├── .gitignore ├── LICENSE ├── README.md ├── app.py ├── assets ├── axis.obj ├── axis.png └── demo.png ├── inference.py ├── paths.py ├── render ├── __init__.py ├── canvas.py ├── core.py ├── model.py └── speedup.py ├── requirements.txt ├── utils.py └── vision_tower.py /.gitignore: -------------------------------------------------------------------------------- 1 | **/__pycache__ 2 | .gradio 3 | .locks 4 | models* -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Attribution 4.0 International 2 | 3 | ======================================================================= 4 | 5 | Creative Commons Corporation ("Creative Commons") is not a law firm and 6 | does not provide legal services or legal advice. Distribution of 7 | Creative Commons public licenses does not create a lawyer-client or 8 | other relationship. 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2 |

Orient Anything: Learning Robust Object Orientation Estimation from Rendering 3D Models

3 | 4 | [**Zehan Wang**](https://scholar.google.com/citations?user=euXK0lkAAAAJ&hl=zh-CN)1* · [**Ziang Zhang**](https://scholar.google.com/citations?hl=zh-CN&user=DptGMnYAAAAJ)1* · [**Tianyu Pang**](https://scholar.google.com/citations?hl=zh-CN&user=wYDbtFsAAAAJ)2 · [**Du Chao**](https://scholar.google.com/citations?hl=zh-CN&user=QOp7xW0AAAAJ)2 · [**Hengshuang Zhao**](https://scholar.google.com/citations?user=4uE10I0AAAAJ&hl&oi=ao)3 · [**Zhou Zhao**](https://scholar.google.com/citations?user=IIoFY90AAAAJ&hl&oi=ao)1 5 | 6 | 1Zhejiang University    2SEA AI Lab    3HKU 7 | 8 | *Equal Contribution 9 | 10 | 11 | Paper PDF 12 | Project Page 13 | 14 | 15 |
16 | 17 | **Orient Anything**, a robust image-based object orientation estimation model. By training on 2M rendered labeled images, it achieves strong zero-shot generalization ability for images in the wild. 18 | 19 | ![teaser](assets/demo.png) 20 | 21 | ## News 22 | * **2025-05-01:** Orient Anything is accepted by ICML 2025! 23 | * **2024-12-24:** [Paper](https://arxiv.org/abs/2412.18605), [Project Page](https://orient-anything.github.io), [Code](https://github.com/SpatialVision/Orient-Anything), Models, and [Demo](https://huggingface.co/spaces/Viglong/Orient-Anything) are released. 24 | 25 | 26 | 27 | ## Pre-trained models 28 | 29 | We provide **three models** of varying scales for robust object orientation estimation in images: 30 | 31 | | Model | Params | Checkpoint | 32 | |:-|-:|:-:| 33 | | Orient-Anything-Small | 23.3 M | [Download](https://huggingface.co/Viglong/OriNet/blob/main/cropsmallEx03/dino_weight.pt) | 34 | | Orient-Anything-Base | 87.8 M | [Download](https://huggingface.co/Viglong/OriNet/blob/main/cropbaseEx032/dino_weight.pt) | 35 | | Orient-Anything-Large | 305 M | [Download](https://huggingface.co/Viglong/OriNet/blob/main/croplargeEX2/dino_weight.pt) | 36 | 37 | ## Usage 38 | 39 | ### 1 Prepraration 40 | 41 | ```bash 42 | pip install -r requirements.txt 43 | ``` 44 | 45 | ### 2 Use our models 46 | #### 2.1 In Gradio app 47 | Start gradio by executing the following script: 48 | 49 | ```bash 50 | python app.py 51 | ``` 52 | then open GUI page(default is https://127.0.0.1:7860) in web browser. 53 | 54 | or, you can try it in our [Huggingface-Space](https://huggingface.co/spaces/Viglong/Orient-Anything) 55 | 56 | #### 2.2 In Python Scripts 57 | ```python 58 | from paths import * 59 | from vision_tower import DINOv2_MLP 60 | from transformers import AutoImageProcessor 61 | import torch 62 | from PIL import Image 63 | 64 | import torch.nn.functional as F 65 | from utils import * 66 | from inference import * 67 | 68 | from huggingface_hub import hf_hub_download 69 | ckpt_path = hf_hub_download(repo_id="Viglong/Orient-Anything", filename="croplargeEX2/dino_weight.pt", repo_type="model", cache_dir='./', resume_download=True) 70 | print(ckpt_path) 71 | 72 | save_path = './' 73 | device = 'cuda' if torch.cuda.is_available() else 'cpu' 74 | dino = DINOv2_MLP( 75 | dino_mode = 'large', 76 | in_dim = 1024, 77 | out_dim = 360+180+180+2, 78 | evaluate = True, 79 | mask_dino = False, 80 | frozen_back = False 81 | ) 82 | 83 | dino.eval() 84 | print('model create') 85 | dino.load_state_dict(torch.load(ckpt_path, map_location='cpu')) 86 | dino = dino.to(device) 87 | print('weight loaded') 88 | val_preprocess = AutoImageProcessor.from_pretrained(DINO_LARGE, cache_dir='./') 89 | 90 | image_path = '/path/to/image' 91 | origin_image = Image.open(image_path).convert('RGB') 92 | angles = get_3angle(origin_image, dino, val_preprocess, device) 93 | azimuth = float(angles[0]) 94 | polar = float(angles[1]) 95 | rotation = float(angles[2]) 96 | confidence = float(angles[3]) 97 | 98 | 99 | ``` 100 | 101 | 102 | ### Best Practice 103 | To avoid ambiguity, our model only supports inputs that contain images of a single object. For daily images that usually contain multiple objects, it is a good choice to isolate each object with DINO-grounding and predict the orientation separately. 104 | ```python 105 | [ToDo] 106 | ``` 107 | ### Test-Time Augmentation 108 | In order to further enhance the robustness of the model，We further propose the test-time ensemble strategy. The input images will be randomly cropped into different variants, and the predicted orientation of different variants will be voted as the final prediction result. We implement this strategy in functions `get_3angle_infer_aug()` and `get_crop_images()`. 109 | 110 | ## Citation 111 | 112 | If you find this project useful, please consider citing: 113 | 114 | ```bibtex 115 | @article{orient_anything, 116 | title={Orient Anything: Learning Robust Object Orientation Estimation from Rendering 3D Models}, 117 | author={Wang, Zehan and Zhang, Ziang and Pang, Tianyu and Du, Chao and Zhao, Hengshuang and Zhao, Zhou}, 118 | journal={arXiv:2412.18605}, 119 | year={2024} 120 | } 121 | ``` 122 | 123 | ## Acknowledgement 124 | Thanks to the open source of the following projects: [Grounded-Segment-Anything](https://github.com/IDEA-Research/Grounded-Segment-Anything), [render-py](https://github.com/tvytlx/render-py) 125 | -------------------------------------------------------------------------------- /app.py: -------------------------------------------------------------------------------- 1 | import gradio as gr 2 | from paths import * 3 | 4 | from vision_tower import DINOv2_MLP 5 | from transformers import AutoImageProcessor 6 | import torch 7 | from inference import * 8 | from utils import * 9 | 10 | from huggingface_hub import hf_hub_download 11 | ckpt_path = hf_hub_download(repo_id="Viglong/Orient-Anything", filename="croplargeEX2/dino_weight.pt", repo_type="model", cache_dir='./', resume_download=True) 12 | print(ckpt_path) 13 | 14 | save_path = './' 15 | device = 'cpu' 16 | dino = DINOv2_MLP( 17 | dino_mode = 'large', 18 | in_dim = 1024, 19 | out_dim = 360+180+180+2, 20 | evaluate = True, 21 | mask_dino = False, 22 | frozen_back = False 23 | ) 24 | 25 | dino.eval() 26 | print('model create') 27 | dino.load_state_dict(torch.load(ckpt_path, map_location='cpu')) 28 | dino = dino.to(device) 29 | print('weight loaded') 30 | val_preprocess = AutoImageProcessor.from_pretrained(DINO_LARGE, cache_dir='./') 31 | 32 | def infer_func(img, do_rm_bkg, do_infer_aug): 33 | origin_img = Image.fromarray(img) 34 | if do_infer_aug: 35 | rm_bkg_img = background_preprocess(origin_img, True) 36 | angles = get_3angle_infer_aug(origin_img, rm_bkg_img, dino, val_preprocess, device) 37 | else: 38 | rm_bkg_img = background_preprocess(origin_img, do_rm_bkg) 39 | angles = get_3angle(rm_bkg_img, dino, val_preprocess, device) 40 | 41 | phi = np.radians(angles[0]) 42 | theta = np.radians(angles[1]) 43 | gamma = angles[2] 44 | confidence = float(angles[3]) 45 | if confidence > 0.5: 46 | render_axis = render_3D_axis(phi, theta, gamma) 47 | res_img = overlay_images_with_scaling(render_axis, rm_bkg_img) 48 | else: 49 | res_img = img 50 | 51 | # axis_model = "axis.obj" 52 | return [res_img, round(float(angles[0]), 2), round(float(angles[1]), 2), round(float(angles[2]), 2), round(float(angles[3]), 2)] 53 | 54 | server = gr.Interface( 55 | flagging_mode='never', 56 | fn=infer_func, 57 | inputs=[ 58 | gr.Image(height=512, width=512, label="upload your image"), 59 | gr.Checkbox(label="Remove Background", value=True), 60 | gr.Checkbox(label="Inference time augmentation", value=False) 61 | ], 62 | outputs=[ 63 | gr.Image(height=512, width=512, label="result image"), 64 | # gr.Model3D(clear_color=[0.0, 0.0, 0.0, 0.0], label="3D Model"), 65 | gr.Textbox(lines=1, label='Azimuth(0~360°)'), 66 | gr.Textbox(lines=1, label='Polar(-90~90°)'), 67 | gr.Textbox(lines=1, label='Rotation(-90~90°)'), 68 | gr.Textbox(lines=1, label='Confidence(0~1)') 69 | ] 70 | ) 71 | 72 | server.launch() 73 | -------------------------------------------------------------------------------- /assets/axis.obj: -------------------------------------------------------------------------------- 1 | # Blender 4.2.1 LTS 2 | # www.blender.org 3 | mtllib axis.mtl 4 | o X 5 | v 28.000000 0.000000 0.000000 6 | v 22.000000 0.292636 -1.471178 7 | v 22.000000 0.574025 -1.385819 8 | v 22.000000 0.833355 -1.247204 9 | v 22.000000 1.060660 -1.060660 10 | v 22.000000 1.247205 -0.833355 11 | v 22.000000 1.385819 -0.574025 12 | v 22.000000 1.471178 -0.292636 13 | v 22.000000 1.500000 -0.000000 14 | v 22.000000 1.471178 0.292636 15 | v 22.000000 1.385819 0.574025 16 | v 22.000000 1.247205 0.833355 17 | v 22.000000 1.060660 1.060660 18 | v 22.000000 0.833355 1.247204 19 | v 22.000000 0.574025 1.385819 20 | v 22.000000 0.292636 1.471178 21 | v 22.000000 0.000000 1.500000 22 | v 22.000000 -0.292635 1.471178 23 | v 22.000000 -0.574025 1.385819 24 | v 22.000000 -0.833355 1.247204 25 | v 