Upload 15 files

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+assets/demo.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,14 +1 @@
----
-title: 3d Object Orientation Estimation
-emoji: 🔥
-colorFrom: pink
-colorTo: gray
-sdk: gradio
-sdk_version: 6.8.0
-app_file: app.py
-pinned: false
-license: mit
-short_description: 3D object orientation estimation using DINOv2 with custom CP
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Spatial-vision

app.py

@@ -0,0 +1,72 @@
+import gradio as gr
+from paths import *
+
+from vision_tower import DINOv2_MLP
+from transformers import AutoImageProcessor
+import torch
+from inference import *
+from utils import *
+
+from huggingface_hub import hf_hub_download
+ckpt_path = hf_hub_download(repo_id="Viglong/Orient-Anything", filename="ronormsigma1/dino_weight.pt", repo_type="model", cache_dir='./', resume_download=True)
+print(ckpt_path)
+
+save_path = './'
+device = 'cpu'
+dino = DINOv2_MLP(
+                    dino_mode   = 'large',
+                    in_dim      = 1024,
+                    out_dim     = 360+180+360+2,
+                    evaluate    = True,
+                    mask_dino   = False,
+                    frozen_back = False
+                )
+
+dino.eval()
+print('model create')
+dino.load_state_dict(torch.load(ckpt_path, map_location='cpu'))
+dino = dino.to(device)
+print('weight loaded')
+val_preprocess   = AutoImageProcessor.from_pretrained(DINO_LARGE, cache_dir='./')
+
+def infer_func(img, do_rm_bkg, do_infer_aug):
+    origin_img = Image.fromarray(img)
+    if do_infer_aug:
+        rm_bkg_img = background_preprocess(origin_img, True)
+        angles = get_3angle_infer_aug(origin_img, rm_bkg_img, dino, val_preprocess, device)
+    else:
+        rm_bkg_img = background_preprocess(origin_img, do_rm_bkg)
+        angles = get_3angle(rm_bkg_img, dino, val_preprocess, device)
+    
+    phi   = np.radians(angles[0])
+    theta = np.radians(angles[1])
+    gamma = angles[2]
+    confidence = float(angles[3])
+    if confidence > 0.5:
+        render_axis = render_3D_axis(phi, theta, gamma)
+        res_img = overlay_images_with_scaling(render_axis, rm_bkg_img)
+    else:
+        res_img = img
+    
+    # axis_model = "axis.obj"
+    return [res_img, round(float(angles[0]), 2), round(float(angles[1]), 2), round(float(angles[2]), 2), round(float(angles[3]), 2)]
+
+server = gr.Interface(
+    flagging_mode='never',
+    fn=infer_func, 
+    inputs=[
+        gr.Image(height=512, width=512, label="upload your image"),
+        gr.Checkbox(label="Remove Background", value=True),
+        gr.Checkbox(label="Inference time augmentation", value=False)
+    ], 
+    outputs=[
+        gr.Image(height=512, width=512, label="result image"),
+        # gr.Model3D(clear_color=[0.0, 0.0, 0.0, 0.0],  label="3D Model"),
+        gr.Textbox(lines=1, label='Azimuth(0~360°)'),
+        gr.Textbox(lines=1, label='Polar(-90~90°)'),
+        gr.Textbox(lines=1, label='Rotation(-90~90°)'),
+        gr.Textbox(lines=1, label='Confidence(0~1)')
+    ]
+)
+
+server.launch()

