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detector.py
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import cv2
import numpy as np
import torch
from dataset.database import BaseDatabase, get_database_split, get_object_vert, get_object_center
from network import name2network
from utils.base_utils import load_cfg, transformation_offset_2d, transformation_scale_2d, \
transformation_compose_2d, transformation_crop, transformation_rotation_2d
from utils.database_utils import select_reference_img_ids_fps, normalize_reference_views
from utils.pose_utils import estimate_pose_from_similarity_transform_compose
def compute_similarity_transform(pts0, pts1):
"""
@param pts0:
@param pts1:
@return: sR @ p0 + t = p1
"""
ref_c = np.mean(pts0, 0)
que_c = np.mean(pts1, 0)
ref_d = pts0 - ref_c[None, :]
que_d = pts1 - que_c[None, :]
scale = np.mean(np.linalg.norm(que_d,2,1)) / np.mean(np.linalg.norm(ref_d,2,1))
ref_d_ = ref_d * scale
U, S, VT = np.linalg.svd(ref_d_.T @ que_d)
rotation = VT.T @ U.T
offset = - scale * (rotation @ ref_c) + que_c
return scale, rotation, offset
def compute_similarity_transform_batch(pts0, pts1):
"""
@param pts0:
@param pts1:
@return: sR @ p0 + t = p1
"""
c0 = np.mean(pts0, 1) # n, 2
c1 = np.mean(pts1, 1) # n, 2
d0 = pts0 - c0[:, None, :]
d1 = pts1 - c1[:, None, :]
scale = np.mean(np.linalg.norm(d1,2,2,keepdims=True),1,keepdims=True) / \
np.mean(np.linalg.norm(d0,2,2,keepdims=True),1,keepdims=True) # n,1,1
d0_ = d0 * scale # n,k,2
U, S, VT = np.linalg.svd(d0_.transpose([0,2,1]) @ d1) # n,2,2
rotation = VT.transpose([0,2,1]) @ U.transpose([0,2,1]) # n,2,2
offset = - scale * (rotation @ c0[:,:,None]) + c1[:,:,None]
return scale, rotation, offset # [n,1,1] [n,2,2] [n,2,1]
def compute_inlier_mask(scale, rotation, offset, corr, thresh):
x0=corr[None, :, :2] # [1,k,2]
x1=corr[None, :, 2:] # [1,k,2]
x1_ = scale * (x0 @ rotation.transpose([0,2,1])) + offset.transpose([0,2,1])
mask = np.linalg.norm(x1-x1_,2,2) < thresh
return mask
def ransac_similarity_transform(corr):
n, _ = corr.shape
batch_size=4096
bad_seed_thresh=4
inlier_thresh=5
best_inlier, best_mask = 0, None
iter_num = 0
confidence = 0.99
while True:
idx = np.random.randint(0,n,[batch_size,2])
seed0 = corr[idx[:,0]] # b,4
seed1 = corr[idx[:,1]] # b,4
bad_mask = np.linalg.norm(seed0 - seed1, 2, 1) < bad_seed_thresh
seed0 = seed0[~bad_mask]
seed1 = seed1[~bad_mask]
seed = np.stack([seed0,seed1],1)
scale, rotation, offset = compute_similarity_transform_batch(seed[:,:,:2],seed[:,:,2:]) #
mask = compute_inlier_mask(scale,rotation,offset,corr,inlier_thresh) # b,n
inlier_num = np.sum(mask,1)
if np.max(inlier_num) >= best_inlier:
best_mask = mask[np.argmax(inlier_num)]
iter_num += seed.shape[0]
inlier_ratio = np.mean(best_mask)
if 1-(1-inlier_ratio**2)**iter_num > confidence:
break
inlier_corr=corr[best_mask]
scale, rotation, offset = compute_similarity_transform_batch(inlier_corr[None,:,:2],inlier_corr[None,:,2:])
scale, rotation, offset = scale[0,0,0], rotation[0], offset[0,:,0]
return scale, rotation, offset, best_mask
def compose_similarity_transform(scale, rotation, offset):
M = transformation_scale_2d(scale)
M = transformation_compose_2d(M, np.concatenate([rotation, np.zeros([2, 1])], 1).astype(np.float32))
M = transformation_compose_2d(M, transformation_offset_2d(offset[0], offset[1]))
