default='./images')
#ecto options
scheduler_options(parser.add_argument_group('Scheduler Options'))
args = parser.parse_args()
if not os.path.exists(args.output):
os.makedirs(args.output)
source = create_source(
package_name='image_pipeline',
source_type='OpenNISource') #optionally pass keyword args here...
depth_saver = ecto.If(
'depth saver',
cell=ImageSaver(
filename_format=os.path.join(args.output, 'depth%04d.png')))
image_saver = ecto.If(
'rgb saver',
cell=ImageSaver(
filename_format=os.path.join(args.output, 'image%04d.png')))
display = imshow(name='RGB', triggers=dict(save=ord('s')))
plasm = ecto.Plasm()
plasm.connect(
source['image'] >> (display['image'], image_saver['image']),
source['depth'] >> (imshow(name='Depth')['image'], depth_saver['image']),
display['save'] >> (image_saver['__test__'], depth_saver['__test__']))
run_plasm(args, plasm, locals=vars())
parser.add_argument('--width', dest='width', default=640)
parser.add_argument('--height', dest='height', default=480)
parser.add_argument('--preview', action='store_true')
#ecto options
scheduler_options(parser.add_argument_group('Scheduler Options'))
args = parser.parse_args()
if not os.path.exists(args.output):
os.makedirs(args.output)
#camera = VideoCapture(video_device=1,width=int(args.width),height=int(args.height))
source = create_source(
package_name='image_pipeline',
source_type='OpenNISource') #optionally pass keyword args here...
depth_saver = ImageSaver(
filename_format=os.path.join(args.output, 'frame_%010d_depth.png'))
image_saver = ImageSaver(
filename_format=os.path.join(args.output, 'frame_%010d_image.png'))
#mono_saver = ImageSaver(filename_format=os.path.join(args.output, 'frame_%010d_mono.png'))
plasm = ecto.Plasm()
plasm.connect(
# camera['image'] >> imshow(name='Mono')['image'],
source['depth'] >> imshow(name='Depth')['image'],
source['image'] >> imshow(name='RGB')['image'])
if not args.preview:
plasm.connect(
source['image'] >> image_saver['image'],
source['depth'] >> depth_saver['image'],
# camera['image'] >> mono_saver['image'],
continue
plasm = ecto.Plasm()
#the db_reader transforms observation id into a set of image,depth,mask,K,R,T
db_reader = capture.ObservationReader("db_reader", db_params=dbtools.args_to_db_params(args))
#this iterates over all of the observation ids.
observation_dealer = ecto.Dealer(tendril=db_reader.inputs.at('observation'), iterable=obs_ids)
plasm.connect(observation_dealer[:] >> db_reader['observation'])
path_names = [ 'image', 'depth', 'mask']
for path_name in path_names:
path = os.path.join(object_id, path_name)
if not os.path.exists(path):
os.makedirs(path)
writer_image = ImageSaver(filename_format=os.path.join(path, '%05d.png'))[:]
plasm.connect(db_reader[path_name] >> (writer_image, imshow(name=path_name)['image']))
sched = ecto.schedulers.Singlethreaded(plasm)
sched.execute()
# write files listing the produced files
for path_name in path_names:
file_object = open(os.path.join(os.getcwd(), object_id, path_name + '.txt'), 'w')
for file_name in [ os.path.join(os.getcwd(), object_id, path_name, file_name)
for file_name in os.listdir(os.path.join(object_id, path_name))
if file_name.endswith('png') ]:
file_object.write(file_name + '\n')
file_object.close
#After done execution upload the resulting model to the db....