assets/axis.obj

@@ -0,0 +1,1663 @@
+# Blender 4.2.1 LTS
+mtllib axis.mtl
+o X
+v 28.000000 0.000000 0.000000
+v 22.000000 0.292636 -1.471178
+v 22.000000 0.574025 -1.385819
+v 22.000000 0.833355 -1.247204
+v 22.000000 1.060660 -1.060660
+v 22.000000 1.247205 -0.833355
+v 22.000000 1.385819 -0.574025
+v 22.000000 1.471178 -0.292636
+v 22.000000 1.500000 -0.000000
+v 22.000000 1.471178 0.292636
+v 22.000000 1.385819 0.574025
+v 22.000000 1.247205 0.833355
+v 22.000000 1.060660 1.060660
+v 22.000000 0.833355 1.247204
+v 22.000000 0.574025 1.385819
+v 22.000000 0.292636 1.471178
+v 22.000000 0.000000 1.500000
+v 22.000000 -0.292635 1.471178
+v 22.000000 -0.574025 1.385819
+v 22.000000 -0.833355 1.247204
+v 22.000000 -1.060660 1.060660
+v 22.000000 -1.247204 0.833355
+v 22.000000 -1.385819 0.574025
+v 22.000000 -1.471178 0.292636
+v 22.000000 -1.500000 0.000000
+v 22.000000 -1.471178 -0.292636
+v 22.000000 -1.385819 -0.574025
+v 22.000000 -1.247204 -0.833355
+v 22.000000 -1.060660 -1.060660
+v 22.000000 -0.833355 -1.247204
+v 22.000000 -0.574025 -1.385819
+v 22.000000 -0.292635 -1.471178
+v 22.000000 -0.000000 -1.500000
+vn -1.0000 -0.0000 -0.0000
+vn -1.0000 0.0001 -0.0000
+vn -1.0000 -0.0001 -0.0000
+vn 0.2414 0.0951 -0.9657
+vn 0.2414 0.2817 -0.9286
+vn 0.2414 0.4575 -0.8558
+vn 0.2414 0.6156 -0.7501
+vn 0.2414 0.7501 -0.6156
+vn 0.2414 0.8558 -0.4574
+vn 0.2414 0.9286 -0.2817
+vn 0.2414 0.9657 -0.0951
+vn 0.2414 0.9657 0.0951
+vn 0.2414 0.9286 0.2817
+vn 0.2414 0.8558 0.4574
+vn 0.2414 0.7501 0.6156
+vn 0.2414 0.6156 0.7501
+vn 0.2414 0.4575 0.8558
+vn 0.2414 0.2817 0.9286
+vn 0.2414 0.0951 0.9657
+vn 0.2414 -0.0951 0.9657
+vn 0.2414 -0.2817 0.9286
+vn 0.2414 -0.4575 0.8558
+vn 0.2414 -0.6156 0.7501
+vn 0.2414 -0.7501 0.6156
+vn 0.2414 -0.8558 0.4575
+vn 0.2414 -0.9286 0.2817
+vn 0.2414 -0.9657 0.0951
+vn 0.2414 -0.9657 -0.0951
+vn 0.2414 -0.9286 -0.2817
+vn 0.2414 -0.8558 -0.4575
+vn 0.2414 -0.7501 -0.6156
+vn 0.2414 -0.6156 -0.7501
+vn 0.2414 -0.4575 -0.8558
+vn 0.2414 -0.2817 -0.9286
+vn 0.2414 -0.0951 -0.9657
+vt 0.597858 0.736041
+vt 0.735114 0.644330
+vt 0.643402 0.507074
+vt 0.506147 0.598785
+vt 0.523575 0.686407
+vt 0.555780 0.524503
+vt 0.717685 0.556707
+vt 0.685480 0.718612
+vt 0.555780 0.718612
+vt 0.506147 0.644330
+vt 0.523575 0.556707
+vt 0.597858 0.507074
+vt 0.685480 0.524503
+vt 0.735114 0.598785
+vt 0.717685 0.686407
+vt 0.643402 0.736041
+vt 0.575961 0.729399
+vt 0.538092 0.704096
+vt 0.512789 0.666227
+vt 0.503903 0.621557
+vt 0.512789 0.576888
+vt 0.538092 0.539019
+vt 0.575961 0.513716
+vt 0.620630 0.504831
+vt 0.665299 0.513716
+vt 0.703168 0.539019
+vt 0.728471 0.576888
+vt 0.737357 0.621557
+vt 0.728471 0.666227
+vt 0.703168 0.704096
+vt 0.665299 0.729399
+vt 0.620630 0.738284
+vt 0.354677 0.736041
+vt 0.377450 0.621557
+vt 0.377450 0.738284
+vt 0.332780 0.729399
+vt 0.312600 0.718612
+vt 0.294911 0.704096
+vt 0.280395 0.686407
+vt 0.269608 0.666227
+vt 0.262966 0.644330
+vt 0.260723 0.621557
+vt 0.262966 0.598785
+vt 0.269608 0.576888
+vt 0.280395 0.556707
+vt 0.294911 0.539019
+vt 0.312600 0.524503
+vt 0.332780 0.513716
+vt 0.354677 0.507074
+vt 0.377450 0.504831
+vt 0.400222 0.507074
+vt 0.422119 0.513716
+vt 0.442299 0.524503
+vt 0.459988 0.539019
+vt 0.474504 0.556707
+vt 0.485291 0.576888
+vt 0.491933 0.598785
+vt 0.494176 0.621557
+vt 0.491933 0.644330
+vt 0.485291 0.666227
+vt 0.474504 0.686407
+vt 0.459988 0.704096
+vt 0.442299 0.718612
+vt 0.422119 0.729399
+vt 0.400222 0.736041
+s 0
+usemtl MI_YamahaMSP3_MatteBlack.004
+f 2/1/1 26/2/1 18/3/1
+f 10/4/1 6/5/1 2/1/1
+f 18/3/1 14/6/1 10/4/1
+f 26/2/1 22/7/1 18/3/1
+f 2/1/1 30/8/1 26/2/1
+f 6/5/1 4/9/1 2/1/1
+f 10/4/1 8/10/1 6/5/1
+f 14/6/1 12/11/1 10/4/1
+f 18/3/1 16/12/1 14/6/1
+f 22/7/1 20/13/1 18/3/1
+f 26/2/1 24/14/1 22/7/1
+f 30/8/1 28/15/1 26/2/1
+f 2/1/1 32/16/1 30/8/1
+f 4/9/1 3/17/1 2/1/1
+f 6/5/2 5/18/2 4/9/2
+f 8/10/3 7/19/3 6/5/3
+f 10/4/1 9/20/1 8/10/1
+f 12/11/3 11/21/3 10/4/3
+f 14/6/2 13/22/2 12/11/2
+f 16/12/1 15/23/1 14/6/1
+f 18/3/1 17/24/1 16/12/1
+f 20/13/1 19/25/1 18/3/1
+f 22/7/3 21/26/3 20/13/3
+f 24/14/2 23/27/2 22/7/2
+f 26/2/1 25/28/1 24/14/1
+f 28/15/2 27/29/2 26/2/2
+f 30/8/3 29/30/3 28/15/3
+f 32/16/1 31/31/1 30/8/1
+f 2/1/1 33/32/1 32/16/1
+f 2/33/4 1/34/4 33/35/4
+f 3/36/5 1/34/5 2/33/5
+f 18/3/1 10/4/1 2/1/1
+f 4/37/6 1/34/6 3/36/6
+f 5/38/7 1/34/7 4/37/7
+f 6/39/8 1/34/8 5/38/8
+f 7/40/9 1/34/9 6/39/9
+f 8/41/10 1/34/10 7/40/10
+f 9/42/11 1/34/11 8/41/11
+f 10/43/12 1/34/12 9/42/12
+f 11/44/13 1/34/13 10/43/13
+f 12/45/14 1/34/14 11/44/14
+f 13/46/15 1/34/15 12/45/15
+f 14/47/16 1/34/16 13/46/16
+f 15/48/17 1/34/17 14/47/17
+f 16/49/18 1/34/18 15/48/18
+f 17/50/19 1/34/19 16/49/19
+f 18/51/20 1/34/20 17/50/20
+f 19/52/21 1/34/21 18/51/21
+f 20/53/22 1/34/22 19/52/22
+f 21/54/23 1/34/23 20/53/23
+f 22/55/24 1/34/24 21/54/24
+f 23/56/25 1/34/25 22/55/25
+f 24/57/26 1/34/26 23/56/26
+f 25/58/27 1/34/27 24/57/27
+f 26/59/28 1/34/28 25/58/28
+f 27/60/29 1/34/29 26/59/29
+f 28/61/30 1/34/30 27/60/30
+f 29/62/31 1/34/31 28/61/31
+f 30/63/32 1/34/32 29/62/32
+f 31/64/33 1/34/33 30/63/33
+f 32/65/34 1/34/34 31/64/34
+f 33/35/35 1/34/35 32/65/35
+o Xzhu
+v 24.000000 0.097545 -0.490393
+v 0.000000 0.097546 -0.490393
+v 24.000000 0.191341 -0.461940
+v 0.000000 0.191342 -0.461940
+v 24.000000 0.277785 -0.415735
+v 0.000000 0.277786 -0.415735
+v 24.000000 0.353553 -0.353553
+v 0.000000 0.353554 -0.353553
+v 24.000000 0.415734 -0.277785
+v 0.000000 0.415735 -0.277785
+v 24.000000 0.461939 -0.191342
+v 0.000000 0.461940 -0.191342
+v 24.000000 0.490392 -0.097545
+v 0.000000 0.490393 -0.097545
+v 24.000000 0.499999 -0.000000
+v 0.000000 0.500001 -0.000000
+v 24.000000 0.490392 0.097545
+v 0.000000 0.490393 0.097545
+v 24.000000 0.461939 0.191342
+v 0.000000 0.461940 0.191342
+v 24.000000 0.415734 0.277785
+v 0.000000 0.415735 0.277785
+v 24.000000 0.353553 0.353553
+v 0.000000 0.353554 0.353553
+v 24.000000 0.277785 0.415735
+v 0.000000 0.277786 0.415735
+v 24.000000 0.191341 0.461940
+v 0.000000 0.191342 0.461940
+v 24.000000 0.097545 0.490393
+v 0.000000 0.097546 0.490393
+v 24.000000 -0.000001 0.500000
+v 0.000000 0.000001 0.500000
+v 24.000000 -0.097546 0.490393
+v 0.000000 -0.097545 0.490393
+v 24.000000 -0.191342 0.461940
+v 0.000000 -0.191341 0.461940
+v 24.000000 -0.277786 0.415735
+v 0.000000 -0.277785 0.415735
+v 24.000000 -0.353554 0.353553
+v 0.000000 -0.353553 0.353553
+v 24.000000 -0.415735 0.277785
+v 0.000000 -0.415734 0.277785
+v 24.000000 -0.461940 0.191342
+v 0.000000 -0.461939 0.191342
+v 24.000000 -0.490393 0.097545
+v 0.000000 -0.490392 0.097545
+v 24.000000 -0.500001 0.000000
+v 0.000000 -0.499999 0.000000
+v 24.000000 -0.490393 -0.097545
+v 0.000000 -0.490392 -0.097545
+v 24.000000 -0.461940 -0.191342
+v 0.000000 -0.461939 -0.191342
+v 24.000000 -0.415735 -0.277785
+v 0.000000 -0.415734 -0.277785
+v 24.000000 -0.353554 -0.353553
+v 0.000000 -0.353553 -0.353553
+v 24.000000 -0.277786 -0.415735
+v 0.000000 -0.277785 -0.415735
+v 24.000000 -0.191342 -0.461940
+v 0.000000 -0.191341 -0.461940
+v 24.000000 -0.097546 -0.490393
+v 0.000000 -0.097545 -0.490393
+v 24.000000 -0.000001 -0.500000
+v 0.000000 0.000001 -0.500000
+vn -1.0000 -0.0000 -0.0000
+vn -0.0000 0.0980 -0.9952
+vn -0.0000 0.2903 -0.9569
+vn 1.0000 -0.0000 -0.0000
+vn -0.0000 0.4714 -0.8819
+vn -0.0000 0.6344 -0.7730
+vn -0.0000 0.7730 -0.6344
+vn -0.0000 0.8819 -0.4714
+vn -0.0000 0.9569 -0.2903
+vn -0.0000 0.9952 -0.0980
+vn -0.0000 0.9952 0.0980
+vn -0.0000 0.9569 0.2903
+vn -0.0000 0.8819 0.4714
+vn -0.0000 0.7730 0.6344
+vn -0.0000 0.6344 0.7730
+vn -0.0000 0.4714 0.8819
+vn -0.0000 0.2903 0.9569
+vn -0.0000 0.0980 0.9952
+vn -0.0000 -0.0980 0.9952
+vn -0.0000 -0.2903 0.9569
+vn -0.0000 -0.4714 0.8819
+vn -0.0000 -0.6344 0.7730
+vn -0.0000 -0.7730 0.6344
+vn -0.0000 -0.8819 0.4714
+vn -0.0000 -0.9569 0.2903
+vn -0.0000 -0.9952 0.0980
+vn -0.0000 -0.9952 -0.0980
+vn -0.0000 -0.9569 -0.2903
+vn -0.0000 -0.8819 -0.4714
+vn -0.0000 -0.7730 -0.6344
+vn -0.0000 -0.6344 -0.7730
+vn -0.0000 -0.4714 -0.8819
+vn -0.0000 -0.2903 -0.9569
+vn -0.0000 -0.0980 -0.9952
+vt 0.597858 0.736041
+vt 0.735114 0.644330
+vt 0.643402 0.507074
+vt 0.506147 0.598785
+vt 0.523575 0.686407
+vt 0.555780 0.524503
+vt 0.717685 0.556707
+vt 0.685480 0.718612
+vt 0.555780 0.718612
+vt 0.506147 0.644330
+vt 0.523575 0.556707
+vt 0.597858 0.507074
+vt 0.685480 0.524503
+vt 0.735114 0.598785
+vt 0.717685 0.686407
+vt 0.643402 0.736041
+vt 0.575961 0.729399
+vt 0.538092 0.704096
+vt 0.512789 0.666227
+vt 0.503903 0.621557
+vt 0.512789 0.576888
+vt 0.538092 0.539019
+vt 0.575961 0.513716
+vt 0.620630 0.504831
+vt 0.665299 0.513716
+vt 0.703168 0.539019
+vt 0.728471 0.576888
+vt 0.737357 0.621557
+vt 0.728471 0.666227
+vt 0.703168 0.704096
+vt 0.665299 0.729399
+vt 0.620630 0.738284
+vt 0.271058 0.986328
+vt 0.255859 0.986328
+vt 0.255859 0.743148
+vt 0.286257 0.986328
+vt 0.271058 0.743148
+vt 0.422119 0.729399
+vt 0.269608 0.666227
+vt 0.332780 0.513716
+vt 0.485291 0.576888
+vt 0.485291 0.666227
+vt 0.422119 0.513716
+vt 0.269608 0.576888
+vt 0.332780 0.729399
+vt 0.459988 0.704096
+vt 0.494176 0.621557
+vt 0.459988 0.539019
+vt 0.377450 0.504831
+vt 0.294911 0.539019
+vt 0.260723 0.621557
+vt 0.294911 0.704096
+vt 0.377450 0.738284
+vt 0.442299 0.718612
+vt 0.474504 0.686407
+vt 0.491933 0.644330
+vt 0.491933 0.598785
+vt 0.474504 0.556707
+vt 0.442299 0.524503
+vt 0.400222 0.507074
+vt 0.354677 0.507074
+vt 0.312600 0.524503
+vt 0.280395 0.556707
+vt 0.262966 0.598785
+vt 0.262966 0.644330
+vt 0.280395 0.686407
+vt 0.312600 0.718612
+vt 0.354677 0.736041
+vt 0.400222 0.736041
+vt 0.301456 0.986328
+vt 0.286257 0.743148
+vt 0.316654 0.986328
+vt 0.301456 0.743148
+vt 0.331853 0.986328
+vt 0.316654 0.743148
+vt 0.347052 0.986328
+vt 0.331853 0.743148
+vt 0.362251 0.986328
+vt 0.347052 0.743148
+vt 0.377450 0.986328
+vt 0.362251 0.743148
+vt 0.392648 0.986328
+vt 0.377450 0.743148
+vt 0.407847 0.986328
+vt 0.392648 0.743148
+vt 0.423046 0.986328
+vt 0.407847 0.743148
+vt 0.438245 0.986328
+vt 0.423046 0.743148
+vt 0.453443 0.986328
+vt 0.438245 0.743148
+vt 0.468642 0.986328
+vt 0.453443 0.743148
+vt 0.483841 0.986328
+vt 0.468642 0.743148
+vt 0.499040 0.986328
+vt 0.483841 0.743148
+vt 0.514239 0.986328
+vt 0.499040 0.743148
+vt 0.529437 0.986328
+vt 0.514239 0.743148
+vt 0.544636 0.986328
+vt 0.529437 0.743148
+vt 0.559835 0.986328
+vt 0.544636 0.743148
+vt 0.575034 0.986328
+vt 0.559835 0.743148
+vt 0.590232 0.986328
+vt 0.575034 0.743148
+vt 0.605431 0.986328
+vt 0.590232 0.743148
+vt 0.620630 0.986328
+vt 0.605431 0.743148
+vt 0.635829 0.986328
+vt 0.620630 0.743148
+vt 0.651028 0.986328
+vt 0.635829 0.743148
+vt 0.666226 0.986328
+vt 0.651028 0.743148
+vt 0.681425 0.986328
+vt 0.666226 0.743148
+vt 0.696624 0.986328
+vt 0.681425 0.743148
+vt 0.711823 0.986328
+vt 0.696624 0.743148
+vt 0.727022 0.986328
+vt 0.711823 0.743148
+vt 0.742220 0.986328
+vt 0.727022 0.743148
+vt 0.742220 0.743148
+s 0
+usemtl MI_YamahaMSP3_MatteBlack.004
+f 35/66/36 83/67/36 67/68/36
+f 51/69/36 43/70/36 35/66/36
+f 67/68/36 59/71/36 51/69/36
+f 83/67/36 75/72/36 67/68/36
+f 35/66/36 91/73/36 83/67/36
+f 43/70/36 39/74/36 35/66/36
+f 51/69/36 47/75/36 43/70/36
+f 59/71/36 55/76/36 51/69/36
+f 67/68/36 63/77/36 59/71/36
+f 75/72/36 71/78/36 67/68/36
+f 83/67/36 79/79/36 75/72/36
+f 91/73/36 87/80/36 83/67/36
+f 35/66/36 95/81/36 91/73/36
+f 39/74/36 37/82/36 35/66/36
+f 43/70/36 41/83/36 39/74/36
+f 47/75/36 45/84/36 43/70/36
+f 51/69/36 49/85/36 47/75/36
+f 55/76/36 53/86/36 51/69/36
+f 59/71/36 57/87/36 55/76/36
+f 63/77/36 61/88/36 59/71/36
+f 67/68/36 65/89/36 63/77/36
+f 71/78/36 69/90/36 67/68/36
+f 75/72/36 73/91/36 71/78/36
+f 79/79/36 77/92/36 75/72/36
+f 83/67/36 81/93/36 79/79/36
+f 87/80/36 85/94/36 83/67/36
+f 91/73/36 89/95/36 87/80/36
+f 95/81/36 93/96/36 91/73/36
+f 35/66/36 97/97/36 95/81/36
+f 34/98/37 96/99/37 97/100/37
+f 36/101/38 34/98/38 35/102/38
+f 92/103/39 44/104/39 60/105/39
+f 76/106/39 84/107/39 92/103/39
+f 60/105/39 68/108/39 76/106/39
