print(location_ori_changed)
location_tilt = np.dot(np.transpose(Rtilt), location)
print("rtilted location")
print(location)
location_changed = [location_tilt[i] for i in sun_permutation]
location_changed[1] *= -1
ct_3d_from_2d= [ct_x * location_ori_changed[2], ct_y * location_ori_changed[2], location_ori_changed[2]]
calib_3 = np.zeros((3,3))
calib_3 = calib[0:3, 0:3]
inv_K = inv(calib_3)
ct_3d_from_2d2 = np.dot(inv_K, ct_3d_from_2d)
ct_3d_from_2d2_world = [ct_3d_from_2d2[i] for i in sun_permutation]
ct_3d_from_2d2_world[2] *= -1
box_3d_new = compute_box_3d_world(dim, ct_3d_from_2d2_world, rotation_y, theta)
box_2d_new = project_to_image(box_3d_new, calib)
image5 = draw_box_3d_world(image, box_2d_new, image_id, c= (255, 0, 0))
print(ct_3d_from_2d2)
print("내가 location_changed")
print(location_changed)
print("dim")
print(dim)
#dim_changed = [dim[i]*2 if dim[i] > 0 else print("error" + str(image_id)) for i in sun_permutation] #왜냐하면 dimension은 크기니까
dim_changed = [dim[i]*2 for i in sun_permutation] #왜냐하면 dimension은 크기니까
#dim_2 = [dim[i]*2 for i in range(0,3)] #왜냐하면 dimension은 크기니까
#print(dim_changed)
#Rtilt 적용된 location을 2d image에 project시킨것.
ct3d_to_ct2d = project_to_image(np.array([location_changed]), calib) # 이제 3d bounding box를 image에 투영시킴
ct3d_to_ct2d_ori = project_to_image(np.array([location_ori_changed]), calib) # 이제 3d bounding box를 image에 투영시킴
'category_id': cat_ids[cat_id],
# 'dim': dim_xyz,
'dim': dim_changed,
'bbox': bbox2D, #(xmin, ymin, w, h)
#'depth': abs(location_changed[1]), ....NotRtilted(2019-12-23)
'depth': location_changed[2],
'alpha': alpha,
# 'truncated': truncated,
# 'occluded': occluded,
'location': location_changed,
'rotation_y': rotation_y,
'ct_2d_from_3d': ct3d_proj
}
ret['annotations'].append(ann)
image = draw_box_3d_world(image, box_2d, image_id)
# cv2.imshow(str(image_id), image)
# cv2.waitKey()
# cv2.imwrite('C:\\obj_detection\\CenterNet-master\\CenterNet-master\\data\\SUNRGBD\\resultImages_mini\\'+str(image_id)+'.jpg',image)
else:
print("3d_ct out")
print(image_id)
print(cat_id)
# depth = np.array([location[2]], dtype=np.float32)
# pt_2d = np.array([(bbox2D[0] + bbox2D[2]) / 2, (bbox2D[1] + bbox2D[3]) / 2],
# dtype=np.float32)
# pt_3d = unproject_2d_to_3d(pt_2d, depth, calib)
# pt_3d[1] += dim[0] / 2 # because the position of KITTI is defined as the center of the bottom face
# print('pt_3d', pt_3d)
# print('location', location)
cv2.line(image, (int(xmax_projected), int(ymin_projected)),
(int(xmax_projected), int(ymax_projected)), (0, 255, 255),
2,
lineType=cv2.LINE_AA)
cv2.line(image, (int(xmin_projected), int(ymax_projected)),
(int(xmin_projected), int(ymin_projected)), (0, 255, 255),
2,
lineType=cv2.LINE_AA)
cv2.line(image, (int(xmax_projected), int(ymax_projected)),
(int(xmin_projected), int(ymax_projected)), (0, 255, 255),
2,
lineType=cv2.LINE_AA)
cv2.circle(image, (int(ct_x_projected), int(ct_y_projected)), 5,
(0, 255, 255), -1)
image = draw_box_3d_world(image, box_2d, image_id)
#print(box_2d.transpose()[0].min(), box_2d.transpose()[0].max(), box_2d.transpose()[1].min(), box_2d.transpose()[1].max())
#bbox2D_projected는 (xmin, ymin, width, height꼴로 넣어줘야함)
bbox2D_projected = [
float(xmin_projected),
float(ymin_projected),
float(width_projected),
float(height_projected)
]
depth = location_ori_changed[2]
P = calib
loc_2d_depth = unproject_2d_to_3d(pt_2d, depth, P)
box_3d = compute_box_3d_Rtilt(dim_changed, loc_2d_depth,
rotation_y, Rtilt)
