def frameCentroidsToDets(frame, mats=None):
'''Extract the centroids for given cameras and undistorts them. Returns a list of x2ds and splits per camera.'''
detRawData, splits = SolveIK.list_of_lists_to_splits(frame,
dtype=np.float32)
detRawData = (detRawData - np.float32([256, 256])) / np.float32(
[256, -256])
if mats is None: return detRawData[:, :2].copy(), splits
return Calibrate.undistort_dets(detRawData, splits, mats)
def get_labels(frames, x3ds_seq, detections_seq, mats, x2d_threshold=0.01):
'''Project all the 3d points in all the views and label the detections.'''
num_cameras = len(mats)
ret = {}
Ps = np.array([m[2] / (m[0][0, 0]) for m in mats], dtype=np.float32)
for fi in frames:
print fi, '\r',
x3ds, x3ds_labels = x3ds_seq[fi]
x2ds_raw_data, splits = detections_seq[fi][0]
assert (num_cameras + 1 == len(splits))
x2ds_labels = -np.ones(len(x2ds_raw_data), dtype=np.int32)
x2ds_data, _ = Calibrate.undistort_dets(x2ds_raw_data, splits, mats)
if len(x2ds_data):
clouds = ISCV.HashCloud2DList(x2ds_data, splits, x2d_threshold)
sc, x2ds_labels, x2ds_vels = Label.project_assign(
clouds, x3ds, x3ds_labels, Ps, x2d_threshold)
zeros = np.where(x2ds_labels == -1)[0]
# these lines remove all the data for the unlabelled points
x2ds_data[zeros] = -1
x2ds_raw_data[zeros] = -1
ret[fi] = x2ds_raw_data, splits, x2ds_labels
return ret
def frameCentroidsToDets(frame, mats=None): '''Extract the centroids for given cameras and undistorts them. Returns a list of x2ds and splits per camera.''' x2ds_raw_data, x2ds_splits = frame[0],frame[1] if mats is None: return x2ds_raw_data[:,:2].copy(), x2ds_splits return Calibrate.undistort_dets(x2ds_raw_data, x2ds_splits, mats)
dx, dy = 10, 10
img[int(r.sy - dy):int(r.sy + dy),
int(r.sx - dx):int(r.sx + dx), 0] = 128
else:
pts0 = pts1 = []
return (pts0, pts1)
def tighten_calibration(
(x3s, x3s_labels), (x2s, x2s_splits, x2s_labels), mats):
x3s_original = x3s.copy()
x2s_labels_original = x2s_labels.copy()
for it in range(10):
x2d_threshold = 0.08 # - it * 0.04/50.
Ps = np.array([m[2] / (m[0][0, 0]) for m in mats], dtype=np.float32)
u2s, _ = Calibrate.undistort_dets(x2s, x2s_splits, mats)
x3s, x3s_labels, E, x2d_labels = Recon.solve_x3ds(
u2s, x2s_splits, x2s_labels_original, Ps, True)
clouds = ISCV.HashCloud2DList(u2s, x2s_splits, x2d_threshold)
sc, x2s_labels, _ = Label.project_assign(clouds, x3s, x3s_labels, Ps,
x2d_threshold)
print 'it', it, sc
tiara_xis = np.where(x3s_labels < len(VICON_tiara_x3ds))[0]
tiara_lis = x3s_labels[tiara_xis]
tiara_true = VICON_tiara_x3ds[tiara_lis] + [0, 1000, 0]
tiara_xs = x3s[tiara_xis]
# now solve the tiara into place by finding a rigid transform
RT, inliers = Calibrate.rigid_align_points_inliers(tiara_xs,
tiara_true,
scale=True)
x3s = np.dot(x3s, RT[:3, :3].T) + RT[:, 3]