+f 44/104/39 52/109/39 60/105/39
+f 92/103/39 36/110/39 44/104/39
+f 84/107/39 88/111/39 92/103/39
+f 76/106/39 80/112/39 84/107/39
+f 68/108/39 72/113/39 76/106/39
+f 60/105/39 64/114/39 68/108/39
+f 52/109/39 56/115/39 60/105/39
+f 44/104/39 48/116/39 52/109/39
+f 36/110/39 40/117/39 44/104/39
+f 92/103/39 96/118/39 36/110/39
+f 88/111/39 90/119/39 92/103/39
+f 84/107/39 86/120/39 88/111/39
+f 80/112/39 82/121/39 84/107/39
+f 76/106/39 78/122/39 80/112/39
+f 72/113/39 74/123/39 76/106/39
+f 68/108/39 70/124/39 72/113/39
+f 64/114/39 66/125/39 68/108/39
+f 60/105/39 62/126/39 64/114/39
+f 56/115/39 58/127/39 60/105/39
+f 52/109/39 54/128/39 56/115/39
+f 48/116/39 50/129/39 52/109/39
+f 44/104/39 46/130/39 48/116/39
+f 40/117/39 42/131/39 44/104/39
+f 36/110/39 38/132/39 40/117/39
+f 96/118/39 34/133/39 36/110/39
+f 92/103/39 94/134/39 96/118/39
+f 38/135/40 36/101/40 37/136/40
+f 40/137/41 38/135/41 39/138/41
+f 42/139/42 40/137/42 41/140/42
+f 44/141/43 42/139/43 43/142/43
+f 46/143/44 44/141/44 45/144/44
+f 48/145/45 46/143/45 47/146/45
+f 50/147/46 48/145/46 49/148/46
+f 52/149/47 50/147/47 51/150/47
+f 54/151/48 52/149/48 53/152/48
+f 56/153/49 54/151/49 55/154/49
+f 58/155/50 56/153/50 57/156/50
+f 60/157/51 58/155/51 59/158/51
+f 62/159/52 60/157/52 61/160/52
+f 64/161/53 62/159/53 63/162/53
+f 66/163/54 64/161/54 65/164/54
+f 68/165/55 66/163/55 67/166/55
+f 70/167/56 68/165/56 69/168/56
+f 72/169/57 70/167/57 71/170/57
+f 74/171/58 72/169/58 73/172/58
+f 76/173/59 74/171/59 75/174/59
+f 78/175/60 76/173/60 77/176/60
+f 80/177/61 78/175/61 79/178/61
+f 82/179/62 80/177/62 81/180/62
+f 84/181/63 82/179/63 83/182/63
+f 86/183/64 84/181/64 85/184/64
+f 88/185/65 86/183/65 87/186/65
+f 90/187/66 88/185/66 89/188/66
+f 92/189/67 90/187/67 91/190/67
+f 94/191/68 92/189/68 93/192/68
+f 96/193/69 94/191/69 95/194/69
+f 67/68/36 51/69/36 35/66/36
+f 34/98/37 97/100/37 35/102/37
+f 36/101/38 35/102/38 37/136/38
+f 60/105/39 76/106/39 92/103/39
+f 38/135/40 37/136/40 39/138/40
+f 40/137/41 39/138/41 41/140/41
+f 42/139/42 41/140/42 43/142/42
+f 44/141/43 43/142/43 45/144/43
+f 46/143/44 45/144/44 47/146/44
+f 48/145/45 47/146/45 49/148/45
+f 50/147/46 49/148/46 51/150/46
+f 52/149/47 51/150/47 53/152/47
+f 54/151/48 53/152/48 55/154/48
+f 56/153/49 55/154/49 57/156/49
+f 58/155/50 57/156/50 59/158/50
+f 60/157/51 59/158/51 61/160/51
+f 62/159/52 61/160/52 63/162/52
+f 64/161/53 63/162/53 65/164/53
+f 66/163/54 65/164/54 67/166/54
+f 68/165/55 67/166/55 69/168/55
+f 70/167/56 69/168/56 71/170/56
+f 72/169/57 71/170/57 73/172/57
+f 74/171/58 73/172/58 75/174/58
+f 76/173/59 75/174/59 77/176/59
+f 78/175/60 77/176/60 79/178/60
+f 80/177/61 79/178/61 81/180/61
+f 82/179/62 81/180/62 83/182/62
+f 84/181/63 83/182/63 85/184/63
+f 86/183/64 85/184/64 87/186/64
+f 88/185/65 87/186/65 89/188/65
+f 90/187/66 89/188/66 91/190/66
+f 92/189/67 91/190/67 93/192/67
+f 94/191/68 93/192/68 95/194/68
+f 96/193/69 95/194/69 97/195/69
+o Y
+v 0.000000 -0.000002 -28.000000
+v -0.292636 -1.471180 -22.000000
+v -0.574025 -1.385821 -22.000000
+v -0.833355 -1.247206 -22.000000
+v -1.060660 -1.060662 -22.000000
+v -1.247204 -0.833357 -22.000000
+v -1.385819 -0.574027 -22.000000
+v -1.471178 -0.292637 -22.000000
+v -1.500000 -0.000002 -22.000000
+v -1.471178 0.292634 -22.000000
+v -1.385819 0.574023 -22.000000
+v -1.247204 0.833353 -22.000000
+v -1.060660 1.060658 -22.000000
+v -0.833355 1.247203 -22.000000
+v -0.574025 1.385817 -22.000000
+v -0.292636 1.471176 -22.000000
+v 0.000000 1.499998 -22.000000
+v 0.292636 1.471176 -22.000000
+v 0.574025 1.385817 -22.000000
+v 0.833355 1.247203 -22.000000
+v 1.060660 1.060658 -22.000000
+v 1.247204 0.833353 -22.000000
+v 1.385819 0.574023 -22.000000
+v 1.471178 0.292634 -22.000000
+v 1.500000 -0.000002 -22.000000
+v 1.471178 -0.292637 -22.000000
+v 1.385819 -0.574027 -22.000000
+v 1.247204 -0.833357 -22.000000
+v 1.060660 -1.060662 -22.000000
+v 0.833355 -1.247206 -22.000000
+v 0.574025 -1.385821 -22.000000
+v 0.292636 -1.471180 -22.000000
+v 0.000000 -1.500002 -22.000000
+vn -0.0000 -0.0000 1.0000
+vn -0.0001 -0.0000 1.0000
+vn 0.0001 -0.0000 1.0000
+vn -0.0951 -0.9657 -0.2414
+vn -0.2817 -0.9286 -0.2414
+vn -0.4575 -0.8558 -0.2414
+vn -0.6156 -0.7501 -0.2414
+vn -0.7501 -0.6156 -0.2414
+vn -0.8558 -0.4575 -0.2414
+vn -0.9286 -0.2817 -0.2414
+vn -0.9657 -0.0951 -0.2414
+vn -0.9657 0.0951 -0.2414
+vn -0.9286 0.2817 -0.2414
+vn -0.8558 0.4575 -0.2414
+vn -0.7501 0.6156 -0.2414
+vn -0.6156 0.7501 -0.2414
+vn -0.4574 0.8558 -0.2414
+vn -0.2817 0.9286 -0.2414
+vn -0.0951 0.9657 -0.2414
+vn 0.0951 0.9657 -0.2414
+vn 0.2817 0.9286 -0.2414
+vn 0.4574 0.8558 -0.2414
+vn 0.6156 0.7501 -0.2414
+vn 0.7501 0.6156 -0.2414
+vn 0.8558 0.4575 -0.2414
+vn 0.9286 0.2817 -0.2414
+vn 0.9657 0.0951 -0.2414
+vn 0.9657 -0.0951 -0.2414
+vn 0.9286 -0.2817 -0.2414
+vn 0.8558 -0.4575 -0.2414
+vn 0.7501 -0.6156 -0.2414
+vn 0.6156 -0.7501 -0.2414
+vn 0.4575 -0.8558 -0.2414
+vn 0.2817 -0.9286 -0.2414
+vn 0.0951 -0.9657 -0.2414
+vt 0.847703 0.234951
+vt 0.974738 0.150069
+vt 0.889856 0.023035
+vt 0.762822 0.107916
+vt 0.778953 0.189013
+vt 0.808759 0.039165
+vt 0.958607 0.068972
+vt 0.928801 0.218820
+vt 0.808759 0.218820
+vt 0.762822 0.150069
+vt 0.778953 0.068972
+vt 0.847703 0.023035
+vt 0.928801 0.039165
+vt 0.974738 0.107916
+vt 0.958607 0.189013
+vt 0.889856 0.234951
+vt 0.827437 0.228803
+vt 0.792388 0.205385
+vt 0.768969 0.170336
+vt 0.760746 0.128993
+vt 0.768969 0.087650
+vt 0.792388 0.052601
+vt 0.827437 0.029182
+vt 0.868780 0.020959
+vt 0.910123 0.029182
+vt 0.945172 0.052601
+vt 0.968590 0.087650
+vt 0.976814 0.128993
+vt 0.968590 0.170336
+vt 0.945172 0.205385
+vt 0.910123 0.228803
+vt 0.868780 0.237027
+vt 0.622632 0.234951
+vt 0.643709 0.128993
+vt 0.643709 0.237027
+vt 0.602366 0.228803
+vt 0.583688 0.218820
+vt 0.567317 0.205385
+vt 0.553881 0.189013
+vt 0.543898 0.170336
+vt 0.537751 0.150069
+vt 0.535675 0.128993
+vt 0.537751 0.107916
+vt 0.543898 0.087650
+vt 0.553881 0.068972
+vt 0.567317 0.052601
+vt 0.583688 0.039165
+vt 0.602366 0.029182
+vt 0.622632 0.023035
+vt 0.643709 0.020959
+vt 0.664785 0.023035
+vt 0.685052 0.029182
+vt 0.703729 0.039165
+vt 0.720101 0.052601
+vt 0.733536 0.068972
+vt 0.743519 0.087650
+vt 0.749667 0.107916
+vt 0.751743 0.128993
+vt 0.749667 0.150069
+vt 0.743519 0.170336
+vt 0.733536 0.189013
+vt 0.720101 0.205385
+vt 0.703729 0.218820
+vt 0.685052 0.228803
+vt 0.664785 0.234951
+s 0
+usemtl MI_YamahaMSP3_MatteBlack.003
+f 99/196/70 123/197/70 115/198/70
+f 107/199/70 103/200/70 99/196/70
+f 115/198/70 111/201/70 107/199/70
+f 123/197/70 119/202/70 115/198/70
+f 99/196/70 127/203/70 123/197/70
+f 103/200/70 101/204/70 99/196/70
+f 107/199/70 105/205/70 103/200/70
+f 111/201/70 109/206/70 107/199/70
+f 115/198/70 113/207/70 111/201/70
+f 119/202/70 117/208/70 115/198/70
+f 123/197/70 121/209/70 119/202/70
+f 127/203/70 125/210/70 123/197/70
+f 99/196/70 129/211/70 127/203/70
+f 101/204/70 100/212/70 99/196/70
+f 103/200/71 102/213/71 101/204/71
+f 105/205/71 104/214/71 103/200/71
+f 107/199/72 106/215/72 105/205/72
+f 109/206/72 108/216/72 107/199/72
+f 111/201/71 110/217/71 109/206/71
+f 113/207/70 112/218/70 111/201/70
+f 115/198/70 114/219/70 113/207/70
+f 117/208/70 116/220/70 115/198/70
+f 119/202/72 118/221/72 117/208/72
+f 121/209/71 120/222/71 119/202/71
+f 123/197/71 122/223/71 121/209/71
+f 125/210/72 124/224/72 123/197/72
+f 127/203/72 126/225/72 125/210/72
+f 129/211/70 128/226/70 127/203/70
+f 99/196/70 130/227/70 129/211/70
+f 99/228/73 98/229/73 130/230/73
+f 100/231/74 98/229/74 99/228/74
+f 115/198/70 107/199/70 99/196/70
+f 101/232/75 98/229/75 100/231/75
+f 102/233/76 98/229/76 101/232/76
+f 103/234/77 98/229/77 102/233/77
+f 104/235/78 98/229/78 103/234/78
+f 105/236/79 98/229/79 104/235/79
+f 106/237/80 98/229/80 105/236/80
+f 107/238/81 98/229/81 106/237/81
+f 108/239/82 98/229/82 107/238/82
+f 109/240/83 98/229/83 108/239/83
+f 110/241/84 98/229/84 109/240/84
+f 111/242/85 98/229/85 110/241/85
+f 112/243/86 98/229/86 111/242/86
+f 113/244/87 98/229/87 112/243/87
+f 114/245/88 98/229/88 113/244/88
+f 115/246/89 98/229/89 114/245/89
+f 116/247/90 98/229/90 115/246/90
+f 117/248/91 98/229/91 116/247/91
+f 118/249/92 98/229/92 117/248/92
+f 119/250/93 98/229/93 118/249/93
+f 120/251/94 98/229/94 119/250/94
+f 121/252/95 98/229/95 120/251/95
+f 122/253/96 98/229/96 121/252/96
+f 123/254/97 98/229/97 122/253/97
+f 124/255/98 98/229/98 123/254/98
+f 125/256/99 98/229/99 124/255/99
+f 126/257/100 98/229/100 125/256/100
+f 127/258/101 98/229/101 126/257/101
+f 128/259/102 98/229/102 127/258/102
+f 129/260/103 98/229/103 128/259/103
+f 130/230/104 98/229/104 129/260/104
+o Yzhu
+v -0.097545 0.490392 -0.000000
+v -0.097545 0.490391 -24.000000
+v -0.191342 0.461939 -0.000000
+v -0.191342 0.461938 -24.000000
+v -0.277785 0.415734 -0.000000
+v -0.277785 0.415733 -24.000000
+v -0.353553 0.353553 -0.000000
+v -0.353553 0.353552 -24.000000
+v -0.415735 0.277785 -0.000000
+v -0.415735 0.277784 -24.000000
+v -0.461940 0.191341 -0.000000
+v -0.461940 0.191340 -24.000000
+v -0.490393 0.097545 -0.000000
+v -0.490393 0.097544 -24.000000
+v -0.500000 -0.000001 0.000000
+v -0.500000 -0.000001 -24.000000
+v -0.490393 -0.097546 0.000000
+v -0.490393 -0.097547 -24.000000
+v -0.461940 -0.191342 0.000000
+v -0.461940 -0.191343 -24.000000
+v -0.415735 -0.277786 0.000000
+v -0.415735 -0.277787 -24.000000
+v -0.353553 -0.353554 0.000000
+v -0.353553 -0.353555 -24.000000
+v -0.277785 -0.415735 0.000000
+v -0.277785 -0.415736 -24.000000
+v -0.191342 -0.461940 0.000000
+v -0.191342 -0.461941 -24.000000
+v -0.097545 -0.490393 0.000000
+v -0.097545 -0.490394 -24.000000
+v 0.000000 -0.500001 0.000000
+v 0.000000 -0.500001 -24.000000
+v 0.097545 -0.490393 0.000000
+v 0.097545 -0.490394 -24.000000
+v 0.191342 -0.461940 0.000000
+v 0.191342 -0.461941 -24.000000
+v 0.277785 -0.415735 0.000000
+v 0.277785 -0.415736 -24.000000
+v 0.353553 -0.353554 0.000000
+v 0.353553 -0.353555 -24.000000
+v 0.415735 -0.277786 0.000000
+v 0.415735 -0.277787 -24.000000
+v 0.461940 -0.191342 0.000000
+v 0.461940 -0.191343 -24.000000
+v 0.490393 -0.097546 0.000000
+v 0.490393 -0.097547 -24.000000
+v 0.500000 -0.000001 0.000000
+v 0.500000 -0.000001 -24.000000
+v 0.490393 0.097545 -0.000000
+v 0.490393 0.097544 -24.000000
+v 0.461940 0.191341 -0.000000
+v 0.461940 0.191340 -24.000000
+v 0.415735 0.277785 -0.000000
+v 0.415735 0.277784 -24.000000
+v 0.353553 0.353553 -0.000000
+v 0.353553 0.353552 -24.000000
+v 0.277785 0.415734 -0.000000
+v 0.277785 0.415733 -24.000000
+v 0.191342 0.461939 -0.000000
+v 0.191342 0.461938 -24.000000
+v 0.097545 0.490392 -0.000000
+v 0.097545 0.490391 -24.000000
+v 0.000000 0.499999 -0.000000
+v 0.000000 0.499999 -24.000000
+vn -0.0000 -0.0000 -1.0000
+vn -0.0001 -0.0000 -1.0000
+vn 0.0001 -0.0000 -1.0000
+vn -0.0980 0.9952 -0.0000
+vn -0.2903 0.9569 -0.0000
+vn -0.0000 -0.0000 1.0000
+vn -0.4714 0.8819 -0.0000
+vn -0.6344 0.7730 -0.0000
+vn -0.7730 0.6344 -0.0000
+vn -0.8819 0.4714 -0.0000
+vn -0.9569 0.2903 -0.0000
+vn -0.9952 0.0980 -0.0000
+vn -0.9952 -0.0980 -0.0000
+vn -0.9569 -0.2903 -0.0000
+vn -0.8819 -0.4714 -0.0000
+vn -0.7730 -0.6344 -0.0000
+vn -0.6344 -0.7730 -0.0000
+vn -0.4714 -0.8819 -0.0000
+vn -0.2903 -0.9569 -0.0000
+vn -0.0980 -0.9952 -0.0000
+vn 0.0980 -0.9952 -0.0000
+vn 0.2903 -0.9569 -0.0000
+vn 0.4714 -0.8819 -0.0000
+vn 0.6344 -0.7730 -0.0000
+vn 0.7730 -0.6344 -0.0000
+vn 0.8819 -0.4714 -0.0000
+vn 0.9569 -0.2903 -0.0000
+vn 0.9952 -0.0980 -0.0000
+vn 0.9952 0.0980 -0.0000
+vn 0.9569 0.2903 -0.0000
+vn 0.8819 0.4714 -0.0000
+vn 0.7730 0.6344 -0.0000
+vn 0.6344 0.7730 -0.0000
+vn 0.4714 0.8819 -0.0000
+vn 0.2903 0.9569 -0.0000
+vn 0.0980 0.9952 -0.0000
+vt 0.847703 0.234951
+vt 0.974738 0.150069
+vt 0.889856 0.023035
+vt 0.762822 0.107916
+vt 0.778953 0.189013
+vt 0.808759 0.039165
+vt 0.958607 0.068972
+vt 0.928801 0.218820
+vt 0.808759 0.218820
+vt 0.762822 0.150069
+vt 0.778953 0.068972
+vt 0.847703 0.023035
