class batchUpdaterPyMulti:
def __init__(self, big_cfg, height, width):
self.big_cfg = big_cfg
self.model_dir = big_cfg.dataset.model_dir
self.rot_coord = big_cfg.network.ROT_COORD
self.pixel_means = big_cfg.network.PIXEL_MEANS[[2, 1, 0]]
self.pixel_means = self.pixel_means.reshape([3, 1,
1]).astype(np.float32)
self.K = big_cfg.dataset.INTRINSIC_MATRIX
self.T_means = big_cfg.dataset.trans_means
self.T_stds = big_cfg.dataset.trans_stds
self.height = height
self.width = width
self.zNear = big_cfg.dataset.ZNEAR
self.zFar = big_cfg.dataset.ZFAR
self.render_machine = None
if big_cfg.dataset.dataset.startswith("ModelNet"):
self.modelnet_root = big_cfg.modelnet_root
self.texture_path = os.path.join(self.modelnet_root,
"gray_texture.png")
from lib.render_glumpy.render_py_light_modelnet_multi import (
Render_Py_Light_ModelNet_Multi, )
self.model_path_list = [
os.path.join(self.model_dir, "{}.obj".format(model_name))
for model_name in big_cfg.dataset.class_name
]
self.render_machine = Render_Py_Light_ModelNet_Multi(
self.model_path_list,
self.texture_path,
self.K,
self.width,
self.height,
self.zNear,
self.zFar,
brightness_ratios=[0.7],
)
else:
self.render_machine = Render_Py(
self.model_dir,
big_cfg.dataset.class_name,
self.K,
self.width,
self.height,
self.zNear,
self.zFar,
)
self.reinit = True
self.batch_size = big_cfg.TRAIN.BATCH_PAIRS # will update according to data
self.Kinv = np.linalg.inv(np.matrix(self.K))
print("build render_machine: ", self.render_machine)
def get_names(self, big_cfg):
"""
:param small_cfg:
:return:
"""
pred = ["image_observed", "image_rendered"]
# pred = []
if big_cfg.network.PRED_FLOW:
pred.append("flow_est_crop")
pred.append("flow_loss")
pred.append("rot_est")
pred.append("rot_gt")
pred.append("trans_est")
pred.append("trans_gt")
if big_cfg.train_iter.SE3_DIST_LOSS:
pred.append("rot_loss")
pred.append("trans_loss")
if big_cfg.train_iter.SE3_PM_LOSS:
pred.append("point_matching_loss")
if self.big_cfg["network"]["PRED_MASK"]:
pred.append("zoom_mask_prob")
pred.append("zoom_mask_gt_observed")
pred.append("mask_pred") # unzoomed
return pred
def forward(self, data_batch, preds, big_cfg):
"""
:param data:
image_observed
image_rendered
depth_gt_observed
- depth_observed
- depth_rendered
- mask_real
src_pose
tgt_pose
:param label:
rot_i2r
trans_i2r
- flow_i2r
- flow_i2r_weights
- point_cloud_model
- point_cloud_weights
- point_cloud_real
:param preds:
image_observed
image_rendered
- flow_i2r_est
- flow_i2r_loss
rot_i2r
trans_i2r
- rot_i2r_loss
- trans_i2r_loss
- point_matching_loss
:return updated_batch:
"""
data_array = data_batch.data
label_array = data_batch.label
num_ctx = len(data_array)
pred_names = self.get_names(big_cfg)
init_time = 0
render_time = 0
image_time = 0
flow_time = 0
update_time = 0
mask_time = 0
io_time = 0
data_names = [x[0] for x in data_batch.provide_data[0]]
label_names = [x[0] for x in data_batch.provide_label[0]]
