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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__':
with open("data/coco/annotations2014/annotations/captions_val2014.json", "r") as json_file:
json_data = json.load(json_file)
capdirs = json_data["annotations"]
import pudb; pudb.set_trace()
#d2 = CocoImagesAndCaptionsTrain2014(size=256)
d2 = CocoImagesAndCaptionsValidation2014(size=256)
print("constructed dataset.")
print(f"length of {d2.__class__.__name__}: {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__)
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