+vt 0.928801 0.039165
+vt 0.974738 0.107916
+vt 0.958607 0.189013
+vt 0.889856 0.234951
+vt 0.827437 0.228803
+vt 0.792388 0.205385
+vt 0.768969 0.170336
+vt 0.760746 0.128993
+vt 0.768969 0.087650
+vt 0.792388 0.052601
+vt 0.827437 0.029182
+vt 0.868780 0.020959
+vt 0.910123 0.029182
+vt 0.945172 0.052601
+vt 0.968590 0.087650
+vt 0.976814 0.128993
+vt 0.968590 0.170336
+vt 0.945172 0.205385
+vt 0.910123 0.228803
+vt 0.868780 0.237027
+vt 0.545240 0.466600
+vt 0.531173 0.466600
+vt 0.531173 0.241528
+vt 0.559307 0.466600
+vt 0.545240 0.241528
+vt 0.685052 0.228803
+vt 0.543898 0.170336
+vt 0.602366 0.029182
+vt 0.743519 0.087650
+vt 0.743519 0.170336
+vt 0.685052 0.029182
+vt 0.543898 0.087650
+vt 0.602366 0.228803
+vt 0.720101 0.205385
+vt 0.751743 0.128993
+vt 0.720101 0.052601
+vt 0.643709 0.020959
+vt 0.567317 0.052601
+vt 0.535675 0.128993
+vt 0.567317 0.205385
+vt 0.643709 0.237027
+vt 0.703729 0.218820
+vt 0.733536 0.189013
+vt 0.749667 0.150069
+vt 0.749667 0.107916
+vt 0.733536 0.068972
+vt 0.703729 0.039165
+vt 0.664785 0.023035
+vt 0.622632 0.023035
+vt 0.583688 0.039165
+vt 0.553881 0.068972
+vt 0.537751 0.107916
+vt 0.537751 0.150069
+vt 0.553881 0.189013
+vt 0.583688 0.218820
+vt 0.622632 0.234951
+vt 0.664785 0.234951
+vt 0.573374 0.466600
+vt 0.559307 0.241528
+vt 0.587441 0.466600
+vt 0.573374 0.241528
+vt 0.601508 0.466600
+vt 0.587441 0.241528
+vt 0.615575 0.466600
+vt 0.601508 0.241528
+vt 0.629642 0.466600
+vt 0.615575 0.241528
+vt 0.643709 0.466600
+vt 0.629642 0.241528
+vt 0.657776 0.466600
+vt 0.643709 0.241528
+vt 0.671843 0.466600
+vt 0.657776 0.241528
+vt 0.685910 0.466600
+vt 0.671843 0.241528
+vt 0.699977 0.466600
+vt 0.685910 0.241528
+vt 0.714043 0.466600
+vt 0.699977 0.241528
+vt 0.728110 0.466600
+vt 0.714043 0.241528
+vt 0.742177 0.466600
+vt 0.728110 0.241528
+vt 0.756244 0.466600
+vt 0.742177 0.241528
+vt 0.770311 0.466600
+vt 0.756244 0.241528
+vt 0.784378 0.466600
+vt 0.770311 0.241528
+vt 0.798445 0.466600
+vt 0.784378 0.241528
+vt 0.812512 0.466600
+vt 0.798445 0.241528
+vt 0.826579 0.466600
+vt 0.812512 0.241528
+vt 0.840646 0.466600
+vt 0.826579 0.241528
+vt 0.854713 0.466600
+vt 0.840646 0.241528
+vt 0.868780 0.466600
+vt 0.854713 0.241528
+vt 0.882847 0.466600
+vt 0.868780 0.241528
+vt 0.896914 0.466600
+vt 0.882847 0.241528
+vt 0.910981 0.466600
+vt 0.896914 0.241528
+vt 0.925048 0.466600
+vt 0.910981 0.241528
+vt 0.939115 0.466600
+vt 0.925048 0.241528
+vt 0.953182 0.466600
+vt 0.939115 0.241528
+vt 0.967249 0.466600
+vt 0.953182 0.241528
+vt 0.981316 0.466600
+vt 0.967249 0.241528
+vt 0.981316 0.241528
+s 0
+usemtl MI_YamahaMSP3_MatteBlack.003
+f 132/261/105 180/262/105 164/263/105
+f 148/264/105 140/265/105 132/261/105
+f 164/263/105 156/266/105 148/264/105
+f 180/262/105 172/267/105 164/263/105
+f 132/261/105 188/268/105 180/262/105
+f 140/265/105 136/269/105 132/261/105
+f 148/264/106 144/270/106 140/265/106
+f 156/266/105 152/271/105 148/264/105
+f 164/263/105 160/272/105 156/266/105
+f 172/267/105 168/273/105 164/263/105
+f 180/262/107 176/274/107 172/267/107
+f 188/268/105 184/275/105 180/262/105
+f 132/261/105 192/276/105 188/268/105
+f 136/269/105 134/277/105 132/261/105
+f 140/265/105 138/278/105 136/269/105
+f 144/270/107 142/279/107 140/265/107
+f 148/264/106 146/280/106 144/270/106
+f 152/271/105 150/281/105 148/264/105
+f 156/266/105 154/282/105 152/271/105
+f 160/272/105 158/283/105 156/266/105
+f 164/263/105 162/284/105 160/272/105
+f 168/273/105 166/285/105 164/263/105
+f 172/267/105 170/286/105 168/273/105
+f 176/274/105 174/287/105 172/267/105
+f 180/262/107 178/288/107 176/274/107
+f 184/275/106 182/289/106 180/262/106
+f 188/268/105 186/290/105 184/275/105
+f 192/276/105 190/291/105 188/268/105
+f 132/261/105 194/292/105 192/276/105
+f 131/293/108 193/294/108 194/295/108
+f 133/296/109 131/293/109 132/297/109
+f 189/298/110 141/299/110 157/300/110
+f 173/301/110 181/302/110 189/298/110
+f 157/300/110 165/303/110 173/301/110
+f 141/299/110 149/304/110 157/300/110
+f 189/298/110 133/305/110 141/299/110
+f 181/302/110 185/306/110 189/298/110
+f 173/301/110 177/307/110 181/302/110
+f 165/303/110 169/308/110 173/301/110
+f 157/300/110 161/309/110 165/303/110
+f 149/304/110 153/310/110 157/300/110
+f 141/299/110 145/311/110 149/304/110
+f 133/305/110 137/312/110 141/299/110
+f 189/298/110 193/313/110 133/305/110
+f 185/306/110 187/314/110 189/298/110
+f 181/302/110 183/315/110 185/306/110
+f 177/307/110 179/316/110 181/302/110
+f 173/301/110 175/317/110 177/307/110
+f 169/308/110 171/318/110 173/301/110
+f 165/303/110 167/319/110 169/308/110
+f 161/309/110 163/320/110 165/303/110
+f 157/300/110 159/321/110 161/309/110
+f 153/310/110 155/322/110 157/300/110
+f 149/304/110 151/323/110 153/310/110
+f 145/311/110 147/324/110 149/304/110
+f 141/299/110 143/325/110 145/311/110
+f 137/312/110 139/326/110 141/299/110
+f 133/305/110 135/327/110 137/312/110
+f 193/313/110 131/328/110 133/305/110
+f 189/298/110 191/329/110 193/313/110
+f 135/330/111 133/296/111 134/331/111
+f 137/332/112 135/330/112 136/333/112
+f 139/334/113 137/332/113 138/335/113
+f 141/336/114 139/334/114 140/337/114
+f 143/338/115 141/336/115 142/339/115
+f 145/340/116 143/338/116 144/341/116
+f 147/342/117 145/340/117 146/343/117
+f 149/344/118 147/342/118 148/345/118
+f 151/346/119 149/344/119 150/347/119
+f 153/348/120 151/346/120 152/349/120
+f 155/350/121 153/348/121 154/351/121
+f 157/352/122 155/350/122 156/353/122
+f 159/354/123 157/352/123 158/355/123
+f 161/356/124 159/354/124 160/357/124
+f 163/358/125 161/356/125 162/359/125
+f 165/360/126 163/358/126 164/361/126
+f 167/362/127 165/360/127 166/363/127
+f 169/364/128 167/362/128 168/365/128
+f 171/366/129 169/364/129 170/367/129
+f 173/368/130 171/366/130 172/369/130
+f 175/370/131 173/368/131 174/371/131
+f 177/372/132 175/370/132 176/373/132
+f 179/374/133 177/372/133 178/375/133
+f 181/376/134 179/374/134 180/377/134
+f 183/378/135 181/376/135 182/379/135
+f 185/380/136 183/378/136 184/381/136
+f 187/382/137 185/380/137 186/383/137
+f 189/384/138 187/382/138 188/385/138
+f 191/386/139 189/384/139 190/387/139
+f 193/388/140 191/386/140 192/389/140
+f 164/263/105 148/264/105 132/261/105
+f 131/293/108 194/295/108 132/297/108
+f 133/296/109 132/297/109 134/331/109
+f 157/300/110 173/301/110 189/298/110
+f 135/330/111 134/331/111 136/333/111
+f 137/332/112 136/333/112 138/335/112
+f 139/334/113 138/335/113 140/337/113
+f 141/336/114 140/337/114 142/339/114
+f 143/338/115 142/339/115 144/341/115
+f 145/340/116 144/341/116 146/343/116
+f 147/342/117 146/343/117 148/345/117
+f 149/344/118 148/345/118 150/347/118
+f 151/346/119 150/347/119 152/349/119
+f 153/348/120 152/349/120 154/351/120
+f 155/350/121 154/351/121 156/353/121
+f 157/352/122 156/353/122 158/355/122
+f 159/354/123 158/355/123 160/357/123
+f 161/356/124 160/357/124 162/359/124
+f 163/358/125 162/359/125 164/361/125
+f 165/360/126 164/361/126 166/363/126
+f 167/362/127 166/363/127 168/365/127
+f 169/364/128 168/365/128 170/367/128
+f 171/366/129 170/367/129 172/369/129
+f 173/368/130 172/369/130 174/371/130
+f 175/370/131 174/371/131 176/373/131
+f 177/372/132 176/373/132 178/375/132
+f 179/374/133 178/375/133 180/377/133
+f 181/376/134 180/377/134 182/379/134
+f 183/378/135 182/379/135 184/381/135
+f 185/380/136 184/381/136 186/383/136
+f 187/382/137 186/383/137 188/385/137
+f 189/384/138 188/385/138 190/387/138
+f 191/386/139 190/387/139 192/389/139
+f 193/388/140 192/389/140 194/390/140
+o Z
+v 0.000000 28.000000 -0.000002
+v -0.292636 22.000000 -1.471180
+v -0.574025 22.000000 -1.385821
+v -0.833355 22.000000 -1.247206
+v -1.060660 22.000000 -1.060662
+v -1.247204 22.000000 -0.833357
+v -1.385819 22.000000 -0.574027
+v -1.471178 22.000000 -0.292638
+v -1.500000 22.000000 -0.000002
+v -1.471178 22.000000 0.292634
+v -1.385819 22.000000 0.574023
+v -1.247204 22.000000 0.833353
+v -1.060660 22.000000 1.060658
+v -0.833355 22.000000 1.247202
+v -0.574025 22.000000 1.385817
+v -0.292636 22.000000 1.471176
+v 0.000000 22.000000 1.499998
+v 0.292636 22.000000 1.471176
+v 0.574025 22.000000 1.385817
+v 0.833355 22.000000 1.247202
+v 1.060660 22.000000 1.060658
+v 1.247204 22.000000 0.833353
+v 1.385819 22.000000 0.574023
+v 1.471178 22.000000 0.292634
+v 1.500000 22.000000 -0.000002
+v 1.471178 22.000000 -0.292638
+v 1.385819 22.000000 -0.574027
+v 1.247204 22.000000 -0.833357
+v 1.060660 22.000000 -1.060662
+v 0.833355 22.000000 -1.247206
+v 0.574025 22.000000 -1.385821
+v 0.292636 22.000000 -1.471180
+v 0.000000 22.000000 -1.500002
+vn -0.0000 -1.0000 -0.0000
+vn -0.0000 -1.0000 0.0001
+vn -0.0000 -1.0000 -0.0001
+vn -0.0951 0.2414 -0.9657
+vn -0.2817 0.2414 -0.9286
+vn -0.4575 0.2414 -0.8558
+vn -0.6156 0.2414 -0.7501
+vn -0.7501 0.2414 -0.6156
+vn -0.8558 0.2414 -0.4575
+vn -0.9286 0.2414 -0.2817
+vn -0.9657 0.2414 -0.0951
+vn -0.9657 0.2414 0.0951
+vn -0.9286 0.2414 0.2817
+vn -0.8558 0.2414 0.4575
+vn -0.7501 0.2414 0.6156
+vn -0.6156 0.2414 0.7501
+vn -0.4575 0.2414 0.8558
+vn -0.2817 0.2414 0.9286
+vn -0.0951 0.2414 0.9657
+vn 0.0951 0.2414 0.9657
+vn 0.2817 0.2414 0.9286
+vn 0.4575 0.2414 0.8558
+vn 0.6156 0.2414 0.7501
+vn 0.7501 0.2414 0.6156
+vn 0.8558 0.2414 0.4575
+vn 0.9286 0.2414 0.2817
+vn 0.9657 0.2414 0.0951
+vn 0.9657 0.2414 -0.0951
+vn 0.9286 0.2414 -0.2817
+vn 0.8558 0.2414 -0.4575
+vn 0.7501 0.2414 -0.6156
+vn 0.6156 0.2414 -0.7501
+vn 0.4575 0.2414 -0.8558
+vn 0.2817 0.2414 -0.9286
+vn 0.0951 0.2414 -0.9657
+vt 0.344619 0.232996
+vt 0.473521 0.146867
+vt 0.387392 0.017966
+vt 0.258490 0.104095
+vt 0.274858 0.186384
+vt 0.305103 0.034334
+vt 0.457153 0.064579
+vt 0.426908 0.216629
+vt 0.305103 0.216629
+vt 0.258490 0.146867
+vt 0.274858 0.064579
+vt 0.344619 0.017966
+vt 0.426908 0.034334
+vt 0.473521 0.104095
+vt 0.457153 0.186384
+vt 0.387392 0.232996
+vt 0.324055 0.226758
+vt 0.288491 0.202996
+vt 0.264728 0.167432
+vt 0.256384 0.125481
+vt 0.264728 0.083531
+vt 0.288491 0.047967
+vt 0.324055 0.024204
+vt 0.366006 0.015859
+vt 0.407956 0.024204
+vt 0.443520 0.047967
+vt 0.467283 0.083531
+vt 0.475627 0.125481
+vt 0.467283 0.167432
+vt 0.443520 0.202996
+vt 0.407956 0.226758
+vt 0.366006 0.235103
+vt 0.116241 0.232996
+vt 0.137627 0.125481
+vt 0.137627 0.235103
+vt 0.095676 0.226758
+vt 0.076724 0.216629
+vt 0.060112 0.202996
+vt 0.046480 0.186384
+vt 0.036350 0.167432
+vt 0.030112 0.146867
+vt 0.028005 0.125481
+vt 0.030112 0.104095
+vt 0.036350 0.083531
+vt 0.046480 0.064579
+vt 0.060112 0.047967
+vt 0.076724 0.034334
+vt 0.095676 0.024204
+vt 0.116241 0.017966
+vt 0.137627 0.015859
+vt 0.159013 0.017966
+vt 0.179577 0.024204
+vt 0.198530 0.034334
+vt 0.215141 0.047967
+vt 0.228774 0.064579
+vt 0.238904 0.083531
+vt 0.245142 0.104095
+vt 0.247249 0.125481
+vt 0.245142 0.146867
+vt 0.238904 0.167432
+vt 0.228774 0.186384
+vt 0.215141 0.202996
+vt 0.198530 0.216629
+vt 0.179577 0.226758
+vt 0.159013 0.232996
+s 0
+usemtl MI_YamahaMSP3_MatteBlack.010
+f 196/391/141 220/392/141 212/393/141
+f 204/394/141 200/395/141 196/391/141
+f 212/393/141 208/396/141 204/394/141
+f 220/392/141 216/397/141 212/393/141
+f 196/391/141 224/398/141 220/392/141
+f 200/395/141 198/399/141 196/391/141
+f 204/394/141 202/400/141 200/395/141
+f 208/396/141 206/401/141 204/394/141
+f 212/393/141 210/402/141 208/396/141
+f 216/397/141 214/403/141 212/393/141
+f 220/392/141 218/404/141 216/397/141
+f 224/398/141 222/405/141 220/392/141
+f 196/391/141 226/406/141 224/398/141
+f 198/399/142 197/407/142 196/391/142
+f 200/395/143 199/408/143 198/399/143
+f 202/400/141 201/409/141 200/395/141
+f 204/394/141 203/410/141 202/400/141
+f 206/401/141 205/411/141 204/394/141
+f 208/396/142 207/412/142 206/401/142
+f 210/402/143 209/413/143 208/396/143
+f 212/393/141 211/414/141 210/402/141
+f 214/403/143 213/415/143 212/393/143
+f 216/397/142 215/416/142 214/403/142
+f 218/404/141 217/417/141 216/397/141
+f 220/392/141 219/418/141 218/404/141
+f 222/405/141 221/419/141 220/392/141
+f 224/398/143 223/420/143 222/405/143
+f 226/406/142 225/421/142 224/398/142
