[package]
name = "rtx-vision-advanced"
version = "0.1.0"
edition = "2021"
authors = ["RustyTorch++ Team"]
license = "Apache-2.0"
description = "State-of-the-art computer vision capabilities with medical imaging, autonomous vehicle support, and production-ready deployment"
repository = "https://github.com/rustytorch/rustytorch"

[dependencies]
# Core RTX dependencies
rtx-tensor = { path = "../rtx-tensor" }
rtx-autograd = { path = "../rtx-autograd" }
rtx-transformers = { path = "../rtx-transformers" }
rtx-flash-attention = { path = "../rtx-flash-attention" }
rtx-vision = { path = "../rtx-vision" }
rtx-memory = { path = "../rtx-memory" }
rtx-inference = { path = "../rtx-inference" }

# Core utilities
anyhow = "1.0"
thiserror = "1.0"
tracing = "0.1"
log = "0.4"

# Image processing and computer vision
image = "0.24"
imageproc = "0.23"
opencv = { version = "0.88", optional = true }
rerun = { version = "0.17", optional = true }

# Medical imaging
dicom = { version = "0.6", optional = true }
dicom-object = { version = "0.6", optional = true }
medical-imaging-toolkit = { version = "0.1", optional = true }

# Point cloud processing for autonomous vehicles
pcd-rs = { version = "0.8", optional = true }
las = { version = "0.8", optional = true }
nalgebra.workspace = true
nalgebra-glm = "0.18"

# Advanced numeric computing
ndarray = "0.15"
ndarray-stats = "0.5"
ndarray-linalg = "0.16"
approx-float = "0.1"
num-traits = "0.2"
half = "2.3"
rand = "0.8"
rand_distr = "0.4"

# Machine learning utilities
candle-core = { version = "0.5", optional = true }
candle-nn = { version = "0.5", optional = true }
ort = { version = "2.0", optional = true }
tch = { version = "0.13", optional = true }

# Serialization and I/O
serde = { version = "1.0", features = ["derive"] }
serde_json = "1.0"
bincode = "1.3"
hdf5 = { version = "0.8", optional = true }
npy = "0.7"

# Concurrency and async
tokio = { version = "1.35", features = ["full"] }
rayon = "1.8"
crossbeam = "0.8"
parking_lot = "0.12"

# GPU computing and optimization
cudarc = { version = "0.9", optional = true }
wgpu = { version = "0.19", optional = true }
metal = { version = "0.27", optional = true }

# Network models and pre-trained weights
safetensors = "0.4"
memmap2 = "0.9"
reqwest = { version = "0.11", features = ["json", "stream"] }
futures = "0.3"

# Evaluation and benchmarking
criterion = { version = "0.5", optional = true }
indicatif = "0.17"
console = "0.15"

# Geometric and mathematical operations
geo = { version = "0.27", optional = true }
rstar = { version = "0.11", optional = true }
kiddo = { version = "4.1", optional = true }

[dev-dependencies]
# Testing framework
proptest = "1.4"
criterion = "0.5"
approx = "0.5"
tempfile = "3.8"
tokio-test = "0.4"

# Benchmarking
divan = "0.1"

[features]
default = ["gpu"]
gpu = ["cudarc", "wgpu"]
cuda = ["cudarc", "rtx-tensor/cuda"]
metal = ["dep:metal"]
opencv = ["dep:opencv"]
medical = ["dicom", "dicom-object", "medical-imaging-toolkit", "hdf5"]
autonomous = ["pcd-rs", "las", "geo", "rstar", "kiddo"]
onnx = ["ort"]
pytorch = ["tch"]
candle = ["candle-core", "candle-nn"]
visualization = ["rerun"]
benchmarks = ["criterion"]

[lib]
name = "rtx_vision_advanced"
path = "src/lib.rs"

[[bench]]
name = "detection_bench"
harness = false
required-features = ["benchmarks"]

[[bench]]
name = "segmentation_bench"
harness = false
required-features = ["benchmarks"]

[[bench]]
name = "medical_bench"
harness = false
required-features = ["benchmarks", "medical"]

[[bench]]
name = "autonomous_bench"
harness = false
required-features = ["benchmarks", "autonomous"]

[[example]]
name = "yolo_detection"
required-features = ["gpu"]

[[example]]
name = "medical_segmentation"
required-features = ["medical"]

[[example]]
name = "lidar_processing"
required-features = ["autonomous"]