def _ground_truth_label(_id, zone):
xml_base_zone_path = dp.REPO_HOME_PATH + '/data/bbox/aps_threats/zone_{2}/{0}_{1}.xml'
for i in range(16):
xml_zone_path = xml_base_zone_path.format(i, _id, zone)
if os.path.exists(xml_zone_path) and len(
parse_bbox_xml(xml_zone_path)) > 0:
return 1
return 0
def _curate_torso_calibration(data_set):
ids = Table().read_table(dp.ids_by_data_set(data_set)).column('Id')
X, y_label, y_zone, example_ids = [], [], [], []
xml_base_zone_path = dp.REPO_HOME_PATH + '/data/bbox/aps_threats/zone_{2}/{0}_{1}.xml'
for _id in ids:
print(_id)
logits, bboxes = threat_detect_inference(_id, 'torso')
_, cropped_bbox = crop_and_pad(_id,
file_format='aps',
region='torso',
slc=0,
keypoint_pad=None)
arcs = build_arcs('torso', logits, bboxes, _id)
for i in range(len(arcs)):
arc = arcs[i]
matched = False
for zone in [5, 6, 7, 17]:
num_matches = 0
original_scale_boxes = arc.original_scale_boxes(cropped_bbox)
for i in range(original_scale_boxes.shape[0]):
gt_xml_path = xml_base_zone_path.format(i, _id, zone)
if os.path.exists(gt_xml_path):
gt_bboxes = np.array(parse_bbox_xml(gt_xml_path))
if gt_bboxes.size > 0:
if np.max(
_iou(gt_bboxes, original_scale_boxes[i][
np.newaxis])) > 0.5:
num_matches += 1
matched = num_matches / np.sum(
np.sum(original_scale_boxes, axis=1) > 0) >= 0.5
if matched:
X.append(arc.to_list())
y_label.append(int(matched))
y_zone.append(zone)
example_ids.append(_id)
break
if not matched:
X.append(arc.to_list())
y_label.append(int(matched))
y_zone.append(zone)
example_ids.append(_id)
X, y_label, y_zone = np.array(X), np.array(y_label), np.array(y_zone)
return X, y_label, y_zone, example_ids
def create_keypoint_tf_record(data_set, region, random_seed):
np.random.seed(random_seed)
if region == 'face':
keypoint = FACE
elif region == 'butt':
keypoint = BUTT
ids = Table().read_table(dp.ids_by_data_set(data_set)).column('Id')
tf_record_name = '{}_keypoint'.format(keypoint.name)
print('Creating: {0}_{1}'.format(data_set, tf_record_name))
writer = tf.python_io.TFRecordWriter(
dp.tf_records(data_set, tf_record_name))
base_xml_path = dp.REPO_HOME_PATH + '/data/bbox/' + keypoint.name
base_png_path = dp.LARGE_DATA_BIN + '/data/raw/'
if keypoint.file_format == 'a3daps':
base_png_path += 'a3daps_png/'
else:
base_png_path += 'aps_png/'
for _id in ids:
for i in keypoint.slices:
image = np.array(
Image.open('{0}/{1}/{1}_{2}.png'.format(
base_png_path, i, _id))).astype(np.float32)
xml_path = '{0}/{1}_{2}.xml'.format(base_xml_path, i, _id)
if os.path.exists(xml_path):
bboxes = np.array(parse_bbox_xml(xml_path)).ravel()
bboxes = np.append(bboxes, (BOX_COUNT * 4 - len(bboxes)) *
[0]).astype(np.int64)
if np.sum(bboxes) == 0:
continue
example = tf.train.Example(features=tf.train.Features(
feature={
'image': _bytes_feature(image.tostring()),
'bbox': _bbox_feature(bboxes),
}))
writer.write(example.SerializeToString())
writer.close()
def _create_localization_tf_record(data_set, region, random_seed):
np.random.seed(random_seed)
ids = Table().read_table(dp.ids_by_data_set(data_set)).column('Id')
tf_record_name = '{0}_localization'.format(region)
print('Creating: {0}_{1}'.format(data_set, tf_record_name))
writer = tf.python_io.TFRecordWriter(
dp.tf_records(data_set, tf_record_name))
base_xml_path = '{0}/data/bbox/aps_threats/zone_'.format(dp.REPO_HOME_PATH)
if region == 'arm':
zones = {1: 0, 2: 1, 3: 2, 4: 3}
if region == 'thigh':
zones = {8: 0, 9: 1, 10: 2, 11: 3, 12: 4}
masks = [