22.000000 -1.060660 1.060660 26 | v 22.000000 -1.247204 0.833355 27 | v 22.000000 -1.385819 0.574025 28 | v 22.000000 -1.471178 0.292636 29 | v 22.000000 -1.500000 0.000000 30 | v 22.000000 -1.471178 -0.292636 31 | v 22.000000 -1.385819 -0.574025 32 | v 22.000000 -1.247204 -0.833355 33 | v 22.000000 -1.060660 -1.060660 34 | v 22.000000 -0.833355 -1.247204 35 | v 22.000000 -0.574025 -1.385819 36 | v 22.000000 -0.292635 -1.471178 37 | v 22.000000 -0.000000 -1.500000 38 | vn -1.0000 -0.0000 -0.0000 39 | vn -1.0000 0.0001 -0.0000 40 | vn -1.0000 -0.0001 -0.0000 41 | vn 0.2414 0.0951 -0.9657 42 | vn 0.2414 0.2817 -0.9286 43 | vn 0.2414 0.4575 -0.8558 44 | vn 0.2414 0.6156 -0.7501 45 | vn 0.2414 0.7501 -0.6156 46 | vn 0.2414 0.8558 -0.4574 47 | vn 0.2414 0.9286 -0.2817 48 | vn 0.2414 0.9657 -0.0951 49 | vn 0.2414 0.9657 0.0951 50 | vn 0.2414 0.9286 0.2817 51 | vn 0.2414 0.8558 0.4574 52 | vn 0.2414 0.7501 0.6156 53 | vn 0.2414 0.6156 0.7501 54 | vn 0.2414 0.4575 0.8558 55 | vn 0.2414 0.2817 0.9286 56 | vn 0.2414 0.0951 0.9657 57 | vn 0.2414 -0.0951 0.9657 58 | vn 0.2414 -0.2817 0.9286 59 | vn 0.2414 -0.4575 0.8558 60 | vn 0.2414 -0.6156 0.7501 61 | vn 0.2414 -0.7501 0.6156 62 | vn 0.2414 -0.8558 0.4575 63 | vn 0.2414 -0.9286 0.2817 64 | vn 0.2414 -0.9657 0.0951 65 | vn 0.2414 -0.9657 -0.0951 66 | vn 0.2414 -0.9286 -0.2817 67 | vn 0.2414 -0.8558 -0.4575 68 | vn 0.2414 -0.7501 -0.6156 69 | vn 0.2414 -0.6156 -0.7501 70 | vn 0.2414 -0.4575 -0.8558 71 | vn 0.2414 -0.2817 -0.9286 72 | vn 0.2414 -0.0951 -0.9657 73 | vt 0.597858 0.736041 74 | vt 0.735114 0.644330 75 | vt 0.643402 0.507074 76 | vt 0.506147 0.598785 77 | vt 0.523575 0.686407 78 | vt 0.555780 0.524503 79 | vt 0.717685 0.556707 80 | vt 0.685480 0.718612 81 | vt 0.555780 0.718612 82 | vt 0.506147 0.644330 83 | vt 0.523575 0.556707 84 | vt 0.597858 0.507074 85 | vt 0.685480 0.524503 86 | vt 0.735114 0.598785 87 | vt 0.717685 0.686407 88 | vt 0.643402 0.736041 89 | vt 0.575961 0.729399 90 | vt 0.538092 0.704096 91 | vt 0.512789 0.666227 92 | vt 0.503903 0.621557 93 | vt 0.512789 0.576888 94 | vt 0.538092 0.539019 95 | vt 0.575961 0.513716 96 | vt 0.620630 0.504831 97 | vt 0.665299 0.513716 98 | vt 0.703168 0.539019 99 | vt 0.728471 0.576888 100 | vt 0.737357 0.621557 101 | vt 0.728471 0.666227 102 | vt 0.703168 0.704096 103 | vt 0.665299 0.729399 104 | vt 0.620630 0.738284 105 | vt 0.354677 0.736041 106 | vt 0.377450 0.621557 107 | vt 0.377450 0.738284 108 | vt 0.332780 0.729399 109 | vt 0.312600 0.718612 110 | vt 0.294911 0.704096 111 | vt 0.280395 0.686407 112 | vt 0.269608 0.666227 113 | vt 0.262966 0.644330 114 | vt 0.260723 0.621557 115 | vt 0.262966 0.598785 116 | vt 0.269608 0.576888 117 | vt 0.280395 0.556707 118 | vt 0.294911 0.539019 119 | vt 0.312600 0.524503 120 | vt 0.332780 0.513716 121 | vt 0.354677 0.507074 122 | vt 0.377450 0.504831 123 | vt 0.400222 0.507074 124 | vt 0.422119 0.513716 125 | vt 0.442299 0.524503 126 | vt 0.459988 0.539019 127 | vt 0.474504 0.556707 128 | vt 0.485291 0.576888 129 | vt 0.491933 0.598785 130 | vt 0.494176 0.621557 131 | vt 0.491933 0.644330 132 | vt 0.485291 0.666227 133 | vt 0.474504 0.686407 134 | vt 0.459988 0.704096 135 | vt 0.442299 0.718612 136 | vt 0.422119 0.729399 137 | vt 0.400222 0.736041 138 | s 0 139 | usemtl MI_YamahaMSP3_MatteBlack.004 140 | f 2/1/1 26/2/1 18/3/1 141 | f 10/4/1 6/5/1 2/1/1 142 | f 18/3/1 14/6/1 10/4/1 143 | f 26/2/1 22/7/1 18/3/1 144 | f 2/1/1 30/8/1 26/2/1 145 | f 6/5/1 4/9/1 2/1/1 146 | f 10/4/1 8/10/1 6/5/1 147 | f 14/6/1 12/11/1 10/4/1 148 | f 18/3/1 16/12/1 14/6/1 149 | f 22/7/1 20/13/1 18/3/1 150 | f 26/2/1 24/14/1 22/7/1 151 | f 30/8/1 28/15/1 26/2/1 152 | f 2/1/1 32/16/1 30/8/1 153 | f 4/9/1 3/17/1 2/1/1 154 | f 6/5/2 5/18/2 4/9/2 155 | f 8/10/3 7/19/3 6/5/3 156 | f 10/4/1 9/20/1 8/10/1 157 | f 12/11/3 11/21/3 10/4/3 158 | f 14/6/2 13/22/2 12/11/2 159 | f 16/12/1 15/23/1 14/6/1 160 | f 18/3/1 17/24/1 16/12/1 161 | f 20/13/1 19/25/1 18/3/1 162 | f 22/7/3 21/26/3 20/13/3 163 | f 24/14/2 23/27/2 22/7/2 164 | f 26/2/1 25/28/1 24/14/1 165 | f 28/15/2 27/29/2 26/2/2 166 | f 30/8/3 29/30/3 28/15/3 167 | f 32/16/1 31/31/1 30/8/1 168 | f 2/1/1 33/32/1 32/16/1 169 | f 2/33/4 1/34/4 33/35/4 170 | f 3/36/5 1/34/5 2/33/5 171 | f 18/3/1 10/4/1 2/1/1 172 | f 4/37/6 1/34/6 3/36/6 173 | f 5/38/7 1/34/7 4/37/7 174 | f 6/39/8 1/34/8 5/38/8 175 | f 7/40/9 1/34/9 6/39/9 176 | f 8/41/10 1/34/10 7/40/10 177 | f 9/42/11 1/34/11 8/41/11 178 | f 10/43/12 1/34/12 9/42/12 179 | f 11/44/13 1/34/13 10/43/13 180 | f 12/45/14 1/34/14 11/44/14 181 | f 13/46/15 1/34/15 12/45/15 182 | f 14/47/16 1/34/16 13/46/16 183 | f 15/48/17 1/34/17 14/47/17 184 | f 16/49/18 1/34/18 15/48/18 185 | f 17/50/19 1/34/19 16/49/19 186 | f 18/51/20 1/34/20 17/50/20 187 | f 19/52/21 1/34/21 18/51/21 188 | f 20/53/22 1/34/22 19/52/22 189 | f 21/54/23 1/34/23 20/53/23 190 | f 22/55/24 1/34/24 21/54/24 191 | f 23/56/25 1/34/25 22/55/25 192 | f 24/57/26 1/34/26 23/56/26 193 | f 25/58/27 1/34/27 24/57/27 194 | f 26/59/28 1/34/28 25/58/28 195 | f 27/60/29 1/34/29 26/59/29 196 | f 28/61/30 1/34/30 27/60/30 197 | f 29/62/31 1/34/31 28/61/31 198 | f 30/63/32 1/34/32 29/62/32 199 | f 31/64/33 1/34/33 30/63/33 200 | f 32/65/34 1/34/34 31/64/34 201 | f 33/35/35 1/34/35 32/65/35 202 | o Xzhu 203 | v 24.000000 0.097545 -0.490393 204 | v 0.000000 0.097546 -0.490393 205 | v 24.000000 0.191341 -0.461940 206 | v 0.000000 0.191342 -0.461940 207 | v 24.000000 0.277785 -0.415735 208 | v 0.000000 0.277786 -0.415735 209 | v 24.000000 0.353553 -0.353553 210 | v 0.000000 0.353554 -0.353553 211 | v 24.000000 0.415734 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-0.7730 298 | vn -0.0000 -0.4714 -0.8819 299 | vn -0.0000 -0.2903 -0.9569 300 | vn -0.0000 -0.0980 -0.9952 301 | vt 0.597858 0.736041 302 | vt 0.735114 0.644330 303 | vt 0.643402 0.507074 304 | vt 0.506147 0.598785 305 | vt 0.523575 0.686407 306 | vt 0.555780 0.524503 307 | vt 0.717685 0.556707 308 | vt 0.685480 0.718612 309 | vt 0.555780 0.718612 310 | vt 0.506147 0.644330 311 | vt 0.523575 0.556707 312 | vt 0.597858 0.507074 313 | vt 0.685480 0.524503 314 | vt 0.735114 0.598785 315 | vt 0.717685 0.686407 316 | vt 0.643402 0.736041 317 | vt 0.575961 0.729399 318 | vt 0.538092 0.704096 319 | vt 0.512789 0.666227 320 | vt 0.503903 0.621557 321 | vt 0.512789 0.576888 322 | vt 0.538092 0.539019 323 | vt 0.575961 0.513716 324 | vt 0.620630 0.504831 325 | vt 0.665299 0.513716 326 | vt 0.703168 0.539019 327 | vt 0.728471 0.576888 328 | vt 0.737357 0.621557 329 | vt 0.728471 0.666227 330 | vt 0.703168 0.704096 331 | vt 0.665299 0.729399 332 | vt 0.620630 0.738284 333 | vt 0.271058 0.986328 334 | vt 0.255859 0.986328 335 | vt 0.255859 0.743148 336 | vt 0.286257 0.986328 337 | vt 0.271058 0.743148 338 | vt 0.422119 0.729399 339 | vt 0.269608 0.666227 340 | vt 0.332780 0.513716 341 | vt 0.485291 0.576888 342 | vt 0.485291 0.666227 343 | vt 0.422119 0.513716 344 | vt 0.269608 0.576888 345 | vt 0.332780 0.729399 346 | vt 0.459988 0.704096 347 | vt 0.494176 0.621557 348 | vt 0.459988 0.539019 349 | vt 0.377450 0.504831 350 | vt 0.294911 0.539019 351 | vt 0.260723 0.621557 352 | vt 0.294911 0.704096 353 | vt 0.377450 0.738284 354 | vt 0.442299 0.718612 355 | vt 0.474504 0.686407 356 | vt 0.491933 0.644330 357 | vt 0.491933 0.598785 358 | vt 0.474504 0.556707 359 | vt 0.442299 0.524503 360 | vt 0.400222 0.507074 361 | vt 0.354677 0.507074 362 | vt 0.312600 0.524503 363 | vt 0.280395 0.556707 364 | vt 0.262966 0.598785 365 | vt 0.262966 0.644330 366 | vt 0.280395 0.686407 367 | vt 0.312600 0.718612 368 | vt 0.354677 0.736041 369 | vt 0.400222 0.736041 370 | vt 0.301456 0.986328 371 | vt 0.286257 0.743148 372 | vt 0.316654 0.986328 373 | vt 0.301456 0.743148 374 | vt 0.331853 0.986328 375 | vt 0.316654 0.743148 376 | vt 0.347052 0.986328 377 | vt 0.331853 0.743148 378 | vt 0.362251 0.986328 379 | vt 0.347052 0.743148 380 | vt 0.377450 0.986328 381 | vt 0.362251 0.743148 382 | vt 0.392648 0.986328 383 | vt 0.377450 0.743148 384 | vt 0.407847 0.986328 385 | vt 0.392648 0.743148 386 | vt 0.423046 0.986328 387 | vt 0.407847 0.743148 388 | vt 0.438245 0.986328 389 | vt 0.423046 0.743148 390 | vt 0.453443 0.986328 391 | vt 0.438245 0.743148 392 | vt 0.468642 0.986328 393 | vt 0.453443 0.743148 394 | vt 0.483841 0.986328 395 | vt 0.468642 0.743148 396 | vt 0.499040 0.986328 397 | vt 0.483841 0.743148 398 | vt 0.514239 0.986328 399 | vt 0.499040 0.743148 400 | vt 0.529437 0.986328 401 | vt 0.514239 0.743148 402 | vt 0.544636 0.986328 403 | vt 0.529437 0.743148 404 | vt 0.559835 0.986328 405 | vt 0.544636 0.743148 406 | vt 0.575034 0.986328 407 | vt 0.559835 0.743148 408 | vt 0.590232 0.986328 409 | vt 0.575034 0.743148 410 | vt 0.605431 0.986328 411 | vt 0.590232 0.743148 412 | vt 0.620630 0.986328 413 | vt 0.605431 0.743148 414 | vt 0.635829 0.986328 415 | vt 0.620630 0.743148 416 | vt 0.651028 0.986328 417 | vt 0.635829 0.743148 418 | vt 0.666226 0.986328 419 | vt 0.651028 0.743148 420 | vt 0.681425 0.986328 421 | vt 0.666226 0.743148 422 | vt 0.696624 0.986328 423 | vt 0.681425 0.743148 424 | vt 0.711823 0.986328 425 | vt 0.696624 0.743148 426 | vt 0.727022 0.986328 427 | vt 0.711823 0.743148 428 | vt 0.742220 0.986328 429 | vt 0.727022 0.743148 430 | vt 0.742220 0.743148 431 | s 0 432 | usemtl MI_YamahaMSP3_MatteBlack.004 433 | f 35/66/36 83/67/36 67/68/36 434 | f 51/69/36 43/70/36 35/66/36 435 | f 67/68/36 59/71/36 51/69/36 436 | f 83/67/36 75/72/36 67/68/36 437 | f 35/66/36 91/73/36 83/67/36 438 | f 43/70/36 39/74/36 35/66/36 439 | f 51/69/36 47/75/36 43/70/36 440 | f 59/71/36 55/76/36 51/69/36 441 | f 67/68/36 63/77/36 59/71/36 442 | f 75/72/36 71/78/36 67/68/36 443 | f 83/67/36 79/79/36 75/72/36 444 | f 91/73/36 