def imgs_generator():
camNetAll, camNetLeft, camNetRight = _getCamNets(args)
camNetLeft.triangulate()
camNetLeft.bundle_adjust(cam_id_list=(0, 1, 2),
unique=False,
prior=True)
camNetRight.triangulate()
camNetRight.bundle_adjust(cam_id_list=(0, 1, 2),
unique=False,
prior=True)
camNetAll.triangulate()
camNetAll.points3d_m = procrustes_seperate(camNetAll.points3d_m)
camNetAll.points3d_m = normalize_pose_3d(camNetAll.points3d_m,
rotate=True)
camNetAll.points3d_m = filter_batch(camNetAll.points3d_m)
for cam in camNetAll:
cam.points2d = smooth_pose2d(cam.points2d)
def stack(img_id):
row1 = np.hstack([
_compute_2d_img(camNetLeft, img_id, cam_id)
for cam_id in (0, 1, 2)
])
row2 = np.hstack([
_compute_2d_img(camNetRight, img_id, cam_id)
for cam_id in (0, 1, 2)
])
row3 = np.hstack([
_compute_3d_img(camNetAll, img_id, cam_id)
for cam_id in (2, 3, 4)
])
img = np.vstack([row1, row2, row3])
return img
for img_id in range(args.num_images):
print("img_id:" + str(img_id))
yield stack(img_id)
def save_pose(self):
pts2d = np.zeros((7, self.state.num_images, config["num_joints"], 2),
dtype=float)
# pts3d = np.zeros((self.cfg.num_images, self.cfg.num_joints, 3), dtype=float)
for cam in self.camNetAll:
pts2d[cam.cam_id, :] = cam.points2d.copy()
# overwrite by manual correction
count = 0
for cam_id in range(config["num_cameras"]):
for img_id in range(self.state.num_images):
if self.state.db.has_key(cam_id, img_id):
pt = self.state.db.read(cam_id,
img_id) * config["image_shape"]
pts2d[cam_id, img_id, :] = pt
count += 1
if "fly" in config["name"]:
# some post-processing for body-coxa
for cam_id in range(len(self.camNetAll.cam_list)):
for j in range(config["skeleton"].num_joints):
if config["skeleton"].camera_see_joint(
cam_id, j) and config["skeleton"].is_tracked_point(
j, config["skeleton"].Tracked.BODY_COXA):
pts2d[cam_id, :, j, 0] = np.median(pts2d[cam_id, :, j,
0])
pts2d[cam_id, :, j, 1] = np.median(pts2d[cam_id, :, j,
1])
dict_merge = self.camNetAll.save_network(path=None)
dict_merge["points2d"] = pts2d
# take a copy of the current points2d
pts2d_orig = np.zeros(
(7, self.state.num_images, config["num_joints"], 2), dtype=float)
for cam_id in range(config["num_cameras"]):
pts2d_orig[cam_id, :] = self.camNetAll[cam_id].points2d.copy()
# ugly hack to temporarly incorporate manual corrections
c = 0
for cam_id in range(config["num_cameras"]):
for img_id in range(self.state.num_images):
if self.state.db.has_key(cam_id, img_id):
pt = self.state.db.read(cam_id,
img_id) * config["image_shape"]
self.camNetAll[cam_id].points2d[img_id, :] = pt
c += 1
print("Replaced points2d with {} manual correction".format(count))
# do the triangulation if we have the calibration
if self.camNetLeft.has_calibration() and self.camNetLeft.has_pose():
self.camNetAll.triangulate()
pts3d = self.camNetAll.points3d_m
dict_merge["points3d"] = pts3d
# apply procrustes
if config["procrustes_apply"]:
print("Applying Procrustes on 3D Points")
dict_merge["points3d"] = procrustes_seperate(
dict_merge["points3d"])
# put old values back
for cam_id in range(config["num_cameras"]):
self.camNetAll[cam_id].points2d = pts2d_orig[cam_id, :].copy()
pickle.dump(
dict_merge,
open(
os.path.join(
self.folder_output,
"pose_result_{}.pkl".format(self.folder.replace("/", "_")),
),
"wb",
),
)
print("Saved the pose at: {}".format(
os.path.join(
self.folder_output,
"pose_result_{}.pkl".format(self.folder.replace("/", "_")),
)))