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Fixed mapping from image to bounding box

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There are multiple bounding boxes per image. Previously bounding boxes
were wrongly matched.

Signed-off-by: Jim Martens <github@2martens.de>
This commit is contained in:
Jim Martens
2019-04-10 17:42:59 +02:00
parent a0f659cea2
commit 28ac1a2027
1 changed files with 15 additions and 26 deletions
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41
src/twomartens/masterthesis/data.py
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@ -49,41 +49,32 @@ def load_coco(data_path: str, category: int,
coco_train = coco.COCO(annotation_file_train) coco_train = coco.COCO(annotation_file_train)
img_ids = coco_train.getImgIds(catIds=[category]) # return all image IDs belonging to given category img_ids = coco_train.getImgIds(catIds=[category]) # return all image IDs belonging to given category
images = coco_train.loadImgs(img_ids) # load all images images = coco_train.loadImgs(img_ids) # load all images
annotation_ids = coco_train.getAnnIds(img_ids) annotation_ids = coco_train.getAnnIds(img_ids, catIds=[category])
annotations = coco_train.loadAnns(annotation_ids) # load all image annotations annotations = coco_train.loadAnns(annotation_ids) # load all image annotations
file_names = [f"{data_path}/images/train2014/{image['file_name']}" for image in images] file_names = {image['id']: f"{data_path}/images/train2014/{image['file_name']}" for image in images}
sizes = [(image['width'], image['height']) for image in images]
bboxes = [annotation['bbox'] for annotation in annotations]
# load validation images # load validation images
coco_val = coco.COCO(annotation_file_val) coco_val = coco.COCO(annotation_file_val)
img_ids = coco_val.getImgIds(catIds=[category]) # return all image IDs belonging to given category img_ids = coco_val.getImgIds(catIds=[category]) # return all image IDs belonging to given category
images = coco_val.loadImgs(img_ids) # load all images images_val = coco_val.loadImgs(img_ids) # load all images
annotation_ids = coco_val.getAnnIds(img_ids) annotation_ids = coco_val.getAnnIds(img_ids, catIds=[category])
annotations = coco_val.loadAnns(annotation_ids) # load all image annotations annotations_val = coco_val.loadAnns(annotation_ids) # load all image annotations
file_names_val = [f"{data_path}/images/val2014/{image['file_name']}" for image in images] file_names_val = {image['id']: f"{data_path}/images/val2014/{image['file_name']}" for image in images_val}
sizes_val = [(image['width'], image['height']) for image in images]
bboxes_val = [annotation['bbox'] for annotation in annotations]
sizes.extend(sizes_val) images.extend(images_val)
annotations.extend(annotations_val)
# remove memory-heavy things ids_to_images = {image['id']: image for image in images}
del annotations
del images
del annotation_ids
del img_ids
del coco_train
del coco_val
file_names.extend(file_names_val)
bboxes.extend(bboxes_val)
checked_file_names = [] checked_file_names = []
checked_bboxes = [] checked_bboxes = []
for file_name, bbox, size in zip(file_names, bboxes, sizes): for annotation in annotations:
img_id = annotation['image_id']
image = ids_to_images[img_id]
file_name = file_names[img_id] if img_id in file_names else file_names_val[img_id]
bbox = annotation['bbox']
target_height = round(bbox[3]) target_height = round(bbox[3])
target_width = round(bbox[2]) target_width = round(bbox[2])
image_width, image_height = size image_width, image_height = image['width'], image['height']
y1 = round(bbox[1]) y1 = round(bbox[1])
x1 = round(bbox[0]) x1 = round(bbox[0])
y2 = round(bbox[1] + bbox[3]) y2 = round(bbox[1] + bbox[3])
@ -111,8 +102,6 @@ def load_coco(data_path: str, category: int,
checked_bboxes.append(bbox) checked_bboxes.append(bbox)
length_dataset = len(checked_file_names) length_dataset = len(checked_file_names)
length_original_dataset = len(file_names)
removed_images = length_original_dataset - length_dataset
def _load_image(paths: Sequence[str], labels: Sequence[Sequence[float]]): def _load_image(paths: Sequence[str], labels: Sequence[Sequence[float]]):
_images = tf.map_fn(lambda path: tf.read_file(path), paths) _images = tf.map_fn(lambda path: tf.read_file(path), paths)