87/80/36 83/67/36 445 | f 35/66/36 95/81/36 91/73/36 446 | f 39/74/36 37/82/36 35/66/36 447 | f 43/70/36 41/83/36 39/74/36 448 | f 47/75/36 45/84/36 43/70/36 449 | f 51/69/36 49/85/36 47/75/36 450 | f 55/76/36 53/86/36 51/69/36 451 | f 59/71/36 57/87/36 55/76/36 452 | f 63/77/36 61/88/36 59/71/36 453 | f 67/68/36 65/89/36 63/77/36 454 | f 71/78/36 69/90/36 67/68/36 455 | f 75/72/36 73/91/36 71/78/36 456 | f 79/79/36 77/92/36 75/72/36 457 | f 83/67/36 81/93/36 79/79/36 458 | f 87/80/36 85/94/36 83/67/36 459 | f 91/73/36 89/95/36 87/80/36 460 | f 95/81/36 93/96/36 91/73/36 461 | f 35/66/36 97/97/36 95/81/36 462 | f 34/98/37 96/99/37 97/100/37 463 | f 36/101/38 34/98/38 35/102/38 464 | f 92/103/39 44/104/39 60/105/39 465 | f 76/106/39 84/107/39 92/103/39 466 | f 60/105/39 68/108/39 76/106/39 467 | f 44/104/39 52/109/39 60/105/39 468 | f 92/103/39 36/110/39 44/104/39 469 | f 84/107/39 88/111/39 92/103/39 470 | f 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0.889856 0.234951 872 | vt 0.827437 0.228803 873 | vt 0.792388 0.205385 874 | vt 0.768969 0.170336 875 | vt 0.760746 0.128993 876 | vt 0.768969 0.087650 877 | vt 0.792388 0.052601 878 | vt 0.827437 0.029182 879 | vt 0.868780 0.020959 880 | vt 0.910123 0.029182 881 | vt 0.945172 0.052601 882 | vt 0.968590 0.087650 883 | vt 0.976814 0.128993 884 | vt 0.968590 0.170336 885 | vt 0.945172 0.205385 886 | vt 0.910123 0.228803 887 | vt 0.868780 0.237027 888 | vt 0.545240 0.466600 889 | vt 0.531173 0.466600 890 | vt 0.531173 0.241528 891 | vt 0.559307 0.466600 892 | vt 0.545240 0.241528 893 | vt 0.685052 0.228803 894 | vt 0.543898 0.170336 895 | vt 0.602366 0.029182 896 | vt 0.743519 0.087650 897 | vt 0.743519 0.170336 898 | vt 0.685052 0.029182 899 | vt 0.543898 0.087650 900 | vt 0.602366 0.228803 901 | vt 0.720101 0.205385 902 | vt 0.751743 0.128993 903 | vt 0.720101 0.052601 904 | vt 0.643709 0.020959 905 | vt 0.567317 0.052601 906 | vt 0.535675 0.128993 907 | vt 0.567317 0.205385 908 | vt 0.643709 0.237027 909 | vt 0.703729 0.218820 910 | vt 0.733536 0.189013 911 | vt 0.749667 0.150069 912 | vt 0.749667 0.107916 913 | vt 0.733536 0.068972 914 | vt 0.703729 0.039165 915 | vt 0.664785 0.023035 916 | vt 0.622632 0.023035 917 | vt 0.583688 0.039165 918 | vt 0.553881 0.068972 919 | vt 0.537751 0.107916 920 | vt 0.537751 0.150069 921 | vt 0.553881 0.189013 922 | vt 0.583688 0.218820 923 | vt 0.622632 0.234951 924 | vt 0.664785 0.234951 925 | vt 0.573374 0.466600 926 | vt 0.559307 0.241528 927 | vt 0.587441 0.466600 928 | vt 0.573374 0.241528 929 | vt 0.601508 0.466600 930 | vt 0.587441 0.241528 931 | vt 0.615575 0.466600 932 | vt 0.601508 0.241528 933 | vt 0.629642 0.466600 934 | vt 0.615575 0.241528 935 | vt 0.643709 0.466600 936 | vt 0.629642 0.241528 937 | vt 0.657776 0.466600 938 | vt 0.643709 0.241528 939 | vt 0.671843 0.466600 940 | vt 0.657776 0.241528 941 | vt 0.685910 0.466600 942 | vt 0.671843 0.241528 943 | vt 0.699977 0.466600 944 | vt 0.685910 0.241528 945 | vt 0.714043 0.466600 946 | vt 0.699977 0.241528 947 | vt 0.728110 0.466600 948 | vt 0.714043 0.241528 949 | vt 0.742177 0.466600 950 | vt 0.728110 0.241528 951 | vt 0.756244 0.466600 952 | vt 0.742177 0.241528 953 | vt 0.770311 0.466600 954 | vt 0.756244 0.241528 955 | vt 0.784378 0.466600 956 | vt 0.770311 0.241528 957 | vt 0.798445 0.466600 958 | vt 0.784378 0.241528 959 | vt 0.812512 0.466600 960 | vt 0.798445 0.241528 961 | vt 0.826579 0.466600 962 | vt 0.812512 0.241528 963 | vt 0.840646 0.466600 964 | vt 0.826579 0.241528 965 | vt 0.854713 0.466600 966 | vt 0.840646 0.241528 967 | vt 0.868780 0.466600 968 | vt 0.854713 0.241528 969 | vt 0.882847 0.466600 970 | vt 0.868780 0.241528 971 | vt 0.896914 0.466600 972 | vt 0.882847 0.241528 973 | vt 0.910981 0.466600 974 | vt 0.896914 0.241528 975 | vt 0.925048 0.466600 976 | vt 0.910981 0.241528 977 | vt 0.939115 0.466600 978 | vt 0.925048 0.241528 979 | vt 0.953182 0.466600 980 | vt 0.939115 0.241528 981 | vt 0.967249 0.466600 982 | vt 0.953182 0.241528 983 | vt 0.981316 0.466600 984 | vt 0.967249 0.241528 985 | vt 0.981316 0.241528 986 | s 0 987 | usemtl MI_YamahaMSP3_MatteBlack.003 988 | f 132/261/105 180/262/105 164/263/105 989 | f 148/264/105 140/265/105 132/261/105 990 | f 164/263/105 156/266/105 148/264/105 991 | f 180/262/105 172/267/105 164/263/105 992 | f 132/261/105 188/268/105 180/262/105 993 | f 140/265/105 136/269/105 132/261/105 994 | f 148/264/106 144/270/106 140/265/106 995 | f 156/266/105 152/271/105 148/264/105 996 | f 164/263/105 160/272/105 156/266/105 997 | f 172/267/105 168/273/105 164/263/105 998 | f 180/262/107 176/274/107 172/267/107 999 | f 188/268/105 184/275/105 180/262/105 1000 | f 132/261/105 192/276/105 188/268/105 1001 | f 136/269/105 134/277/105 132/261/105 1002 | f 140/265/105 138/278/105 136/269/105 1003 | f 144/270/107 142/279/107 140/265/107 1004 | f 148/264/106 146/280/106 144/270/106 1005 | f 152/271/105 150/281/105 148/264/105 1006 | f 156/266/105 154/282/105 152/271/105 1007 | f 160/272/105 158/283/105 156/266/105 1008 | f 164/263/105 162/284/105 160/272/105 1009 | f 168/273/105 166/285/105 164/263/105 1010 | f 172/267/105 170/286/105 168/273/105 1011 | f 176/274/105 174/287/105 172/267/105 1012 | f 180/262/107 178/288/107 176/274/107 1013 | f 184/275/106 182/289/106 180/262/106 1014 | f 188/268/105 186/290/105 184/275/105 1015 | f 192/276/105 190/291/105 188/268/105 1016 | f 132/261/105 194/292/105 192/276/105 1017 | f 131/293/108 193/294/108 194/295/108 1018 | f 133/296/109 131/293/109 132/297/109 1019 | f 189/298/110 141/299/110 157/300/110 1020 | f 173/301/110 181/302/110 189/298/110 1021 | f 157/300/110 165/303/110 173/301/110 1022 | f 141/299/110 149/304/110 157/300/110 1023 | f 189/298/110 133/305/110 141/299/110 1024 | f 181/302/110 185/306/110 189/298/110 1025 | f 173/301/110 177/307/110 181/302/110 1026 | f 165/303/110 169/308/110 173/301/110 1027 | f 157/300/110 161/309/110 165/303/110 1028 | f 149/304/110 153/310/110 157/300/110 1029 | f 141/299/110 145/311/110 149/304/110 1030 | f 133/305/110 137/312/110 141/299/110 1031 | f 189/298/110 193/313/110 133/305/110 1032 | f 185/306/110 187/314/110 189/298/110 1033 | f 181/302/110 183/315/110 185/306/110 1034 | f 177/307/110 179/316/110 181/302/110 1035 | f 173/301/110 175/317/110 177/307/110 1036 | f 169/308/110 171/318/110 173/301/110 1037 | f 165/303/110 167/319/110 169/308/110 1038 | f 161/309/110 163/320/110 165/303/110 1039 | f 157/300/110 159/321/110 161/309/110 1040 | f 153/310/110 155/322/110 157/300/110 1041 | f 149/304/110 151/323/110 153/310/110 1042 | f 145/311/110 147/324/110 149/304/110 1043 | f 141/299/110 143/325/110 145/311/110 1044 | f 137/312/110 139/326/110 141/299/110 1045 | f 133/305/110 135/327/110 137/312/110 1046 | f 193/313/110 131/328/110 133/305/110 1047 | f 189/298/110 191/329/110 193/313/110 1048 | f 135/330/111 133/296/111 134/331/111 1049 | f 137/332/112 135/330/112 136/333/112 1050 | f 139/334/113 137/332/113 138/335/113 1051 | f 141/336/114 139/334/114 140/337/114 1052 | f 143/338/115 141/336/115 142/339/115 1053 | f 145/340/116 143/338/116 144/341/116 1054 | f 147/342/117 145/340/117 146/343/117 1055 | f 149/344/118 147/342/118 148/345/118 1056 | f 151/346/119 149/344/119 150/347/119 1057 | f 153/348/120 151/346/120 152/349/120 1058 | f 155/350/121 153/348/121 154/351/121 1059 | f 157/352/122 155/350/122 156/353/122 1060 | f 159/354/123 157/352/123 158/355/123 1061 | f 161/356/124 159/354/124 160/357/124 1062 | f 163/358/125 161/356/125 162/359/125 1063 | f 165/360/126 163/358/126 164/361/126 1064 | f 167/362/127 165/360/127 166/363/127 1065 | f 169/364/128 167/362/128 168/365/128 1066 | f 171/366/129 169/364/129 170/367/129 1067 | f 173/368/130 171/366/130 172/369/130 1068 | f 175/370/131 173/368/131 174/371/131 1069 | f 177/372/132 175/370/132 176/373/132 1070 | f 179/374/133 177/372/133 178/375/133 1071 | f 181/376/134 179/374/134 180/377/134 1072 | f 183/378/135 181/376/135 182/379/135 1073 | f 185/380/136 183/378/136 184/381/136 1074 | f 187/382/137 185/380/137 186/383/137 1075 | f 189/384/138 187/382/138 188/385/138 1076 | f 191/386/139 189/384/139 190/387/139 1077 | f 193/388/140 191/386/140 192/389/140 1078 | f 164/263/105 148/264/105 132/261/105 1079 | f 131/293/108 194/295/108 132/297/108 1080 | f 133/296/109 132/297/109 134/331/109 1081 | f 157/300/110 173/301/110 189/298/110 1082 | f 135/330/111 134/331/111 136/333/111 1083 | f 137/332/112 136/333/112 138/335/112 1084 | f 139/334/113 138/335/113 140/337/113 1085 | f 141/336/114 140/337/114 142/339/114 1086 | f 143/338/115 142/339/115 144/341/115 1087 | f 145/340/116 144/341/116 146/343/116 1088 | f 147/342/117 146/343/117 148/345/117 1089 | f 149/344/118 148/345/118 150/347/118 1090 | f 151/346/119 150/347/119 152/349/119 1091 | f 153/348/120 152/349/120 154/351/120 1092 | f 155/350/121 154/351/121 156/353/121 1093 | f 157/352/122 156/353/122 158/355/122 1094 | f 159/354/123 158/355/123 160/357/123 1095 | f 161/356/124 160/357/124 162/359/124 1096 | f 163/358/125 162/359/125 164/361/125 1097 | f 165/360/126 164/361/126 166/363/126 1098 | f 167/362/127 166/363/127 168/365/127 1099 | f 169/364/128 168/365/128 170/367/128 1100 | f 171/366/129 170/367/129 172/369/129 1101 | f 173/368/130 172/369/130 174/371/130 1102 | f 175/370/131 174/371/131 176/373/131 1103 | f 177/372/132 176/373/132 178/375/132 1104 | f 179/374/133 178/375/133 180/377/133 1105 | f 181/376/134 180/377/134 182/379/134 1106 | f 183/378/135 182/379/135 184/381/135 1107 | f 185/380/136 184/381/136 186/383/136 1108 | f 187/382/137 186/383/137 188/385/137 1109 | f 189/384/138 188/385/138 190/387/138 1110 | f 191/386/139 190/387/139 192/389/139 1111 | f 193/388/140 192/389/140 194/390/140 1112 | o Z 1113 | v 0.000000 28.000000 -0.000002 1114 | v -0.292636 22.000000 -1.471180 1115 | v -0.574025 22.000000 -1.385821 1116 | v -0.833355 22.000000 -1.247206 1117 | v -1.060660 22.000000 -1.060662 1118 | v -1.247204 22.000000 -0.833357 1119 | v -1.385819 22.000000 -0.574027 1120 | v -1.471178 22.000000 -0.292638 1121 | v -1.500000 22.000000 -0.000002 1122 | v -1.471178 22.000000 0.292634 1123 | v -1.385819 22.000000 0.574023 1124 | v -1.247204 22.000000 0.833353 1125 | v -1.060660 22.000000 1.060658 1126 | v -0.833355 22.000000 1.247202 1127 | v -0.574025 22.000000 1.385817 1128 | v -0.292636 22.000000 1.471176 1129 | v 0.000000 22.000000 1.499998 1130 | v 0.292636 22.000000 1.471176 1131 | v 0.574025 22.000000 1.385817 1132 | v 0.833355 22.000000 1.247202 1133 | v 1.060660 22.000000 1.060658 1134 | v 1.247204 22.000000 0.833353 1135 | v 1.385819 22.000000 0.574023 1136 | v 1.471178 22.000000 0.292634 1137 | v 1.500000 22.000000 -0.000002 1138 | v 1.471178 22.000000 -0.292638 1139 | v 1.385819 22.000000 -0.574027 1140 | v 1.247204 22.000000 -0.833357 1141 | v 1.060660 22.000000 -1.060662 1142 | v 0.833355 22.000000 -1.247206 1143 | v 0.574025 22.000000 -1.385821 1144 | v 0.292636 22.000000 -1.471180 1145 | v 0.000000 22.000000 -1.500002 1146 | vn -0.0000 -1.0000 -0.0000 1147 | vn -0.0000 -1.0000 0.0001 1148 | vn -0.0000 -1.0000 -0.0001 1149 | vn -0.0951 0.2414 -0.9657 1150 | vn -0.2817 0.2414 -0.9286 1151 | vn -0.4575 0.2414 -0.8558 1152 | vn -0.6156 0.2414 -0.7501 1153 | vn -0.7501 0.2414 -0.6156 1154 | vn -0.8558 0.2414 -0.4575 1155 | vn -0.9286 0.2414 -0.2817 1156 | vn -0.9657 0.2414 -0.0951 1157 | vn -0.9657 0.2414 0.0951 1158 | vn -0.9286 0.2414 0.2817 1159 | vn -0.8558 0.2414 0.4575 1160 | vn -0.7501 0.2414 0.6156 1161 | vn -0.6156 0.2414 0.7501 1162 | vn -0.4575 0.2414 0.8558 1163 | vn -0.2817 0.2414 0.9286 1164 | vn -0.0951 0.2414 0.9657 1165 | vn 0.0951 0.2414 0.9657 1166 | vn 0.2817 0.2414 0.9286 1167 | vn 0.4575 0.2414 0.8558 1168 | vn 0.6156 0.2414 0.7501 1169 | vn 0.7501 0.2414 0.6156 1170 | vn 0.8558 0.2414 0.4575 1171 | vn 0.9286 0.2414 0.2817 1172 | vn 0.9657 0.2414 0.0951 1173 | vn 0.9657 0.2414 -0.0951 1174 | vn 0.9286 0.2414 -0.2817 1175 | vn 0.8558 0.2414 -0.4575 1176 | vn 0.7501 0.2414 -0.6156 1177 | vn 0.6156 0.2414 -0.7501 1178 | vn 0.4575 0.2414 -0.8558 1179 | vn 0.2817 0.2414 -0.9286 1180 | vn 0.0951 0.2414 -0.9657 1181 | vt 0.344619 0.232996 1182 | vt 0.473521 0.146867 1183 | vt 0.387392 0.017966 1184 | vt 0.258490 0.104095 1185 | vt 0.274858 0.186384 1186 | vt 0.305103 0.034334 1187 | vt 0.457153 0.064579 1188 | vt 0.426908 0.216629 1189 | vt 0.305103 0.216629 1190 | vt 0.258490 0.146867 1191 | vt 0.274858 0.064579 1192 | vt 0.344619 0.017966 1193 | vt 0.426908 0.034334 1194 | vt 0.473521 0.104095 1195 | vt 0.457153 0.186384 1196 | vt 0.387392 0.232996 1197 | vt 0.324055 0.226758 1198 | vt 0.288491 0.202996 1199 | vt 0.264728 0.167432 1200 | vt 0.256384 0.125481 1201 | vt 0.264728 0.083531 1202 | vt 0.288491 0.047967 1203 | vt 0.324055 0.024204 1204 | vt 0.366006 0.015859 1205 | vt 0.407956 0.024204 1206 | vt 0.443520 0.047967 1207 | vt 0.467283 0.083531 1208 | vt 0.475627 0.125481 1209 | vt 0.467283 0.167432 1210 | vt 0.443520 0.202996 1211 | vt 0.407956 0.226758 1212 | vt 0.366006 0.235103 1213 | vt 0.116241 0.232996 1214 | vt 0.137627 0.125481 1215 | vt 0.137627 0.235103 1216 | vt 0.095676 0.226758 1217 | vt 0.076724 0.216629 1218 | vt 0.060112 0.202996 1219 | vt 0.046480 0.186384 1220 | vt 0.036350 0.167432 1221 | vt 0.030112 0.146867 1222 | vt 0.028005 0.125481 1223 | vt 0.030112 0.104095 1224 | vt 0.036350 0.083531 1225 | vt 0.046480 0.064579 1226 | vt 0.060112 0.047967 1227 | vt 0.076724 0.034334 1228 | vt 0.095676 0.024204 1229 | vt 0.116241 0.017966 1230 | vt 0.137627 0.015859 1231 | vt 0.159013 0.017966 1232 | vt 0.179577 0.024204 1233 | vt 0.198530 0.034334 1234 | vt 0.215141 0.047967 1235 | vt 0.228774 0.064579 1236 | vt 0.238904 0.083531 1237 | vt 0.245142 0.104095 1238 | vt 0.247249 0.125481 1239 | vt 0.245142 0.146867 1240 | vt 0.238904 0.167432 1241 | vt 0.228774 0.186384 1242 | vt 0.215141 0.202996 1243 | vt 0.198530 0.216629 1244 | vt 0.179577 0.226758 1245 | vt 0.159013 0.232996 1246 | s 0 1247 | usemtl MI_YamahaMSP3_MatteBlack.010 1248 | f 196/391/141 220/392/141 212/393/141 1249 | f 204/394/141 200/395/141 196/391/141 1250 | f 212/393/141 208/396/141 204/394/141 1251 | f 220/392/141 216/397/141 212/393/141 1252 | f 196/391/141 224/398/141 220/392/141 1253 | f 200/395/141 198/399/141 196/391/141 1254 | f 204/394/141 202/400/141 200/395/141 1255 | f 208/396/141 206/401/141 204/394/141 1256 | f 212/393/141 210/402/141 208/396/141 1257 | f 216/397/141 214/403/141 212/393/141 1258 | f 220/392/141 218/404/141 216/397/141 1259 | f 224/398/141 222/405/141 220/392/141 1260 | f 196/391/141 226/406/141 224/398/141 1261 | f 198/399/142 197/407/142 196/391/142 1262 | f 200/395/143 199/408/143 198/399/143 1263 | f 202/400/141 201/409/141 200/395/141 1264 | f 204/394/141 203/410/141 202/400/141 1265 | f 206/401/141 205/411/141 204/394/141 1266 | f 208/396/142 207/412/142 206/401/142 1267 | f 210/402/143 209/413/143 208/396/143 1268 | f 212/393/141 211/414/141 210/402/141 1269 | f 214/403/143 213/415/143 212/393/143 1270 | f 216/397/142 215/416/142 214/403/142 1271 | f 218/404/141 217/417/141 216/397/141 1272 | f 220/392/141 219/418/141 218/404/141 1273 | f 222/405/141 221/419/141 220/392/141 1274 | f 224/398/143 223/420/143 222/405/143 1275 | f 226/406/142 225/421/142 224/398/142 1276 | f 196/391/141 227/422/141 226/406/141 1277 | f 196/423/144 195/424/144 227/425/144 1278 | f 197/426/145 195/424/145 196/423/145 1279 | f 212/393/141 204/394/141 196/391/141 1280 | f 198/427/146 195/424/146 197/426/146 1281 | f 199/428/147 195/424/147 198/427/147 1282 | f 200/429/148 195/424/148 199/428/148 1283 | f 201/430/149 195/424/149 200/429/149 1284 | f 202/431/150 195/424/150 201/430/150 1285 | f 203/432/151 195/424/151 202/431/151 1286 | f 204/433/152 195/424/152 203/432/152 1287 | f 205/434/153 195/424/153 204/433/153 1288 | f 206/435/154 195/424/154 205/434/154 1289 | f 207/436/155 195/424/155 206/435/155 1290 | f 208/437/156 195/424/156 207/436/156 1291 | f 209/438/157 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0.047967 1431 | vt 0.324055 0.024204 1432 | vt 0.366006 0.015859 1433 | vt 0.407956 0.024204 1434 | vt 0.443520 0.047967 1435 | vt 0.467283 0.083531 1436 | vt 0.475627 0.125481 1437 | vt 0.467283 0.167432 1438 | vt 0.443520 0.202996 1439 | vt 0.407956 0.226758 1440 | vt 0.366006 0.235103 1441 | vt 0.037711 0.468049 1442 | vt 0.023437 0.468049 1443 | vt 0.023437 0.239671 1444 | vt 0.051985 0.468049 1445 | vt 0.037711 0.239671 1446 | vt 0.179577 0.226758 1447 | vt 0.036350 0.167432 1448 | vt 0.095676 0.024204 1449 | vt 0.238904 0.083531 1450 | vt 0.238904 0.167432 1451 | vt 0.179577 0.024204 1452 | vt 0.036350 0.083531 1453 | vt 0.095676 0.226758 1454 | vt 0.215141 0.202996 1455 | vt 0.247249 0.125481 1456 | vt 0.215141 0.047967 1457 | vt 0.137627 0.015859 1458 | vt 0.060112 0.047967 1459 | vt 0.028005 0.125481 1460 | vt 0.060112 0.202996 1461 | vt 0.137627 0.235103 1462 | vt 0.198530 0.216629 1463 | vt 0.228774 0.186384 1464 | vt 0.245142 0.146867 1465 | vt 0.245142 0.104095 1466 | vt 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0.239671 1538 | vt 0.480195 0.239671 1539 | s 0 1540 | usemtl MI_YamahaMSP3_MatteBlack.010 1541 | f 229/456/176 277/457/176 261/458/176 1542 | f 245/459/176 237/460/176 229/456/176 1543 | f 261/458/176 253/461/176 245/459/176 1544 | f 277/457/176 269/462/176 261/458/176 1545 | f 229/456/176 285/463/176 277/457/176 1546 | f 237/460/176 233/464/176 229/456/176 1547 | f 245/459/176 241/465/176 237/460/176 1548 | f 253/461/176 249/466/176 245/459/176 1549 | f 261/458/176 257/467/176 253/461/176 1550 | f 269/462/176 265/468/176 261/458/176 1551 | f 277/457/176 273/469/176 269/462/176 1552 | f 285/463/176 281/470/176 277/457/176 1553 | f 229/456/176 289/471/176 285/463/176 1554 | f 233/464/176 231/472/176 229/456/176 1555 | f 237/460/176 235/473/176 233/464/176 1556 | f 241/465/176 239/474/176 237/460/176 1557 | f 245/459/176 243/475/176 241/465/176 1558 | f 249/466/176 247/476/176 245/459/176 1559 | f 253/461/176 251/477/176 249/466/176 1560 | f 257/467/176 255/478/176 253/461/176 1561 | f 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230/500/179 234/507/179 238/494/179 1584 | f 286/493/179 290/508/179 230/500/179 1585 | f 282/501/179 284/509/179 286/493/179 1586 | f 278/497/179 280/510/179 282/501/179 1587 | f 274/502/179 276/511/179 278/497/179 1588 | f 270/496/179 272/512/179 274/502/179 1589 | f 266/503/179 268/513/179 270/496/179 1590 | f 262/498/179 264/514/179 266/503/179 1591 | f 258/504/179 260/515/179 262/498/179 1592 | f 254/495/179 256/516/179 258/504/179 1593 | f 250/505/179 252/517/179 254/495/179 1594 | f 246/499/179 248/518/179 250/505/179 1595 | f 242/506/179 244/519/179 246/499/179 1596 | f 238/494/179 240/520/179 242/506/179 1597 | f 234/507/179 236/521/179 238/494/179 1598 | f 230/500/179 232/522/179 234/507/179 1599 | f 290/508/179 228/523/179 230/500/179 1600 | f 286/493/179 288/524/179 290/508/179 1601 | f 232/525/180 230/491/180 231/526/180 1602 | f 234/527/181 232/525/181 233/528/181 1603 | f 236/529/182 234/527/182 235/530/182 1604 | f 238/531/183 236/529/183 237/532/183 1605 | f 240/533/184 238/531/184 239/534/184 1606 | f 242/535/185 240/533/185 241/536/185 1607 | f 244/537/186 242/535/186 243/538/186 1608 | f 246/539/187 244/537/187 245/540/187 1609 | f 248/541/188 246/539/188 247/542/188 1610 | f 250/543/189 248/541/189 249/544/189 1611 | f 252/545/190 250/543/190 251/546/190 1612 | f 254/547/191 252/545/191 253/548/191 