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 single_image1():
url = 'https://www.dropbox.com/s/bzezpw8ivlfu4b0/frame0002.jpg?dl=0'
p = get_file_from_url(url)
image_cv = image_cv_from_jpg_fn(p)
line_detector_name = 'baseline'
image_prep_name = 'prep_200_100'
res = run_pipeline(image_cv, line_detector_name, image_prep_name)
write_jpgs_to_dir(res, 'single_image1')
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 go(self):
line_detector = self.options.line_detector
image_prep = self.options.image_prep
self.info('Line detector: %s' % line_detector)
self.info('image_prep: %s' % image_prep)
image_filename = self.options.image
if image_filename.startswith('http'):
image_filename = get_file_from_url(image_filename)
image_cv = image_cv_from_jpg_fn(image_filename)
res = run_pipeline(image_cv, line_detector, image_prep)
output = self.options.output
if output is None:
output = 'out-pipeline-' + get_md5(self.options.image)[:6]
self.info('No --output given, using %s' % output)
write_jpgs_to_dir(res, output)
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 test_pair(transform, jpg, mat, out):
"""
jpg = filename
mat = filename
"""
data = scipy.io.loadmat(mat)
regions = data["regions"].flatten()
max_type = 0
for r in regions:
max_type = max(max_type, r["type"][0][0][0][0])
r_vals = {}
for t in np.arange(max_type):
r_vals[t + 1] = np.array([], "float32")
g_vals = copy.deepcopy(r_vals)
b_vals = copy.deepcopy(r_vals)
h_vals = copy.deepcopy(r_vals)
s_vals = copy.deepcopy(r_vals)
v_vals = copy.deepcopy(r_vals)
result_stats = {
"average_abs_err": [],
"total_pixels": 0,
"total_error": 0,
"total_regions": 0,
"r_vals": r_vals,
"g_vals": g_vals,
"b_vals": b_vals,
"h_vals": h_vals,
"s_vals": s_vals,
"v_vals": v_vals,
}
for r in regions:
logger.info("region")
x = r["x"][0][0].flatten()
y = r["y"][0][0].flatten()
mask = r["mask"][0][0]
mask3 = cv2.merge([mask, mask, mask])
print "x", x
print "y", y
print "mask shape", mask.shape
print "type", r["type"][0][0][0][
0
] # type in 1- based / matlab-based indices from the list of region types (i.e road, white, yellow, red, or what ever types were annotated)
print "color", r["color"][0] # color in [r,g,b] where [r,g,b]are between 0 and 1
t = r["type"][0][0][0][0]
# print 'guy look here'
region_color = r["color"][0]
region_color = region_color[0][0]
rval = region_color[0] * 255.0
gval = region_color[1] * 255.0
bval = region_color[2] * 255.0
image = image_cv_from_jpg_fn(jpg)
transformed = transform(image)
[b2, g2, r2] = cv2.split(transformed)
thsv = cv2.cvtColor(transformed, cv2.cv.CV_BGR2HSV)
[h2, s2, v2] = cv2.split(thsv)
r2_ = r2[mask.nonzero()]
g2_ = g2[mask.nonzero()]
b2_ = b2[mask.nonzero()]
h2_ = h2[mask.nonzero()]
s2_ = s2[mask.nonzero()]
v2_ = v2[mask.nonzero()]
# ipython_if_guy()
result_stats["r_vals"][t] = np.concatenate((result_stats["r_vals"][t], r2_), 0)
result_stats["g_vals"][t] = np.concatenate((result_stats["g_vals"][t], g2_), 0)
result_stats["b_vals"][t] = np.concatenate((result_stats["b_vals"][t], b2_), 0)
result_stats["h_vals"][t] = np.concatenate((result_stats["h_vals"][t], h2_), 0)
result_stats["s_vals"][t] = np.concatenate((result_stats["s_vals"][t], s2_), 0)
result_stats["v_vals"][t] = np.concatenate((result_stats["v_vals"][t], v2_), 0)
absdiff_img = cv2.absdiff(transformed, np.array([bval, gval, rval, 0.0]))
masked_diff = cv2.multiply(np.array(absdiff_img, "float32"), np.array(mask3, "float32"))
