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app.py

@@ -1,181 +0,0 @@
-"""SAM 3D Objects – pinned torch 2.8.0 for kaolin ABI compat."""
-import os, sys, subprocess
-os.environ.setdefault("CUDA_HOME", "/usr/local/cuda")
-os.environ.setdefault("CONDA_PREFIX", "/usr/local")
-os.environ["LIDRA_SKIP_INIT"] = "true"
-
-import spaces
-import gradio as gr
-import numpy as np
-from PIL import Image
-from huggingface_hub import snapshot_download, login
-import tempfile, uuid
-from pathlib import Path
-
-if os.environ.get("HF_TOKEN"):
-    login(token=os.environ["HF_TOKEN"])
-
-# Runtime installs for things that need source builds
-def _pip(*a):
-    r = subprocess.run([sys.executable, "-m", "pip", "install", "--no-cache-dir"] + list(a),
-                       capture_output=True, text=True, timeout=1200)
-    return r.returncode == 0
-
-_pip("utils3d")
-_pip("iopath")
-_pip("--no-deps", "pytorch3d")
-
-# gsplat
-for idx in ["https://docs.gsplat.studio/whl/pt28cu128",
-            "https://docs.gsplat.studio/whl/pt27cu128",
-            "https://docs.gsplat.studio/whl/pt26cu124"]:
-    if _pip("--no-deps", f"--extra-index-url={idx}", "gsplat"):
-        break
-
-_pip("--no-deps", "git+https://github.com/microsoft/MoGe.git@a8c37341bc0325ca99b9d57981cc3bb2bd3e255b")
-
-# Clone sam-3d-objects
-SAM3D_PATH = Path("/home/user/app/sam-3d-objects")
-if not SAM3D_PATH.exists():
-    subprocess.run(["git", "clone", "--depth", "1",
-        "https://github.com/facebookresearch/sam-3d-objects.git", str(SAM3D_PATH)], check=True)
-subprocess.run([sys.executable, "-m", "pip", "install", "-e", str(SAM3D_PATH), "--no-deps"],
-               capture_output=True, text=True)
-patch = SAM3D_PATH / "patching" / "hydra"
-if patch.exists():
-    subprocess.run(["bash", str(patch)], capture_output=True, cwd=str(SAM3D_PATH))
-sys.path.insert(0, str(SAM3D_PATH))
-sys.path.insert(0, str(SAM3D_PATH / "notebook"))
-
-# Pre-download checkpoints
-CKPT_DIR = snapshot_download(repo_id="facebook/sam-3d-objects", token=os.environ.get("HF_TOKEN"))
-hf_ckpt = Path(CKPT_DIR) / "checkpoints"
-local_ckpt = SAM3D_PATH / "checkpoints" / "hf"
-if hf_ckpt.exists() and not local_ckpt.exists():
-    local_ckpt.parent.mkdir(parents=True, exist_ok=True)
-    local_ckpt.symlink_to(hf_ckpt)
-CONFIG_PATH = str(local_ckpt / "pipeline.yaml")
-print(f"Config exists: {Path(CONFIG_PATH).exists()}")
-
-# Verify
-for mod in ["torch", "kaolin", "gsplat", "open3d", "sam2"]:
-    try:
-        m = __import__(mod)
-        print(f"  {mod}={getattr(m, '__version__', 'ok')}")
-    except Exception as e:
-        print(f"  {mod}: {e}")
-try:
-    import kaolin; kaolin.ops.mesh
-    print("  kaolin C++: OK")
-except Exception as e:
-    print(f"  kaolin C++: {e}")
-
-print("=== Setup done ===")
-
-SAM3D_MODEL = None
-SAM2_GEN = None
-
-@spaces.GPU(duration=60)
-def diagnose():
-    import torch
-    lines = [f"torch={torch.__version__}", f"cuda={torch.cuda.is_available()}"]
-    if torch.cuda.is_available():
-        lines.append(f"gpu={torch.cuda.get_device_name()}")
-    for mod in ["kaolin", "gsplat", "open3d", "sam2", "utils3d"]:
-        try:
-            m = __import__(mod)
-            lines.append(f"{mod}={getattr(m, '__version__', 'ok')}")
-        except Exception as e:
-            lines.append(f"{mod}: {e}")
-    try:
-        import kaolin; kaolin.ops.mesh
-        lines.append("kaolin C++: OK")
-    except Exception as e:
-        lines.append(f"kaolin C++: {e}")
-    return "\n".join(lines)
-
-@spaces.GPU(duration=300)
-def reconstruct_objects(image: np.ndarray):
-    global SAM3D_MODEL, SAM2_GEN
