OBJECT_WIDTH,
scale=(1000, -1000, -1000))
rgbA, depthA = normalize_scale(rgbA, depthA, bb,
real_dataset.camera, IMAGE_SIZE)
rgbB, depthB = normalize_scale(rotated_rgb, rotated_depth, bb,
real_dataset.camera, IMAGE_SIZE)
index = output_dataset.add_pose(rgbA, depthA, previous_pose)
output_dataset.add_pair(rgbB, depthB, random_transform, index)
iteration = i * SAMPLE_QUANTITY + j
sys.stdout.write(
"Progress: %d%% \r" %
(int(iteration /
(SAMPLE_QUANTITY * real_dataset.size()) * 100)))
sys.stdout.flush()
if iteration % 500 == 0:
output_dataset.dump_images_on_disk()
if iteration % 5000 == 0:
output_dataset.save_json_files(metadata)
if args.verbose:
show_frames(rgbA, depthA, rgbB, depthB)
cv2.imshow("testB", rgbB[:, :, ::-1])
k = cv2.waitKey(1)
if k == ESCAPE_KEY:
break
output_dataset.dump_images_on_disk()
output_dataset.save_json_files(metadata)
detection_offset.rotate(z=math.radians(1))
elif key == NUM_PAD_8_KEY:
detection_offset.translate(z=-0.001)
elif key == NUM_PAD_9_KEY:
detection_offset.rotate(x=math.radians(1))
elif key == ARROW_UP_KEY:
detection_offset.translate(y=-0.001)
elif key == ARROW_DOWN_KEY:
detection_offset.translate(y=0.001)
elif key == ARROW_LEFT_KEY:
detection_offset.rotate(y=math.radians(-1))
elif key == ARROW_RIGHT_KEY:
detection_offset.rotate(y=math.radians(1))
print("Compute detections")
for i in range(dataset.size()):
frame, pose = dataset.data_pose[i]
# if pose is identity, compute the detection
if pose == Transform():
rgb, depth = dataset.data_pose[i][0].get_rgb_depth(dataset.path)
pose = detector.detect(rgb)
if detector.get_likelihood() < 0.1:
print(
"[WARNING] : Detector returns uncertain pose at frame {}".
format(i))
#Todo : need better way to handle viewpoint's pose change in dataset...
dataset.data_pose[i] = (Frame(rgb, depth, str(i)), pose)
np.save(os.path.join(dataset.path, "offset"), detection_offset.matrix)
dataset.dump_images_on_disk()
dataset.save_json_files({"save_type": "png"})
sensor.stop()
import cv2
import os
if __name__ == '__main__':
folder = "/home/mathieu/Dataset/DeepTrack/dragon/"
dataset_path = os.path.join(folder, "train_raw_real")
new_dataset_path = os.path.join(folder, "train_raw_real_resized")
if not os.path.exists(new_dataset_path):
os.mkdir(new_dataset_path)
dataset = Dataset(dataset_path)
if not dataset.load():
print("[Error]: Train dataset empty")
sys.exit(-1)
new_dataset = Dataset(new_dataset_path)
new_dataset.camera = dataset.camera.copy()
new_dataset.camera.set_ratio(2)
for i in range(dataset.size()):
rgb, depth, pose = dataset.load_image(i)
new_rgb = cv2.resize(
rgb, (new_dataset.camera.width, new_dataset.camera.height))
new_depth = cv2.resize(
depth, (new_dataset.camera.width, new_dataset.camera.height))
new_dataset.add_pose(new_rgb, new_depth, pose)
if i % (1 * dataset.size() / 100) == 0:
print("Progress : {}%".format(i * 100 / dataset.size()))
new_dataset.set_save_type(dataset.metadata["save_type"])
new_dataset.dump_images_on_disk()
new_dataset.save_json_files(dataset.metadata)
pair = combine_view_transform(random_pose, random_transform)
rgbA, depthA = vpRender.render(random_pose.transpose())
rgbB, depthB = vpRender.render(pair.transpose(),
sphere_sampler.random_direction())
bb = compute_2Dboundingbox(random_pose,
dataset.camera,
OBJECT_WIDTH,
scale=(1000, -1000, -1000))
rgbA, depthA = normalize_scale(rgbA, depthA, bb, dataset.camera,
IMAGE_SIZE)
rgbB, depthB = normalize_scale(rgbB, depthB, bb, dataset.camera,
IMAGE_SIZE)
index = dataset.add_pose(rgbA, depthA, random_pose)
dataset.add_pair(rgbB, depthB, random_transform, index)
if i % 500 == 0:
dataset.dump_images_on_disk()
if i % 5000 == 0:
dataset.save_json_files(metadata)
if args.verbose:
show_frames(rgbA, depthA, rgbB, depthB)
cv2.imshow("testB", rgbB[:, :, ::-1])
k = cv2.waitKey(1)
if k == ESCAPE_KEY:
break
dataset.dump_images_on_disk()
dataset.save_json_files(metadata)