make_vertical_stripe_mask((4, 4), (4, 4)),
make_vertical_stripe_mask((4, 4), (4, 15)),
make_horizontal_stripe_mask((4, 4), (4, 4)),
make_horizontal_stripe_mask((4, 4), (15, 4)),
make_squares_mask((10, 10), (10, 25)),
make_squares_mask((5, 5), (10, 25)),
make_squares_mask((5, 5), (5, 15)),
make_vertical_stripe_mask((2, 2), (2, 2))
]
examples = []
for _id in ids:
for slc in range(16):
for zone in zones.keys():
xml_path = base_xml_path + '{0}/{1}_{2}.xml'.format(
zone, slc, _id)
if os.path.exists(xml_path):
boxes = parse_bbox_xml(xml_path)
if len(boxes) > 0:
examples += [(_id, slc, zone)]
np.random.shuffle(examples)
for _id, slc, zone in examples:
image, cropped_bbox = crop_and_pad(_id,
region,
'aps',
slc,
keypoint_pad=None)
pad = pad_dim((cropped_bbox[3] - cropped_bbox[1],
cropped_bbox[2] - cropped_bbox[0]))
xml_path = base_xml_path + '{0}/{1}_{2}.xml'.format(zone, slc, _id)
box = parse_bbox_xml(xml_path)[0]
rescaled_box = [
box[0] - cropped_bbox[0] + pad[1],
box[1] - cropped_bbox[1] + pad[0],
box[2] - cropped_bbox[0] + pad[1],
box[3] - cropped_bbox[1] + pad[0]
]
rescaled_box[0] = max(rescaled_box[0], pad[1])
rescaled_box[1] = max(rescaled_box[1], pad[0])
rescaled_box[2] = min(rescaled_box[2],
cropped_bbox[2] - cropped_bbox[0] + pad[1])
rescaled_box[3] = min(rescaled_box[3],
cropped_bbox[3] - cropped_bbox[1] + pad[0])
bboxes = np.array(rescaled_box).astype(np.int64)
image = image.astype(np.float32)
label = zones[zone]
example = tf.train.Example(features=tf.train.Features(
feature={
'image':
_bytes_feature(image.tostring()),
'bbox':
_bbox_feature(bboxes),
'dim':
tf.train.Feature(int64_list=tf.train.Int64List(
value=[image.shape[0]])),
'label':
tf.train.Feature(int64_list=tf.train.Int64List(value=[label])),
'slc':
tf.train.Feature(int64_list=tf.train.Int64List(value=[slc]))
}))
writer.write(example.SerializeToString())
if 'train' in data_set:
for _ in range(len(masks) - 1):
writer.write(example.SerializeToString())
for mask in masks:
masked_image = (image * mask(image.shape)).astype(np.float32)
example = tf.train.Example(features=tf.train.Features(
feature={
'image':
_bytes_feature(masked_image.tostring()),
'bbox':
_bbox_feature(bboxes),
'dim':
tf.train.Feature(int64_list=tf.train.Int64List(
value=[image.shape[0]])),
'label':
tf.train.Feature(int64_list=tf.train.Int64List(
value=[label])),
'slc':
tf.train.Feature(int64_list=tf.train.Int64List(
value=[slc]))
}))
writer.write(example.SerializeToString())
writer.close()
def _create_full_tf_record(data_set,
file_format,
region,
keypoint_pad,
random_seed,
example_multiple=3):
np.random.seed(random_seed)
ids = Table().read_table(dp.ids_by_data_set(data_set)).column('Id')
tf_record_name = '{0}_crop_patch_full_{1}'.format(file_format, region)
print('Creating: {0}_{1}'.format(data_set, tf_record_name))
writer = tf.python_io.TFRecordWriter(
dp.tf_records(data_set, tf_record_name))
base_xml_path = '{0}/data/bbox/{1}_merged_threats_{2}'.format(
dp.REPO_HOME_PATH, file_format, region)
masks = [
make_vertical_stripe_mask((4, 4), (4, 4)),
make_vertical_stripe_mask((4, 4), (4, 15)),
make_horizontal_stripe_mask((4, 4), (4, 4)),
make_horizontal_stripe_mask((4, 4), (15, 4)),
make_squares_mask((10, 10), (10, 25)),
make_squares_mask((5, 5), (10, 25)),
make_squares_mask((5, 5), (5, 15)),
make_vertical_stripe_mask((2, 2), (2, 2))
]
if file_format == 'aps':
slcs = range(16)
if file_format == 'a3daps':
slcs = [t for t in range(64) if t % 2 == 0]