src_pose_all = [
data_array[ctx_i][data_names.index("src_pose")].asnumpy()
for ctx_i in range(num_ctx)
]
tgt_pose_all = [
data_array[ctx_i][data_names.index("tgt_pose")].asnumpy()
for ctx_i in range(num_ctx)
]
class_index_all = [
data_array[ctx_i][data_names.index("class_index")].asnumpy()
for ctx_i in range(num_ctx)
]
t = time.time()
# print("pred lens: {}".format(len(preds)))
# for i in preds:
# print(i[0].shape)
rot_est_all = [
preds[pred_names.index("rot_est")][ctx_i].asnumpy()
for ctx_i in range(num_ctx)
]
trans_est_all = [
preds[pred_names.index("trans_est")][ctx_i].asnumpy()
for ctx_i in range(num_ctx)
]
init_time += time.time() - t
if self.big_cfg.network.PRED_FLOW:
depth_gt_observed_all = [
data_array[ctx_i][data_names.index(
"depth_gt_observed")].asnumpy() for ctx_i in range(num_ctx)
]
for ctx_i in range(num_ctx):
batch_size = data_array[ctx_i][0].shape[0]
assert batch_size == self.batch_size, "{} vs. {}".format(
batch_size, self.batch_size)
cur_ctx = data_array[ctx_i][0].context
t = time.time()
src_pose = src_pose_all[
ctx_i] # data_array[ctx_i][data_names.index('src_pose')].asnumpy()
tgt_pose = tgt_pose_all[
ctx_i] # data_array[ctx_i][data_names.index('tgt_pose')].asnumpy()
if self.big_cfg.network.PRED_FLOW:
depth_gt_observed = depth_gt_observed_all[
ctx_i] # data_array[ctx_i][data_names.index('depth_gt_observed')] # ndarray
class_index = class_index_all[
ctx_i] # data_array[ctx_i][data_names.index('class_index')].asnumpy()
rot_est = rot_est_all[
ctx_i] # preds[pred_names.index('rot_est')][ctx_i].asnumpy()
trans_est = trans_est_all[
ctx_i] # preds[pred_names.index('trans_est')][ctx_i].asnumpy()
init_time += time.time() - t
refined_image_array = np.zeros(
(batch_size, 3, self.height, self.width))
refined_depth_array = np.zeros(
(batch_size, 1, self.height, self.width))
rot_res_array = np.zeros((batch_size, 4))
trans_res_array = np.zeros((batch_size, 3))
refined_pose_array = np.zeros((batch_size, 3, 4))
KT_array = np.zeros((batch_size, 3, 4))
for batch_idx in range(batch_size):
pre_pose = np.squeeze(src_pose[batch_idx])
r_delta = np.squeeze(rot_est[batch_idx])
t_delta = np.squeeze(trans_est[batch_idx])
refined_pose = RT_transform.RT_transform(
pre_pose,
r_delta,
t_delta,
self.T_means,
self.T_stds,
rot_coord=self.rot_coord,
)
t = time.time()
if not self.big_cfg.dataset.dataset.startswith("ModelNet"):
refined_image, refined_depth = self.render_machine.render(
class_index[batch_idx].astype("int"),
refined_pose[:3, :3],
refined_pose[:3, 3],
r_type="mat",
)
else:
idx = 2 # random.randint(0, 100)
# generate random light_position
if idx % 6 == 0:
light_position = [1, 0, 1]
elif idx % 6 == 1:
light_position = [1, 1, 1]
elif idx % 6 == 2:
light_position = [0, 1, 1]
elif idx % 6 == 3:
light_position = [-1, 1, 1]
elif idx % 6 == 4:
light_position = [-1, 0, 1]
elif idx % 6 == 5:
light_position = [0, 0, 1]
else:
raise Exception("???")
