def find_closest_auto(demofile, new_xyz):
if args.parallel:
from joblib import Parallel, delayed
demo_clouds = [asarray(seg["cloud_xyz"]) for seg in demofile.values()]
keys = demofile.keys()
if args.parallel:
costs = Parallel(n_jobs=3,verbose=100)(delayed(registration_cost)(demo_cloud, new_xyz) for demo_cloud in demo_clouds)
else:
costs = []
for (i,ds_cloud) in enumerate(demo_clouds):
costs.append(registration_cost(ds_cloud, new_xyz))
print "completed %i/%i"%(i+1, len(demo_clouds))
print "costs\n",costs
if args.show_neighbors:
nshow = min(5, len(keys))
import cv2, rapprentice.cv_plot_utils as cpu
sortinds = np.argsort(costs)[:nshow]
near_rgbs = [asarray(demofile[keys[i]]["rgb"]) for i in sortinds]
bigimg = cpu.tile_images(near_rgbs, 1, nshow)
cv2.imshow("neighbors", bigimg)
print "press any key to continue"
cv2.waitKey()
ibest = np.argmin(costs)
return keys[ibest]
def find_closest_auto(demofile, new_xyz):
if args.parallel:
from joblib import Parallel, delayed
demo_clouds = [asarray(seg["cloud_xyz"]) for seg in demofile.values()]
keys = demofile.keys()
if args.parallel:
costs = Parallel(n_jobs=3, verbose=100)(
delayed(registration_cost)(demo_cloud, new_xyz)
for demo_cloud in demo_clouds)
else:
costs = []
for (i, ds_cloud) in enumerate(demo_clouds):
costs.append(registration_cost(ds_cloud, new_xyz))
print "completed %i/%i" % (i + 1, len(demo_clouds))
print "costs\n", costs
if args.show_neighbors:
nshow = min(5, len(keys))
import cv2, rapprentice.cv_plot_utils as cpu
sortinds = np.argsort(costs)[:nshow]
near_rgbs = [asarray(demofile[keys[i]]["rgb"]) for i in sortinds]
bigimg = cpu.tile_images(near_rgbs, 1, nshow)
cv2.imshow("neighbors", bigimg)
print "press any key to continue"
cv2.waitKey()
ibest = np.argmin(costs)
return keys[ibest]
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("h5file")
parser.add_argument("--pattern")
args = parser.parse_args()
import h5py
import rapprentice.cv_plot_utils as cpu
import numpy as np
import cv2
import fnmatch
hdf = h5py.File(args.h5file,"r")
all_imgnames = [(np.asarray(seg["rgb"]),name) for (name,seg) in hdf.items() if (args.pattern is None) or fnmatch.fnmatch(name, args.pattern)]
nrows = 7
chunksize = nrows**2
for i in xrange(0,len(all_imgnames),chunksize):
imgnames = all_imgnames[i:i+chunksize]
imgs = []
for (img, name) in imgnames:
cv2.putText(img, name,(30,30), cv2.FONT_HERSHEY_PLAIN, 3, (255, 0, 255), thickness = 3)
imgs.append(img)
bigimg = cpu.tile_images(imgs, nrows, nrows)
cv2.imshow("bigimg", bigimg)
cv2.waitKey()
args = parser.parse_args()
import h5py
import rapprentice.cv_plot_utils as cpu
import numpy as np
import cv2
import fnmatch
hdf = h5py.File(args.h5file, "r")
all_imgnames = [
(np.asarray(seg["rgb"]), name) for (name, seg) in hdf.items()
if (args.pattern is None) or fnmatch.fnmatch(name, args.pattern)
]
nrows = 7
chunksize = nrows**2
for i in xrange(0, len(all_imgnames), chunksize):
imgnames = all_imgnames[i:i + chunksize]
imgs = []
for (img, name) in imgnames:
cv2.putText(img,
name, (30, 30),
cv2.FONT_HERSHEY_PLAIN,
3, (255, 0, 255),
thickness=3)
imgs.append(img)
bigimg = cpu.tile_images(imgs, nrows, nrows)
cv2.imshow("bigimg", bigimg)
cv2.waitKey()