-    if image is None:
-        return None, None, "No image"
-    try:
-        import torch, trimesh, time
-        t0 = time.time()
-
-        if SAM2_GEN is None:
-            from sam2.automatic_mask_generator import SAM2AutomaticMaskGenerator
-            SAM2_GEN = SAM2AutomaticMaskGenerator.from_pretrained("facebook/sam2-hiera-large")
-        image_np = np.array(image) if not isinstance(image, np.ndarray) else image
-        masks = SAM2_GEN.generate(image_np)
-        if not masks:
-            return None, image_np, "No objects"
-        masks = sorted(masks, key=lambda x: x["area"], reverse=True)
-        best_mask = masks[0]["segmentation"]
-        preview = image_np.copy()
-        preview[best_mask] = (preview[best_mask]*0.5 + np.array([0,255,0])*0.5).astype(np.uint8)
-        print(f"  SAM2: {len(masks)} masks ({time.time()-t0:.0f}s)")
-
-        if SAM3D_MODEL is None:
-            from inference import Inference
-            SAM3D_MODEL = Inference(CONFIG_PATH, compile=False)
-        print(f"  SAM3D loaded ({time.time()-t0:.0f}s)")
-
-        result = SAM3D_MODEL(image=image_np, mask=best_mask, seed=42)
-        print(f"  Reconstructed ({time.time()-t0:.0f}s)")
-
-        if result is None:
-            return None, preview, "Reconstruction None"
-
-        od = tempfile.mkdtemp()
-        glb = f"{od}/obj.glb"
-        gs=None
-        if hasattr(result,"save_ply"): gs=result
-        elif isinstance(result,dict):
-            for k in("gs","gaussian","gaussians"):
-                v=result.get(k)
-                if v: gs=v[0] if isinstance(v,(list,tuple)) else v; break
-        if gs and hasattr(gs,"save_ply"):
-            ply=f"{od}/t.ply"; gs.save_ply(ply)
-            import open3d as o3d
-            p=o3d.io.read_point_cloud(ply); p.estimate_normals()
-            m,_=o3d.geometry.TriangleMesh.create_from_point_cloud_poisson(p,depth=8)
-            o3d.io.write_triangle_mesh(glb,m)
-        elif gs and hasattr(gs,"_xyz"):
-            import open3d as o3d
-            p=o3d.geometry.PointCloud()
-            p.points=o3d.utility.Vector3dVector(gs._xyz.detach().cpu().numpy())
-            p.estimate_normals()
-            m,_=o3d.geometry.TriangleMesh.create_from_point_cloud_poisson(p,depth=8)
-            o3d.io.write_triangle_mesh(glb,m)
-        else:
-            return None,preview,f"No 3D: {type(result)}"
-
-        n=0
-        try: n=len(trimesh.load(glb,force="mesh").faces)
-        except: pass
-        return glb,preview,f"OK: {n:,} faces ({int(time.time()-t0)}s)"
-    except Exception as e:
-        import traceback; traceback.print_exc()
-        return None,None,f"Error: {e}"
-
-with gr.Blocks(title="SAM 3D Objects") as demo:
-    gr.Markdown("# SAM 3D Objects\nImage -> 3D (GLB)")
-    with gr.Tab("Reconstruct"):
-        with gr.Row():
-            with gr.Column():
-                inp=gr.Image(label="Input",type="numpy")
-                btn=gr.Button("Reconstruct",variant="primary",size="lg")
-            with gr.Column():
-                prev=gr.Image(label="Detection",type="numpy",interactive=False)
-                stat=gr.Textbox(label="Status")
-        with gr.Row():
-            m3d=gr.Model3D(label="3D Preview")
-            dl=gr.File(label="Download GLB")
-        btn.click(reconstruct_objects,inputs=[inp],outputs=[m3d,prev,stat])
-        m3d.change(lambda x:x,inputs=[m3d],outputs=[dl])
-    with gr.Tab("Diagnose"):
-        dbtn=gr.Button("GPU Diagnose")
-        dout=gr.Textbox(label="Env",lines=15)
-        dbtn.click(diagnose,outputs=[dout])
-demo.launch(mcp_server=True)