def test_mean_smooth(pts):
#factor = 640 / 368.0
C0, C1, d0, d1 = load_camera_from_calibr('../calibr-1211/camchain-homeyihuaDesktopCPM3D_kalibrfinal3.yaml')
pts3d = []
for k in range(14):
p0 = pts[k,:2]
p1 = pts[k,2:]
p0, p1 = p1, p0
p3d = triangulate(C0, C1, p0, p1)
pts3d.append(p3d)
pts3d = np.array(pts3d)
return pts3d
import sys, os
from tensorpack.utils.fs import mkdir_p
from runner import get_runner, get_parallel_runner
from model import colorize
from calibr import load_camera_from_calibr
if __name__ == '__main__':
dir = sys.argv[1]
undistdir = sys.argv[2]
outdir = sys.argv[3]
mkdir_p(undistdir)
mkdir_p(outdir)
runner, _ = get_runner('../data/cpm.npy')
C0, C1, d0, d1 = load_camera_from_calibr(
'../calibr-1211/camchain-homeyihuaDesktopCPM3D_kalibrfinal3.yaml')
for f in sorted(glob.glob(os.path.join(dir, '*.jpg'))):
im = cv2.imread(f, cv2.IMREAD_COLOR)
im = cv2.undistort(im, C0.K, d0)
cv2.imwrite(os.path.join(undistdir, os.path.basename(f)), im)
im = cv2.resize(im, (368, 368))
out = runner(im)
np.save(os.path.join(outdir, os.path.basename(f)), out)
#viz = colorize(im, out[:,:,:-1].sum(axis=2))
#cv2.imshow("", viz/255.0)
#cv2.waitKey()
print f
def final():
camera = libcpm.Camera()
camera.setup()
# cpp matcher:
pmatcher = libcpm.PatchMatch()
pmatcher.init(camera, 20)
# python matcher:
#bgs0, bgs1 = [], []
#for k in range(20):
#m1 = camera.get_for_py(0)
#m1 = np.array(m1, copy=True)
#m2 = camera.get_for_py(1)
#m2 = np.array(m2, copy=True)
#bgs0.append(m1)
#bgs1.append(m2)
#matcher = Matcher(BackgroundSegmentor(bgs0), BackgroundSegmentor(bgs1))
runner = get_parallel_runner('../data/cpm.npy')
viewer = libcpm.StereoCameraViewer(camera)
viewer.start()
C1, C0, d1, d0 = load_camera_from_calibr(
'../calibr-1211/camchain-homeyihuaDesktopCPM3D_kalibrfinal3.yaml')
queue = deque(maxlen=2)
ctx = zmq.Context()
sok = ctx.socket(zmq.PUSH)
global args
sok.connect('tcp://{}:8888'.format(args.host))
def cpp_matcher(m1, m2, o1, o2):
o1 = libcpm.Mat(o1)
o2 = libcpm.Mat(o2)
out = pmatcher.match_with_hm(m1, m2, o1, o2)
return np.asarray(out).reshape(14, 4) #14 x 2image x (x,y)
pts3ds = []
cnt = 0
while True:
cnt += 1
print 'begin---', time.time()
m1 = camera.get_for_py(0)
m1r = np.array(m1, copy=False)
m2 = camera.get_for_py(1)
m2r = np.array(m2, copy=False)
m1s = cv2.resize(m1r, (368, 368))
m2s = cv2.resize(m2r, (368, 368))
print 'after resize---', time.time()
o1, o2 = runner(m1s, m2s)
print 'after cpm---', time.time()
#pts14x4 = matcher.match(m1r, m2r, o1, o2)
pts14x4 = cpp_matcher(m1, m2, o1, o2)
#to_save = (m1s, m2s, o1, o2, pts14x4)
#fout = open('full-recording/{:04d}.dat'.format(cnt), 'wb')
#fout.write(dumps(to_save))
#fout.close()
print 'after match---', time.time()
queue.append(pts14x4)
p2d = np.mean(queue, axis=0)
p3ds = np.zeros((14, 3))
for c in range(14):
p3d = triangulate(C0, C1, p2d[c, :2], p2d[c, 2:])
p3ds[c, :] = p3d
sok.send(dumps(p3ds))
print p3ds
print 'after send---', time.time()
print '-----------------'