# print("light_position a: {}".format(light_position))
light_position = np.array(light_position) * 0.5
# inverse yz
light_position[0] += refined_pose[0, 3]
light_position[1] -= refined_pose[1, 3]
light_position[2] -= refined_pose[2, 3]
# print("light_position b: {}".format(light_position))
colors = np.array([1, 1, 1]) # white light
intensity = np.random.uniform(0.9, 1.1, size=(3, ))
colors_randk = 0 # random.randint(0, colors.shape[0] - 1)
light_intensity = colors[colors_randk] * intensity
# print('light intensity: ', light_intensity)
# randomly choose a render machine
rm_randk = 0 # random.randint(0, len(brightness_ratios) - 1)
refined_image, refined_depth = self.render_machine.render(
class_index[batch_idx].astype("int"),
refined_pose[:3, :3],
refined_pose[:3, 3],
light_position,
light_intensity,
brightness_k=rm_randk,
r_type="mat",
)
render_time += time.time() - t
# process refined_image
t = time.time()
refined_image = (refined_image[:, :, [2, 1, 0]].transpose(
[2, 0, 1]).astype(np.float32))
refined_image -= self.pixel_means
image_time += time.time() - t
# get se3_res
rot_res, trans_res = RT_transform.calc_RT_delta(
refined_pose,
np.squeeze(tgt_pose[batch_idx]),
self.T_means,
self.T_stds,
rot_coord=self.rot_coord,
rot_type="QUAT",
)
# print('{}, {}: {}, {}'.format(ctx_i, batch_idx, r_delta, rot_res))
refined_pose_array[batch_idx] = refined_pose
refined_image_array[batch_idx] = refined_image
refined_depth_array[batch_idx] = refined_depth.reshape(
(1, self.height, self.width))
rot_res_array[batch_idx] = rot_res
trans_res_array[batch_idx] = trans_res
se3_m = np.zeros([3, 4])
se3_rotm, se3_t = RT_transform.calc_se3(
refined_pose, np.squeeze(tgt_pose[batch_idx]))
se3_m[:, :3] = se3_rotm
se3_m[:, 3] = se3_t
KT_array[batch_idx] = np.dot(self.K, se3_m)
if self.big_cfg.network.PRED_MASK:
t = time.time()
refined_mask_rendered_array = np.zeros(
refined_depth_array.shape)
refined_mask_rendered_array[
refined_depth_array >
0.2] = 1 # if the mask_rendered input is depth
mask_time += time.time() - t
update_package = {
"image_rendered": refined_image_array,
"depth_rendered": refined_depth_array,
"src_pose": refined_pose_array,
"rot": rot_res_array,
"trans": trans_res_array,
}
if self.big_cfg.network.PRED_FLOW:
t = time.time()
gpu_flow_machine = gpu_flow_wrapper(cur_ctx.device_id)
# import matplotlib.pyplot as plt
# plt.figure()
# plt.subplot(1,2,1)
# plt.imshow(refined_depth_array[0,0])
# plt.subplot(1,2,2)
# plt.imshow(depth_gt_observed[0,0])
# plt.show()
refined_flow, refined_flow_valid = gpu_flow_machine(
refined_depth_array.astype(np.float32),
depth_gt_observed.astype(np.float32),
KT_array.astype(np.float32),
np.array(self.Kinv).astype(np.float32),
)
# problem with py3
# print('updater, flow: ', refined_flow.shape, np.unique(refined_flow))
# print('updater, flow weights: ', refined_flow_valid.shape, np.unique(refined_flow_valid))
# print('KT: ', KT_array[0])
# print('Kinv: ', self.Kinv)
flow_time += time.time() - t
refined_flow_weights = np.tile(refined_flow_valid,
[1, 2, 1, 1])
update_package["flow"] = refined_flow
update_package["flow_weights"] = refined_flow_weights
if self.big_cfg.network.INPUT_MASK:
update_package["mask_rendered"] = refined_mask_rendered_array
t = time.time()
data_array[ctx_i] = self.update_data_batch(data_array[ctx_i],
data_names,
