def run(generator, args, anchor_params):
# display images, one at a time
for i in range(generator.size()):
# load the data
image = generator.load_image(i)
annotations = generator.load_annotations(i)
# apply random transformations
if args.random_transform:
image, annotations = generator.random_transform_group_entry(image, annotations)
# resize the image and annotations
if args.resize:
image, image_scale = generator.resize_image(image)
annotations['bboxes'] *= image_scale
for m in range(len(annotations['masks'])):
annotations['masks'][m], _ = generator.resize_image(annotations['masks'][m])
anchors = anchors_for_shape(image.shape, anchor_params=anchor_params)
positive_indices, _, max_indices = compute_gt_annotations(anchors, annotations['bboxes'])
# draw anchors on the image
if args.anchors:
anchors = generator.generate_anchors(image.shape)
positive_indices, _, max_indices = compute_gt_annotations(anchors, annotations['bboxes'])
draw_boxes(image, anchors[positive_indices], (255, 255, 0), thickness=1)
# draw annotations on the image
if args.annotations:
# draw annotations in red
draw_annotations(image, annotations, color=(0, 0, 255), label_to_name=generator.label_to_name)
# draw regressed anchors in green to override most red annotations
# result is that annotations without anchors are red, with anchors are green
draw_boxes(image, annotations['bboxes'][max_indices[positive_indices], :], (0, 255, 0))
# Draw masks over the image with random colours
if args.masks:
for m in range(len(annotations['masks'])):
# crop the mask with the related bbox size, and then draw them
box = annotations['bboxes'][m].astype(int)
mask = annotations['masks'][m][box[1]:box[3], box[0]:box[2]]
draw_mask(image, box, mask, annotations['labels'][m].astype(int))
# add the label caption
caption = '{}'.format(generator.label_to_name(annotations['labels'][m]))
draw_caption(image, box, caption)
cv2.imshow('Image', image)
if cv2.waitKey() == ord('q'):
return False
return True
def main(args=None):
# parse arguments
if args is None:
args = sys.argv[1:]
args = parse_args(args)
# optionally choose specific GPU
# if args.gpu:
# os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
# create the generators
test_generator = create_test_generator(args)
if os.path.exists(
'/app/Ant_mcs_detection/output/test_set_all_detections.pkl'):
with open('/app/Ant_mcs_detection/output/test_set_all_detections.pkl',
'rb') as f:
all_detections = pickle.load(f)
else:
with tf.device('/device:GPU:3'):
config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False,
device_count={
'CPU': 1,
'GPU': 1
})
config.gpu_options.allow_growth = True
session = tf.Session(config=config)
keras.backend.set_session(session)
print('Creating model, this may take a second...')
model = models.load_model(os.path.abspath(args.weights),
backbone_name=args.backbone)
model = models.convert_model(model)
# print model summary
print(model.summary())
score_threshold = 0.05
max_detections = 0
save_path = None
eval_batch_size = 16
all_detections = _get_detections(test_generator,
model,
score_threshold=score_threshold,
max_detections=max_detections,
save_path=save_path,
eval_batch_size=eval_batch_size)
with open('/app/Ant_mcs_detection/output/test_set_all_detections.pkl',
'wb') as f:
pickle.dump(all_detections, f)
all_annotations = _get_annotations(
test_generator, predicted_items_count=len(all_detections))
proba_percentiles = np.linspace(10, 99, 90)
iou_values_per_example = []
for example_idx in tqdm(range(len(all_annotations)),
total=len(all_annotations)):
image = np.copy(test_generator.load_image(example_idx))
_, example_mask, _, _ = test_generator.data_manager.get_all_data(
test_generator.image_names[example_idx])
example_mask = np.copy(example_mask)
image = np.copy(image)
image[:, :, 0] = 255 - image[:, :, 0]
image[:, :, 1] = 255 - image[:, :, 1]
# image = np.ma.array(image)
image_mask = np.tile(1 - example_mask, (1, 1, 3))
image = image * image_mask
annotations = test_generator.load_annotations(example_idx)
anchors = anchors_for_shape(image.shape, anchor_params=None)
positive_indices, _, max_indices = compute_gt_annotations(
anchors, annotations['bboxes'])
curr_detections = all_detections[example_idx][0]
# selected_detections = curr_detections[curr_detections[:, -1] >= args.proba_threshold, :]
# bboxes = selected_detections[:, :4]
# probas = selected_detections[:, -1]
bboxes = curr_detections[:, :4]
probas = curr_detections[:, -1]
# region filter detections
bboxes_filtered = np.copy(bboxes)
scores_filtered = np.copy(probas)
# proba_threshold = np.percentile(scores_filtered, args.shrinking_proba_perc_threshold)
proba_threshold = args.proba_threshold
filter_indices = np.where(scores_filtered >= proba_threshold)[0]
bboxes_filtered = bboxes_filtered[filter_indices, :]
scores_filtered = scores_filtered[filter_indices]
#region IR_only
f = plt.figure(figsize=(6, 6), dpi=300)
plt.imshow(image[:, :, 0], cmap='gray', vmin=0, vmax=255)
for i in range(annotations['bboxes'].shape[0]):
label = annotations['labels'][i]
box = annotations['bboxes'][i]
x1, y1, x2, y2 = np.array(box).astype(int)
plt.plot([x1, x1, x2, x2, x1], [y1, y2, y2, y1, y1],
color='red',
linewidth=2)
for bbox, proba in zip(bboxes_filtered, scores_filtered):
x1, y1, x2, y2 = bbox.astype(int)
plt.plot([x1, x1, x2, x2, x1], [y1, y2, y2, y1, y1],
color='lightgreen',
linewidth=2,
alpha=proba / probas.max())
plt.text(x1, y1, '%.3f' % proba, fontsize=10)
plt.axis('off')
plt.savefig(
os.path.join('/app/Ant_mcs_detection/output/images_IRonly/',
'test_img_IR_withLabels_%05d.png' % example_idx))
plt.close()
#endregion IR_only
# region IR_WV_SLP
f = plt.figure(figsize=(6, 6), dpi=300)
plt.imshow(image)
for i in range(annotations['bboxes'].shape[0]):
label = annotations['labels'][i]
box = annotations['bboxes'][i]
x1, y1, x2, y2 = np.array(box).astype(int)
plt.plot([x1, x1, x2, x2, x1], [y1, y2, y2, y1, y1],
color='red',
linewidth=2)
for bbox, proba in zip(bboxes_filtered, scores_filtered):
x1, y1, x2, y2 = bbox.astype(int)
plt.plot([x1, x1, x2, x2, x1], [y1, y2, y2, y1, y1],
color='lightgreen',
linewidth=2,
alpha=proba / probas.max())
plt.text(x1, y1, '%.3f' % proba, fontsize=10)
plt.axis('off')
plt.savefig(
os.path.join(
'/app/Ant_mcs_detection/output/images_IR_WV_SLP/',
'test_img_IR_WV_SLP_withLabels_%05d.png' % example_idx))
plt.close()