def try_undistort(desired_count):
undist = cc.CameraConverter()
bagdir = '/data/bags/didi-round2/release/car/training/suburu_leading_at_distance'
bt = mb.find_bag_tracklets(bagdir, '/data/tracklets')
multi = mb.MultiBagStream(bt, ns.generate_numpystream)
generator = multi.generate(infinite = False)
count = 0
output_count = 0
for numpydata in generator:
im = numpydata.image
frame_idx, obs = numpydata.obs
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
undistorted = undist.undistort_image(im)
if count % 25 == 0:
cv2.imwrite('/data/dev/camera/orig_{}.png'.format(count), im)
# Print center.
img_point = undist.project_point(obs.position)
cv2.circle(undistorted, (int(img_point[0]), int(img_point[1])), radius = 5, color = (255, 0, 0), thickness=2)
# Print bbox corners.
img_points = undist.project_points(obs.get_bbox().transpose())
for img_point in img_points:
cv2.circle(undistorted, (int(img_point[0]), int(img_point[1])), radius = 5, color = (0, 255, 0), thickness=2)
cv2.imwrite('/data/dev/camera/undist_{}.png'.format(count), undistorted)
output_count += 1
count += 1
if desired_count is not None and output_count == desired_count:
return
def try_detector():
detector = get_latest_detector()
bagdir = '/data/bags/didi-round2/release/car/training/suburu_leading_front_left'
bt = mb.find_bag_tracklets(bagdir, '/data/tracklets')
multi = mb.MultiBagStream(bt, numpystream.generate_numpystream)
generator = generate_birdseye_boxes_single(multi, infinite=False)
for birdseye_box, yaw in generator:
prediction = detector.detect_rotation(birdseye_box)
print('gt_yaw: [{}], predicted_yaw: [{}]'.format(yaw, prediction))
def train_model(model):
validation_batch_size = 128
train_batch_size = 128
bag_tracklets = multibag.find_bag_tracklets(
'/data/didi/didi-round1/Didi-Release-2/Data/',
'/old_data/output/tracklet/')
# Good shuffle seeds: (7, 0.15)
shuffleseed = 7
multibag.shuffle(bag_tracklets, shuffleseed)
split = multibag.train_validation_split(bag_tracklets, 0.15)
validation_stream = multibag.MultiBagStream(split.validation_bags)
validation_generator = TrainDataGenerator(validation_stream,
include_ground_truth=True)
training_stream = multibag.MultiBagStream(split.train_bags)
training_generator = TrainDataGenerator(training_stream,
include_ground_truth=True)
print('train: ', training_generator.get_count(), ', validation: ',
validation_generator.get_count())
checkpoint_path = get_model_filename(
CHECKPOINT_DIR, suffix='e{epoch:02d}-vl{val_loss:.2f}')