+f 196/391/141 227/422/141 226/406/141
+f 196/423/144 195/424/144 227/425/144
+f 197/426/145 195/424/145 196/423/145
+f 212/393/141 204/394/141 196/391/141
+f 198/427/146 195/424/146 197/426/146
+f 199/428/147 195/424/147 198/427/147
+f 200/429/148 195/424/148 199/428/148
+f 201/430/149 195/424/149 200/429/149
+f 202/431/150 195/424/150 201/430/150
+f 203/432/151 195/424/151 202/431/151
+f 204/433/152 195/424/152 203/432/152
+f 205/434/153 195/424/153 204/433/153
+f 206/435/154 195/424/154 205/434/154
+f 207/436/155 195/424/155 206/435/155
+f 208/437/156 195/424/156 207/436/156
+f 209/438/157 195/424/157 208/437/157
+f 210/439/158 195/424/158 209/438/158
+f 211/440/159 195/424/159 210/439/159
+f 212/441/160 195/424/160 211/440/160
+f 213/442/161 195/424/161 212/441/161
+f 214/443/162 195/424/162 213/442/162
+f 215/444/163 195/424/163 214/443/163
+f 216/445/164 195/424/164 215/444/164
+f 217/446/165 195/424/165 216/445/165
+f 218/447/166 195/424/166 217/446/166
+f 219/448/167 195/424/167 218/447/167
+f 220/449/168 195/424/168 219/448/168
+f 221/450/169 195/424/169 220/449/169
+f 222/451/170 195/424/170 221/450/170
+f 223/452/171 195/424/171 222/451/171
+f 224/453/172 195/424/172 223/452/172
+f 225/454/173 195/424/173 224/453/173
+f 226/455/174 195/424/174 225/454/174
+f 227/425/175 195/424/175 226/455/175
+o Zzhu
+v -0.097545 24.000000 -0.490395
+v -0.097545 -0.000000 -0.490393
+v -0.191342 24.000000 -0.461942
+v -0.191342 -0.000000 -0.461940
+v -0.277785 24.000000 -0.415737
+v -0.277785 -0.000000 -0.415735
+v -0.353553 24.000000 -0.353555
+v -0.353553 -0.000000 -0.353553
+v -0.415735 24.000000 -0.277787
+v -0.415735 -0.000000 -0.277785
+v -0.461940 24.000000 -0.191344
+v -0.461940 -0.000000 -0.191342
+v -0.490393 24.000000 -0.097547
+v -0.490393 -0.000000 -0.097545
+v -0.500000 24.000000 -0.000002
+v -0.500000 0.000000 0.000000
+v -0.490393 24.000000 0.097543
+v -0.490393 0.000000 0.097545
+v -0.461940 24.000000 0.191340
+v -0.461940 0.000000 0.191342
+v -0.415735 24.000000 0.277783
+v -0.415735 0.000000 0.277785
+v -0.353553 24.000000 0.353551
+v -0.353553 0.000000 0.353553
+v -0.277785 24.000000 0.415733
+v -0.277785 0.000000 0.415735
+v -0.191342 24.000000 0.461938
+v -0.191342 0.000000 0.461940
+v -0.097545 24.000000 0.490391
+v -0.097545 0.000000 0.490393
+v 0.000000 24.000000 0.499998
+v 0.000000 0.000000 0.500000
+v 0.097545 24.000000 0.490391
+v 0.097545 0.000000 0.490393
+v 0.191342 24.000000 0.461938
+v 0.191342 0.000000 0.461940
+v 0.277785 24.000000 0.415733
+v 0.277785 0.000000 0.415735
+v 0.353553 24.000000 0.353551
+v 0.353553 0.000000 0.353553
+v 0.415735 24.000000 0.277783
+v 0.415735 0.000000 0.277785
+v 0.461940 24.000000 0.191340
+v 0.461940 0.000000 0.191342
+v 0.490393 24.000000 0.097543
+v 0.490393 0.000000 0.097545
+v 0.500000 24.000000 -0.000002
+v 0.500000 0.000000 0.000000
+v 0.490393 24.000000 -0.097547
+v 0.490393 -0.000000 -0.097545
+v 0.461940 24.000000 -0.191344
+v 0.461940 -0.000000 -0.191342
+v 0.415735 24.000000 -0.277787
+v 0.415735 -0.000000 -0.277785
+v 0.353553 24.000000 -0.353555
+v 0.353553 -0.000000 -0.353553
+v 0.277785 24.000000 -0.415737
+v 0.277785 -0.000000 -0.415735
+v 0.191342 24.000000 -0.461942
+v 0.191342 -0.000000 -0.461940
+v 0.097545 24.000000 -0.490395
+v 0.097545 -0.000000 -0.490393
+v 0.000000 24.000000 -0.500002
+v 0.000000 -0.000000 -0.500000
+vn -0.0000 -1.0000 -0.0000
+vn -0.0980 -0.0000 -0.9952
+vn -0.2903 -0.0000 -0.9569
+vn -0.0000 1.0000 -0.0000
+vn -0.4714 -0.0000 -0.8819
+vn -0.6344 -0.0000 -0.7730
+vn -0.7730 -0.0000 -0.6344
+vn -0.8819 -0.0000 -0.4714
+vn -0.9569 -0.0000 -0.2903
+vn -0.9952 -0.0000 -0.0980
+vn -0.9952 -0.0000 0.0980
+vn -0.9569 -0.0000 0.2903
+vn -0.8819 -0.0000 0.4714
+vn -0.7730 -0.0000 0.6344
+vn -0.6344 -0.0000 0.7730
+vn -0.4714 -0.0000 0.8819
+vn -0.2903 -0.0000 0.9569
+vn -0.0980 -0.0000 0.9952
+vn 0.0980 -0.0000 0.9952
+vn 0.2903 -0.0000 0.9569
+vn 0.4714 -0.0000 0.8819
+vn 0.6344 -0.0000 0.7730
+vn 0.7730 -0.0000 0.6344
+vn 0.8819 -0.0000 0.4714
+vn 0.9569 -0.0000 0.2903
+vn 0.9952 -0.0000 0.0980
+vn 0.9952 -0.0000 -0.0980
+vn 0.9569 -0.0000 -0.2903
+vn 0.8819 -0.0000 -0.4714
+vn 0.7730 -0.0000 -0.6344
+vn 0.6344 -0.0000 -0.7730
+vn 0.4714 -0.0000 -0.8819
+vn 0.2903 -0.0000 -0.9569
+vn 0.0980 -0.0000 -0.9952
+vt 0.344619 0.232996
+vt 0.473521 0.146867
+vt 0.387392 0.017966
+vt 0.258490 0.104095
+vt 0.274858 0.186384
+vt 0.305103 0.034334
+vt 0.457153 0.064579
+vt 0.426908 0.216629
+vt 0.305103 0.216629
+vt 0.258490 0.146867
+vt 0.274858 0.064579
+vt 0.344619 0.017966
+vt 0.426908 0.034334
+vt 0.473521 0.104095
+vt 0.457153 0.186384
+vt 0.387392 0.232996
+vt 0.324055 0.226758
+vt 0.288491 0.202996
+vt 0.264728 0.167432
+vt 0.256384 0.125481
+vt 0.264728 0.083531
+vt 0.288491 0.047967
+vt 0.324055 0.024204
+vt 0.366006 0.015859
+vt 0.407956 0.024204
+vt 0.443520 0.047967
+vt 0.467283 0.083531
+vt 0.475627 0.125481
+vt 0.467283 0.167432
+vt 0.443520 0.202996
+vt 0.407956 0.226758
+vt 0.366006 0.235103
+vt 0.037711 0.468049
+vt 0.023437 0.468049
+vt 0.023437 0.239671
+vt 0.051985 0.468049
+vt 0.037711 0.239671
+vt 0.179577 0.226758
+vt 0.036350 0.167432
+vt 0.095676 0.024204
+vt 0.238904 0.083531
+vt 0.238904 0.167432
+vt 0.179577 0.024204
+vt 0.036350 0.083531
+vt 0.095676 0.226758
+vt 0.215141 0.202996
+vt 0.247249 0.125481
+vt 0.215141 0.047967
+vt 0.137627 0.015859
+vt 0.060112 0.047967
+vt 0.028005 0.125481
+vt 0.060112 0.202996
+vt 0.137627 0.235103
+vt 0.198530 0.216629
+vt 0.228774 0.186384
+vt 0.245142 0.146867
+vt 0.245142 0.104095
+vt 0.228774 0.064579
+vt 0.198530 0.034334
+vt 0.159013 0.017966
+vt 0.116241 0.017966
+vt 0.076724 0.034334
+vt 0.046480 0.064579
+vt 0.030112 0.104095
+vt 0.030112 0.146867
+vt 0.046480 0.186384
+vt 0.076724 0.216629
+vt 0.116241 0.232996
+vt 0.159013 0.232996
+vt 0.066259 0.468049
+vt 0.051985 0.239671
+vt 0.080532 0.468049
+vt 0.066259 0.239671
+vt 0.094806 0.468049
+vt 0.080532 0.239671
+vt 0.109080 0.468049
+vt 0.094806 0.239671
+vt 0.123353 0.468049
+vt 0.109080 0.239671
+vt 0.137627 0.468049
+vt 0.123353 0.239671
+vt 0.151901 0.468049
+vt 0.137627 0.239671
+vt 0.166174 0.468049
+vt 0.151901 0.239671
+vt 0.180448 0.468049
+vt 0.166174 0.239671
+vt 0.194722 0.468049
+vt 0.180448 0.239671
+vt 0.208995 0.468049
+vt 0.194722 0.239671
+vt 0.223269 0.468049
+vt 0.208995 0.239671
+vt 0.237543 0.468049
+vt 0.223269 0.239671
+vt 0.251816 0.468049
+vt 0.237543 0.239671
+vt 0.266090 0.468049
+vt 0.251816 0.239671
+vt 0.280364 0.468049
+vt 0.266090 0.239671
+vt 0.294637 0.468049
+vt 0.280364 0.239671
+vt 0.308911 0.468049
+vt 0.294637 0.239671
+vt 0.323185 0.468049
+vt 0.308911 0.239671
+vt 0.337458 0.468049
+vt 0.323185 0.239671
+vt 0.351732 0.468049
+vt 0.337458 0.239671
+vt 0.366006 0.468049
+vt 0.351732 0.239671
+vt 0.380279 0.468049
+vt 0.366006 0.239671
+vt 0.394553 0.468049
+vt 0.380279 0.239671
+vt 0.408827 0.468049
+vt 0.394553 0.239671
+vt 0.423100 0.468049
+vt 0.408827 0.239671
+vt 0.437374 0.468049
+vt 0.423100 0.239671
+vt 0.451648 0.468049
+vt 0.437374 0.239671
+vt 0.465921 0.468049
+vt 0.451648 0.239671
+vt 0.480195 0.468049
+vt 0.465921 0.239671
+vt 0.480195 0.239671
+s 0
+usemtl MI_YamahaMSP3_MatteBlack.010
+f 229/456/176 277/457/176 261/458/176
+f 245/459/176 237/460/176 229/456/176
+f 261/458/176 253/461/176 245/459/176
+f 277/457/176 269/462/176 261/458/176
+f 229/456/176 285/463/176 277/457/176
+f 237/460/176 233/464/176 229/456/176
+f 245/459/176 241/465/176 237/460/176
+f 253/461/176 249/466/176 245/459/176
+f 261/458/176 257/467/176 253/461/176
+f 269/462/176 265/468/176 261/458/176
+f 277/457/176 273/469/176 269/462/176
+f 285/463/176 281/470/176 277/457/176
+f 229/456/176 289/471/176 285/463/176
+f 233/464/176 231/472/176 229/456/176
+f 237/460/176 235/473/176 233/464/176
+f 241/465/176 239/474/176 237/460/176
+f 245/459/176 243/475/176 241/465/176
+f 249/466/176 247/476/176 245/459/176
+f 253/461/176 251/477/176 249/466/176
+f 257/467/176 255/478/176 253/461/176
+f 261/458/176 259/479/176 257/467/176
+f 265/468/176 263/480/176 261/458/176
+f 269/462/176 267/481/176 265/468/176
+f 273/469/176 271/482/176 269/462/176
+f 277/457/176 275/483/176 273/469/176
+f 281/470/176 279/484/176 277/457/176
+f 285/463/176 283/485/176 281/470/176
+f 289/471/176 287/486/176 285/463/176
+f 229/456/176 291/487/176 289/471/176
+f 228/488/177 290/489/177 291/490/177
+f 230/491/178 228/488/178 229/492/178
+f 286/493/179 238/494/179 254/495/179
+f 270/496/179 278/497/179 286/493/179
+f 254/495/179 262/498/179 270/496/179
+f 238/494/179 246/499/179 254/495/179
+f 286/493/179 230/500/179 238/494/179
+f 278/497/179 282/501/179 286/493/179
+f 270/496/179 274/502/179 278/497/179
+f 262/498/179 266/503/179 270/496/179
+f 254/495/179 258/504/179 262/498/179
+f 246/499/179 250/505/179 254/495/179
+f 238/494/179 242/506/179 246/499/179
+f 230/500/179 234/507/179 238/494/179
+f 286/493/179 290/508/179 230/500/179
+f 282/501/179 284/509/179 286/493/179
+f 278/497/179 280/510/179 282/501/179
+f 274/502/179 276/511/179 278/497/179
+f 270/496/179 272/512/179 274/502/179
+f 266/503/179 268/513/179 270/496/179
+f 262/498/179 264/514/179 266/503/179
+f 258/504/179 260/515/179 262/498/179
+f 254/495/179 256/516/179 258/504/179
+f 250/505/179 252/517/179 254/495/179
+f 246/499/179 248/518/179 250/505/179
+f 242/506/179 244/519/179 246/499/179
+f 238/494/179 240/520/179 242/506/179
+f 234/507/179 236/521/179 238/494/179
+f 230/500/179 232/522/179 234/507/179
+f 290/508/179 228/523/179 230/500/179
+f 286/493/179 288/524/179 290/508/179
+f 232/525/180 230/491/180 231/526/180
+f 234/527/181 232/525/181 233/528/181
+f 236/529/182 234/527/182 235/530/182
+f 238/531/183 236/529/183 237/532/183
+f 240/533/184 238/531/184 239/534/184
+f 242/535/185 240/533/185 241/536/185
+f 244/537/186 242/535/186 243/538/186
+f 246/539/187 244/537/187 245/540/187
+f 248/541/188 246/539/188 247/542/188
+f 250/543/189 248/541/189 249/544/189
+f 252/545/190 250/543/190 251/546/190
+f 254/547/191 252/545/191 253/548/191
+f 256/549/192 254/547/192 255/550/192
+f 258/551/193 256/549/193 257/552/193
+f 260/553/194 258/551/194 259/554/194
+f 262/555/195 260/553/195 261/556/195
+f 264/557/196 262/555/196 263/558/196
+f 266/559/197 264/557/197 265/560/197
+f 268/561/198 266/559/198 267/562/198
+f 270/563/199 268/561/199 269/564/199
+f 272/565/200 270/563/200 271/566/200
+f 274/567/201 272/565/201 273/568/201
+f 276/569/202 274/567/202 275/570/202
+f 278/571/203 276/569/203 277/572/203
+f 280/573/204 278/571/204 279/574/204
+f 282/575/205 280/573/205 281/576/205
+f 284/577/206 282/575/206 283/578/206
+f 286/579/207 284/577/207 285/580/207
+f 288/581/208 286/579/208 287/582/208
+f 290/583/209 288/581/209 289/584/209
+f 261/458/176 245/459/176 229/456/176
+f 228/488/177 291/490/177 229/492/177
+f 230/491/178 229/492/178 231/526/178
+f 254/495/179 270/496/179 286/493/179
+f 232/525/180 231/526/180 233/528/180
+f 234/527/181 233/528/181 235/530/181
+f 236/529/182 235/530/182 237/532/182
+f 238/531/183 237/532/183 239/534/183
+f 240/533/184 239/534/184 241/536/184
+f 242/535/185 241/536/185 243/538/185
+f 244/537/186 243/538/186 245/540/186
+f 246/539/187 245/540/187 247/542/187
+f 248/541/188 247/542/188 249/544/188
+f 250/543/189 249/544/189 251/546/189
+f 252/545/190 251/546/190 253/548/190
+f 254/547/191 253/548/191 255/550/191
+f 256/549/192 255/550/192 257/552/192
+f 258/551/193 257/552/193 259/554/193
+f 260/553/194 259/554/194 261/556/194
+f 262/555/195 261/556/195 263/558/195
+f 264/557/196 263/558/196 265/560/196
+f 266/559/197 265/560/197 267/562/197
+f 268/561/198 267/562/198 269/564/198
+f 270/563/199 269/564/199 271/566/199
+f 272/565/200 271/566/200 273/568/200
+f 274/567/201 273/568/201 275/570/201
+f 276/569/202 275/570/202 277/572/202
+f 278/571/203 277/572/203 279/574/203
+f 280/573/204 279/574/204 281/576/204
+f 282/575/205 281/576/205 283/578/205
+f 284/577/206 283/578/206 285/580/206
+f 286/579/207 285/580/207 287/582/207
+f 288/581/208 287/582/208 289/584/208
+f 290/583/209 289/584/209 291/585/209

