Update app.py
Browse files
app.py
CHANGED
@@ -30,7 +30,57 @@ from src.utils.image import process_image
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os.environ["ANYSPLAT_PROCESSED"] = f"{os.getcwd()}/proprocess_results"
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def get_reconstructed_scene(outdir, model, device):
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@@ -63,6 +113,7 @@ def get_reconstructed_scene(outdir, model, device):
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)
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plyfile = os.path.join(outdir, "gaussians.ply")
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export_ply(
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gaussians.means[0],
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@@ -73,10 +124,12 @@ def get_reconstructed_scene(outdir, model, device):
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Path(plyfile),
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save_sh_dc_only=True,
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)
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# Clean up
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torch.cuda.empty_cache()
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-
return
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# 2) Handle uploaded video/images --> produce target_dir + images
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os.environ["ANYSPLAT_PROCESSED"] = f"{os.getcwd()}/proprocess_results"
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from plyfile import PlyData
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import numpy as np
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import argparse
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from io import BytesIO
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def process_ply_to_splat(ply_file_path):
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plydata = PlyData.read(ply_file_path)
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vert = plydata["vertex"]
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sorted_indices = np.argsort(
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-np.exp(vert["scale_0"] + vert["scale_1"] + vert["scale_2"])
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/ (1 + np.exp(-vert["opacity"]))
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)
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buffer = BytesIO()
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for idx in sorted_indices:
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v = plydata["vertex"][idx]
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position = np.array([v["x"], v["y"], v["z"]], dtype=np.float32)
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scales = np.exp(
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np.array(
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[v["scale_0"], v["scale_1"], v["scale_2"]],
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dtype=np.float32,
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)
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)
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rot = np.array(
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[v["rot_0"], v["rot_1"], v["rot_2"], v["rot_3"]],
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dtype=np.float32,
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)
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SH_C0 = 0.28209479177387814
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color = np.array(
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[
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0.5 + SH_C0 * v["f_dc_0"],
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0.5 + SH_C0 * v["f_dc_1"],
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0.5 + SH_C0 * v["f_dc_2"],
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1 / (1 + np.exp(-v["opacity"])),
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]
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)
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buffer.write(position.tobytes())
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buffer.write(scales.tobytes())
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buffer.write((color * 255).clip(0, 255).astype(np.uint8).tobytes())
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buffer.write(
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((rot / np.linalg.norm(rot)) * 128 + 128)
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.clip(0, 255)
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.astype(np.uint8)
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.tobytes()
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)
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return buffer.getvalue()
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def save_splat_file(splat_data, output_path):
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with open(output_path, "wb") as f:
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f.write(splat_data)
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def get_reconstructed_scene(outdir, model, device):
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)
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plyfile = os.path.join(outdir, "gaussians.ply")
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splatfile = os.path.join(outdir, "gaussians.splat")
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export_ply(
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gaussians.means[0],
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Path(plyfile),
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save_sh_dc_only=True,
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)
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splat_data = process_ply_to_splat(plyfile)
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save_splat_file(splat_data, splatfile)
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# Clean up
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torch.cuda.empty_cache()
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return splatfile, video, depth_colored
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# 2) Handle uploaded video/images --> produce target_dir + images
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