def __init__(self):
# defaults overwritten by param
self.robot_name = "shamrock"
self.rectified_input = False
# read calibrations
self.extrinsics_filename = (get_ros_package_path('duckietown') +
"/config/baseline/calibration/camera_extrinsic/" +
self.robot_name + ".yaml")
if not os.path.isfile(extrinsics_filename):
logger.warn("no robot specific extrinsic calibration, trying default")
alternate_extrinsics_filename = (get_ros_package_path('duckietown') +
"/config/baseline/calibration/camera_extrinsic/default.yaml")
if not os.path.isfile(alternate_extrinsics_filename):
logger.warn("can't find default either, something's wrong")
else:
self.H = self.load_homography(alternate_extrinsics_filename)
else:
self.H = self.load_homography(self.extrinsics_filename)
self.H_inv = np.linalg.inv(self.H)
intrinsics_filename = (get_ros_package_path('duckietown') + "/config/baseline/calibration/camera_intrinsics/" + self.robot_name + ".yaml")
if not os.path.isfile(intrinsics_filename):
logger.warn("no robot specific calibration, trying default")
intrinsics_filename = (get_ros_package_path('duckietown') +
"/config/baseline/calibration/camera_extrinsic/default.yaml")
if not os.path.isfile(extrinsics_filename):
logger.error("can't find default either, something's wrong")
self.ci_ = self.load_camera_info(intrinsics_filename)
self.pcm_ = PinholeCameraModel()
self.pcm_.fromCameraInfo(self.ci)
def get_unique_filename(rt_name, rdbe):
commit = rdbe.commit[-8:]
d = rdbe.date.replace('-', '')
basename = rt_name + '_%s_%s_%s.rdbe.yaml' % (d, rdbe.branch, commit)
dr = get_ros_package_path('easy_regression')
filename = os.path.join(dr, 'db', rt_name, basename)
return filename
def single_image_histograms():
url = 'https://www.dropbox.com/s/bzezpw8ivlfu4b0/frame0002.jpg?dl=0'
p = os.path.join(get_ros_package_path('line_detector2'), 'include',
'line_detector2_tests', 'frame0002.jpg')
download_if_not_exist(url, p)
image_cv = image_cv_from_jpg_fn(p)
res = go(image_cv)
write_images_as_jpegs('single_image_histograms', res)
def load_board_info(self, filename=''):
'''Load calibration checkerboard info'''
if not os.path.isfile(filename):
filename = get_ros_package_path('ground_projection') + '/config/ground_projection_node/default.yaml'
target_data = yaml_load_file(filename)
target_info = {
'width': target_data['board_w'],
'height': target_data['board_h'],
'square_size': target_data['square_size'],
'x_offset': target_data['x_offset'],
'y_offset': target_data['y_offset'],
'offset': np.array([target_data['x_offset'], -target_data['y_offset']]),
'size': (target_data['board_w'], target_data['board_h']),
}
logger.info("Loaded checkerboard parameters")
return target_info
def examine_dataset(dirname, out):
logger.info(dirname)
dirname = expand_all(dirname)
jpgs = locate_files(dirname, '*.jpg')
mats = locate_files(dirname, '*.mat')
logger.debug('I found %d JPGs and %d .mat.' % (len(jpgs), len(mats)))
if len(jpgs) == 0:
msg = 'Not JPGs found in %r.' % dirname
raise ValueError(msg)