inference.py

@@ -0,0 +1,49 @@
+import torch
+from PIL import Image
+from utils import *
+import torch.nn.functional as F
+import numpy as np
+
+def get_3angle(image, dino, val_preprocess, device):
+    
+    # image = Image.open(image_path).convert('RGB')
+    image_inputs = val_preprocess(images = image)
+    image_inputs['pixel_values'] = torch.from_numpy(np.array(image_inputs['pixel_values'])).to(device)
+    with torch.no_grad():
+        dino_pred = dino(image_inputs)
+
+    gaus_ax_pred   = torch.argmax(dino_pred[:, 0:360], dim=-1)
+    gaus_pl_pred   = torch.argmax(dino_pred[:, 360:360+180], dim=-1)
+    gaus_ro_pred   = torch.argmax(dino_pred[:, 360+180:360+180+360], dim=-1)
+    confidence     = F.softmax(dino_pred[:, -2:], dim=-1)[0][0]
+    angles = torch.zeros(4)
+    angles[0]  = gaus_ax_pred
+    angles[1]  = gaus_pl_pred - 90
+    angles[2]  = gaus_ro_pred - 180
+    angles[3]  = confidence
+    return angles
+
+def get_3angle_infer_aug(origin_img, rm_bkg_img, dino, val_preprocess, device):
+    
+    # image = Image.open(image_path).convert('RGB')
+    image = get_crop_images(origin_img, num=3) + get_crop_images(rm_bkg_img, num=3)
+    image_inputs = val_preprocess(images = image)
+    image_inputs['pixel_values'] = torch.from_numpy(np.array(image_inputs['pixel_values'])).to(device)
+    with torch.no_grad():
+        dino_pred = dino(image_inputs)
+
+    gaus_ax_pred   = torch.argmax(dino_pred[:, 0:360], dim=-1).to(torch.float32)
+    gaus_pl_pred   = torch.argmax(dino_pred[:, 360:360+180], dim=-1).to(torch.float32)
+    gaus_ro_pred   = torch.argmax(dino_pred[:, 360+180:360+180+360], dim=-1).to(torch.float32)
+    
+    gaus_ax_pred   = remove_outliers_and_average_circular(gaus_ax_pred)
+    gaus_pl_pred   = remove_outliers_and_average(gaus_pl_pred)
+    gaus_ro_pred   = remove_outliers_and_average(gaus_ro_pred)
+    
+    confidence     = torch.mean(F.softmax(dino_pred[:, -2:], dim=-1), dim=0)[0]
+    angles = torch.zeros(4)
+    angles[0]  = gaus_ax_pred
+    angles[1]  = gaus_pl_pred - 90
+    angles[2]  = gaus_ro_pred - 180
+    angles[3]  = confidence
+    return angles

paths.py

@@ -0,0 +1,4 @@
+DINO_SMALL  = "facebook/dinov2-small"
+DINO_BASE   = "facebook/dinov2-base"
+DINO_LARGE  = "facebook/dinov2-large"
+DINO_GIANT  = "facebook/dinov2-giant"

render/__init__.py

@@ -0,0 +1,3 @@
+# flake8: noqa
+from .core import render
+from .model import Model

render/canvas.py

@@ -0,0 +1,51 @@
+import typing as t
+
+from PIL import Image, ImageColor, ImageOps, ImageChops, ImageFilter
+import numpy as np
+
+class Canvas:
+    def __init__(self, filename=None, height=500, width=500):
+        self.filename = filename
+        self.height, self.width = height, width
+        self.img = Image.new("RGBA", (self.height, self.width), (0, 0, 0, 0))
+
+    def draw(self, dots, color: t.Union[tuple, str]):
+        if isinstance(color, str):
+            color = ImageColor.getrgb(color)
+        if isinstance(dots, tuple):
+            dots = [dots]
+        for dot in dots:
+            if dot[0]>=self.height or dot[1]>=self.width or dot[0]<0 or dot[1]<0:
+                # print(dot)
+                continue
+            self.img.putpixel(dot, color + (255,))
+
+    def add_white_border(self, border_size=5):
+        # Ensure the input image is in RGBA mode
+        if self.img.mode != "RGBA":
+            self.img = self.img.convert("RGBA")
+        
+        # Extract the alpha channel
+        alpha = self.img.getchannel("A")
+        # print(alpha.size)
+        dilated_alpha = alpha.filter(ImageFilter.MaxFilter(size=5))
+        # # print(dilated_alpha.size)
+
+        # Create a white area using the dilated alpha mask
+        white_area = Image.new("RGBA", self.img.size, (255, 255, 255, 255))
+        white_area.putalpha(dilated_alpha)
+        
+        # Merge the dilated white area with the original image
+        result = Image.alpha_composite(white_area, self.img)
+        # expanded_alpha = ImageOps.expand(alpha, border=border_size, fill=255)
+        # white_border = Image.new("RGBA", image.size, (255, 255, 255, 255))
+        # white_border.putalpha(alpha)
+        return result
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, type, value, traceback):
+        # self.img = add_white_border(self.img)
+        self.img.save(self.filename)
+        pass

render/core.py

@@ -0,0 +1,378 @@
+import typing as t
+from functools import partial
+
+import numpy as np
+from copy import deepcopy
+from .canvas import Canvas
+
+from . import speedup
+
+
+# 2D part
+
+
+class Vec2d:
+    __slots__ = "x", "y", "arr"
+
+    def __init__(self, *args):
+        if len(args) == 1 and isinstance(args[0], Vec3d):
+            self.arr = Vec3d.narr
+        else:
+            assert len(args) == 2
+            self.arr = list(args)
+
+        self.x, self.y = [d if isinstance(d, int) else int(d + 0.5) for d in self.arr]
+
+    def __repr__(self):
+        return f"Vec2d({self.x}, {self.y})"
+
+    def __truediv__(self, other):
+        return (self.y - other.y) / (self.x - other.x)
+
+    def __eq__(self, other):
+        return self.x == other.x and self.y == other.y
+
+
+def draw_line(
+    v1: Vec2d, v2: Vec2d, canvas: Canvas, color: t.Union[tuple, str] = "white"
+):
+    """
+    Draw a line with a specified color
+
+    https://en.wikipedia.org/wiki/Bresenham%27s_line_algorithm
+    """
+    v1, v2 = deepcopy(v1), deepcopy(v2)
+    if v1 == v2:
+        canvas.draw((v1.x, v1.y), color=color)
+        return
+
+    steep = abs(v1.y - v2.y) > abs(v1.x - v2.x)
+    if steep:
+        v1.x, v1.y = v1.y, v1.x
+        v2.x, v2.y = v2.y, v2.x
+    v1, v2 = (v1, v2) if v1.x < v2.x else (v2, v1)
+    slope = abs((v1.y - v2.y) / (v1.x - v2.x))
+    y = v1.y
+    error: float = 0
+    incr = 1 if v1.y < v2.y else -1
+    dots = []
+    for x in range(int(v1.x), int(v2.x + 0.5)):
+        dots.append((int(y), x) if steep else (x, int(y)))
+        error += slope
+        if abs(error) >= 0.5:
+            y += incr
+            error -= 1
+
+    canvas.draw(dots, color=color)
+
+
+def draw_triangle(v1, v2, v3, canvas, color, wireframe=False):
+    """
+    Draw a triangle with 3 ordered vertices
+
+    http://www.sunshine2k.de/coding/java/TriangleRasterization/TriangleRasterization.html
+    """
+    _draw_line = partial(draw_line, canvas=canvas, color=color)
+
+    if wireframe:
+        _draw_line(v1, v2)
+        _draw_line(v2, v3)
+        _draw_line(v1, v3)
+        return
+
+    def sort_vertices_asc_by_y(vertices):
+        return sorted(vertices, key=lambda v: v.y)
+
+    def fill_bottom_flat_triangle(v1, v2, v3):
+        invslope1 = (v2.x - v1.x) / (v2.y - v1.y)
+        invslope2 = (v3.x - v1.x) / (v3.y - v1.y)
+
+        x1 = x2 = v1.x
+        y = v1.y
+
+        while y <= v2.y:
+            _draw_line(Vec2d(x1, y), Vec2d(x2, y))
+            x1 += invslope1
+            x2 += invslope2
+            y += 1
+
+    def fill_top_flat_triangle(v1, v2, v3):
+        invslope1 = (v3.x - v1.x) / (v3.y - v1.y)
+        invslope2 = (v3.x - v2.x) / (v3.y - v2.y)
+
+        x1 = x2 = v3.x
+        y = v3.y
+
+        while y > v2.y:
+            _draw_line(Vec2d(x1, y), Vec2d(x2, y))
+            x1 -= invslope1
+            x2 -= invslope2
+            y -= 1
+
+    v1, v2, v3 = sort_vertices_asc_by_y((v1, v2, v3))
+
+    # 填充
+    if v1.y == v2.y == v3.y:
+        pass
+    elif v2.y == v3.y:
+        fill_bottom_flat_triangle(v1, v2, v3)
+    elif v1.y == v2.y:
+        fill_top_flat_triangle(v1, v2, v3)
+    else:
+        v4 = Vec2d(int(v1.x + (v2.y - v1.y) / (v3.y - v1.y) * (v3.x - v1.x)), v2.y)
+        fill_bottom_flat_triangle(v1, v2, v4)
+        fill_top_flat_triangle(v2, v4, v3)
+
+
+# 3D part
+
+
+class Vec3d:
+    __slots__ = "x", "y", "z", "arr"
+
+    def __init__(self, *args):
+        # for Vec4d cast
+        if len(args) == 1 and isinstance(args[0], Vec4d):
+            vec4 = args[0]
+            arr_value = (vec4.x, vec4.y, vec4.z)
+        else:
+            assert len(args) == 3
+            arr_value = args
+        self.arr = np.array(arr_value, dtype=np.float64)
+        self.x, self.y, self.z = self.arr
+
+    def __repr__(self):
+        return repr(f"Vec3d({','.join([repr(d) for d in self.arr])})")
+
+    def __sub__(self, other):
+        return self.__class__(*[ds - do for ds, do in zip(self.arr, other.arr)])
+
+    def __bool__(self):
+        """ False for zero vector (0, 0, 0)
+        """
+        return any(self.arr)
+
+
+class Mat4d:
+    def __init__(self, narr=None, value=None):
+        self.value = np.matrix(narr) if value is None else value
+
+    def __repr__(self):
+        return repr(self.value)
+
+    def __mul__(self, other):
+        return self.__class__(value=self.value * other.value)
+
+
+class Vec4d(Mat4d):
+    def __init__(self, *narr, value=None):
+        if value is not None:
+            self.value = value
+        elif len(narr) == 1 and isinstance(narr[0], Mat4d):
+            self.value = narr[0].value
+        else:
+            assert len(narr) == 4
+            self.value = np.matrix([[d] for d in narr])
+
+        self.x, self.y, self.z, self.w = (
+            self.value[0, 0],
+            self.value[1, 0],
+            self.value[2, 0],
+            self.value[3, 0],
+        )
+        self.arr = self.value.reshape((1, 4))
+
+
+# Math util
+def normalize(v: Vec3d):
+    return Vec3d(*speedup.normalize(*v.arr))
+
+
+def dot_product(a: Vec3d, b: Vec3d):
+    return speedup.dot_product(*a.arr, *b.arr)
+
+
+def cross_product(a: Vec3d, b: Vec3d):
+    return Vec3d(*speedup.cross_product(*a.arr, *b.arr))
+
+BASE_LIGHT = 0.9
+def get_light_intensity(face) -> float:
+    # lights = [Vec3d(-2, 4, -10), Vec3d(10, 4, -2), Vec3d(8, 8, -8), Vec3d(0, 0, -8)]
+    lights = [Vec3d(-2, 4, -10)]
+    # lights = []
+
+    v1, v2, v3 = face
+    up = normalize(cross_product(v2 - v1, v3 - v1))
+    intensity = BASE_LIGHT
+    for light in lights:
+        intensity += dot_product(up, normalize(light))*0.2
+    return intensity
+
+
+def look_at(eye: Vec3d, target: Vec3d, up: Vec3d = Vec3d(0, -1, 0)) -> Mat4d:
+    """
+    http://www.songho.ca/opengl/gl_camera.html#lookat
+
+    Args:
+        eye: Camera position in world coordinates
+        target: The point the camera is looking at
+        up: The direction you want the camera to stand upright in.
+            https://stackoverflow.com/questions/10635947/what-exactly-is-the-up-vector-in-opengls-lookat-function
+            The default is set to (0, -1, 0) because the model data exported from Blender
+            seems to have flipped the Y-axis, so we flip the camera's up vector as well.
+    """
+    f = normalize(eye - target)
+    l = normalize(cross_product(up, f))  # noqa: E741
+    u = cross_product(f, l)
+
+    rotate_matrix = Mat4d(
+        [[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]]
+    )
+    translate_matrix = Mat4d(
+        [[1, 0, 0, -eye.x], [0, 1, 0, -eye.y], [0, 0, 1, -eye.z], [0, 0, 0, 1.0]]
+    )
+
+    return Mat4d(value=(rotate_matrix * translate_matrix).value)
+
+
+def perspective_project(r, t, n, f, b=None, l=None):  # noqa: E741
+    """
+    Purpose:
+        Convert camera coordinates into Cartesian coordinates within (-1, 1)
+        as projected onto the image plane (the “retina”).
+
+    Principle:
+        For x and y coordinates, the projected point is computed using similar triangles.
+        For the z coordinate, we assume near = -1 and far = 1 and derive the mapping.
+        http://www.songho.ca/opengl/gl_projectionmatrix.html
+        https://www.scratchapixel.com/lessons/3d-basic-rendering/perspective-and-orthographic-projection-matrix/opengl-perspective-projection-matrix
+
+    Derived projection matrix:
+        [
+            2n/(r-l)   0         (r+l)/(r-l)      0
+            0          2n/(t-b)  (t+b)/(t-b)      0
+            0          0         -(f+n)/(f-n)   (-2*f*n)/(f-n)
+            0          0         -1              0
+        ]
+
+    Since our near-plane (“retina”) is symmetric around the optical center,
+    the matrix simplifies to:
+        [
+            n/r        0         0                0
+            0          n/t       0                0
+            0          0         -(f+n)/(f-n)   (-2*f*n)/(f-n)
+            0          0         -1               0
+        ]
+
+    Args:
+        r: right
+        t: top
+        n: near
+        f: far
+        b: bottom
+        l: left
+    """
+    return Mat4d(
+        [
+            [n / r, 0, 0, 0],
+            [0, n / t, 0, 0],
+            [0, 0, -(f + n) / (f - n), (-2 * f * n) / (f - n)],
+            [0, 0, -1, 0],
+        ]
+    )
+
+
+def draw(screen_vertices, world_vertices, model, canvas, wireframe=True):
+    """standard algorithm
+    """
+    for triangle_indices in model.indices:
+        vertex_group = [screen_vertices[idx - 1] for idx in triangle_indices]
+        face = [Vec3d(world_vertices[idx - 1]) for idx in triangle_indices]
+        if wireframe:
+            draw_triangle(*vertex_group, canvas=canvas, color="black", wireframe=True)
+        else:
+            intensity = get_light_intensity(face)
+            if intensity > 0:
+                draw_triangle(
+                    *vertex_group, canvas=canvas, color=(int(intensity * 255),) * 3
+                )
+
+
+def draw_with_z_buffer(screen_vertices, world_vertices, model, canvas):
+    """ z-buffer algorithm
+    """
+    intensities = []
+    triangles = []
+    for i, triangle_indices in enumerate(model.indices):
+        screen_triangle = [screen_vertices[idx - 1] for idx in triangle_indices]
+        uv_triangle = [model.uv_vertices[idx - 1] for idx in model.uv_indices[i]]
+        world_triangle = [Vec3d(world_vertices[idx - 1]) for idx in triangle_indices]
+        intensities.append(abs(get_light_intensity(world_triangle)))
+        # take off the class to let Cython work
+        triangles.append(
+            [np.append(screen_triangle[i].arr, uv_triangle[i]) for i in range(3)]
+        )
+
+    faces = speedup.generate_faces(
+        np.array(triangles, dtype=np.float64), model.texture_width, model.texture_height
+    )
+    for face_dots in faces:
+        for dot in face_dots:
+            intensity = intensities[dot[0]]
+            u, v = dot[3], dot[4]
+            color = model.texture_array[u, v]
+            canvas.draw((dot[1], dot[2]), tuple(int(c * intensity) for c in color[:3]))
+            # TODO: add object rendering mode (no texture)
+            # canvas.draw((dot[1], dot[2]), (int(255 * intensity),) * 3)
+
+
+def render(model, height, width, filename, cam_loc, wireframe=False):
+    """
+    Args:
+        model: the Model object
+        height: cavas height
+        width: cavas width
+        picname: picture file name
+    """
+    model_matrix = Mat4d([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]])
+    # TODO: camera configration
+    view_matrix = look_at(Vec3d(cam_loc[0], cam_loc[1], cam_loc[2]), Vec3d(0, 0, 0))
+    projection_matrix = perspective_project(0.5, 0.5, 3, 1000)
+
+    world_vertices = []
+
+    def mvp(v):
+        world_vertex = model_matrix * v
+        world_vertices.append(Vec4d(world_vertex))
+        return projection_matrix * view_matrix * world_vertex
+
+    def ndc(v):
+        """
+        各个坐标同时除以 w，得到 NDC 坐标
+        """
+        v = v.value
+        w = v[3, 0]
+        x, y, z = v[0, 0] / w, v[1, 0] / w, v[2, 0] / w
+        return Mat4d([[x], [y], [z], [1 / w]])
+
+    def viewport(v):
+        x = y = 0
+        w, h = width, height
+        n, f = 0.3, 1000
+        return Vec3d(
+            w * 0.5 * v.value[0, 0] + x + w * 0.5,
+            h * 0.5 * v.value[1, 0] + y + h * 0.5,
+            0.5 * (f - n) * v.value[2, 0] + 0.5 * (f + n),
+        )
+
+    # the render pipeline
+    screen_vertices = [viewport(ndc(mvp(v))) for v in model.vertices]
+
+    with Canvas(filename, height, width) as canvas:
+        if wireframe:
+            draw(screen_vertices, world_vertices, model, canvas)
+        else:
+            draw_with_z_buffer(screen_vertices, world_vertices, model, canvas)
+        
+        render_img = canvas.add_white_border().copy()
+    return render_img

