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coco_loader.py
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from paths import DATASETS_ROOT
from pycocotools.coco import COCO
from pycocotools import mask
import cv2
import numpy as np
import logging
log = logging.getLogger()
COCO_VOC_CATS = ['__background__', 'airplane', 'bicycle', 'bird', 'boat',
'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'dining table',
'dog', 'horse', 'motorcycle', 'person', 'potted plant',
'sheep', 'couch', 'train', 'tv']
COCO_NONVOC_CATS = ['apple', 'backpack', 'banana', 'baseball bat',
'baseball glove', 'bear', 'bed', 'bench', 'book', 'bowl',
'broccoli', 'cake', 'carrot', 'cell phone', 'clock', 'cup',
'donut', 'elephant', 'fire hydrant', 'fork', 'frisbee',
'giraffe', 'hair drier', 'handbag', 'hot dog', 'keyboard',
'kite', 'knife', 'laptop', 'microwave', 'mouse', 'orange',
'oven', 'parking meter', 'pizza', 'refrigerator', 'remote',
'sandwich', 'scissors', 'sink', 'skateboard', 'skis',
'snowboard', 'spoon', 'sports ball', 'stop sign',
'suitcase', 'surfboard', 'teddy bear', 'tennis racket',
'tie', 'toaster', 'toilet', 'toothbrush', 'traffic light',
'truck', 'umbrella', 'vase', 'wine glass', 'zebra']
COCO_CATS = COCO_VOC_CATS+COCO_NONVOC_CATS
coco_ids = {'airplane': 5, 'apple': 53, 'backpack': 27, 'banana': 52,
'baseball bat': 39, 'baseball glove': 40, 'bear': 23, 'bed': 65,
'bench': 15, 'bicycle': 2, 'bird': 16, 'boat': 9, 'book': 84,
'bottle': 44, 'bowl': 51, 'broccoli': 56, 'bus': 6, 'cake': 61,
'car': 3, 'carrot': 57, 'cat': 17, 'cell phone': 77, 'chair': 62,
'clock': 85, 'couch': 63, 'cow': 21, 'cup': 47, 'dining table':
67, 'dog': 18, 'donut': 60, 'elephant': 22, 'fire hydrant': 11,
'fork': 48, 'frisbee': 34, 'giraffe': 25, 'hair drier': 89,
'handbag': 31, 'horse': 19, 'hot dog': 58, 'keyboard': 76, 'kite':
38, 'knife': 49, 'laptop': 73, 'microwave': 78, 'motorcycle': 4,
'mouse': 74, 'orange': 55, 'oven': 79, 'parking meter': 14,
'person': 1, 'pizza': 59, 'potted plant': 64, 'refrigerator': 82,
'remote': 75, 'sandwich': 54, 'scissors': 87, 'sheep': 20, 'sink':
81, 'skateboard': 41, 'skis': 35, 'snowboard': 36, 'spoon': 50,
'sports ball': 37, 'stop sign': 13, 'suitcase': 33, 'surfboard':
42, 'teddy bear': 88, 'tennis racket': 43, 'tie': 32, 'toaster':
80, 'toilet': 70, 'toothbrush': 90, 'traffic light': 10, 'train':
7, 'truck': 8, 'tv': 72, 'umbrella': 28, 'vase': 86, 'wine glass':
46, 'zebra': 24}
coco_ids_to_cats = dict(map(reversed, list(coco_ids.items())))
class COCOLoader():
cats_to_ids = dict(map(reversed, enumerate(COCO_CATS)))
ids_to_cats = dict(enumerate(COCO_CATS))
num_classes = len(COCO_CATS)
categories = COCO_CATS[1:]
def __init__(self, split, memoization=False):
assert not memoization
self.dataset = 'coco'
self.coco_ids_to_internal = {k: self.cats_to_ids[v] for k, v in coco_ids_to_cats.items()}
self.ids_to_coco_ids = dict(map(reversed, self.coco_ids_to_internal.items()))
self.split = split
assert self.split in ['train2014', 'val2014', 'test2014', 'test2015', 'minival2014', 'valminusminival2014', 'test-dev2015']
self.root = DATASETS_ROOT + 'coco/'
self.included_coco_ids = list(coco_ids.values())
if 'test' in self.split:
json = '%s/annotations/image_info_%s.json'
else:
json = '%s/annotations/instances_%s.json'
self.coco = COCO(json % (self.root, self.split))
self.filenames = self.coco.getImgIds()
self.num_classes = COCOLoader.num_classes
self.real_split = self.split
if self.real_split in ['minival2014', 'valminusminival2014']:
self.real_split = 'val2014'
self.coco = COCO('%s/annotations/instances_%s.json' % (self.root, self.real_split))
if 'test' in self.real_split:
self.real_split = 'test2015'
log.info("Created a COCO loader %s with %i images" % (split, len(self.coco.getImgIds())))
def load_image(self, img_id):
img = self.coco.loadImgs(img_id)[0]
img_str = '%simages/%s/%s' % (self.root, self.real_split, img['file_name'])
if self.split == 'test-dev2015':
img_str = img_str.replace('test-dev2015', 'test2015')
im = cv2.imread(img_str, cv2.IMREAD_COLOR)
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)/255.0
im = im.astype(np.float32)
return im
def get_filenames(self):
# strictly speaking those are not filenames,
# but the usage is consistent in this class
return self.filenames
def _get_coco_annotations(self, img_id, only_instances=True):
iscrowd = False if only_instances else None
return self.coco.loadAnns(self.coco.getAnnIds(
imgIds=img_id, catIds=self.included_coco_ids, iscrowd=iscrowd))
def read_annotations(self, img_id):
anns = self._get_coco_annotations(img_id)
bboxes = [ann['bbox'] for ann in anns]
cats = [coco_ids_to_cats[ann['category_id']] for ann in anns]
labels = [COCOLoader.cats_to_ids[cat_name] for cat_name in cats]
img = self.coco.loadImgs(img_id)[0]
# removed rounding to int32
return np.array(bboxes).reshape((-1, 4)), np.array(labels),\
img['width'], img['height'], np.zeros_like(labels, dtype=np.bool)
def _read_segmentation(self, ann, H, W):
s = ann['segmentation']
s = s if type(s) == list else [s]
return mask.decode(mask.frPyObjects(s, H, W)).max(axis=2)
def get_semantic_segmentation(self, img_id):
img = self.coco.loadImgs(img_id)[0]
h, w = img['height'], img['width']
segmentation = np.zeros((h, w), dtype=np.uint8)
coco_anns = self._get_coco_annotations(img_id, only_instances=False)
for ann in coco_anns:
mask = self._read_segmentation(ann, h, w)
cid = self.coco_ids_to_internal[ann['category_id']]
assert mask.shape == segmentation.shape
segmentation[mask > 0] = cid
return segmentation