num_pixels = cv2.sumElems(mask)[0]
region_error = cv2.sumElems(cv2.sumElems(masked_diff))[0]
avg_abs_err = region_error / (num_pixels + 1.0)
print "Average abs. error", avg_abs_err
result_stats["average_abs_err"].append(avg_abs_err)
result_stats["total_pixels"] = result_stats["total_pixels"] + num_pixels
result_stats["total_error"] = result_stats["total_error"] + region_error
result_stats["total_regions"] = result_stats["total_regions"] + 1
# XXX: to finish
return result_stats
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 test_pair(transform, jpg, mat, out):
"""
jpg = filename
mat = filename
"""
data = scipy.io.loadmat(mat)
regions = data['regions'].flatten()
max_type = 0
for r in regions:
max_type = max(max_type, r['type'][0][0][0][0])
r_vals = {}
for t in np.arange(max_type):
r_vals[t + 1] = np.array([], 'float32')
g_vals = copy.deepcopy(r_vals)
b_vals = copy.deepcopy(r_vals)
h_vals = copy.deepcopy(r_vals)
s_vals = copy.deepcopy(r_vals)
v_vals = copy.deepcopy(r_vals)
result_stats = {
'average_abs_err': [],
'total_pixels': 0,
'total_error': 0,
'total_regions': 0,
'r_vals': r_vals,
'g_vals': g_vals,
'b_vals': b_vals,
'h_vals': h_vals,
's_vals': s_vals,
'v_vals': v_vals
}
for r in regions:
logger.info('region')
x = r['x'][0][0].flatten()
y = r['y'][0][0].flatten()
mask = r['mask'][0][0]
mask3 = cv2.merge([mask, mask, mask])
print 'x', x
print 'y', y
print 'mask shape', mask.shape
# type in 1- based / matlab-based indices from the list of region types (i.e road, white,
# yellow, red, or what ever types were annotated)
print 'type', r['type'][0][0][0][0]
# color in [r,g,b] where [r,g,b]are between 0 and 1
print 'color', r['color'][0]
t = r['type'][0][0][0][0]
# print 'guy look here'
region_color = r['color'][0]
region_color = region_color[0][0]
rval = region_color[0] * 255.
gval = region_color[1] * 255.
bval = region_color[2] * 255.
image = image_cv_from_jpg_fn(jpg)
transformed = transform(image)
[b2, g2, r2] = cv2.split(transformed)
thsv = cv2.cvtColor(transformed, cv2.COLOR_BGR2HSV)
[h2, s2, v2] = cv2.split(thsv)
r2_ = r2[mask.nonzero()]
g2_ = g2[mask.nonzero()]
b2_ = b2[mask.nonzero()]
h2_ = h2[mask.nonzero()]
s2_ = s2[mask.nonzero()]
v2_ = v2[mask.nonzero()]
result_stats['r_vals'][t] = np.concatenate(
(result_stats['r_vals'][t], r2_), 0)
result_stats['g_vals'][t] = np.concatenate(
(result_stats['g_vals'][t], g2_), 0)
result_stats['b_vals'][t] = np.concatenate(
(result_stats['b_vals'][t], b2_), 0)
result_stats['h_vals'][t] = np.concatenate(
(result_stats['h_vals'][t], h2_), 0)
result_stats['s_vals'][t] = np.concatenate(
(result_stats['s_vals'][t], s2_), 0)
result_stats['v_vals'][t] = np.concatenate(
(result_stats['v_vals'][t], v2_), 0)
absdiff_img = cv2.absdiff(transformed, np.array([bval, gval, rval,
0.]))
masked_diff = cv2.multiply(np.array(absdiff_img, 'float32'),
np.array(mask3, 'float32'))
num_pixels = cv2.sumElems(mask)[0]
region_error = cv2.sumElems(cv2.sumElems(masked_diff))[0]
avg_abs_err = region_error / (num_pixels + 1.)
print 'Average abs. error', avg_abs_err
result_stats['average_abs_err'].append(avg_abs_err)
result_stats[
'total_pixels'] = result_stats['total_pixels'] + num_pixels
result_stats[
'total_error'] = result_stats['total_error'] + region_error
result_stats['total_regions'] = result_stats['total_regions'] + 1
# XXX: to finish
return result_stats
