"""
Copyright 2017-2018 Fizyr (https://fizyr.com)
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import numpy as np
import cv2
import keras
import os
from pycocotools.coco import COCO
from maskrcnn_modanet.train.generator import Generator
from keras_retinanet.utils.image import read_image_bgr
class CocoGenerator(Generator):
def __init__(
self,
data_dir,
set_name,
**kwargs):
self.data_dir = data_dir
self.set_name = set_name
self.coco = COCO(os.path.join(data_dir, 'annotations', 'instances_' + set_name + '.json'))
self.image_ids = self.coco.getImgIds()
self.load_classes()
super(CocoGenerator, self).__init__(**kwargs)
def load_classes(self):
# load class names (name -> label)
categories = self.coco.loadCats(self.coco.getCatIds())
categories.sort(key=lambda x: x['id'])
self.classes = {}
self.coco_labels = {}
self.coco_labels_inverse = {}
for c in categories:
self.coco_labels[len(self.classes)] = c['id']
self.coco_labels_inverse[c['id']] = len(self.classes)
self.classes[c['name']] = len(self.classes)
# also load the reverse (label -> name)
self.labels = {}
for key, value in self.classes.items():
self.labels[value] = key
def size(self):
return len(self.image_ids)
def num_classes(self):
return len(self.classes)
def name_to_label(self, name):
return self.classes[name]
def label_to_name(self, label):
return self.labels[label]
def coco_label_to_label(self, coco_label):
return self.coco_labels_inverse[coco_label]
def coco_label_to_name(self, coco_label):
return self.label_to_name(self.coco_label_to_label(coco_label))
def label_to_coco_label(self, label):
return self.coco_labels[label]
def image_aspect_ratio(self, image_index):
image = self.coco.loadImgs(self.image_ids[image_index])[0]
return float(image['width']) / float(image['height'])
def load_image(self, image_index):
image_info = self.coco.loadImgs(self.image_ids[image_index])[0]
path = os.path.join(self.data_dir, 'images', image_info['file_name']) #, self.set_name
return read_image_bgr(path)
def load_annotations(self, image_index):
# get image info
image_info = self.coco.loadImgs(self.image_ids[image_index])[0]
# get ground truth annotations
annotations_ids = self.coco.getAnnIds(imgIds=self.image_ids[image_index], iscrowd=False)
annotations = {
'labels': np.empty((0,)),
'bboxes': np.empty((0, 4)),
'masks': [],
}
# some images might miss annotations
if len(annotations_ids) == 0:
return annotations
# parse annotations
coco_annotations = self.coco.loadAnns(annotations_ids)
for idx, a in enumerate(coco_annotations):
if 'segmentation' not in a:
raise ValueError('Expected \'segmentation\' key in annotation, got: {}'.format(a))
# some annotations have basically no width / height, skip them
if a['bbox'][2] < 1 or a['bbox'][3] < 1:
continue
annotations['labels'] = np.concatenate([annotations['labels'], [self.coco_label_to_label(a['category_id'])]], axis=0)
annotations['bboxes'] = np.concatenate([annotations['bboxes'], [[
a['bbox'][0],
a['bbox'][1],
a['bbox'][0] + a['bbox'][2] if a['bbox'][0] + a['bbox'][2] <= image_info['width'] else image_info['width'],
a['bbox'][1] + a['bbox'][3] if a['bbox'][1] + a['bbox'][3] <= image_info['height'] else image_info['height'],
]]], axis=0) # normalizing annotations that exit boundaries
mask = np.zeros((image_info['height'], image_info['width'], 1), dtype=np.uint8)
for seg in a['segmentation']:
points = np.array(seg).reshape((len(seg) // 2, 2)).astype(int)
# draw mask
cv2.fillPoly(mask, [points.astype(int)], (1,))
annotations['masks'].append(mask.astype(float))
return annotations