render/model.py

@@ -0,0 +1,31 @@
+import numpy
+from PIL import Image
+from .core import Vec4d
+
+
+class Model:
+    def __init__(self, filename, texture_filename):
+        """
+        https://en.wikipedia.org/wiki/Wavefront_.obj_file#Vertex_normal_indices
+        """
+        self.vertices = []
+        self.uv_vertices = []
+        self.uv_indices = []
+        self.indices = []
+
+        texture = Image.open(texture_filename)
+        self.texture_array = numpy.array(texture)
+        self.texture_width, self.texture_height = texture.size
+
+        with open(filename) as f:
+            for line in f:
+                if line.startswith("v "):
+                    x, y, z = [float(d) for d in line.strip("v").strip().split(" ")]
+                    self.vertices.append(Vec4d(x, y, z, 1))
+                elif line.startswith("vt "):
+                    u, v = [float(d) for d in line.strip("vt").strip().split(" ")]
+                    self.uv_vertices.append([u, v])
+                elif line.startswith("f "):
+                    facet = [d.split("/") for d in line.strip("f").strip().split(" ")]
+                    self.indices.append([int(d[0]) for d in facet])
+                    self.uv_indices.append([int(d[1]) for d in facet])

render/speedup.py

@@ -0,0 +1,102 @@
+# import cython
+import numpy as np
+from math import sqrt
+
+
+def normalize(x, y, z):
+    unit = sqrt(x * x + y * y + z * z)
+    if unit == 0:
+        return 0, 0, 0
+    return x / unit, y / unit, z / unit
+
+
+def get_min_max(a, b, c):
+    min = a
+    max = a
+    if min > b:
+        min = b
+    if min > c:
+        min = c
+    if max < b:
+        max = b
+    if max < c:
+        max = c
+    return int(min), int(max)
+
+def dot_product(a0, a1, a2, b0, b1, b2):
+    r = a0 * b0 + a1 * b1 + a2 * b2
+    return r
+
+
+def cross_product(a0, a1, a2, b0, b1, b2):
+    x = a1 * b2 - a2 * b1
+    y = a2 * b0 - a0 * b2
+    z = a0 * b1 - a1 * b0
+    return x,y,z
+
+
+# @cython.boundscheck(False)
+def generate_faces(triangles, width, height):
+    """ draw the triangle faces with z buffer
+
+    Args:
+        triangles: groups of vertices
+
+    FYI:
+        * zbuffer, https://github.com/ssloy/tinyrenderer/wiki/Lesson-3:-Hidden-faces-removal-(z-buffer)
+        * uv mapping and perspective correction
+    """
+    i, j, k, length     = 0, 0, 0, 0
+    bcy, bcz, x, y, z   = 0.,0.,0.,0.,0.
+    a, b, c             = [0.,0.,0.],[0.,0.,0.],[0.,0.,0.]
+    m, bc               = [0.,0.,0.],[0.,0.,0.]
+    uva, uvb, uvc       = [0.,0.],[0.,0.],[0.,0.]
+    minx, maxx, miny, maxy = 0,0,0,0
+    length = triangles.shape[0]
+    zbuffer = {}
+    faces = []
+
+    for i in range(length):
+        a = triangles[i, 0, 0], triangles[i, 0, 1], triangles[i, 0, 2]
+        b = triangles[i, 1, 0], triangles[i, 1, 1], triangles[i, 1, 2]
+        c = triangles[i, 2, 0], triangles[i, 2, 1], triangles[i, 2, 2]
+        uva = triangles[i, 0, 3], triangles[i, 0, 4]
+        uvb = triangles[i, 1, 3], triangles[i, 1, 4]
+        uvc = triangles[i, 2, 3], triangles[i, 2, 4]
+        minx, maxx = get_min_max(a[0], b[0], c[0])
+        miny, maxy = get_min_max(a[1], b[1], c[1])
+        pixels = []
+        for j in range(minx, maxx + 2):
+            for k in range(miny - 1, maxy + 2):
+                # Must explicitly convert to double for the calculations below
+                x = j
+                y = k
+
+                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)
+                if abs(m[2]) > 0:
+                    bcy = m[1] / m[2]
+                    bcz = m[0] / m[2]
+                    bc = (1 - bcy - bcz, bcy, bcz)
+                else:
+                    continue
+
+                # here, -0.00001 because of the precision lose
+                if bc[0] < -0.00001 or bc[1] < -0.00001 or bc[2] < -0.00001:
+                    continue
+
+                z = 1 / (bc[0] / a[2] + bc[1] / b[2] + bc[2] / c[2])
+
+
+                # The UV data exported from Blender has the same issue as the vertex data:
+                # the Y-axis is flipped. So the texture image must be flipped to work correctly.
+                v = (uva[0] * bc[0] / a[2] + uvb[0] * bc[1] / b[2] + uvc[0] * bc[2] / c[2]) * z * width
+                u = height - (uva[1] * bc[0] / a[2] + uvb[1] * bc[1] / b[2] + uvc[1] * bc[2] / c[2]) * z * height
+
+                # https://en.wikipedia.org/wiki/Pairing_function
+                idx = ((x + y) * (x + y + 1) + y) / 2
+                if zbuffer.get(idx) is None or zbuffer[idx] < z:
+                    zbuffer[idx] = z
+                    pixels.append((i, j, k, int(u) - 1, int(v) - 1))
+
+        faces.append(pixels)
+    return faces

requirements.txt

@@ -0,0 +1,9 @@
+torch==2.2.1
+transformers==4.38
+matplotlib
+pillow==10.2.0
+huggingface-hub==0.26.5
+gradio==5.9.0
+numpy==1.26.4
+onnxruntime
+rembg

