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import json
import os
import pickle
import random
import sys
from io import StringIO
from typing import List, Tuple, Dict
import torch
import torch.utils.data.dataset
from PIL import Image, ImageOps
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
from torch import Tensor
from torchvision.datasets import CocoDetection
from tqdm import tqdm
from bbox import BBox
from dataset.base import Base
from dataset.coco2017 import COCO2017
class COCO2017Car(Base):
class Annotation(object):
class Object(object):
def __init__(self, bbox: BBox, label: int):
super().__init__()
self.bbox = bbox
self.label = label
def __repr__(self) -> str:
return 'Object[label={:d}, bbox={!s}]'.format(
self.label, self.bbox)
def __init__(self, filename: str, objects: List[Object]):
super().__init__()
self.filename = filename
self.objects = objects
CATEGORY_TO_LABEL_DICT = {
'background': 0, 'car': 1
}
LABEL_TO_CATEGORY_DICT = {v: k for k, v in CATEGORY_TO_LABEL_DICT.items()}
def __init__(self, path_to_data_dir: str, mode: Base.Mode, image_min_side: float, image_max_side: float):
super().__init__(path_to_data_dir, mode, image_min_side, image_max_side)
path_to_coco_dir = os.path.join(self._path_to_data_dir, 'COCO')
path_to_annotations_dir = os.path.join(path_to_coco_dir, 'annotations')
path_to_caches_dir = os.path.join('caches', 'coco2017-car', f'{self._mode.value}')
path_to_image_ids_pickle = os.path.join(path_to_caches_dir, 'image-ids.pkl')
path_to_image_id_dict_pickle = os.path.join(path_to_caches_dir, 'image-id-dict.pkl')
path_to_image_ratios_pickle = os.path.join(path_to_caches_dir, 'image-ratios.pkl')
if self._mode == COCO2017Car.Mode.TRAIN:
path_to_jpeg_images_dir = os.path.join(path_to_coco_dir, 'train2017')
path_to_annotation = os.path.join(path_to_annotations_dir, 'instances_train2017.json')
elif self._mode == COCO2017Car.Mode.EVAL:
path_to_jpeg_images_dir = os.path.join(path_to_coco_dir, 'val2017')
path_to_annotation = os.path.join(path_to_annotations_dir, 'instances_val2017.json')
else:
raise ValueError('invalid mode')
coco_dataset = CocoDetection(root=path_to_jpeg_images_dir, annFile=path_to_annotation)
if os.path.exists(path_to_image_ids_pickle) and os.path.exists(path_to_image_id_dict_pickle):
print('loading cache files...')
with open(path_to_image_ids_pickle, 'rb') as f:
self._image_ids = pickle.load(f)
with open(path_to_image_id_dict_pickle, 'rb') as f:
self._image_id_to_annotation_dict = pickle.load(f)
with open(path_to_image_ratios_pickle, 'rb') as f:
self._image_ratios = pickle.load(f)
else:
print('generating cache files...')
