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__)