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from osdsynth.Orient_Anything.paths import *
from osdsynth.Orient_Anything.vision_tower import DINOv2_MLP
from transformers import AutoImageProcessor
import torch
from PIL import Image
import torch.nn.functional as F
from osdsynth.Orient_Anything.utils import *
from osdsynth.Orient_Anything.inference import *
from osdsynth.utils.logger import SkipImageException
class Rotation:
def __init__(self, cfg, logger, device):
self.rotation_model = DINOv2_MLP(
dino_mode = 'large',
in_dim = 1024,
out_dim = 360+180+180+2,
evaluate = True,
mask_dino = False,
frozen_back = False
).eval()
self.rotation_model.load_state_dict(torch.load(cfg.rotation_model_ckpt_path, map_location='cpu'))
self.rotation_model = self.rotation_model.to(device)
self.val_preprocess = AutoImageProcessor.from_pretrained(DINO_LARGE, cache_dir='./')
self.logger = logger
self.device = device
def get_R(self, phi, theta, gamma):
R = np.array([[-math.sin(phi), 0, -math.cos(phi)],
[math.cos(phi) * math.sin(theta), -math.cos(theta), -math.sin(phi) * math.sin(theta)],
[-math.cos(phi) * math.cos(theta), -math.sin(theta), math.sin(phi) * math.cos(theta)]
]) # The position of the object as seen by the camera
# R rotates gamma around the z-axis of the world coordinate system
gamma = gamma * math.pi / 180
R = np.dot(R, np.array([[math.cos(gamma), -math.sin(gamma), 0],
[math.sin(gamma), math.cos(gamma), 0],
[0, 0, 1]
]))
return R
def process_single(self, image_segment_wbg, do_rm_bkg=True, do_infer_aug=False):
origin_img_wbg = Image.fromarray(image_segment_wbg) if isinstance(image_segment_wbg, np.ndarray) else image_segment_wbg
if do_infer_aug:
rm_bkg_img = background_preprocess(origin_img_wbg, True)
angles = get_3angle_infer_aug(origin_img_wbg, rm_bkg_img, self.rotation_model, self.val_preprocess, self.device)
else:
rm_bkg_img = background_preprocess(origin_img_wbg, False)
angles = get_3angle(rm_bkg_img, self.rotation_model, self.val_preprocess, self.device)
confidence = float(angles[3])
if confidence < 0.5:
do_rm_bkg_img = background_preprocess(origin_img_wbg, True)
do_angles = get_3angle(do_rm_bkg_img, self.rotation_model, self.val_preprocess, self.device)
do_confidence = float(do_angles[3])
if do_confidence >0.5 :
rm_bkg_img = do_rm_bkg_img
angles = do_angles
phi = np.radians(angles[0])
theta = np.radians(angles[1])
gamma = angles[2]
confidence = float(angles[3])
render_axis = render_3D_axis(phi, theta, gamma)
res_img = overlay_images_with_scaling(render_axis, rm_bkg_img)
res_img.save("res_img.png")
rotation_matrix = self.get_R(phi=phi, theta=theta, gamma=gamma)
return rotation_matrix, confidence
def process(self, detection_list, do_rm_bkg=True, do_infer_aug=False):
skip_index = []
for i in range(len(detection_list)):
# image_segment_wobg = detection_list[i]['image_segment']
image_segment_wbg = detection_list[i]['image_crop']
# object-centered reference system, the camera's bit position
rotation_matrix, confidence = self.process_single(image_segment_wbg, do_rm_bkg=do_rm_bkg, do_infer_aug=do_infer_aug)
# For the camera, the position and rotation of the object
detection_list[i]['rotation_matrix'] = rotation_matrix
detection_list[i]['rotation_matrix_confidence'] = confidence
# 删除不符合条件的项
for i in skip_index[::-1]:
del detection_list[i]
return detection_list |