update_package)
label_array[ctx_i] = self.update_data_batch(
label_array[ctx_i], label_names, update_package)
update_time += time.time() - t
t = time.time()
new_data_batch = mx.io.DataBatch(
data=data_array,
label=label_array,
pad=data_batch.pad,
index=data_batch.index,
provide_data=data_batch.provide_data,
provide_label=data_batch.provide_label,
)
io_time += time.time() - t
# print("---------------------------------")
# print("init_time: {:.3f} sec".format(init_time))
# print("render_time: {:.3f} sec".format(render_time))
# print("image_time: {:.3f} sec".format(image_time))
# print("flow_time: {:.3f} sec".format(flow_time))
# print("mask_time: {:.3f} sec".format(mask_time))
# print("update_time: {:.3f} sec".format(update_time))
# print("io_time: {:.3f} sec".format(io_time))
# print("all_time: {:.3f} sec".format(time.time() - t_all))
# print("---------------------------------")
return new_data_batch
def update_data_batch(self, data, data_names, update_package):
import mxnet.ndarray as nd
for blob_idx, blob_name in enumerate(data_names):
if blob_name not in update_package:
continue
# print('blob_idx: {}, blob_name: {} -- {}'.format(blob_idx, blob_name, np.max(update_package[blob_name])))
data[blob_idx] = nd.array(update_package[blob_name])
return data
def main():
sel_classes = classes
model_dir = os.path.join(cur_dir, '../../data/LINEMOD_6D/LM6d_converted/models')
render_machine = Render_Py(model_dir, classes, K, width, height, ZNEAR, ZFAR)
for cls_idx, cls_name in enumerate(classes):
if not cls_name in sel_classes:
continue
print(cls_idx, cls_name)
real_indices = []
images = [fn for fn in os.listdir(os.path.join(LM6d_origin_root,
'{:02d}'.format(class2idx(cls_name)), 'rgb')) if '.png' in fn]
images = sorted(images)
gt_path = os.path.join(LM6d_origin_root, '{:02d}'.format(class2idx(cls_name)), 'gt.yml')
gt_dict = load_gt(gt_path)
info_path = os.path.join(LM6d_origin_root, '{:02d}'.format(class2idx(cls_name)), 'info.yml')
info_dict = load_info(info_path)
for real_img in tqdm(images):
old_color_path = os.path.join(LM6d_origin_root, '{:02d}'.format(class2idx(cls_name)), "rgb/{}".format(real_img))
assert os.path.exists(old_color_path), old_color_path
old_depth_path = os.path.join(LM6d_origin_root, '{:02d}'.format(class2idx(cls_name)), "depth/{}".format(real_img))
assert os.path.exists(old_depth_path), old_depth_path
img_id = int(real_img.replace('.png', ''))
new_img_id = img_id + 1
# K
# K = np.array(info_dict[img_id]['cam_K']).reshape((3, 3))
color_img = cv2.imread(old_color_path, cv2.IMREAD_COLOR)
## depth
depth = read_img(old_depth_path, 1)
# print(np.max(depth), np.min(depth))
# print(color_img.shape)
new_color_path = os.path.join(LM6d_new_root, '{:02d}'.format(class2idx(cls_name)),
"{:06d}-color.png".format(new_img_id))
new_depth_path = os.path.join(LM6d_new_root, '{:02d}'.format(class2idx(cls_name)),
"{:06d}-depth.png".format(new_img_id))
mkdir_if_missing(os.path.dirname(new_color_path))
copyfile(old_color_path, new_color_path)
copyfile(old_depth_path, new_depth_path)
# meta and label
meta_dict = {}
num_instance = len(gt_dict[img_id])
meta_dict['cls_indexes'] = np.zeros((1, num_instance), dtype=np.int32)
meta_dict['boxes'] = np.zeros((num_instance, 4), dtype='float32')
meta_dict['poses'] = np.zeros((3,4,num_instance), dtype='float32')