existing_paths, non_existing_paths = 0, 0
for _id in ids:
for i in slcs:
xml_path = '{0}/{1}_{2}.xml'.format(base_xml_path, i, _id)
if os.path.exists(xml_path):
existing_paths += 1
else:
non_existing_paths += 1
ids_and_slcs = []
for _id in ids:
for i in slcs:
xml_path = '{0}/{1}_{2}.xml'.format(base_xml_path, i, _id)
if os.path.exists(xml_path):
ids_and_slcs += example_multiple * [(_id, i)]
elif np.random.binomial(
1,
min(1, example_multiple * existing_paths /
non_existing_paths)):
ids_and_slcs += [((_id, i))]
np.random.shuffle(ids_and_slcs)
for _id, i in ids_and_slcs:
image, cropped_bbox = crop_and_pad(_id,
region,
file_format,
i,
keypoint_pad=keypoint_pad)
pad = pad_dim((cropped_bbox[3] - cropped_bbox[1],
cropped_bbox[2] - cropped_bbox[0]))
xml_path = '{0}/{1}_{2}.xml'.format(base_xml_path, i, _id)
if os.path.exists(xml_path):
bboxes = parse_bbox_xml(xml_path)
if np.sum(np.array(bboxes).ravel()) == 0:
continue
cropped_bboxes = []
for box in bboxes:
rescaled_box = [
box[0] - cropped_bbox[0] + pad[1],
box[1] - cropped_bbox[1] + pad[0],
box[2] - cropped_bbox[0] + pad[1],
box[3] - cropped_bbox[1] + pad[0]
]
rescaled_box[0] = max(rescaled_box[0], pad[1])
rescaled_box[1] = max(rescaled_box[1], pad[0])
rescaled_box[2] = min(
rescaled_box[2],
cropped_bbox[2] - cropped_bbox[0] + pad[1])
rescaled_box[3] = min(
rescaled_box[3],
cropped_bbox[3] - cropped_bbox[1] + pad[0])
cropped_bboxes.append(rescaled_box)
bboxes = np.array(cropped_bboxes).ravel()
bboxes = np.append(bboxes, (BOX_COUNT * 4 - len(bboxes)) *
[0]).astype(np.int64)
else:
bboxes = np.array(BOX_COUNT * 4 * [0]).astype(np.int64)
image = image.astype(np.float32)
example = tf.train.Example(features=tf.train.Features(
feature={
'image':
_bytes_feature(image.tostring()),
'bbox':
_bbox_feature(bboxes),
'dim':
tf.train.Feature(int64_list=tf.train.Int64List(
value=[image.shape[0]]))
}))
writer.write(example.SerializeToString())
if 'train' in data_set:
for _ in range(len(masks) - 1):
writer.write(example.SerializeToString())
for mask in masks:
masked_image = (image * mask(image.shape)).astype(np.float32)
example = tf.train.Example(features=tf.train.Features(
feature={
'image':
_bytes_feature(masked_image.tostring()),
'bbox':
_bbox_feature(bboxes),
'dim':
tf.train.Feature(int64_list=tf.train.Int64List(
value=[image.shape[0]]))
}))
writer.write(example.SerializeToString())
writer.close()
def _create_pure_tf_record(data_set,
file_format,
region,
keypoint_pad,
random_seed,
example_multiple=3):
np.random.seed(random_seed)
ids = Table().read_table(dp.ids_by_data_set(data_set)).column('Id')
tf_record_name = '{0}_crop_patch_pure_{1}'.format(file_format, region)
print('Creating: {0}_{1}'.format(data_set, tf_record_name))
writer = tf.python_io.TFRecordWriter(
dp.tf_records(data_set, tf_record_name))
base_xml_path = '{0}/data/bbox/{1}_merged_threats_{2}'.format(
dp.REPO_HOME_PATH, file_format, region)
if file_format == 'aps':
slcs = range(16)
if file_format == 'a3daps':
slcs = [t for t in range(64) if t % 2 == 0]
ids_and_slcs = []
for _id in ids:
for i in slcs:
xml_path = '{0}/{1}_{2}.xml'.format(base_xml_path, i, _id)
if os.path.exists(xml_path):
ids_and_slcs += [(_id, i)]
np.random.shuffle(ids_and_slcs)
for _id, i in ids_and_slcs:
image, cropped_bbox = crop_and_pad(_id,
region,
file_format,
i,
keypoint_pad=keypoint_pad)
pad = pad_dim((cropped_bbox[3] - cropped_bbox[1],
cropped_bbox[2] - cropped_bbox[0]))
xml_path = '{0}/{1}_{2}.xml'.format(base_xml_path, i, _id)