1613 | f 256/549/192 254/547/192 255/550/192 1614 | f 258/551/193 256/549/193 257/552/193 1615 | f 260/553/194 258/551/194 259/554/194 1616 | f 262/555/195 260/553/195 261/556/195 1617 | f 264/557/196 262/555/196 263/558/196 1618 | f 266/559/197 264/557/197 265/560/197 1619 | f 268/561/198 266/559/198 267/562/198 1620 | f 270/563/199 268/561/199 269/564/199 1621 | f 272/565/200 270/563/200 271/566/200 1622 | f 274/567/201 272/565/201 273/568/201 1623 | f 276/569/202 274/567/202 275/570/202 1624 | f 278/571/203 276/569/203 277/572/203 1625 | f 280/573/204 278/571/204 279/574/204 1626 | f 282/575/205 280/573/205 281/576/205 1627 | f 284/577/206 282/575/206 283/578/206 1628 | f 286/579/207 284/577/207 285/580/207 1629 | f 288/581/208 286/579/208 287/582/208 1630 | f 290/583/209 288/581/209 289/584/209 1631 | f 261/458/176 245/459/176 229/456/176 1632 | f 228/488/177 291/490/177 229/492/177 1633 | f 230/491/178 229/492/178 231/526/178 1634 | f 254/495/179 270/496/179 286/493/179 1635 | f 232/525/180 231/526/180 233/528/180 1636 | f 234/527/181 233/528/181 235/530/181 1637 | f 236/529/182 235/530/182 237/532/182 1638 | f 238/531/183 237/532/183 239/534/183 1639 | f 240/533/184 239/534/184 241/536/184 1640 | f 242/535/185 241/536/185 243/538/185 1641 | f 244/537/186 243/538/186 245/540/186 1642 | f 246/539/187 245/540/187 247/542/187 1643 | f 248/541/188 247/542/188 249/544/188 1644 | f 250/543/189 249/544/189 251/546/189 1645 | f 252/545/190 251/546/190 253/548/190 1646 | f 254/547/191 253/548/191 255/550/191 1647 | f 256/549/192 255/550/192 257/552/192 1648 | f 258/551/193 257/552/193 259/554/193 1649 | f 260/553/194 259/554/194 261/556/194 1650 | f 262/555/195 261/556/195 263/558/195 1651 | f 264/557/196 263/558/196 265/560/196 1652 | f 266/559/197 265/560/197 267/562/197 1653 | f 268/561/198 267/562/198 269/564/198 1654 | f 270/563/199 269/564/199 271/566/199 1655 | f 272/565/200 271/566/200 273/568/200 1656 | f 274/567/201 273/568/201 275/570/201 1657 | f 276/569/202 275/570/202 277/572/202 1658 | f 278/571/203 277/572/203 279/574/203 1659 | f 280/573/204 279/574/204 281/576/204 1660 | f 282/575/205 281/576/205 283/578/205 1661 | f 284/577/206 283/578/206 285/580/206 1662 | f 286/579/207 285/580/207 287/582/207 1663 | f 288/581/208 287/582/208 289/584/208 1664 | f 290/583/209 289/584/209 291/585/209 1665 | -------------------------------------------------------------------------------- /assets/axis.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SpatialVision/Orient-Anything/fbbaf97e25e9ce1c6b5cbeb515f9c5ce9e83b1fc/assets/axis.png -------------------------------------------------------------------------------- /assets/demo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SpatialVision/Orient-Anything/fbbaf97e25e9ce1c6b5cbeb515f9c5ce9e83b1fc/assets/demo.png -------------------------------------------------------------------------------- /inference.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from PIL import Image 3 | from utils import * 4 | import torch.nn.functional as F 5 | import numpy as np 6 | 7 | def get_3angle(image, dino, val_preprocess, device): 8 | 9 | # image = Image.open(image_path).convert('RGB') 10 | image_inputs = val_preprocess(images = image) 11 | image_inputs['pixel_values'] = torch.from_numpy(np.array(image_inputs['pixel_values'])).to(device) 12 | with torch.no_grad(): 13 | dino_pred = dino(image_inputs) 14 | 15 | gaus_ax_pred = torch.argmax(dino_pred[:, 0:360], dim=-1) 16 | gaus_pl_pred = torch.argmax(dino_pred[:, 360:360+180], dim=-1) 17 | gaus_ro_pred = torch.argmax(dino_pred[:, 360+180:360+180+180], dim=-1) 18 | confidence = F.softmax(dino_pred[:, -2:], dim=-1)[0][0] 19 | angles = torch.zeros(4) 20 | angles[0] = gaus_ax_pred 21 | angles[1] = gaus_pl_pred - 90 22 | angles[2] = gaus_ro_pred - 90 23 | angles[3] = confidence 24 | return angles 25 | 26 | def get_3angle_infer_aug(origin_img, rm_bkg_img, dino, val_preprocess, device): 27 | 28 | # image = Image.open(image_path).convert('RGB') 29 | image = get_crop_images(origin_img, num=3) + get_crop_images(rm_bkg_img, num=3) 30 | image_inputs = val_preprocess(images = image) 31 | image_inputs['pixel_values'] = torch.from_numpy(np.array(image_inputs['pixel_values'])).to(device) 32 | with torch.no_grad(): 33 | dino_pred = dino(image_inputs) 34 | 35 | gaus_ax_pred = torch.argmax(dino_pred[:, 0:360], dim=-1).to(torch.float32) 36 | gaus_pl_pred = torch.argmax(dino_pred[:, 360:360+180], dim=-1).to(torch.float32) 37 | gaus_ro_pred = torch.argmax(dino_pred[:, 360+180:360+180+180], dim=-1).to(torch.float32) 38 | 39 | gaus_ax_pred = remove_outliers_and_average_circular(gaus_ax_pred) 40 | gaus_pl_pred = remove_outliers_and_average(gaus_pl_pred) 41 | gaus_ro_pred = remove_outliers_and_average(gaus_ro_pred) 42 | 43 | confidence = torch.mean(F.softmax(dino_pred[:, -2:], dim=-1), dim=0)[0] 44 | angles = torch.zeros(4) 45 | angles[0] = gaus_ax_pred 46 | angles[1] = gaus_pl_pred - 90 47 | angles[2] = gaus_ro_pred - 90 48 | angles[3] = confidence 49 | return angles -------------------------------------------------------------------------------- /paths.py: -------------------------------------------------------------------------------- 1 | DINO_SMALL = "facebook/dinov2-small" 2 | DINO_BASE = "facebook/dinov2-base" 3 | DINO_LARGE = "facebook/dinov2-large" 4 | DINO_GIANT = "facebook/dinov2-giant" -------------------------------------------------------------------------------- /render/__init__.py: -------------------------------------------------------------------------------- 1 | # flake8: noqa 2 | from .core import render 3 | from .model import Model 4 | -------------------------------------------------------------------------------- /render/canvas.py: -------------------------------------------------------------------------------- 1 | import typing as t 2 | 3 | from PIL import Image, ImageColor, ImageOps, ImageChops, ImageFilter 4 | import numpy as np 5 | 6 | class Canvas: 7 | def __init__(self, filename=None, height=500, width=500): 8 | self.filename = filename 9 | self.height, self.width = height, width 10 | self.img = Image.new("RGBA", (self.height, self.width), (0, 0, 0, 0)) 11 | 12 | def draw(self, dots, color: t.Union[tuple, str]): 13 | if isinstance(color, str): 14 | color = ImageColor.getrgb(color) 15 | if isinstance(dots, tuple): 16 | dots = [dots] 17 | for dot in dots: 18 | if dot[0]>=self.height or dot[1]>=self.width or dot[0]<0 or dot[1]<0: 19 | # print(dot) 20 | continue 21 | self.img.putpixel(dot, color + (255,)) 22 | 23 | def add_white_border(self, border_size=5): 24 | # 确保输入图像是 RGBA 模式 25 | if self.img.mode != "RGBA": 26 | self.img = self.img.convert("RGBA") 27 | 28 | # 提取 alpha 通道 29 | alpha = self.img.getchannel("A") 30 | # print(alpha.size) 31 | dilated_alpha = alpha.filter(ImageFilter.MaxFilter(size=5)) 32 | # # print(dilated_alpha.size) 33 | white_area = Image.new("RGBA", self.img.size, (255, 255, 255, 255)) 34 | white_area.putalpha(dilated_alpha) 35 | 36 | # 合并膨胀后的白色区域与原图像 37 | result = Image.alpha_composite(white_area, self.img) 38 | # expanded_alpha = ImageOps.expand(alpha, border=border_size, fill=255) 39 | # white_border = Image.new("RGBA", image.size, (255, 255, 255, 255)) 40 | # white_border.putalpha(alpha) 41 | return result 42 | 43 | def __enter__(self): 44 | return self 45 | 46 | def __exit__(self, type, value, traceback): 47 | # self.img = add_white_border(self.img) 48 | self.img.save(self.filename) 49 | pass 50 | -------------------------------------------------------------------------------- /render/core.py: -------------------------------------------------------------------------------- 1 | import typing as t 2 | from functools import partial 3 | 4 | import numpy as np 5 | from copy import deepcopy 6 | from .canvas import Canvas 7 | 8 | from . import speedup 9 | 10 | 11 | # 2D part 12 | 13 | 14 | class Vec2d: 15 | __slots__ = "x", "y", "arr" 16 | 17 | def __init__(self, *args): 18 | if len(args) == 1 and isinstance(args[0], Vec3d): 19 | self.arr = Vec3d.narr 20 | else: 21 | assert len(args) == 2 22 | self.arr = list(args) 23 | 24 | self.x, self.y = [d if isinstance(d, int) else int(d + 0.5) for d in self.arr] 25 | 26 | def __repr__(self): 27 | return f"Vec2d({self.x}, {self.y})" 28 | 29 | def __truediv__(self, other): 30 | return (self.y - other.y) / (self.x - other.x) 31 | 32 | def __eq__(self, other): 33 | return self.x == other.x and self.y == other.y 34 | 35 | 36 | def draw_line( 37 | v1: Vec2d, v2: Vec2d, canvas: Canvas, color: t.Union[tuple, str] = "white" 38 | ): 39 | """ 40 | Draw a line with a specified color 41 | 42 | https://en.wikipedia.org/wiki/Bresenham%27s_line_algorithm 43 | """ 44 | v1, v2 = deepcopy(v1), deepcopy(v2) 45 | if v1 == v2: 46 | canvas.draw((v1.x, v1.y), color=color) 47 | return 48 | 49 | steep = abs(v1.y - v2.y) > abs(v1.x - v2.x) 50 | if steep: 51 | v1.x, v1.y = v1.y, v1.x 52 | v2.x, v2.y = v2.y, v2.x 53 | v1, v2 = (v1, v2) if v1.x < v2.x else (v2, v1) 54 | slope = abs((v1.y - v2.y) / (v1.x - v2.x)) 55 | y = v1.y 56 | error: float = 0 57 | incr = 1 if v1.y < v2.y else -1 58 | dots = [] 59 | for x in range(int(v1.x), int(v2.x + 0.5)): 60 | dots.append((int(y), x) if steep else (x, int(y))) 61 | error += slope 62 | if abs(error) >= 0.5: 63 | y += incr 64 | error -= 1 65 | 66 | canvas.draw(dots, color=color) 67 | 68 | 69 | def draw_triangle(v1, v2, v3, canvas, color, wireframe=False): 70 | """ 71 | Draw a triangle with 3 ordered vertices 72 | 73 | http://www.sunshine2k.de/coding/java/TriangleRasterization/TriangleRasterization.html 74 | """ 75 | _draw_line = partial(draw_line, canvas=canvas, color=color) 76 | 77 | if wireframe: 78 | _draw_line(v1, v2) 79 | _draw_line(v2, v3) 80 | _draw_line(v1, v3) 