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Cleaned up data set to prevent awkward cases

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Signed-off-by: Jim Martens <github@2martens.de>
This commit is contained in:
Jim Martens
2019-04-10 17:00:32 +02:00
parent b173746784
commit a0f659cea2
1 changed files with 54 additions and 9 deletions
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63
src/twomartens/masterthesis/data.py
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@ -52,6 +52,7 @@ def load_coco(data_path: str, category: int,
annotation_ids = coco_train.getAnnIds(img_ids)
annotations = coco_train.loadAnns(annotation_ids) # load all image annotations
file_names = [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
@ -61,12 +62,57 @@ def load_coco(data_path: str, category: int,
annotation_ids = coco_val.getAnnIds(img_ids)
annotations = coco_val.loadAnns(annotation_ids) # load all image annotations
file_names_val = [f"{data_path}/images/val2014/{image['file_name']}" for image in images]
sizes_val = [(image['width'], image['height']) for image in images]
bboxes_val = [annotation['bbox'] for annotation in annotations]
sizes.extend(sizes_val)
# remove memory-heavy things
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)
length_dataset = len(file_names)
checked_file_names = []
checked_bboxes = []
for file_name, bbox, size in zip(file_names, bboxes, sizes):
target_height = round(bbox[3])
target_width = round(bbox[2])
image_width, image_height = size
y1 = round(bbox[1])
x1 = round(bbox[0])
y2 = round(bbox[1] + bbox[3])
x2 = round(bbox[0] + bbox[2])
if target_width <= 0 or target_height <= 0:
continue
if x2 <= 0 or y2 <= 0:
continue
if x1 < 0 or y1 < 0:
continue
if x2 + 1 - x1 <= 0 or y2 + 1 - y1 <= 0:
continue
if image_width < x2:
target_width = image_width - x1
if image_height < y2:
target_height = image_height - y1
if target_width <= 0:
continue
if target_height <= 0:
continue
bbox[2] = target_width
bbox[3] = target_height
checked_file_names.append(file_name)
checked_bboxes.append(bbox)
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]]):
_images = tf.map_fn(lambda path: tf.read_file(path), paths)
@ -76,12 +122,11 @@ def load_coco(data_path: str, category: int,
image_shape = tf.shape(image)
image = tf.reshape(image, [image_shape[0], image_shape[1], 3])
label = image_data[1]
x1, x2, y1, y2 = tf.cast(tf.round(label[0]), dtype=tf.int32), \
tf.cast(tf.round(label[0] + label[2]), dtype=tf.int32), \
tf.cast(tf.round(label[1]), dtype=tf.int32), \
tf.cast(tf.round(label[1] + label[3]), dtype=tf.int32)
# image_cut = image[x1:x2 + 1, y1:y2 + 1]
image_resized = tf.image.resize_image_with_pad(image, resized_shape[0], resized_shape[1])
image_cut = tf.image.crop_to_bounding_box(image, tf.cast(tf.floor(label[1]), dtype=tf.int32),
tf.cast(tf.floor(label[0]), dtype=tf.int32),
tf.cast(tf.floor(label[3]), dtype=tf.int32),
tf.cast(tf.floor(label[2]), dtype=tf.int32))
image_resized = tf.image.resize_image_with_pad(image_cut, resized_shape[0], resized_shape[1])
return [image_resized, label]
@ -92,8 +137,8 @@ def load_coco(data_path: str, category: int,
return processed_images
# build image data set
path_dataset = tf.data.Dataset.from_tensor_slices(file_names)
label_dataset = tf.data.Dataset.from_tensor_slices(bboxes)
path_dataset = tf.data.Dataset.from_tensor_slices(checked_file_names)
label_dataset = tf.data.Dataset.from_tensor_slices(checked_bboxes)
dataset = tf.data.Dataset.zip((path_dataset, label_dataset))
dataset = dataset.apply(tf.data.experimental.shuffle_and_repeat(buffer_size=length_dataset, count=num_epochs))
dataset = dataset.batch(batch_size=batch_size)