# Set up callbacks. Stop early if the model does not improve. Save model checkpoints.
# Source: http://stackoverflow.com/questions/37293642/how-to-tell-keras-stop-training-based-on-loss-value
callbacks = [
EarlyStopping(monitor='val_loss', patience=2, verbose=0),
ModelCheckpoint(checkpoint_path,
monitor='val_loss',
save_best_only=False,
verbose=0),
]
hist = model.fit_generator(
training_generator.generate(train_batch_size),
steps_per_epoch=(training_generator.get_count() / train_batch_size),
epochs=100,
# Values for quick testing:
# steps_per_epoch = (128 / batch_size),
# epochs = 2,
validation_data=validation_generator.generate(validation_batch_size),
validation_steps=(validation_generator.get_count() /
validation_batch_size),
callbacks=callbacks)
model.save(get_model_filename(MODEL_DIR))
# print(hist)
with open(get_model_filename(HISTORY_DIR, '', 'p'), 'wb') as f:
pickle.dump(hist.history, f)
def try_augmenting_camera_boxes():
camera_converter = cc.CameraConverter()
bagdir = '/data/bags/didi-round2/release/car/training/suburu_leading_front_left'
bt = mb.find_bag_tracklets(bagdir, '/data/tracklets')
generator = generate_training_data_multi(bt)
count = 0
for bbox, label, im in generator:
new_bbox, new_label = augment_example(bbox, label, camera_converter)
print('bbox', bbox)
print('new_bbox', new_bbox)
print('label', label)
print('new_label', new_label)
count += 1
if count == 10:
return
def try_draw_panoramas():
import cv2
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
bagdir = '/data/bags/'
# bagdir = '/data/bags/didi-round2/release/car/training/suburu_leading_front_left'
# bagdir = '/data/bags/didi-round2/release/pedestrian/'
# bag_file = '/data/bags/didi-round2/release/car/testing/ford02.bag'
bt = mb.find_bag_tracklets(bagdir, '/data/tracklets')
multi = mb.MultiBagStream(bt, ns.generate_numpystream)
# numpystream = ns.generate_numpystream(bag_file, tracklet)
generator = generate_panoramas_multi(multi)
id = 1
for im, bbox, obs in generator:
cv2.rectangle(im, tuple(bbox[0]), tuple(bbox[1]), color = (255, 0, 0))
im = cv2.resize(im, (0,0), fx=1.0, fy=8.0)
plt.imshow(im)
plt.show()
def try_rotating_images(train_dir):
bagdir = '/data/bags/didi-round2/release/car/training/suburu_leading_front_left'
bt = mb.find_bag_tracklets(bagdir, '/data/tracklets')
multi = mb.MultiBagStream(bt, numpystream.generate_numpystream)
generator = generate_birdseye_boxes_single(multi, infinite=False)
count = 0
frames_since_last_conversion = 0
for birdseye_box, yaw in generator:
if (yaw > (math.pi / 4) or yaw <
(-math.pi / 4)) and frames_since_last_conversion > 10:
# Try to undo rotation with negative yaw.
rotated = rotate_image(birdseye_box, -yaw)
# Expect car to have zero rotation in image.
cv2.imwrite('rotate_test_{}.png'.format(count), rotated)
print('count: {}, orig_yaw: {}'.format(count, yaw))
count += 1
frames_since_last_conversion = 0
if count % 10 == 0:
return
else:
frames_since_last_conversion += 1
def try_write():
bagdir = '/data/bags/'
# bagdir = '/data/bags/didi-round2/release/car/training/suburu_leading_front_left'
bt = mb.find_bag_tracklets(bagdir, '/data/tracklets')
multi = mb.MultiBagStream(bt, ns.generate_numpystream)
write_train_data(multi, '/home/eljefec/repo/squeezeDet/data/KITTI/panorama912x48')
crop = ci.crop_image(birdseye, expected_shape, new_shape)
image_file = os.path.join(imagedir, '{:06d}.png'.format(id))
imlib.save_np_image(crop, image_file, bbox_tuple)
label_path = os.path.join(labeldir, '{:06d}.txt'.format(id))
write_kitti_annotation(obs, birdseye_bbox, label_path)
id += 1
if id % 1000 == 0:
print('Working... Processed {} samples.'.format(id))
stopwatch.stop()
print('Elapsed time: {}'.format(
stopwatch.format_duration()))
stopwatch.start()
print('DONE. Processed {} samples.'.format(id))
stopwatch.stop()
print('Elapsed time: {}'.format(stopwatch.format_duration()))
if __name__ == '__main__':
bagdir = '/data/bags/'
# bagdir = '/data/bags/didi-round2/release/car/training/suburu_leading_at_distance'
bag_tracklets = multibag.find_bag_tracklets(bagdir, '/data/tracklets')
slice_config = ld.slice_config()
generate_kitti(
bag_tracklets,
'/home/eljefec/repo/squeezeDet/data/KITTI/training_64x64/image_2',
'/home/eljefec/repo/squeezeDet/data/KITTI/training_64x64/label_2',
output_bbox=False,
slice_config=slice_config)