annotation[0, 1] = minY
annotation[0, 2] = maxX
annotation[0, 3] = maxY
annotation[0, 4] = annot['rbbox']['label']
annotations = np.append(annotations, annotation, axis=0)
return annotations
def coco_label_to_label(self, coco_label):
return self.coco_labels_inverse[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 1024 / 1024 #float(image['width']) / float(image['height'])
def num_classes(self):
return len(self.classes)
if __name__ == '__main__':
from augmentation import get_augumentation
dataset = CocoDataset("/content/gdrive/My Drive/findShip/test.pickle",
transform=get_augumentation(phase='train',
width=1024,
height=1024))
sample = dataset[0]
print('sample: ', sample)
annotation[0, :4] = a['bbox']
annotation[0, 4] = self.coco_label_to_label(a['category_id'])
annotations = np.append(annotations, annotation, axis=0)
# transform from [x, y, w, h] to [x1, y1, x2, y2]
annotations[:, 2] = annotations[:, 0] + annotations[:, 2]
annotations[:, 3] = annotations[:, 1] + annotations[:, 3]
return annotations
def coco_label_to_label(self, coco_label):
return self.coco_labels_inverse[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 num_classes(self):
return 9
if __name__ == '__main__':
from augmentation import get_augumentation
dataset = CocoDataset(root_dir='/root/data/coco', set_name='trainval35k',
transform=get_augumentation(phase='train'))
sample = dataset[0]
print('sample: ', sample)
def visualize(annotations, category_id_to_name):
img = annotations['image'].copy()
for idx, bbox in enumerate(annotations['bboxes']):
img = visualize_bbox(img, bbox, annotations['category_id'][idx],
category_id_to_name)
plt.figure(figsize=(12, 12))
plt.imshow(img)
dataset_root = '/home/toandm2/data/VOCdevkit'
network = 'efficientdet-d0'
dataset = VOCDetection(root=dataset_root,
transform=get_augumentation(
phase='train',
width=EFFICIENTDET[network]['input_size'],
height=EFFICIENTDET[network]['input_size']))
def visual_data(data):
img = data['image']
bboxes = data['bboxes']
print(len(data['bboxes']))
annotations = {
'image': data['image'],
'bboxes': data['bboxes'],
'category_id': range(len(data['bboxes']))
}
category_id_to_name = {v: v for v in range(len(data['bboxes']))}
visualize(annotations, category_id_to_name)
