehot_segmentation,
|
|
use_stuffthing=use_stuffthing, crop_size=crop_size, force_no_crop=force_no_crop,
|
|
given_files=given_files,
|
|
use_segmentation=False,
|
|
crop_type=crop_type)
|
|
|
|
def get_split(self):
|
|
return "validation"
|
|
|
|
def year(self):
|
|
return '2014'
|
|
|
|
if __name__ == '__main__':
|
|
d2 = CocoImagesAndCaptionsValidation2014(size=256)
|
|
print("construced val set.")
|
|
print(f"length of train split: {len(d2)}")
|
|
|
|
ex2 = d2[0]
|
|
# ex3 = d3[0]
|
|
# print(ex1["image"].shape)
|
|
print(ex2["image"].shape)
|
|
# print(ex3["image"].shape)
|
|
# print(ex1["segmentation"].shape)
|
|
print(ex2["caption"].__class__.__name__)
|