81 | return 82 | 83 | def sort_vertices_asc_by_y(vertices): 84 | return sorted(vertices, key=lambda v: v.y) 85 | 86 | def fill_bottom_flat_triangle(v1, v2, v3): 87 | invslope1 = (v2.x - v1.x) / (v2.y - v1.y) 88 | invslope2 = (v3.x - v1.x) / (v3.y - v1.y) 89 | 90 | x1 = x2 = v1.x 91 | y = v1.y 92 | 93 | while y <= v2.y: 94 | _draw_line(Vec2d(x1, y), Vec2d(x2, y)) 95 | x1 += invslope1 96 | x2 += invslope2 97 | y += 1 98 | 99 | def fill_top_flat_triangle(v1, v2, v3): 100 | invslope1 = (v3.x - v1.x) / (v3.y - v1.y) 101 | invslope2 = (v3.x - v2.x) / (v3.y - v2.y) 102 | 103 | x1 = x2 = v3.x 104 | y = v3.y 105 | 106 | while y > v2.y: 107 | _draw_line(Vec2d(x1, y), Vec2d(x2, y)) 108 | x1 -= invslope1 109 | x2 -= invslope2 110 | y -= 1 111 | 112 | v1, v2, v3 = sort_vertices_asc_by_y((v1, v2, v3)) 113 | 114 | # 填充 115 | if v1.y == v2.y == v3.y: 116 | pass 117 | elif v2.y == v3.y: 118 | fill_bottom_flat_triangle(v1, v2, v3) 119 | elif v1.y == v2.y: 120 | fill_top_flat_triangle(v1, v2, v3) 121 | else: 122 | v4 = Vec2d(int(v1.x + (v2.y - v1.y) / (v3.y - v1.y) * (v3.x - v1.x)), v2.y) 123 | fill_bottom_flat_triangle(v1, v2, v4) 124 | fill_top_flat_triangle(v2, v4, v3) 125 | 126 | 127 | # 3D part 128 | 129 | 130 | class Vec3d: 131 | __slots__ = "x", "y", "z", "arr" 132 | 133 | def __init__(self, *args): 134 | # for Vec4d cast 135 | if len(args) == 1 and isinstance(args[0], Vec4d): 136 | vec4 = args[0] 137 | arr_value = (vec4.x, vec4.y, vec4.z) 138 | else: 139 | assert len(args) == 3 140 | arr_value = args 141 | self.arr = np.array(arr_value, dtype=np.float64) 142 | self.x, self.y, self.z = self.arr 143 | 144 | def __repr__(self): 145 | return repr(f"Vec3d({','.join([repr(d) for d in self.arr])})") 146 | 147 | def __sub__(self, other): 148 | return self.__class__(*[ds - do for ds, do in zip(self.arr, other.arr)]) 149 | 150 | def __bool__(self): 151 | """ False for zero vector (0, 0, 0) 152 | """ 153 | return any(self.arr) 154 | 155 | 156 | class Mat4d: 157 | def __init__(self, narr=None, value=None): 158 | self.value = np.matrix(narr) if value is None else value 159 | 160 | def __repr__(self): 161 | return repr(self.value) 162 | 163 | def __mul__(self, other): 164 | return self.__class__(value=self.value * other.value) 165 | 166 | 167 | class Vec4d(Mat4d): 168 | def __init__(self, *narr, value=None): 169 | if value is not None: 170 | self.value = value 171 | elif len(narr) == 1 and isinstance(narr[0], Mat4d): 172 | self.value = narr[0].value 173 | else: 174 | assert len(narr) == 4 175 | self.value = np.matrix([[d] for d in narr]) 176 | 177 | self.x, self.y, self.z, self.w = ( 178 | self.value[0, 0], 179 | self.value[1, 0], 180 | self.value[2, 0], 181 | self.value[3, 0], 182 | ) 183 | self.arr = self.value.reshape((1, 4)) 184 | 185 | 186 | # Math util 187 | def normalize(v: Vec3d): 188 | return Vec3d(*speedup.normalize(*v.arr)) 189 | 190 | 191 | def dot_product(a: Vec3d, b: Vec3d): 192 | return speedup.dot_product(*a.arr, *b.arr) 193 | 194 | 195 | def cross_product(a: Vec3d, b: Vec3d): 196 | return Vec3d(*speedup.cross_product(*a.arr, *b.arr)) 197 | 198 | BASE_LIGHT = 0.9 199 | def get_light_intensity(face) -> float: 200 | # lights = [Vec3d(-2, 4, -10), Vec3d(10, 4, -2), Vec3d(8, 8, -8), Vec3d(0, 0, -8)] 201 | lights = [Vec3d(-2, 4, -10)] 202 | # lights = [] 203 | 204 | v1, v2, v3 = face 205 | up = normalize(cross_product(v2 - v1, v3 - v1)) 206 | intensity = BASE_LIGHT 207 | for light in lights: 208 | intensity += dot_product(up, normalize(light))*0.2 209 | return intensity 210 | 211 | 212 | def look_at(eye: Vec3d, target: Vec3d, up: Vec3d = Vec3d(0, -1, 0)) -> Mat4d: 213 | """ 214 | http://www.songho.ca/opengl/gl_camera.html#lookat 215 | 216 | Args: 217 | eye: 摄像机的世界坐标位置 218 | target: 观察点的位置 219 | up: 就是你想让摄像机立在哪个方向 220 | https://stackoverflow.com/questions/10635947/what-exactly-is-the-up-vector-in-opengls-lookat-function 221 | 这里默认使用了 0, -1, 0， 因为 blender 导出来的模型数据似乎有问题，导致y轴总是反的，于是把摄像机的up也翻一下得了。 222 | """ 223 | f = normalize(eye - target) 224 | l = normalize(cross_product(up, f)) # noqa: E741 225 | u = cross_product(f, l) 226 | 227 | rotate_matrix = Mat4d( 228 | [[l.x, l.y, l.z, 0], [u.x, u.y, u.z, 0], [f.x, f.y, f.z, 0], [0, 0, 0, 1.0]] 229 | ) 230 | translate_matrix = Mat4d( 231 | [[1, 0, 0, -eye.x], [0, 1, 0, -eye.y], [0, 0, 1, -eye.z], [0, 0, 0, 1.0]] 232 | ) 233 | 234 | return Mat4d(value=(rotate_matrix * translate_matrix).value) 235 | 236 | 237 | def perspective_project(r, t, n, f, b=None, l=None): # noqa: E741 238 | """ 239 | 目的： 240 | 把相机坐标转换成投影在视网膜的范围在(-1, 1)的笛卡尔坐标 241 | 242 | 原理： 243 | 对于x，y坐标，相似三角形可以算出投影点的x，y 244 | 对于z坐标，是假设了near是-1，far是1，然后带进去算的 245 | http://www.songho.ca/opengl/gl_projectionmatrix.html 246 | https://www.scratchapixel.com/lessons/3d-basic-rendering/perspective-and-orthographic-projection-matrix/opengl-perspective-projection-matrix 247 | 248 | 推导出来的矩阵： 249 | [ 250 | 2n/(r-l) 0 (r+l/r-l) 0 251 | 0 2n/(t-b) (t+b)/(t-b) 0 252 | 0 0 -(f+n)/f-n (-2*f*n)/(f-n) 253 | 0 0 -1 0 254 | ] 255 | 256 | 实际上由于我们用的视网膜(near pane)是个关于远点对称的矩形，所以矩阵简化为： 257 | [ 258 | n/r 0 0 0 259 | 0 n/t 0 0 260 | 0 0 -(f+n)/f-n (-2*f*n)/(f-n) 261 | 0 0 -1 0 262 | ] 263 | 264 | Args: 265 | r: right, t: top, n: near, f: far, b: bottom, l: left 266 | """ 267 | return Mat4d( 268 | [ 269 | [n / r, 0, 0, 0], 270 | [0, n / t, 0, 0], 271 | [0, 0, -(f + n) / (f - n), (-2 * f * n) / (f - n)], 272 | [0, 0, -1, 0], 273 | ] 274 | ) 275 | 276 | 277 | def draw(screen_vertices, world_vertices, model, canvas, wireframe=True): 278 | """standard algorithm 279 | """ 280 | for triangle_indices in model.indices: 281 | vertex_group = [screen_vertices[idx - 1] for idx in triangle_indices] 282 | face = [Vec3d(world_vertices[idx - 1]) for idx in triangle_indices] 283 | if wireframe: 284 | draw_triangle(*vertex_group, canvas=canvas, color="black", wireframe=True) 285 | else: 286 | intensity = get_light_intensity(face) 287 | if intensity > 0: 288 | draw_triangle( 289 | *vertex_group, canvas=canvas, color=(int(intensity * 255),) * 3 290 | ) 291 | 292 | 293 | def draw_with_z_buffer(screen_vertices, world_vertices, model, canvas): 294 | """ z-buffer algorithm 295 | """ 296 | intensities = [] 297 | triangles = [] 298 | for i, triangle_indices in enumerate(model.indices): 299 | screen_triangle = [screen_vertices[idx - 1] for idx in triangle_indices] 300 | uv_triangle = [model.uv_vertices[idx - 1] for idx in model.uv_indices[i]] 301 | world_triangle = [Vec3d(world_vertices[idx - 1]) for idx in triangle_indices] 302 | intensities.append(abs(get_light_intensity(world_triangle))) 303 | # take off the class to let Cython work 304 | triangles.append( 305 | [np.append(screen_triangle[i].arr, uv_triangle[i]) for i in range(3)] 306 | ) 307 | 308 | faces = speedup.generate_faces( 309 | np.array(triangles, dtype=np.float64), model.texture_width, model.texture_height 310 | ) 311 | for face_dots in faces: 312 | for dot in face_dots: 313 | intensity = intensities[dot[0]] 314 | u, v = dot[3], dot[4] 315 | color = model.texture_array[u, v] 316 | canvas.draw((dot[1], dot[2]), tuple(int(c * intensity) for c in color[:3])) 317 | # TODO: add object rendering mode (no texture) 318 | # canvas.draw((dot[1], dot[2]), (int(255 * intensity),) * 3) 319 | 320 | 321 | def render(model, height, width, filename, cam_loc, wireframe=False): 322 | """ 323 | Args: 324 | model: the Model object 325 | height: cavas height 326 | width: cavas width 327 | picname: picture file name 328 | """ 329 | model_matrix = Mat4d([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]]) 330 | # TODO: camera configration 331 | view_matrix = look_at(Vec3d(cam_loc[0], cam_loc[1], cam_loc[2]), Vec3d(0, 0, 0)) 332 | projection_matrix = perspective_project(0.5, 0.5, 3, 1000) 333 | 334 | world_vertices = [] 335 | 336 | def mvp(v): 337 | world_vertex = model_matrix * v 338 | world_vertices.append(Vec4d(world_vertex)) 339 | return projection_matrix * view_matrix * world_vertex 340 | 341 | def ndc(v): 342 | """ 343 | 各个坐标同时除以 w，得到 NDC 坐标 344 | """ 345 | v = v.value 346 | w = v[3, 0] 347 | x, y, z = v[0, 0] / w, v[1, 0] / w, v[2, 0] / w 348 | return Mat4d([[x], [y], [z], [1 / w]]) 349 | 350 | def viewport(v): 351 | x = y = 0 352 | w, h = width, height 353 | n, f = 0.3, 1000 354 | return Vec3d( 355 | w * 0.5 * v.value[0, 0] + x + w * 0.5, 356 | h * 0.5 * v.value[1, 0] + y + h * 0.5, 357 | 0.5 * (f - n) * v.value[2, 0] + 0.5 * (f + n), 358 | ) 359 | 360 | # the render pipeline 361 | screen_vertices = [viewport(ndc(mvp(v))) for v in model.vertices] 362 | 363 | with Canvas(filename, height, width) as canvas: 364 | if wireframe: 365 | draw(screen_vertices, world_vertices, model, canvas) 366 | else: 367 | draw_with_z_buffer(screen_vertices, world_vertices, model, canvas) 368 | 369 | render_img = canvas.add_white_border().copy() 370 | return render_img -------------------------------------------------------------------------------- /render/model.py: -------------------------------------------------------------------------------- 1 | import numpy 2 | from PIL import Image 3 | from .core import Vec4d 4 | 5 | 6 | class Model: 7 | def __init__(self, filename, texture_filename): 8 | """ 9 | https://en.wikipedia.org/wiki/Wavefront_.obj_file#Vertex_normal_indices 10 | """ 11 | self.vertices = [] 12 | self.uv_vertices = [] 13 | self.uv_indices = [] 14 | self.indices = [] 15 | 16 | texture = Image.open(texture_filename) 17 | self.texture_array = numpy.array(texture) 18 | self.texture_width, self.texture_height = texture.size 19 | 20 | with open(filename) as f: 21 | for line in f: 22 | if line.startswith("v "): 23 | x, y, z = [float(d) for d in line.strip("v").strip().split(" ")] 24 | self.vertices.append(Vec4d(x, y, z, 1)) 25 | elif line.startswith("vt "): 26 | u, v = [float(d) for d in line.strip("vt").strip().split(" ")] 27 | self.uv_vertices.append([u, v]) 28 | elif line.startswith("f "): 29 | facet = [d.split("/") for d in line.strip("f").strip().split(" ")] 30 | self.indices.append([int(d[0]) for d in facet]) 31 | self.uv_indices.append([int(d[1]) for d in facet]) 32 | -------------------------------------------------------------------------------- /render/speedup.py: -------------------------------------------------------------------------------- 1 | # import cython 2 | import numpy as np 3 | from math import sqrt 4 | 5 | 6 | def normalize(x, y, z): 7 | unit = sqrt(x * x + y * y + z * z) 8 | if unit == 0: 9 | return 0, 0, 0 10 | return x / unit, y / unit, z / unit 11 | 12 | 13 | def get_min_max(a, b, c): 14 | min = a 15 | max = a 16 | if min > b: 17 | min = b 18 | if min > c: 19 | min = c 20 | if max < b: 21 | max = b 22 | if max < c: 23 | max = c 24 | return int(min), int(max) 25 | 26 | def dot_product(a0, a1, a2, b0, b1, b2): 27 | r = a0 * b0 + a1 * b1 + a2 * b2 28 | return r 29 | 30 | 31 | def cross_product(a0, a1, a2, b0, b1, b2): 32 | x = a1 * b2 - a2 * b1 33 | y = a2 * b0 - a0 * b2 34 | z = a0 * b1 - a1 * b0 35 | return x,y,z 36 | 37 | 38 | # @cython.boundscheck(False) 39 | def generate_faces(triangles, width, height): 40 | """ draw the triangle faces with z buffer 41 | 42 | Args: 43 | triangles: groups of vertices 44 | 45 | FYI: 46 | * zbuffer, https://github.com/ssloy/tinyrenderer/wiki/Lesson-3:-Hidden-faces-removal-(z-buffer) 47 | * uv mapping and perspective correction 48 | """ 49 | i, j, k, length = 0, 0, 0, 0 50 | bcy, bcz, x, y, z = 0.,0.,0.,0.,0. 51 | a, b, c = [0.,0.,0.],[0.,0.,0.],[0.,0.,0.] 52 | m, bc = [0.,0.,0.],[0.,0.,0.] 53 | uva, uvb, uvc = [0.,0.],[0.,0.],[0.,0.] 54 | minx, maxx, miny, maxy = 0,0,0,0 55 | length = triangles.shape[0] 56 | zbuffer = {} 57 | faces = [] 58 | 59 | for i in range(length): 60 | a = triangles[i, 0, 0], triangles[i, 0, 1], triangles[i, 0, 2] 61 | b = triangles[i, 1, 0], triangles[i, 1, 1], triangles[i, 1, 2] 62 | c = triangles[i, 2, 0], triangles[i, 2, 1], triangles[i, 2, 2] 63 | uva = triangles[i, 0, 3], triangles[i, 0, 4] 64 | uvb = triangles[i, 1, 3], triangles[i, 1, 4] 65 | uvc = triangles[i, 2, 3], triangles[i, 2, 4] 66 | minx, maxx = get_min_max(a[0], b[0], c[0]) 67 | miny, maxy = get_min_max(a[1], b[1], c[1]) 68 | pixels = [] 69 | for j in range(minx, maxx + 2): 70 | for k in range(miny - 1, maxy + 2): 71 | # 必须显式转换成 double 参与底下的运算，不然结果是错的 72 | x = j 73 | y = k 74 | 75 | m[0], m[1], m[2] = cross_product(c[0] - a[0], b[0] - a[0], a[0] - x, c[1] - a[1], b[1] - a[1], a[1] - y) 76 | if abs(m[2]) > 0: 77 | bcy = m[1] / m[2] 78 | bcz = m[0] / m[2] 79 | bc = (1 - bcy - bcz, bcy, bcz) 80 | else: 81 | continue 82 | 83 | # here, -0.00001 because of the precision lose 84 | if bc[0] < -0.00001 or bc[1] < -0.00001 or bc[2] < -0.00001: 85 | continue 86 | 87 | z = 1 / (bc[0] / a[2] + bc[1] / b[2] + bc[2] / c[2]) 88 | 89 | # Blender 导出来的 uv 数据，跟之前的顶点数据有一样的问题，Y轴是个反的， 90 | # 所以这里的纹理图片要旋转一下才能 work 91 | v = (uva[0] * bc[0] / a[2] + uvb[0] * bc[1] / b[2] + uvc[0] * bc[2] / c[2]) * z * width 92 | u = height - (uva[1] * bc[0] / a[2] + uvb[1] * bc[1] / b[2] + uvc[1] * bc[2] / c[2]) * z * height 93 | 94 | # https://en.wikipedia.org/wiki/Pairing_function 95 | idx = ((x + y) * (x + y + 1) + y) / 2 96 | if zbuffer.get(idx) is None or zbuffer[idx] < z: 97 | zbuffer[idx] = z 98 | pixels.append((i, j, k, int(u) - 1, int(v) - 1)) 99 | 100 | faces.append(pixels) 101 | return faces 102 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | torch==2.2.1 2 | transformers==4.38 3 | matplotlib 4 | pillow==10.2.0 5 | huggingface-hub==0.26.5 6 | gradio==5.9.0 7 | numpy==1.26.4 8 | onnxruntime 9 | rembg -------------------------------------------------------------------------------- /utils.py: -------------------------------------------------------------------------------- 1 | import rembg 2 | import random 3 | import torch 4 | import numpy as np 5 | from PIL import Image, ImageOps 6 | import PIL 7 | from typing import Any 8 | import matplotlib.pyplot as plt 9 | import io 10 | 11 | def resize_foreground( 12 | image: Image, 13 | ratio: float, 14 | ) -> Image: 15 | image = np.array(image) 16 | assert image.shape[-1] == 4 17 | alpha = np.where(image[..., 3] > 0) 18 | y1, y2, x1, x2 = ( 19 | alpha[0].min(), 20 | alpha[0].max(), 21 | alpha[1].min(), 22 | alpha[1].max(), 23 | ) 24 | # crop the foreground 25 | fg = image[y1:y2, x1:x2] 26 | # pad to square 27 | size = max(fg.shape[0], fg.shape[1]) 28 | ph0, pw0 = (size - fg.shape[0]) // 2, (size - fg.shape[1]) // 2 29 | ph1, pw1 = size - fg.shape[0] - ph0, size - fg.shape[1] - pw0 30 | new_image = np.pad( 31 | fg, 32 | ((ph0, ph1), (pw0, pw1), (0, 0)), 33 | mode="constant", 34 | constant_values=((0, 0), (0, 0), (0, 0)), 35 | ) 36 | 37 | # compute padding according to the ratio 38 | new_size = int(new_image.shape[0] / ratio) 39 | # pad to size, double side 40 | ph0, pw0 = (new_size - size) // 2, (new_size - size) // 2 41 | ph1, pw1 = new_size - size - ph0, new_size - size - pw0 42 | new_image = np.pad( 43 | new_image, 44 | ((ph0, ph1), (pw0, pw1), (0, 0)), 45 | mode="constant", 46 | constant_values=((0, 0), (0, 0), (0, 0)), 47 | ) 48 | new_image = Image.fromarray(new_image) 49 | return new_image 50 | 51 | def remove_background(image: Image, 52 | rembg_session: Any = None, 53 | force: bool = False, 54 | **rembg_kwargs, 55 | ) -> Image: 56 | do_remove = True 57 | if image.mode == "RGBA" and image.getextrema()[3][0] < 255: 58 | do_remove = False 59 | do_remove = do_remove or force 60 | if do_remove: 61 | image = rembg.remove(image, session=rembg_session, **rembg_kwargs) 62 | return image 63 | 64 | def random_crop(image, crop_scale=(0.8, 0.95)): 65 | """ 66 | 随机裁切图片 67 | image (numpy.ndarray): (H, W, C)。 68 | crop_scale (tuple): (min_scale, max_scale)。 69 | """ 70 | assert isinstance(image, Image.Image), "iput must be PIL.Image.Image" 71 | assert len(crop_scale) == 2 and 0 < crop_scale[0] <= crop_scale[1] <= 1 72 | 73 | width, height = image.size 74 | 75 | # 计算裁切的高度和宽度 76 | crop_width = random.randint(int(width * crop_scale[0]), int(width * crop_scale[1])) 77 | crop_height = random.randint(int(height * crop_scale[0]), int(height * crop_scale[1])) 78 | 79 | # 随机选择裁切的起始点 80 | left = random.randint(0, width - crop_width) 81 | top = random.randint(0, height - crop_height) 82 | 83 | # 裁切图片 84 | cropped_image = image.crop((left, top, left + crop_width, top + crop_height)) 85 | 86 | return cropped_image 87 | 88 | def get_crop_images(img, num=3): 89 | cropped_images = [] 90 | for i in range(num): 91 | cropped_images.append(random_crop(img)) 92 | return cropped_images 93 | 94 | def background_preprocess(input_image, do_remove_background): 95 | 96 | rembg_session = rembg.new_session() if do_remove_background else None 97 | 98 | if do_remove_background: 99 | input_image = remove_background(input_image, rembg_session) 100 | input_image = resize_foreground(input_image, 0.85) 101 | 102 | return input_image 103 | 104 | def remove_outliers_and_average(tensor, threshold=1.5): 105 | assert tensor.dim() == 1, "dimension of input Tensor must equal to 1" 106 | 107 | q1 = torch.quantile(tensor, 0.25) 108 | q3 = torch.quantile(tensor, 0.75) 109 | iqr = q3 - q1 110 | 111 | lower_bound = q1 - threshold * iqr 112 | upper_bound = q3 + threshold * iqr 113 | 114 | non_outliers = tensor[(tensor >= lower_bound) & (tensor <= upper_bound)] 115 | 116 | if len(non_outliers) == 0: 117 | return tensor.mean().item() 118 | 119 | return non_outliers.mean().item() 120 | 121 | 122 | def remove_outliers_and_average_circular(tensor, threshold=1.5): 123 | assert tensor.dim() == 1, "dimension of input Tensor must equal to 1" 124 | 125 | # 将角度转换为二维平面上的点 126 | radians = tensor * torch.pi / 180.0 127 | x_coords = torch.cos(radians) 128 | y_coords = torch.sin(radians) 129 | 130 | # 计算平均向量 131 | mean_x = torch.mean(x_coords) 132 | mean_y = torch.mean(y_coords) 133 | 134 | differences = torch.sqrt((x_coords - mean_x) * (x_coords - mean_x) + (y_coords - mean_y) * (y_coords - mean_y)) 135 | 136 | # 计算四分位数和 IQR 137 | q1 = torch.quantile(differences, 0.25) 138 | q3 = torch.quantile(differences, 0.75) 139 | iqr = q3 - q1 140 | 141 | # 计算上下限 142 | lower_bound = q1 - threshold * iqr 143 | upper_bound = q3 + threshold * iqr 144 | 145 | # 筛选非离群点 146 | non_outliers = tensor[(differences >= lower_bound) & (differences <= upper_bound)] 147 | 148 | if len(non_outliers) == 0: 149 | mean_angle = torch.atan2(mean_y, mean_x) * 180.0 / torch.pi 150 | mean_angle = (mean_angle + 360) % 360 151 | return mean_angle # 如果没有非离群点，返回 None 152 | 153 | # 对非离群点再次计算平均向量 154 | radians = non_outliers * torch.pi / 180.0 155 | x_coords = torch.cos(radians) 156 | y_coords = torch.sin(radians) 157 | 158 | mean_x = torch.mean(x_coords) 159 | mean_y = torch.mean(y_coords) 160 | 161 | mean_angle = torch.atan2(mean_y, mean_x) * 180.0 / torch.pi 162 | mean_angle = (mean_angle + 360) % 360 163 | 164 | return mean_angle 165 | 166 | def scale(x): 167 | # print(x) 168 | # if abs(x[0])<0.1 and abs(x[1])<0.1: 169 | 170 | # return x*5 171 | # else: 172 | # return x 173 | return x*3 174 | 175 | def get_proj2D_XYZ(phi, theta, gamma): 176 | x = np.array([-1*np.sin(phi)*np.cos(gamma) - np.cos(phi)*np.sin(theta)*np.sin(gamma), np.sin(phi)*np.sin(gamma) - np.cos(phi)*np.sin(theta)*np.cos(gamma)]) 177 | y = np.array([-1*np.cos(phi)*np.cos(gamma) + np.sin(phi)*np.sin(theta)*np.sin(gamma), np.cos(phi)*np.sin(gamma) + np.sin(phi)*np.sin(theta)*np.cos(gamma)]) 178 | z = np.array([np.cos(theta)*np.sin(gamma), np.cos(theta)*np.cos(gamma)]) 179 | x = scale(x) 180 | y = scale(y) 181 | z = scale(z) 182 | return x, y, z 183 | 184 | # 绘制3D坐标轴 185 | def draw_axis(ax, origin, vector, color, label=None): 186 | ax.quiver(origin[0], origin[1], vector[0], vector[1], angles='xy', scale_units='xy', scale=1, color=color) 187 | if label!