if os.path.exists(xml_path):
bboxes = parse_bbox_xml(xml_path)
if np.sum(np.array(bboxes).ravel()) == 0:
continue
cropped_bboxes = []
for box in bboxes:
rescaled_box = [
box[0] - cropped_bbox[0] + pad[1],
box[1] - cropped_bbox[1] + pad[0],
box[2] - cropped_bbox[0] + pad[1],
box[3] - cropped_bbox[1] + pad[0]
]
rescaled_box[0] = max(rescaled_box[0], pad[1])
rescaled_box[1] = max(rescaled_box[1], pad[0])
rescaled_box[2] = min(
rescaled_box[2],
cropped_bbox[2] - cropped_bbox[0] + pad[1])
rescaled_box[3] = min(
rescaled_box[3],
cropped_bbox[3] - cropped_bbox[1] + pad[0])
cropped_bboxes.append(rescaled_box)
bboxes = np.array(cropped_bboxes).ravel()
bboxes = np.append(bboxes, (BOX_COUNT * 4 - len(bboxes)) *
[0]).astype(np.int64)
else:
continue
image = image.astype(np.float32)
example = tf.train.Example(features=tf.train.Features(
feature={
'image':
_bytes_feature(image.tostring()),
'bbox':
_bbox_feature(bboxes),
'dim':
tf.train.Feature(int64_list=tf.train.Int64List(
value=[image.shape[0]]))
}))
writer.write(example.SerializeToString())
writer.close()
def _get_keypoint_bbox(_id, keypoint):
return parse_bbox_xml(dp.REPO_HOME_PATH +
'/data/bbox/keypoint_inference/{0}/{1}.xml'.format(
keypoint.name, _id))[0]
def get_arcs_and_ground_truth(data_set, zones):
ids = Table().read_table(dp.ids_by_data_set(data_set)).column('Id')
images_tensor = tf.placeholder(tf.float32, (None, 600, 600, 1))
model = AnchorBabyModel(batch_size=16, image_dim=(600, 600))
logits_tensor, bboxes_tensor = model.inference(images_tensor, test=True)
logits_tensor = tf.sigmoid(logits_tensor)
bboxes_tensor = target_to_box(bboxes_tensor, model.anchors)
checkpoint_dir = '/home/alon/sda1/tsa-kaggle/train_dir/train_11-25_20:14:05' #patch crop torso w/ masks
xml_base_path = '/home/alon/Documents/tsa-kaggle/data/bbox/aps_merged_threats/{0}_{1}.xml'
xml_base_zone_path = '/home/alon/Documents/tsa-kaggle/data/bbox/aps_threats/zone_{2}/{0}_{1}.xml'
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(
sess,
tf.train.get_checkpoint_state(
checkpoint_dir).model_checkpoint_path)
arcs_matrix, arcs_labels, arc_ids, arc_zone_labels = [], [], [], []
for _id in ids:
print(_id)
images, cropped_bbox = images_and_crop_by_id(_id)
logits, bboxes = sess.run([logits_tensor, bboxes_tensor],
feed_dict={images_tensor: images})
arcs = build_arcs(bboxes, logits)
for arc in arcs:
matched = False
for zone in zones:
num_matches = 0
original_scale_boxes = arc.original_scale_boxes(
cropped_bbox)
for i in range(original_scale_boxes.shape[0]):
gt_xml_path = xml_base_zone_path.format(i, _id, zone)
if os.path.exists(gt_xml_path):
gt_bboxes = np.array(parse_bbox_xml(gt_xml_path))
if gt_bboxes.size > 0:
if np.max(
_iou(
gt_bboxes, original_scale_boxes[i][
np.newaxis])) > 0.5:
num_matches += 1
arc_match = num_matches / np.sum(
np.sum(original_scale_boxes, axis=1) > 0) >= 0.5
if arc_match:
matched = True
arcs_matrix.append(arc.to_list())
arcs_labels.append(True)
arc_ids.append(_id)
arc_zone_labels.append(zone)
break
if not matched:
arcs_matrix.append(arc.to_list())
arcs_labels.append(False)
arc_ids.append(_id)
arc_zone_labels.append(None)
arcs_matrix, arcs_labels, arc_ids = np.array(arcs_matrix), np.array(
arcs_labels), np.array(arc_ids)
arc_zone_labels = np.array(arc_zone_labels)
np.save(data_set + '_arc_X', arcs_matrix)
np.save(data_set + '_arc_Y', arcs_labels)
np.save(data_set + '_arc_ids', arc_ids)
np.save(data_set + '_arc_zone_labels', arc_zone_labels)