distances = []
label_dict = {}
for ins_id, instance in enumerate(gt_dict[img_id]):
obj_id = instance['obj_id']
meta_dict['cls_indexes'][0, ins_id] = obj_id
obj_bb = np.array(instance['obj_bb'])
meta_dict['boxes'][ins_id, :] = obj_bb
# pose
pose = np.zeros((3, 4))
R = np.array(instance['cam_R_m2c']).reshape((3, 3))
t = np.array(instance['cam_t_m2c']) / 1000. # mm -> m
pose[:3, :3] = R
pose[:3, 3] = t
distances.append(t[2])
meta_dict['poses'][:,:,ins_id] = pose
image_gl, depth_gl = render_machine.render(obj_id-1, pose[:3, :3], pose[:3, 3],
r_type='mat')
image_gl = image_gl.astype('uint8')
label = np.zeros(depth_gl.shape)
label[depth_gl!=0] = 1
label_dict[obj_id] = label
meta_path = os.path.join(LM6d_new_root, '{:02d}'.format(class2idx(cls_name)),
"{:06d}-meta.mat".format(new_img_id))
sio.savemat(meta_path, meta_dict)
dis_inds = sorted(range(len(distances)), key=lambda k: -distances[k]) # put deeper objects first
# label
res_label = np.zeros((480, 640))
for dis_id in dis_inds:
cls_id = meta_dict['cls_indexes'][0, dis_id]
tmp_label = label_dict[cls_id]
# label
res_label[tmp_label == 1] = cls_id
label_path = os.path.join(LM6d_new_root, '{:02d}'.format(class2idx(cls_name)),
"{:06d}-label.png".format(new_img_id))
cv2.imwrite(label_path, res_label)
def vis_check():
fig = plt.figure(figsize=(8, 6), dpi=120)
plt.subplot(2, 3, 1)
plt.imshow(color_img[:,:,[2,1,0]])
plt.title('color_img')
plt.subplot(2, 3, 2)
plt.imshow(depth_gl)
plt.title('depth')
plt.subplot(2, 3, 3)
plt.imshow(depth_gl)
plt.title('depth_gl')
plt.subplot(2, 3, 4)
plt.imshow(res_label)
plt.title('res_label')
plt.subplot(2,3,5)
label_v1_path = os.path.join('/data/wanggu/Storage/LINEMOD_SIXD_wods/LM6d_render_v1/data/real',
'{:02d}'.format(class2idx(cls_name)),
"{:06d}-label.png".format(new_img_id))
assert os.path.exists(label_v1_path), label_v1_path
label_v1 = read_img(label_v1_path, 1)
plt.imshow(label_v1)
plt.title('label_v1')
plt.show()
# vis_check()
# real idx
real_indices.append("{:02d}/{:06d}".format(class2idx(cls_name), new_img_id))
# one idx file for each video of each class
real_idx_file = os.path.join(real_set_dir, "{}_all.txt".format(cls_name))
with open(real_idx_file, 'w') as f:
for real_idx in real_indices:
f.write(real_idx + '\n')
def main():
sel_classes = classes
render_machine = Render_Py(model_dir, classes, K, width, height, ZNEAR,
ZFAR)
for cls_idx, cls_name in enumerate(classes):
if not cls_name in sel_classes:
continue
print(cls_idx, cls_name)
observed_indices = []
images = [
fn for fn in os.listdir(
os.path.join(LM6d_origin_root, '{:02d}'.format(
class2idx(cls_name)), 'rgb')) if '.png' in fn
]
images = sorted(images)
gt_path = os.path.join(LM6d_origin_root,
'{:02d}'.format(class2idx(cls_name)), 'gt.yml')
gt_dict = load_gt(gt_path)
info_path = os.path.join(LM6d_origin_root,
'{:02d}'.format(class2idx(cls_name)),
'info.yml')
info_dict = load_info(info_path)
for observed_img in tqdm(images):
old_color_path = os.path.join(LM6d_origin_root,
'{:02d}'.format(class2idx(cls_name)),
"rgb/{}".format(observed_img))
assert os.path.exists(old_color_path), old_color_path
old_depth_path = os.path.join(LM6d_origin_root,
'{:02d}'.format(class2idx(cls_name)),
"depth/{}".format(observed_img))
assert os.path.exists(old_depth_path), old_depth_path
img_id = int(observed_img.replace('.png', ''))
