promto-c

GSplat from Multi‑View Images

Photos → COLMAP (poses) → Brush (GSplat) → View

Requisites

• COLMAP: https://github.com/colmap/colmap/releases
• Chrome / Edge (WebGPU) — required for Brush Web Demo: https://arthurbrussee.github.io/brush-demo/
• Images: .jpg or .png, static scene, sharp, consistent exposure and white balance, 60–80% overlap

promto-c

import torch

print("PyTorch version:", torch.__version__)

# CUDA / ROCm (both use torch.cuda API in PyTorch)
# ------------------------------------------------
has_cuda_api = torch.cuda.is_available()
cuda_version = torch.version.cuda
rocm_version = torch.version.hip  # None if not ROCm build

promto-c

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""Copyright (C) 2026 promto-c

Permission Notice:
- You are free to use, copy, modify, and distribute this software for any purpose.
- No restrictions are imposed on its use.
- Credit is appreciated but not required.
- Use at your own risk; this software is provided "AS IS", without any warranty — express or implied — including, but not limited to, warranties of merchantability or fitness for a particular purpose.
- This notice does not apply to any third-party libraries or dependencies; those are subject to their respective licenses.

promto-c

"""Copyright (C) 2025 promto-c

Permission Notice:
- You are free to use, copy, modify, and distribute this software for any purpose.
- No restrictions are imposed on its use.
- Credit is appreciated but not required.
- Use at your own risk; this software is provided "AS IS", without any warranty — express or implied — including, but not limited to, warranties of merchantability or fitness for a particular purpose.
- This notice does not apply to any third-party libraries or dependencies; those are subject to their respective licenses.
"""

promto-c

"""Copyright (C) 2025 promto-c

Permission Notice:
- You are free to use, copy, modify, and distribute this software for any purpose.
- No restrictions are imposed on its use.
- Credit is appreciated but not required.
- Use at your own risk; this software is provided "AS IS", without any warranty — express or implied — including, but not limited to, warranties of merchantability or fitness for a particular purpose.
- This notice does not apply to any third-party libraries or dependencies; those are subject to their respective licenses.
"""
import fx

promto-c

"""Copyright (C) 2020 promto-c

Permission Notice:
- You are free to use, copy, modify, and distribute this software for any purpose.
- No restrictions are imposed on the use of this software.
- You do not need to give credit or include this notice in your work.
- Use at your own risk.
- This software is provided "AS IS" without any warranty, either expressed or implied.
- This license does not cover any third-party libraries or dependencies used in this software. Those libraries are subject to their respective licenses.
"""

promto-c

"""Copyright (C) 2024 promto-c

Permission Notice:
- You are free to use, copy, modify, and distribute this software for any purpose.
- No restrictions are imposed on the use of this software.
- You do not need to give credit or include this notice in your work.
- Use at your own risk.
- This software is provided "AS IS" without any warranty, either expressed or implied.
"""
from difflib import SequenceMatcher

promto-c

Model-View-Projection (MVP) Matrix in 3D Graphics

In 3D graphics programming, MVP stands for Model-View-Projection matrix. It is a transformation matrix that combines three separate transformations—Model, View, and Projection—into a single matrix to convert 3D coordinates into 2D screen coordinates for rendering.

Components of MVP

1. Model Matrix (M)

• Represents the transformation of an object from model space to world space.
• This matrix is used to position, rotate, and scale the model within the world.
• Example transformations include:

promto-c

import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
import category_encoders as ce
import time

# Load the dataset
file_path = 'car1.csv'

promto-c

import nuke
from typing import List, Tuple


class NodeAlignment:
    """Handles the alignment of nodes in Nuke."""

    DEFAULT_SPACING = 100

    @staticmethod