os.makedirs(path_to_caches_dir, exist_ok=True)
self._image_id_to_annotation_dict: Dict[str, COCO2017Car.Annotation] = {}
self._image_ratios = []
for idx, (image, annotation) in enumerate(tqdm(coco_dataset)):
if len(annotation) > 0:
image_id = str(annotation[0]['image_id']) # all image_id in annotation are the same
annotation = COCO2017Car.Annotation(
filename=os.path.join(path_to_jpeg_images_dir, '{:012d}.jpg'.format(int(image_id))),
objects=[COCO2017Car.Annotation.Object(
bbox=BBox( # `ann['bbox']` is in the format [left, top, width, height]
left=ann['bbox'][0],
top=ann['bbox'][1],
right=ann['bbox'][0] + ann['bbox'][2],
bottom=ann['bbox'][1] + ann['bbox'][3]
),
label=ann['category_id'])
for ann in annotation]
)
annotation.objects = [obj for obj in annotation.objects
if obj.label in [COCO2017.CATEGORY_TO_LABEL_DICT['car']]] # filtering label should refer to original `COCO2017` dataset
if len(annotation.objects) > 0:
self._image_id_to_annotation_dict[image_id] = annotation
ratio = float(image.width / image.height)
self._image_ratios.append(ratio)
self._image_ids = list(self._image_id_to_annotation_dict.keys())
with open(path_to_image_ids_pickle, 'wb') as f:
pickle.dump(self._image_ids, f)
with open(path_to_image_id_dict_pickle, 'wb') as f:
pickle.dump(self._image_id_to_annotation_dict, f)
with open(path_to_image_ratios_pickle, 'wb') as f:
pickle.dump(self.image_ratios, f)
def __len__(self) -> int:
return len(self._image_id_to_annotation_dict)
def __getitem__(self, index: int) -> Tuple[str, Tensor, Tensor, Tensor, Tensor]:
image_id = self._image_ids[index]
annotation = self._image_id_to_annotation_dict[image_id]
bboxes = [obj.bbox.tolist() for obj in annotation.objects]
labels = [COCO2017Car.CATEGORY_TO_LABEL_DICT[COCO2017.LABEL_TO_CATEGORY_DICT[obj.label]] for obj in annotation.objects] # mapping from original `COCO2017` dataset
bboxes = torch.tensor(bboxes, dtype=torch.float)
labels = torch.tensor(labels, dtype=torch.long)
image = Image.open(annotation.filename).convert('RGB') # for some grayscale images
# random flip on only training mode
if self._mode == COCO2017Car.Mode.TRAIN and random.random() > 0.5:
image = ImageOps.mirror(image)
bboxes[:, [0, 2]] = image.width - bboxes[:, [2, 0]] # index 0 and 2 represent `left` and `right` respectively
image, scale = COCO2017Car.preprocess(image, self._image_min_side, self._image_max_side)
scale = torch.tensor(scale, dtype=torch.float)
bboxes *= scale
return image_id, image, scale, bboxes, labels
def evaluate(self, path_to_results_dir: str, image_ids: List[str], bboxes: List[List[float]], classes: List[int], probs: List[float]) -> Tuple[float, str]:
self._write_results(path_to_results_dir, image_ids, bboxes, classes, probs)
annType = 'bbox'
path_to_coco_dir = os.path.join(self._path_to_data_dir, 'COCO')
path_to_annotations_dir = os.path.join(path_to_coco_dir, 'annotations')
path_to_annotation = os.path.join(path_to_annotations_dir, 'instances_val2017.json')
cocoGt = COCO(path_to_annotation)
cocoDt = cocoGt.loadRes(os.path.join(path_to_results_dir, 'results.json'))
cocoEval = COCOeval(cocoGt, cocoDt, annType)
cocoEval.params.catIds = COCO2017.CATEGORY_TO_LABEL_DICT['car'] # filtering label should refer to original `COCO2017` dataset
cocoEval.evaluate()
cocoEval.accumulate()
original_stdout = sys.stdout
string_stdout = StringIO()
sys.stdout = string_stdout
cocoEval.summarize()
sys.stdout = original_stdout
mean_ap = cocoEval.stats[0].item() # stats[0] records AP@[0.5:0.95]
detail = string_stdout.getvalue()
return mean_ap, detail
def _write_results(self, path_to_results_dir: str, image_ids: List[str], bboxes: List[List[float]], classes: List[int], probs: List[float]):
results = []
for image_id, bbox, cls, prob in zip(image_ids, bboxes, classes, probs):
results.append(
{
'image_id': int(image_id), # COCO evaluation requires `image_id` to be type `int`
'category_id': COCO2017.CATEGORY_TO_LABEL_DICT[COCO2017Car.LABEL_TO_CATEGORY_DICT[cls]], # mapping to original `COCO2017` dataset
'bbox': [ # format [left, top, width, height] is expected
bbox[0],
bbox[1],
bbox[2] - bbox[0],
bbox[3] - bbox[1]
],
'score': prob
}
)
with open(os.path.join(path_to_results_dir, 'results.json'), 'w') as f:
json.dump(results, f)
@property
def image_ratios(self) -> List[float]:
return self._image_ratios
@staticmethod
def num_classes() -> int:
return 2
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