def match(options,
scale=1.0,
train_scale=1 / 3.0,
scale_factor=1.05,
n_levels=5,
n_features_train=10000,
n_features_test=1000,
match_distance=120,
):
if not os.path.exists(options.output):
os.makedirs(options.output)
train = imread('Train image', image_file=options.train,
mode=ImageMode.GRAYSCALE)
test = imread('Test image', image_file=options.test,
mode=ImageMode.GRAYSCALE)
train_name, _extension = os.path.splitext(os.path.basename(options.train))
test_name, _extension = os.path.splitext(os.path.basename(options.test))
print 'Scale %d is %f' % (0, scale)
for i in range(n_levels - 1):
scale *= 1.0 / scale_factor
print 'Scale %d is %f' % (i + 1, scale)
orb_train = ORB(n_features=n_features_train,
n_levels=n_levels, scale_factor=scale_factor)
orb_test = ORB(n_features=n_features_test, n_levels=1)
plasm = ecto.Plasm()
scale = Scale(factor=train_scale, interpolation=AREA)
plasm.connect(train['image'] >> scale['image'])
train_pts = hookup_orb(plasm, scale, orb_train)
test_pts = hookup_orb(plasm, test, orb_test)
matcher = Matcher()
plasm.connect(orb_train['descriptors'] >> matcher['train'],
orb_test['descriptors'] >> matcher['test'],
)
matchesMat = MatchesToMat()
h_est = MatchRefinement(match_distance=match_distance)
plasm.connect(train_pts['points'] >> h_est['train'],
test_pts['points'] >> h_est['test'],
matcher['matches'] >> h_est['matches'],
h_est['matches'] >> matchesMat['matches']
)
match_draw = DrawMatches()
output_base = os.path.join(options.output,
'%(train)s_%(test)s' %
dict(train=train_name, test=test_name))
match_image = output_base + '.png'
image_saver = ImageSaver(filename=match_image)
plasm.connect(train_pts['points'] >> match_draw['train'],
test_pts['points'] >> match_draw['test'],
h_est['matches'] >> match_draw['matches'],
h_est['matches_mask'] >> match_draw['matches_mask'],
scale['image'] >> match_draw['train_image'],
test['image'] >> match_draw['test_image'],
match_draw[:] >> image_saver[:]
)
match_eval = MatchEval(output=options.output)
plasm.connect(matchesMat['matches'] >> match_eval['matches'],
train_pts['points'] >> match_eval['train'],
test_pts['points'] >> match_eval['test'],
h_est['matches_mask'] >> match_eval['inliers'],
)
run_plasm(options, plasm, locals=vars())
#!/usr/bin/env python
import ecto
from ecto_opencv.highgui import VideoCapture, imshow, FPSDrawer, ImageSaver
video_cap = VideoCapture(video_device=0)
fps = FPSDrawer()
video_display = imshow(name='video_cap', triggers=dict(save=ord('s')))
saver = ecto.If(
cell=ImageSaver('saver', filename_format='ecto_image_%05d.jpg', start=1))
plasm = ecto.Plasm()
plasm.connect(
video_cap['image'] >> fps['image'],
fps['image'] >> video_display['image'],
video_display['save'] >> saver['__test__'],
fps['image'] >> saver['image'],
)
if __name__ == '__main__':
from ecto.opts import doit
doit(plasm, description='Save images from video stream.')
import ecto
from ecto_opencv.highgui import VideoCapture, FPSDrawer, imshow, ImageSaver
from ecto_opencv.imgproc import BilateralFilter
video_cap = VideoCapture(video_device=0)
fps = FPSDrawer()
video_display = imshow(
'imshow',
name='video_cap',
triggers=dict(save=ord('s')),
)
saver = ecto.If(
cell=ImageSaver('saver', filename_format='bilateral_%05d.jpg', start=1))
bl_begin = None
bl_end = None
plasm = ecto.Plasm()
#Build up a chain of bilateral filters.
for i in range(1, 10):
next = BilateralFilter(d=-1, sigmaColor=10, sigmaSpace=5)
if bl_begin == None: # beginning case
bl_begin = next
bl_end = next
continue
plasm.connect(bl_end[:] >> next[:])
bl_end = next
for x in files:
if '.png' in x:
num = x.split('_')[1].split('.')[0]
if num.isdigit():
return int(num) + 1
return 0
rgb_start = init_image_dir('rgb')
ir_start = init_image_dir('ir')
print 'rgb start', rgb_start, 'ir_start', ir_start
if (rgb_start != ir_start):
print 'please ensure that the images in ir, and rgb are aligned lexographically'
sys.exit(1)
#saving images
rgb_saver = ecto.If(
cell=ImageSaver(filename_format='rgb/rgb_%04d.png', start=rgb_start))
ir_saver = ecto.If(
cell=ImageSaver(filename_format='ir/ir_%04d.png', start=ir_start))
plasm.connect(rgb2gray[:] >> rgb_saver['image'],
conversion[:] >> ir_saver['image'], ander[:] >>
(rgb_saver['__test__'], ir_saver['__test__']))
sched = ecto.schedulers.Singlethreaded(plasm)
while sched.execute(niter=5) == 0:
#swap it modes
next_mode, capture.params.stream_mode = capture.params.stream_mode, next_mode
print sched.stats()