new_img_id = img_id + 1
# K
# K = np.array(info_dict[img_id]['cam_K']).reshape((3, 3))
color_img = cv2.imread(old_color_path, cv2.IMREAD_COLOR)
## depth
depth = read_img(old_depth_path, 1)
# print(np.max(depth), np.min(depth))
# print(color_img.shape)
new_color_path = os.path.join(
LM6d_new_root, '{:02d}'.format(class2idx(cls_name)),
"{:06d}-color.png".format(new_img_id))
new_depth_path = os.path.join(
LM6d_new_root, '{:02d}'.format(class2idx(cls_name)),
"{:06d}-depth.png".format(new_img_id))
mkdir_if_missing(os.path.dirname(new_color_path))
copyfile(old_color_path, new_color_path)
copyfile(old_depth_path, new_depth_path)
# meta and label
meta_dict = {}
num_instance = len(gt_dict[img_id])
meta_dict['cls_indexes'] = np.zeros((1, num_instance),
dtype=np.int32)
meta_dict['boxes'] = np.zeros((num_instance, 4), dtype='float32')
meta_dict['poses'] = np.zeros((3, 4, num_instance),
dtype='float32')
distances = []
label_dict = {}
for ins_id, instance in enumerate(gt_dict[img_id]):
obj_id = instance['obj_id']
meta_dict['cls_indexes'][0, ins_id] = obj_id
obj_bb = np.array(instance['obj_bb'])
meta_dict['boxes'][ins_id, :] = obj_bb
# pose
pose = np.zeros((3, 4))
R = np.array(instance['cam_R_m2c']).reshape((3, 3))
t = np.array(instance['cam_t_m2c']) / 1000. # mm -> m
pose[:3, :3] = R
pose[:3, 3] = t
distances.append(t[2])
meta_dict['poses'][:, :, ins_id] = pose
image_gl, depth_gl = render_machine.render(obj_id - 1,
pose[:3, :3],
pose[:3, 3],
r_type='mat')
image_gl = image_gl.astype('uint8')
label = np.zeros(depth_gl.shape)
label[depth_gl != 0] = 1
label_dict[obj_id] = label
meta_path = os.path.join(LM6d_new_root,
'{:02d}'.format(class2idx(cls_name)),
"{:06d}-meta.mat".format(new_img_id))
sio.savemat(meta_path, meta_dict)
dis_inds = sorted(
range(len(distances)),
key=lambda k: -distances[k]) # put deeper objects first
# label
res_label = np.zeros((480, 640))
for dis_id in dis_inds:
cls_id = meta_dict['cls_indexes'][0, dis_id]
tmp_label = label_dict[cls_id]
# label
res_label[tmp_label == 1] = cls_id
label_path = os.path.join(LM6d_new_root,
'{:02d}'.format(class2idx(cls_name)),
"{:06d}-label.png".format(new_img_id))
cv2.imwrite(label_path, res_label)
# observed idx
observed_indices.append("{:02d}/{:06d}".format(
class2idx(cls_name), new_img_id))
# print(rot_sym_m)
rot_res = R_transform(pose_gt[:3, :3], rot_sym_m, rot_coord='model')
rot_res_q = mat2quat(rot_res)
rgb_gl, depth_gl = render_machine.render(cls_idx, rot_res_q, pose_gt[:, 3] + p_center)
rgb_gl = rgb_gl.astype('uint8')
return rgb_gl, depth_gl
# transform the points to 2D
for img_idx in img_indices:
print(img_idx)
pose_gt = np.loadtxt('./data/render_v5/data/render_real/{}/{}-pose.txt'.format(cls_name, img_idx),
skiprows=1)
im_c = cv2.imread('./data/render_v5/data/real/{}-color.png'.format(img_idx))
im_render_gt, _ = render_machine.render(cls_idx, mat2quat(pose_gt[:3, :3]), pose_gt[:, 3])
# symmetry axis
angle = np.pi
p_center = np.array([0, 0, 0])
rot_axis = np.array([0, 0, -1])
im_rot, depth_rot = rotate(angle, rot_axis, pose_gt, p_center=p_center)
im_rot_1, depth_rot_1 = rotate(angle=np.pi * 0.5, rot_axis=rot_axis, pose_gt=pose_gt, p_center=p_center)
fig = plt.figure(figsize=(8, 6), dpi=120)
plt.subplot(2, 3, 1)
plt.imshow(im_c[:,:,[2, 1, 0]])
plt.title('im_render_real')
plt.subplot(2, 3, 2)