utils.py

@@ -0,0 +1,305 @@
+import rembg
+import random
+import torch
+import numpy as np
+from PIL import Image, ImageOps
+import PIL
+from typing import Any
+import matplotlib.pyplot as plt
+import io
+
+def resize_foreground(
+    image: Image,
+    ratio: float,
+) -> Image:
+    image = np.array(image)
+    assert image.shape[-1] == 4
+    alpha = np.where(image[..., 3] > 0)
+    y1, y2, x1, x2 = (
+        alpha[0].min(),
+        alpha[0].max(),
+        alpha[1].min(),
+        alpha[1].max(),
+    )
+    # crop the foreground
+    fg = image[y1:y2, x1:x2]
+    # pad to square
+    size = max(fg.shape[0], fg.shape[1])
+    ph0, pw0 = (size - fg.shape[0]) // 2, (size - fg.shape[1]) // 2
+    ph1, pw1 = size - fg.shape[0] - ph0, size - fg.shape[1] - pw0
+    new_image = np.pad(
+        fg,
+        ((ph0, ph1), (pw0, pw1), (0, 0)),
+        mode="constant",
+        constant_values=((0, 0), (0, 0), (0, 0)),
+    )
+
+    # compute padding according to the ratio
+    new_size = int(new_image.shape[0] / ratio)
+    # pad to size, double side
+    ph0, pw0 = (new_size - size) // 2, (new_size - size) // 2
+    ph1, pw1 = new_size - size - ph0, new_size - size - pw0
+    new_image = np.pad(
+        new_image,
+        ((ph0, ph1), (pw0, pw1), (0, 0)),
+        mode="constant",
+        constant_values=((0, 0), (0, 0), (0, 0)),
+    )
+    new_image = Image.fromarray(new_image)
+    return new_image
+
+def remove_background(image: Image,
+    rembg_session: Any = None,
+    force: bool = False,
+    **rembg_kwargs,
+) -> Image:
+    do_remove = True
+    if image.mode == "RGBA" and image.getextrema()[3][0] < 255:
+        do_remove = False
+    do_remove = do_remove or force
+    if do_remove:
+        image = rembg.remove(image, session=rembg_session, **rembg_kwargs)
+    return image
+
+def random_crop(image, crop_scale=(0.8, 0.95)):
+    """
+    Randomly crop an image
+        image (numpy.ndarray): (H, W, C).
+        crop_scale (tuple): (min_scale, max_scale).
+    """
+    assert isinstance(image, Image.Image), "iput must be PIL.Image.Image"
+    assert len(crop_scale) == 2 and 0 < crop_scale[0] <= crop_scale[1] <= 1
+
+    width, height = image.size
+
+    # Calculate the crop width and height
+    crop_width = random.randint(int(width * crop_scale[0]), int(width * crop_scale[1]))
+    crop_height = random.randint(int(height * crop_scale[0]), int(height * crop_scale[1]))
+
+    # Randomly choose the top-left corner of the crop
+    left = random.randint(0, width - crop_width)
+    top = random.randint(0, height - crop_height)
+
+    # Crop the image
+    cropped_image = image.crop((left, top, left + crop_width, top + crop_height))
+
+    return cropped_image
+
+def get_crop_images(img, num=3):
+    cropped_images = []
+    for i in range(num):
+        cropped_images.append(random_crop(img))
+    return cropped_images
+
+def background_preprocess(input_image, do_remove_background):
+
+    rembg_session = rembg.new_session() if do_remove_background else None
+
+    if do_remove_background:
+        input_image = remove_background(input_image, rembg_session)
+        input_image = resize_foreground(input_image, 0.85)
+
+    return input_image
+
+def remove_outliers_and_average(tensor, threshold=1.5):
+    assert tensor.dim() == 1, "dimension of input Tensor must equal to 1"
+
+    q1 = torch.quantile(tensor, 0.25)
+    q3 = torch.quantile(tensor, 0.75)
+    iqr = q3 - q1
+
+    lower_bound = q1 - threshold * iqr
+    upper_bound = q3 + threshold * iqr
+
+    non_outliers = tensor[(tensor >= lower_bound) & (tensor <= upper_bound)]
+
+    if len(non_outliers) == 0:
+        return tensor.mean().item()
+
+    return non_outliers.mean().item()
+
+
+def remove_outliers_and_average_circular(tensor, threshold=1.5):
+    assert tensor.dim() == 1, "dimension of input Tensor must equal to 1"
+
+    # Convert angles into points on a 2D plane
+    radians = tensor * torch.pi / 180.0
+    x_coords = torch.cos(radians)
+    y_coords = torch.sin(radians)
+
+    # Compute the mean vector
+    mean_x = torch.mean(x_coords)
+    mean_y = torch.mean(y_coords)
+
+    differences = torch.sqrt((x_coords - mean_x) * (x_coords - mean_x) + (y_coords - mean_y) * (y_coords - mean_y))
+
+    # Compute quartiles and IQR
+    q1 = torch.quantile(differences, 0.25)
+    q3 = torch.quantile(differences, 0.75)
+    iqr = q3 - q1
+
+    # Compute lower and upper bounds
+    lower_bound = q1 - threshold * iqr
+    upper_bound = q3 + threshold * iqr
+
+    # Select non-outlier points
+    non_outliers = tensor[(differences >= lower_bound) & (differences <= upper_bound)]
+
+    if len(non_outliers) == 0:
+        mean_angle = torch.atan2(mean_y, mean_x) * 180.0 / torch.pi
+        mean_angle = (mean_angle + 360) % 360
+        return mean_angle   # If there are no non-outliers, return None
+
+    # Recalculate the mean vector using only non-outliers
+    radians = non_outliers * torch.pi / 180.0
+    x_coords = torch.cos(radians)
+    y_coords = torch.sin(radians)
+
+    mean_x = torch.mean(x_coords)
+    mean_y = torch.mean(y_coords)
+
+    mean_angle = torch.atan2(mean_y, mean_x) * 180.0 / torch.pi
+    mean_angle = (mean_angle + 360) % 360
+
+    return mean_angle
+
+def scale(x):
+    # print(x)
+    # if abs(x[0])<0.1 and abs(x[1])<0.1:
+        
+    #     return x*5
+    # else:
+    #     return x
+    return x*3
+
+def get_proj2D_XYZ(phi, theta, gamma):
+    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)])
+    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)])
+    z = np.array([np.cos(theta)*np.sin(gamma), np.cos(theta)*np.cos(gamma)])
+    x = scale(x)
+    y = scale(y)
+    z = scale(z)
+    return x, y, z
+
+# Draw 3D coordinate axes
+def draw_axis(ax, origin, vector, color, label=None):
+    ax.quiver(origin[0], origin[1], vector[0], vector[1], angles='xy', scale_units='xy', scale=1, color=color)
+    if label!=None:
+        ax.text(origin[0] + vector[0] * 1.1, origin[1] + vector[1] * 1.1, label, color=color, fontsize=12)
+
+def matplotlib_2D_arrow(angles, rm_bkg_img):
+    fig, ax = plt.subplots(figsize=(8, 8))
+
+    # Set rotation angles
+    phi   = np.radians(angles[0])
+    theta = np.radians(angles[1])
+    gamma = np.radians(-1*angles[2])
+
+    w, h = rm_bkg_img.size
+    if h>w:
+        extent = [-5*w/h, 5*w/h, -5, 5]
+    else:
+        extent = [-5, 5, -5*h/w, 5*h/w]
+    ax.imshow(rm_bkg_img, extent=extent, zorder=0, aspect ='auto')  # extent sets the displayed range of the background image
+
+    origin = np.array([0, 0])
+
+    # Rotated vectors
+    rot_x, rot_y, rot_z = get_proj2D_XYZ(phi, theta, gamma)
+
+    # draw arrow
+    arrow_attr = [{'point':rot_x, 'color':'r', 'label':'front'}, 
+                  {'point':rot_y, 'color':'g', 'label':'right'}, 
+                  {'point':rot_z, 'color':'b', 'label':'top'}]
+    
+    if phi> 45 and phi<=225:
+        order = [0,1,2]
+    elif phi > 225 and phi < 315:
+        order = [2,0,1]
+    else:
+        order = [2,1,0]
+    
+    for i in range(3):
+        draw_axis(ax, origin, arrow_attr[order[i]]['point'], arrow_attr[order[i]]['color'], arrow_attr[order[i]]['label'])
+        # draw_axis(ax, origin, rot_y, 'g', label='right')
+        # draw_axis(ax, origin, rot_z, 'b', label='top')
+        # draw_axis(ax, origin, rot_x, 'r', label='front')
+
+    # Turn off axes and grid
+    ax.set_axis_off()
+    ax.grid(False)
+
+    # Set coordinate limits
+    ax.set_xlim(-5, 5)
+    ax.set_ylim(-5, 5)
+
+def figure_to_img(fig):
+    with io.BytesIO() as buf:
+        fig.savefig(buf, format='JPG', bbox_inches='tight')
+        buf.seek(0)
+        image = Image.open(buf).copy()
+    return image
+
+from render import render, Model
+import math
+axis_model = Model("./assets/axis.obj", texture_filename="./assets/axis.png")
+def render_3D_axis(phi, theta, gamma):
+    radius = 240
+    # camera_location = [radius * math.cos(phi), radius * math.sin(phi), radius * math.tan(theta)]
+    # print(camera_location)
+    camera_location = [-1*radius * math.cos(phi), -1*radius * math.tan(theta), radius * math.sin(phi)]
+    img = render(
+        # Model("res/jinx.obj", texture_filename="res/jinx.tga"),
+        axis_model,
+        height=512,
+        width=512,
+        filename="tmp_render.png",
+        cam_loc = camera_location
+    )
+    img = img.rotate(gamma)
+    return img
+
+def overlay_images_with_scaling(center_image: Image.Image, background_image, target_size=(512, 512)):
+    """
+    Resize the foreground image to 512x512, scale the background image to fit,
+    and center-align the overlay.
+    :param center_image: Foreground image
+    :param background_image: Background image
+    :param target_size: Target size for the foreground image, default (512, 512)
+    :return: The overlaid image
+    """
+    # Ensure the input images are in RGBA mode
+    if center_image.mode != "RGBA":
+        center_image = center_image.convert("RGBA")
+    if background_image.mode != "RGBA":
+        background_image = background_image.convert("RGBA")
+    
+    # Resize the foreground image
+    center_image = center_image.resize(target_size)
+    
+    # Scale the background image so it fits within the foreground size
+    bg_width, bg_height = background_image.size
+    
+    # Scale the background proportionally by width or height
+    scale = target_size[0] / max(bg_width, bg_height)
+    new_width = int(bg_width * scale)
+    new_height = int(bg_height * scale)
+    resized_background = background_image.resize((new_width, new_height))
+    # Compute required padding
+    pad_width = target_size[0] - new_width
+    pad_height = target_size[0] - new_height
+
+    # Compute padding for each side
+    left = pad_width // 2
+    right = pad_width - left
+    top = pad_height // 2
+    bottom = pad_height - top
+
+    # Add padding
+    resized_background = ImageOps.expand(resized_background, border=(left, top, right, bottom), fill=(255,255,255,255))
+    
+    # Overlay the foreground image onto the background image
+    result = resized_background.copy()
+    result.paste(center_image, (0, 0), mask=center_image)
+    
+    return result

vision_tower.py

@@ -0,0 +1,161 @@
+import torch
+from torch import nn
+import torch.nn.init as init
+import torch.nn.functional as F
+
+from paths import *
+
+from typing import Dict, List, Optional, Set, Tuple, Union
+from transformers import AutoImageProcessor, AutoModel, Dinov2Model
+from transformers.models.dinov2.modeling_dinov2 import Dinov2Embeddings
+from transformers.models.dinov2.configuration_dinov2 import Dinov2Config
+import numpy as np
+from contextlib import nullcontext
+
+def get_activation(activation):
+    if activation.lower() == 'gelu':
+        return nn.GELU()
+    elif activation.lower() == 'rrelu':
+        return nn.RReLU(inplace=True)
+    elif activation.lower() == 'selu':
+        return nn.SELU(inplace=True)
+    elif activation.lower() == 'silu':
+        return nn.SiLU(inplace=True)
+    elif activation.lower() == 'hardswish':
+        return nn.Hardswish(inplace=True)
+    elif activation.lower() == 'leakyrelu':
+        return nn.LeakyReLU(inplace=True)
+    elif activation.lower() == 'sigmoid':
+        return nn.Sigmoid()
+    elif activation.lower() == 'tanh':
+        return nn.Tanh()
+    else:
+        return nn.ReLU(inplace=True)
+
+
+
+class MLP_dim(nn.Module):
+    def __init__(
+        self, in_dim=512, out_dim=1024, bias=True, activation='relu'):
+        super().__init__()
+        self.act = get_activation(activation)
+        self.net1 = nn.Sequential(
+            nn.Linear(in_dim, int(out_dim), bias=bias),
+            nn.BatchNorm1d(int(out_dim)),
+            self.act
+        )
+        self.net2 = nn.Sequential(
+            nn.Linear(int(out_dim), out_dim, bias=bias),
+            nn.BatchNorm1d(out_dim)
+        )
+
+    def forward(self, x):
+        return self.net2(self.net1(x))
+
+class FLIP_Dinov2Embeddings(Dinov2Embeddings):
+    """
+    Construct the CLS token, mask token, position and patch embeddings.
+    """
+
+    def __init__(self, config: Dinov2Config) -> None:
+        super().__init__(config)
+
+    def forward(self, pixel_values: torch.Tensor, bool_masked_pos: Optional[torch.Tensor] = None) -> torch.Tensor:
+        batch_size, _, height, width = pixel_values.shape
+        target_dtype = self.patch_embeddings.projection.weight.dtype
+        embeddings = self.patch_embeddings(pixel_values.to(dtype=target_dtype))
+
+        # add the [CLS] token to the embedded patch tokens
+        cls_tokens = self.cls_token.expand(batch_size, -1, -1)
+        embeddings = torch.cat((cls_tokens, embeddings), dim=1)
+
+        # add positional encoding to each token
+        embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
+
+        if bool_masked_pos is not None:
+            # embeddings = torch.where(
+            #     bool_masked_pos.unsqueeze(-1), self.mask_token.to(embeddings.dtype).unsqueeze(0), embeddings
+            # )
+            B,S,D = embeddings.shape
+            batch_indices = torch.arange(B).unsqueeze(1)
+            embeddings = embeddings[batch_indices, bool_masked_pos]
+
+        embeddings = self.dropout(embeddings)
+
+        return embeddings
+
+class FLIP_DINOv2(Dinov2Model):
+    def __init__(self, config):
+        super().__init__(config)
+        
+        self.embeddings = FLIP_Dinov2Embeddings(config)
+        
+class DINOv2_MLP(nn.Module):
+    def __init__(self, 
+                 dino_mode,
+                 in_dim,
+                 out_dim,
+                 evaluate,
+                 mask_dino,
+                 frozen_back
+                ) -> None:
+        super().__init__()
+        # self.dinov2 = AutoModel.from_pretrained(DINO_BASE)
+        if dino_mode == 'base':
+            self.dinov2 = FLIP_DINOv2.from_pretrained(DINO_BASE, cache_dir='./')
+        elif dino_mode == 'large':
+            self.dinov2 = FLIP_DINOv2.from_pretrained(DINO_LARGE, cache_dir='./')
+        elif dino_mode == 'small':
+            self.dinov2 = FLIP_DINOv2.from_pretrained(DINO_SMALL, cache_dir='./')
+        elif dino_mode == 'giant':
+            self.dinov2 = FLIP_DINOv2.from_pretrained(DINO_GIANT, cache_dir='./')
+        
+        self.down_sampler = MLP_dim(in_dim=in_dim, out_dim=out_dim)
+        self.random_mask  = False
+        if not evaluate:
+            self.init_weights(self.down_sampler)
+            self.random_mask = mask_dino
+        if frozen_back:
+            self.forward_mode = torch.no_grad()
+        else:
+            self.forward_mode = nullcontext()
+        
+    def forward(self, img_inputs):
+        device = self.get_device()
+        # print(img_inputs['pixel_values'].shape)
+        
+        with self.forward_mode:
+            if self.random_mask:
+                B = len(img_inputs['pixel_values'])
+                S = 256
+                indices = []
+                for i in range(B):
+                    tmp = torch.randperm(S)[:S//2]
+                    tmp = tmp.sort().values + 1
+                    indices.append(tmp)
+                indices = torch.stack(indices, dim=0)
+                indices = torch.cat([torch.zeros(B, 1, dtype=torch.long, device='cpu'), indices], dim=1)
+                # print(indices.shape)
+                img_inputs['bool_masked_pos'] = indices.to(device)
+                
+            dino_outputs = self.dinov2(**img_inputs)
+            dino_seq = dino_outputs.last_hidden_state
+            # B,S,_ = dino_seq.shape
+            # dino_seq = dino_seq.view(B*S,-1)
+            dino_seq = dino_seq[:,0,:]
+        
+        down_sample_out = self.down_sampler(dino_seq)
+        # down_sample_out = down_sample_out.view(B,S,-1)
+        # down_sample_out = down_sample_out[:,0,:]
+        
+        return down_sample_out
+    
+    def get_device(self):
+        return next(self.parameters()).device
+    
+    def init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            init.xavier_uniform_(m.weight)
+            if m.bias is not None:
+                init.constant_(m.bias, 0)
+