def run_detection(transform, jpg, out, shape, interpolation, name,
LineDetectorClass):
image = image_cv_from_jpg_fn(jpg)
image = cv2.resize(image, shape, interpolation=interpolation)
# bgr = bgr[bgr.shape[0] / 2:, :, :]
image_detections = line_detection(LineDetectorClass, image)
transformed = transform(image)
transformed_clipped = image_clip_255(transformed)
transformed_detections = line_detection(LineDetectorClass,
transformed_clipped)
if not os.path.exists(out):
os.makedirs(out)
bn = os.path.splitext(os.path.basename(jpg))[0]
def write(postfix, im):
fn = os.path.join(out, '%s.%s.%s.png' % (bn, name, postfix))
cv2.imwrite(fn, zoom_image(im, 4))
together = make_images_grid(
[
image, # transformed,
merge_masks_res(image_detections),
gray2rgb(image_detections['edges']),
image_detections['annotated'],
transformed_clipped,
merge_masks_res(transformed_detections),
gray2rgb(transformed_detections['edges']),
transformed_detections['annotated'],
],
cols=4,
pad=35,
bgcolor=[1, 1, 1])
# write the string "name" in the upper left of image together
cv2.putText(together, name, (0, 20), cv2.FONT_HERSHEY_SIMPLEX, 1,
(0, 0, 255), 2)
return together
def run_detection(transform, jpg, out, shape, interpolation, name, LineDetectorClass):
image = image_cv_from_jpg_fn(jpg)
image = cv2.resize(image, shape, interpolation=interpolation)
# bgr = bgr[bgr.shape[0] / 2:, :, :]
image_detections = line_detection(LineDetectorClass, image)
transformed = transform(image)
transformed_clipped = image_clip_255(transformed)
transformed_detections = line_detection(LineDetectorClass, transformed_clipped)
if not os.path.exists(out):
os.makedirs(out)
bn = os.path.splitext(os.path.basename(jpg))[0]
def write(postfix, im):
fn = os.path.join(out, "%s.%s.%s.png" % (bn, name, postfix))
cv2.imwrite(fn, zoom_image(im, 4))
together = make_images_grid(
[
image, # transformed,
merge_masks_res(image_detections),
gray2rgb(image_detections["edges"]),
image_detections["annotated"],
transformed_clipped,
merge_masks_res(transformed_detections),
gray2rgb(transformed_detections["edges"]),
transformed_detections["annotated"],
],
cols=4,
pad=35,
bgcolor=[1, 1, 1],
)
# write the string "name" in the upper left of image together
cv2.putText(together, name, (0, 20), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
return together
def examine_dataset(dirname, out):
logger.info(dirname)
dirname = expand_environment(dirname)
jpgs = locate_files(dirname, "*.jpg")
mats = locate_files(dirname, "*.mat")
logger.debug("I found %d jpgs and %d mats" % (len(jpgs), len(mats)))
if len(jpgs) == 0:
msg = "Not enough jpgs."
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)
config_dir = "${DUCKIETOWN_ROOT}/catkin_ws/src/duckietown/config/baseline/line_detector/line_detector_node/"
config_dir = expand_environment(config_dir)
configurations = locate_files(config_dir, "*.yaml")
# 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)
from duckietown_utils.instantiate_utils import instantiate
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=[0.5, 0.5, 0.5])
cv2.imwrite(fn, zoom_image(together, 4))
# ipython_if_guy()
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"
# ipython_if_guy()
print "finished mats: " + dirname
return overall_results
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