# if len(mats) == 0:
# msg = 'Not enough mats.'
# raise ValueError(msg)
first_jpg = sorted(jpgs)[0]
logger.debug('Using jpg %r to learn transformation.' % first_jpg)
first_jpg_image = image_cv_from_jpg_fn(first_jpg)
success, health, parameters = calculate_transform(first_jpg_image)
s = ""
s += 'success: %s\n' % str(success)
s += 'health: %s\n' % str(health)
s += 'parameters: %s\n' % str(parameters)
w = os.path.join(out, 'learned_transform.txt')
with open(w, 'w') as f:
f.write(s)
logger.info(s)
transform = ScaleAndShift(**parameters)
duckietown_package_dir = get_ros_package_path('duckietown')
config_dir = os.path.join(
duckietown_package_dir,
'config/baseline/line_detector/line_detector_node')
if not os.path.exists(config_dir):
msg = 'Could not find configuration dir %s' % config_dir
raise Exception(msg)
config_dir = expand_all(config_dir)
configurations = locate_files(config_dir, '*.yaml')
if not configurations:
msg = 'Could not find any configuration file in %s.' % config_dir
raise Exception(msg)
#logger.info('configurations: %r' % configurations)
for j in jpgs:
summaries = []
shape = (200, 160)
interpolation = cv2.INTER_NEAREST
bn = os.path.splitext(os.path.basename(j))[0]
fn = os.path.join(out, '%s.all.png' % (bn))
if os.path.exists(fn):
logger.debug('Skipping because file exists: %r' % fn)
else:
for c in configurations:
logger.info('Trying %r' % c)
name = os.path.splitext(os.path.basename(c))[0]
if name in ['oreo', 'myrtle', 'bad_lighting', '226-night']:
continue
with open(c) as f:
stuff = yaml.load(f)
if not 'detector' in stuff:
msg = 'Cannot find "detector" section in %r' % c
raise ValueError(msg)
detector = stuff['detector']
logger.info(detector)
if not isinstance(detector, list) and len(detector) == 2:
raise ValueError(detector)
def LineDetectorClass():
return instantiate(detector[0], detector[1])
s = run_detection(transform,
j,
out,
shape=shape,
interpolation=interpolation,
name=name,
LineDetectorClass=LineDetectorClass)
summaries.append(s)
together = make_images_grid(summaries,
cols=1,
pad=10,
bgcolor=[.5, .5, .5])
cv2.imwrite(fn, zoom_image(together, 4))
overall_results = []
comparison_results = {}
for m in mats:
logger.debug(m)
jpg = os.path.splitext(m)[0] + '.jpg'
if not os.path.exists(jpg):
msg = 'JPG %r for mat %r does not exist' % (jpg, m)
logger.error(msg)
else:
frame_results = test_pair(transform, jpg, m, out)
comparison_results[m] = frame_results
overall_results = merge_comparison_results(comparison_results[m],
overall_results)
print "comparison_results[m]=frame_results"
print "finished mats: " + dirname
return overall_results
def run_pipeline(image, line_detector_name, image_prep_name):
"""
Image: numpy (H,W,3) == BGR
Returns a dictionary, res with the following fields:
res['input_image']
"""
res = OrderedDict()
res['image_input'] = image
algo_db = get_easy_algo_db()
line_detector = algo_db.create_instance('line_detector',
line_detector_name)
image_prep = algo_db.create_instance('image_prep', image_prep_name)
context = FakeContext()
segment_list = image_prep.process(context,
image,
line_detector,
transform=None)
res['segments_on_image_input'] = vs_fancy_display(image_prep.image_cv,
segment_list)
res['segments_on_image_resized'] = vs_fancy_display(
image_prep.image_resized, segment_list)
ai = AntiInstagram()
ai.calculateTransform(res['image_input'])
transform = ai.applyTransform
res['image_input_transformed'] = transform(res['image_input'])
res['image_input_transformed_then_convertScaleAbs'] = cv2.convertScaleAbs(
res['image_input_transformed'])
segment_list2 = image_prep.process(context,
image,
line_detector,
transform=transform)
res['segments2_on_image_input_transformed'] = \
vs_fancy_display(image_prep.image_cv, segment_list2)
res['segments2_on_image_input_transformed_resized'] = \
vs_fancy_display(image_prep.image_resized, segment_list2)
filename = (get_ros_package_path('duckietown') +
"/config/baseline/calibration/camera_intrinsic/default.yaml")
ci = load_camera_info(filename)
# (h,w) = image.shape[:2]
ci_W = ci.width
ci_H = ci.height
mapx, mapy = cv2.initUndistortRectifyMap(ci.K, ci.D, ci.R, ci.P,
(ci_W, ci_H), cv2.CV_32FC1)
# mapx and mapy are (h, w) matrices that tell you
# the x coordinate and the y coordinate for each point
# in the first image
# print mapx.shape(),mapy.shape()
res['grid'] = get_grid(image.shape[:2])
res['grid_remap'] = cv2.remap(res['grid'], mapx, mapy, cv2.INTER_LINEAR)
res['image_input_rect'] = cv2.remap(res['image_input'], mapx, mapy,
cv2.INTER_LINEAR)
segment_list_rect = apply_transform_to_segment(segment_list, mapx, mapy)
res['segments2_on_image_input_rect'] = \
vs_fancy_display(res['image_input_rect'], segment_list_rect)
res['grid_undistort'] = cv2.undistort(res['grid'], ci.K, ci.D)
res['image_input_undistort'] = cv2.undistort(res['image_input'], ci.K,
ci.D)
homography = np.array([
-4.89775e-05, -0.0002150858, -0.1818273, 0.00099274, 1.202336e-06,
-0.3280241, -0.0004281805, -0.007185673, 1
]).reshape((3, 3))
segment_list_undistort = undistort_segments(segment_list,
ci_W,
ci_H,
ci.K,
ci.D,
ci.P,
ci.R,
homography=homography)
res['segments_und_image_input_und'] = vs_fancy_display(
res['image_input_undistort'], segment_list_undistort)
# homography_inv = np.linalg.inv(homography)
res['image_input_undistort_warp'] = cv2.warpPerspective(
res['image_input'],
homography, (ci_W, ci_H),
flags=cv2.WARP_INVERSE_MAP)
# add flags=cv2.WARP_INVERSE_MAP to do the inverse
return res