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//! Depth Anything V2
//! https://huggingface.co/spaces/depth-anything/Depth-Anything-V2
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
use clap::Parser;
use std::{ffi::OsString, path::PathBuf, sync::Arc};
use candle::DType::{F32, U8};
use candle::{DType, Device, Module, Result, Tensor};
use candle_examples::{load_image, load_image_and_resize, save_image};
use candle_nn::VarBuilder;
use candle_transformers::models::depth_anything_v2::{DepthAnythingV2, DepthAnythingV2Config};
use candle_transformers::models::dinov2;
use crate::color_map::SpectralRColormap;
mod color_map;
// taken these from: https://huggingface.co/spaces/depth-anything/Depth-Anything-V2/blob/main/depth_anything_v2/dpt.py#L207
const MAGIC_MEAN: [f32; 3] = [0.485, 0.456, 0.406];
const MAGIC_STD: [f32; 3] = [0.229, 0.224, 0.225];
const DINO_IMG_SIZE: usize = 518;
#[derive(Parser)]
struct Args {
#[arg(long)]
dinov2_model: Option<PathBuf>,
#[arg(long)]
depth_anything_v2_model: Option<PathBuf>,
#[arg(long)]
image: PathBuf,
#[arg(long)]
output_dir: Option<PathBuf>,
#[arg(long)]
cpu: bool,
#[arg(long)]
color_map: bool,
}
pub fn main() -> anyhow::Result<()> {
let args = Args::parse();
let device = candle_examples::device(args.cpu)?;
let dinov2_model_file = match args.dinov2_model {
None => {
let api = hf_hub::api::sync::Api::new()?;
let api = api.model("lmz/candle-dino-v2".into());
api.get("dinov2_vits14.safetensors")?
}
Some(dinov2_model) => dinov2_model,
};
println!("Using file {:?}", dinov2_model_file);
let vb = unsafe { VarBuilder::from_mmaped_safetensors(&[dinov2_model_file], F32, &device)? };
let dinov2 = dinov2::vit_small(vb)?;
println!("DinoV2 model built");
let depth_anything_model_file = match args.depth_anything_v2_model {
None => {
let api = hf_hub::api::sync::Api::new()?;
let api = api.model("jeroenvlek/depth-anything-v2-safetensors".into());
api.get("depth_anything_v2_vits.safetensors")?
}
Some(depth_anything_model) => depth_anything_model,
};
println!("Using file {:?}", depth_anything_model_file);
let vb = unsafe {
VarBuilder::from_mmaped_safetensors(&[depth_anything_model_file], DType::F32, &device)?
};
let config = DepthAnythingV2Config::vit_small();
let depth_anything = DepthAnythingV2::new(Arc::new(dinov2), config, vb)?;
let (original_height, original_width, image) = load_and_prep_image(&args.image, &device)?;
println!("Loaded image {image:?}");
let depth = depth_anything.forward(&image)?;
println!("Got predictions {:?}", depth.shape());
let output_image = post_process_image(&depth, original_height, original_width, args.color_map)?;
let output_path = full_output_path(&args.image, &args.output_dir);
println!("Saving image to {}", output_path.to_string_lossy());
save_image(&output_image, output_path)?;
Ok(())
}
fn full_output_path(image_path: &PathBuf, output_dir: &Option<PathBuf>) -> PathBuf {
let input_file_name = image_path.file_name().unwrap();
let mut output_file_name = OsString::from("depth_");
output_file_name.push(input_file_name);
let mut output_path = match output_dir {
None => image_path.parent().unwrap().to_path_buf(),
Some(output_path) => output_path.clone(),
};
output_path.push(output_file_name);
output_path
}
fn load_and_prep_image(
image_path: &PathBuf,
device: &Device,
) -> anyhow::Result<(usize, usize, Tensor)> {
let (_original_image, original_height, original_width) = load_image(&image_path, None)?;
let image = load_image_and_resize(&image_path, DINO_IMG_SIZE, DINO_IMG_SIZE)?
.unsqueeze(0)?
.to_dtype(F32)?
.to_device(&device)?;
let max_pixel_val = Tensor::try_from(255.0f32)?
.to_device(&device)?
.broadcast_as(image.shape())?;
let image = (image / max_pixel_val)?;
let image = normalize_image(&image, &MAGIC_MEAN, &MAGIC_STD)?;
Ok((original_height, original_width, image))
}
fn normalize_image(image: &Tensor, mean: &[f32; 3], std: &[f32; 3]) -> Result<Tensor> {
let mean_tensor =
Tensor::from_vec(mean.to_vec(), (3, 1, 1), &image.device())?.broadcast_as(image.shape())?;
let std_tensor =
Tensor::from_vec(std.to_vec(), (3, 1, 1), &image.device())?.broadcast_as(image.shape())?;
image.sub(&mean_tensor)?.div(&std_tensor)
}
fn post_process_image(
image: &Tensor,
original_height: usize,
original_width: usize,
color_map: bool,
) -> Result<Tensor> {
let out = image.interpolate2d(original_height, original_width)?;
let out = scale_image(&out)?;
let out = if color_map {
let spectral_r = SpectralRColormap::new();
spectral_r.gray2color(&out)?
} else {
let rgb_slice = [&out, &out, &out];
Tensor::cat(&rgb_slice, 0)?.squeeze(1)?
};
let max_pixel_val = Tensor::try_from(255.0f32)?
.to_device(out.device())?
.broadcast_as(out.shape())?;
let out = (out * max_pixel_val)?;
out.to_dtype(U8)
}
fn scale_image(depth: &Tensor) -> Result<Tensor> {
let flat_values: Vec<f32> = depth.flatten_all()?.to_vec1()?;
let min_val = flat_values.iter().min_by(|a, b| a.total_cmp(b)).unwrap();
let max_val = flat_values.iter().max_by(|a, b| a.total_cmp(b)).unwrap();
let min_val_tensor = Tensor::try_from(*min_val)?
.to_device(depth.device())?
.broadcast_as(depth.shape())?;
let depth = (depth - min_val_tensor)?;
let range = max_val - min_val;
let range_tensor = Tensor::try_from(range)?
.to_device(depth.device())?
.broadcast_as(depth.shape())?;
depth / range_tensor
}
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