=None: 188 | ax.text(origin[0] + vector[0] * 1.1, origin[1] + vector[1] * 1.1, label, color=color, fontsize=12) 189 | 190 | def matplotlib_2D_arrow(angles, rm_bkg_img): 191 | fig, ax = plt.subplots(figsize=(8, 8)) 192 | 193 | # 设置旋转角度 194 | phi = np.radians(angles[0]) 195 | theta = np.radians(angles[1]) 196 | gamma = np.radians(-1*angles[2]) 197 | 198 | w, h = rm_bkg_img.size 199 | if h>w: 200 | extent = [-5*w/h, 5*w/h, -5, 5] 201 | else: 202 | extent = [-5, 5, -5*h/w, 5*h/w] 203 | ax.imshow(rm_bkg_img, extent=extent, zorder=0, aspect ='auto') # extent 设置图片的显示范围 204 | 205 | origin = np.array([0, 0]) 206 | 207 | # 旋转后的向量 208 | rot_x, rot_y, rot_z = get_proj2D_XYZ(phi, theta, gamma) 209 | 210 | # draw arrow 211 | arrow_attr = [{'point':rot_x, 'color':'r', 'label':'front'}, 212 | {'point':rot_y, 'color':'g', 'label':'right'}, 213 | {'point':rot_z, 'color':'b', 'label':'top'}] 214 | 215 | if phi> 45 and phi<=225: 216 | order = [0,1,2] 217 | elif phi > 225 and phi < 315: 218 | order = [2,0,1] 219 | else: 220 | order = [2,1,0] 221 | 222 | for i in range(3): 223 | draw_axis(ax, origin, arrow_attr[order[i]]['point'], arrow_attr[order[i]]['color'], arrow_attr[order[i]]['label']) 224 | # draw_axis(ax, origin, rot_y, 'g', label='right') 225 | # draw_axis(ax, origin, rot_z, 'b', label='top') 226 | # draw_axis(ax, origin, rot_x, 'r', label='front') 227 | 228 | # 关闭坐标轴和网格 229 | ax.set_axis_off() 230 | ax.grid(False) 231 | 232 | # 设置坐标范围 233 | ax.set_xlim(-5, 5) 234 | ax.set_ylim(-5, 5) 235 | 236 | def figure_to_img(fig): 237 | with io.BytesIO() as buf: 238 | fig.savefig(buf, format='JPG', bbox_inches='tight') 239 | buf.seek(0) 240 | image = Image.open(buf).copy() 241 | return image 242 | 243 | from render import render, Model 244 | import math 245 | axis_model = Model("./assets/axis.obj", texture_filename="./assets/axis.png") 246 | def render_3D_axis(phi, theta, gamma): 247 | radius = 240 248 | # camera_location = [radius * math.cos(phi), radius * math.sin(phi), radius * math.tan(theta)] 249 | # print(camera_location) 250 | camera_location = [-1*radius * math.cos(phi), -1*radius * math.tan(theta), radius * math.sin(phi)] 251 | img = render( 252 | # Model("res/jinx.obj", texture_filename="res/jinx.tga"), 253 | axis_model, 254 | height=512, 255 | width=512, 256 | filename="tmp_render.png", 257 | cam_loc = camera_location 258 | ) 259 | img = img.rotate(gamma) 260 | return img 261 | 262 | def overlay_images_with_scaling(center_image: Image.Image, background_image, target_size=(512, 512)): 263 | """ 264 | 调整前景图像大小为 512x512，将背景图像缩放以适配，并中心对齐叠加 265 | :param center_image: 前景图像 266 | :param background_image: 背景图像 267 | :param target_size: 前景图像的目标大小，默认 (512, 512) 268 | :return: 叠加后的图像 269 | """ 270 | # 确保输入图像为 RGBA 模式 271 | if center_image.mode != "RGBA": 272 | center_image = center_image.convert("RGBA") 273 | if background_image.mode != "RGBA": 274 | background_image = background_image.convert("RGBA") 275 | 276 | # 调整前景图像大小 277 | center_image = center_image.resize(target_size) 278 | 279 | # 缩放背景图像，确保其适合前景图像的尺寸 280 | bg_width, bg_height = background_image.size 281 | 282 | # 按宽度或高度等比例缩放背景 283 | scale = target_size[0] / max(bg_width, bg_height) 284 | new_width = int(bg_width * scale) 285 | new_height = int(bg_height * scale) 286 | resized_background = background_image.resize((new_width, new_height)) 287 | # 计算需要的填充量 288 | pad_width = target_size[0] - new_width 289 | pad_height = target_size[0] - new_height 290 | 291 | # 计算上下左右的 padding 292 | left = pad_width // 2 293 | right = pad_width - left 294 | top = pad_height // 2 295 | bottom = pad_height - top 296 | 297 | # 添加 padding 298 | resized_background = ImageOps.expand(resized_background, border=(left, top, right, bottom), fill=(255,255,255,255)) 299 | 300 | # 将前景图像叠加到背景图像上 301 | result = resized_background.copy() 302 | result.paste(center_image, (0, 0), mask=center_image) 303 | 304 | return result 305 | -------------------------------------------------------------------------------- /vision_tower.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch import nn 3 | import torch.nn.init as init 4 | import torch.nn.functional as F 5 | 6 | from paths import * 7 | 8 | from typing import Dict, List, Optional, Set, Tuple, Union 9 | from transformers import AutoImageProcessor, AutoModel, Dinov2Model 10 | from transformers.models.dinov2.modeling_dinov2 import Dinov2Embeddings 11 | from transformers.models.dinov2.configuration_dinov2 import Dinov2Config 12 | import numpy as np 13 | from contextlib import nullcontext 14 | 15 | def get_activation(activation): 16 | if activation.lower() == 'gelu': 17 | return nn.GELU() 18 | elif activation.lower() == 'rrelu': 19 | return nn.RReLU(inplace=True) 20 | elif activation.lower() == 'selu': 21 | return nn.SELU(inplace=True) 22 | elif activation.lower() == 'silu': 23 | return nn.SiLU(inplace=True) 24 | elif activation.lower() == 'hardswish': 25 | return nn.Hardswish(inplace=True) 26 | elif activation.lower() == 'leakyrelu': 27 | return nn.LeakyReLU(inplace=True) 28 | elif activation.lower() == 'sigmoid': 29 | return nn.Sigmoid() 30 | elif activation.lower() == 'tanh': 31 | return nn.Tanh() 32 | else: 33 | return nn.ReLU(inplace=True) 34 | 35 | 36 | 37 | class MLP_dim(nn.Module): 38 | def __init__( 39 | self, in_dim=512, out_dim=1024, bias=True, activation='relu'): 40 | super().__init__() 41 | self.act = get_activation(activation) 42 | self.net1 = nn.Sequential( 43 | nn.Linear(in_dim, int(out_dim), bias=bias), 44 | nn.BatchNorm1d(int(out_dim)), 45 | self.act 46 | ) 47 | self.net2 = nn.Sequential( 48 | nn.Linear(int(out_dim), out_dim, bias=bias), 49 | nn.BatchNorm1d(out_dim) 50 | ) 51 | 52 | def forward(self, x): 53 | return self.net2(self.net1(x)) 54 | 55 | class FLIP_Dinov2Embeddings(Dinov2Embeddings): 56 | """ 57 | Construct the CLS token, mask token, position and patch embeddings. 58 | """ 59 | 60 | def __init__(self, config: Dinov2Config) -> None: 61 | super().__init__(config) 62 | 63 | def forward(self, pixel_values: torch.Tensor, bool_masked_pos: Optional[torch.Tensor] = None) -> torch.Tensor: 64 | batch_size, _, height, width = pixel_values.shape 65 | target_dtype = self.patch_embeddings.projection.weight.dtype 66 | embeddings = self.patch_embeddings(pixel_values.to(dtype=target_dtype)) 67 | 68 | # add the [CLS] token to the embedded patch tokens 69 | cls_tokens = self.cls_token.expand(batch_size, -1, -1) 70 | embeddings = torch.cat((cls_tokens, embeddings), dim=1) 71 | 72 | # add positional encoding to each token 73 | embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width) 74 | 75 | if bool_masked_pos is not None: 76 | # embeddings = torch.where( 77 | # bool_masked_pos.unsqueeze(-1), self.mask_token.to(embeddings.dtype).unsqueeze(0), embeddings 78 | # ) 79 | B,S,D = embeddings.shape 80 | batch_indices = torch.arange(B).unsqueeze(1) 81 | embeddings = embeddings[batch_indices, bool_masked_pos] 82 | 83 | embeddings = self.dropout(embeddings) 84 | 85 | return embeddings 86 | 87 | class FLIP_DINOv2(Dinov2Model): 88 | def __init__(self, config): 89 | super().__init__(config) 90 | 91 | self.embeddings = FLIP_Dinov2Embeddings(config) 92 | 93 | class DINOv2_MLP(nn.Module): 94 | def __init__(self, 95 | dino_mode, 96 | in_dim, 97 | out_dim, 98 | evaluate, 99 | mask_dino, 100 | frozen_back 101 | ) -> None: 102 | super().__init__() 103 | # self.dinov2 = AutoModel.from_pretrained(DINO_BASE) 104 | if dino_mode == 'base': 105 | self.dinov2 = FLIP_DINOv2.from_pretrained(DINO_BASE, cache_dir='./') 106 | elif dino_mode == 'large': 107 | self.dinov2 = FLIP_DINOv2.from_pretrained(DINO_LARGE, cache_dir='./') 108 | elif dino_mode == 'small': 109 | self.dinov2 = FLIP_DINOv2.from_pretrained(DINO_SMALL, cache_dir='./') 110 | elif dino_mode == 'giant': 111 | self.dinov2 = FLIP_DINOv2.from_pretrained(DINO_GIANT, cache_dir='./') 112 | 113 | self.down_sampler = MLP_dim(in_dim=in_dim, out_dim=out_dim) 114 | self.random_mask = False 115 | if not evaluate: 116 | self.init_weights(self.down_sampler) 117 | self.random_mask = mask_dino 118 | if frozen_back: 119 | self.forward_mode = torch.no_grad() 120 | else: 121 | self.forward_mode = nullcontext() 122 | 123 | def forward(self, img_inputs): 124 | device = self.get_device() 125 | # print(img_inputs['pixel_values'].shape) 126 | 127 | with self.forward_mode: 128 | if self.random_mask: 129 | B = len(img_inputs['pixel_values']) 130 | S = 256 131 | indices = [] 132 | for i in range(B): 133 | tmp = torch.randperm(S)[:S//2] 134 | tmp = tmp.sort().values + 1 135 | indices.append(tmp) 136 | indices = torch.stack(indices, dim=0) 137 | indices = torch.cat([torch.zeros(B, 1, dtype=torch.long, device='cpu'), indices], dim=1) 138 | # print(indices.shape) 139 | img_inputs['bool_masked_pos'] = indices.to(device) 140 | 141 | dino_outputs = self.dinov2(**img_inputs) 142 | dino_seq = dino_outputs.last_hidden_state 143 | # B,S,_ = dino_seq.shape 144 | # dino_seq = dino_seq.view(B*S,-1) 145 | dino_seq = dino_seq[:,0,:] 146 | 147 | down_sample_out = self.down_sampler(dino_seq) 148 | # down_sample_out = down_sample_out.view(B,S,-1) 149 | # down_sample_out = down_sample_out[:,0,:] 150 | 151 | return down_sample_out 152 | 153 | def get_device(self): 154 | return next(self.parameters()).device 155 | 156 | def init_weights(self, m): 157 | if isinstance(m, nn.Linear): 158 | init.xavier_uniform_(m.weight) 159 | if m.bias is not None: 160 | init.constant_(m.bias, 0) 161 | 162 | --------------------------------------------------------------------------------