FROM nvidia/cuda:11.8.0-devel-ubuntu22.04

ENV DEBIAN_FRONTEND=noninteractive
ENV TZ=Europe/Paris

# Install system packages + Miniforge for PDAL >= 2.5 (Ubuntu 22.04 ships PDAL 2.3 which can't read COPC v1.1)
RUN apt-get update && apt-get install -y --no-install-recommends \
    gdal-bin \
    python3-gdal \
    python3-pip \
    python3-dev \
    build-essential \
    wget \
    && rm -rf /var/lib/apt/lists/* \
    && wget -q https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh -O /tmp/miniforge.sh \
    && bash /tmp/miniforge.sh -b -p /opt/conda \
    && rm /tmp/miniforge.sh \
    && /opt/conda/bin/conda install -y -c conda-forge pdal \
    && ln -sf /opt/conda/bin/pdal /usr/local/bin/pdal \
    && /opt/conda/bin/conda clean -afy

WORKDIR /app

# Install Python packages via pip
COPY requirements.txt .
RUN pip3 install --no-cache-dir \
    numpy \
    matplotlib \
    whitebox \
    rasterio \
    'laspy[lazrs]' \
    lazrs \
    scikit-image \
    scikit-learn \
    scipy \
    tqdm \
    Pillow \
    pytest \
    numba \
    rio-cogeo \
    titiler.core \
    fastapi \
    uvicorn \
    piexif \
    pillow-avif-plugin \
    cmcrameri

# Install CuPy for GPU acceleration (optional - will fallback to numpy if not available)
# We use CuPy 13.4 (CUDA 11.x wheel) because CuPy 14.x dropped JIT compilation
# support.  With CUPY_CUDA_COMPILE_WITH_CACHE=1, CuPy 13.4 compiles kernels at
# runtime for GPU architectures not in the pre-built wheel (e.g. sm_89 / RTX 4060 Ti).
# The devel image includes nvcc, required for JIT compilation.
# nvcc stays in PATH for the 'lidar' user.
ENV CUPY_CUDA_COMPILE_WITH_CACHE=1
ENV PATH=/usr/local/cuda/bin:${PATH}
RUN pip3 install --no-cache-dir cupy-cuda11x==13.4.0 || echo "CuPy not available - GPU acceleration disabled"

# Copy and install the pipeline package
COPY setup.py .
COPY lidar_pipeline/ ./lidar_pipeline/
RUN pip3 install --no-cache-dir .

# Copy backward-compatible entry point
COPY process_lidar.py /usr/local/bin/
RUN chmod +x /usr/local/bin/process_lidar.py

# Create user with uid/gid 1000:1000 and run as that user
RUN groupadd -g 1000 lidar && \
    useradd -u 1000 -g lidar -m lidar && \
    mkdir -p /data/output /data/input && \
    chown -R lidar:lidar /data /data/output /data/input

WORKDIR /data

USER lidar

VOLUME ["/data"]

CMD ["python3", "-m", "lidar_pipeline", "/data/input", "-o", "/data/output"]