return M
class Gen6DEstimator:
default_cfg={
'ref_resolution': 128,
"ref_view_num": 64,
"det_ref_view_num": 32,
'detector': None,
}
def __init__(self,cfg):
self.cfg={**self.default_cfg,**cfg}
self.ref_info={}
self.detector = self._load_module(self.cfg['detector'])
@staticmethod
def _load_module(cfg):
refiner_cfg = load_cfg(cfg)
refiner = name2network[refiner_cfg['network']](refiner_cfg)
state_dict = torch.load(f'checkpoints/{refiner_cfg["name"]}/model_best.pth')
refiner.load_state_dict(state_dict['network_state_dict'])
print(f'load from {refiner_cfg["name"]}/model_best.pth step {state_dict["step"]}')
refiner.cuda().eval()
return refiner
# def _check(self, ref_point_cloud, ref_imgs, ref_poses, ref_Ks, ref_ids, database):
# rfn = ref_imgs.shape[0]
# output_imgs = []
# for rfi in range(rfn):
# pts2d, _ = project_points(ref_point_cloud, ref_poses[rfi], ref_Ks[rfi])
# kps_img = draw_keypoints(ref_imgs[rfi],pts2d)//2+ref_imgs[rfi]//2
# img_raw = database.get_image(ref_ids[rfi])
# output_imgs.append(concat_images_list(img_raw,kps_img,vert=True))
#
# imsave(f'data/vis_val/check.jpg',concat_images_list(*output_imgs))
# import ipdb; ipdb.set_trace()
def build(self, database: BaseDatabase, split_type: str):
object_center = get_object_center(database)
object_vert = get_object_vert(database)
ref_ids_all, _ = get_database_split(database, split_type)
# use fps to select reference images for detection and selection
ref_ids = select_reference_img_ids_fps(database, ref_ids_all, self.cfg['ref_view_num'])
ref_imgs, ref_masks, ref_Ks, ref_poses, ref_Hs = \
normalize_reference_views(database, ref_ids, self.cfg['ref_resolution'], 0.05)
# in-plane rotation for viewpoint selection
rfn, h, w, _ = ref_imgs.shape
ref_imgs_rots = []
angles = [-np.pi/2, -np.pi/4, 0, np.pi/4, np.pi/2]
for angle in angles:
M = transformation_offset_2d(-w/2,-h/2)
M = transformation_compose_2d(M, transformation_rotation_2d(angle))
M = transformation_compose_2d(M, transformation_offset_2d(w/2,h/2))
H_ = np.identity(3).astype(np.float32)
H_[:2,:3] = M
ref_imgs_rot = []
for rfi in range(rfn):
H_new = H_ @ ref_Hs[rfi]
ref_imgs_rot.append(cv2.warpPerspective(database.get_image(ref_ids[rfi]), H_new, (w,h), flags=cv2.INTER_LINEAR))
ref_imgs_rots.append(np.stack(ref_imgs_rot, 0))
ref_imgs_rots = np.stack(ref_imgs_rots, 0) # an,rfn,h,w,3
self.detector.load_ref_imgs(ref_imgs[:self.cfg['det_ref_view_num']])
self.ref_info={'imgs': ref_imgs, 'ref_imgs': ref_imgs_rots, 'masks': ref_masks, 'Ks': ref_Ks, 'poses': ref_poses, 'center': object_center}
self.ref_geo_dis = self.detector.ref_dis_estimation(torch.from_numpy(ref_poses[:self.cfg['det_ref_view_num']]).float().cuda())
self.detector.ref_knn(self.ref_geo_dis, k=self.detector.knn)
def predict(self, que_img, que_K, pose_init=None):
inter_results={}
if pose_init is None:
# stage 1: detection
with torch.no_grad():
detection_outputs = self.detector.detect_que_imgs(que_img[None])
position = detection_outputs['positions'][0]
scale_r2q = detection_outputs['scales'][0]
que_img_, _ = transformation_crop(que_img, position, 1/scale_r2q, 0, self.cfg['ref_resolution']) # h,w,3
inter_results['det_position'] = position
inter_results['det_scale_r2q'] = scale_r2q
inter_results['det_que_img'] = que_img_
return None, inter_results
name2estimator={
'gen6d': Gen6DEstimator,
}