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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,145 @@ | ||
| use anyhow::Result; | ||
| use image::DynamicImage; | ||
| use ndarray::{Array, Dim, IxDyn, IxDynImpl}; | ||
|
|
||
| use crate::ops; | ||
|
|
||
| #[derive(Debug, Clone, Default)] | ||
| pub struct X { | ||
| pub tensor: Array<f32, IxDyn>, | ||
| images: Option<Vec<DynamicImage>>, | ||
| } | ||
|
|
||
| impl From<Array<f32, IxDyn>> for X { | ||
| fn from(tensor: Array<f32, IxDyn>) -> Self { | ||
| Self { | ||
| tensor, | ||
| ..Default::default() | ||
| } | ||
| } | ||
| } | ||
|
|
||
| impl From<Vec<DynamicImage>> for X { | ||
| fn from(images: Vec<DynamicImage>) -> Self { | ||
| Self { | ||
| images: Some(images.to_vec()), | ||
| ..Default::default() | ||
| } | ||
| } | ||
| } | ||
|
|
||
| impl X { | ||
| pub fn load(images: &[DynamicImage]) -> Self { | ||
| Self { | ||
| images: Some(images.to_vec()), | ||
| ..Default::default() | ||
| } | ||
| } | ||
|
|
||
| pub fn zeros(shape: &[usize]) -> Self { | ||
| Self { | ||
| tensor: Array::zeros(Dim(IxDynImpl::from(shape.to_vec()))), | ||
| ..Default::default() | ||
| } | ||
| } | ||
|
|
||
| pub fn data(&self) -> &Array<f32, IxDyn> { | ||
| &self.tensor | ||
| } | ||
|
|
||
| pub fn images(&self) -> Option<&Vec<DynamicImage>> { | ||
| self.images.as_ref() | ||
| } | ||
|
|
||
| pub fn shape(&self) -> &[usize] { | ||
| self.tensor.shape() | ||
| } | ||
|
|
||
| pub fn dims(&self) -> &[usize] { | ||
| self.tensor.shape() | ||
| } | ||
|
|
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| pub fn normalize(mut self, min_: f32, max_: f32) -> Self { | ||
| self.tensor = (self.tensor - min_) / (max_ - min_); | ||
| self | ||
| } | ||
|
|
||
| pub fn standardize(mut self, mean: &[f32], std: &[f32]) -> Self { | ||
| let mean = Array::from_shape_vec((1, mean.len(), 1, 1), mean.to_vec()).unwrap(); | ||
| let std = Array::from_shape_vec((1, std.len(), 1, 1), std.to_vec()).unwrap(); | ||
| self.tensor = (self.tensor - mean) / std; | ||
| self | ||
| } | ||
|
|
||
| pub fn letterbox( | ||
| mut self, | ||
| height: u32, | ||
| width: u32, | ||
| filter: &str, | ||
| bg: Option<u8>, | ||
| ) -> Result<Self> { | ||
| match &self.images { | ||
| None => anyhow::bail!("No images found"), | ||
| Some(xs) => { | ||
| self.tensor = ops::letterbox(xs, height, width, filter, bg)?; | ||
| Ok(self) | ||
| } | ||
| } | ||
| } | ||
|
|
||
| pub fn resize(mut self, height: u32, width: u32, filter: &str) -> Result<Self> { | ||
| match &self.images { | ||
| None => anyhow::bail!("No images found"), | ||
| Some(xs) => { | ||
| self.tensor = ops::resize(xs, height, width, filter)?; | ||
| Ok(self) | ||
| } | ||
| } | ||
| } | ||
| } | ||
|
|
||
| // #[derive(Debug, Clone)] | ||
| // pub struct BaseTensor(pub Array<f32, IxDyn>); | ||
|
|
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| // impl From<Array<f32, IxDyn>> for BaseTensor { | ||
| // fn from(x: Array<f32, IxDyn>) -> Self { | ||
| // Self(x) | ||
| // } | ||
| // } | ||
|
|
||
| // // TODO: from_dynamic_image | ||
|
|
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| // impl BaseTensor { | ||
| // pub fn zeros(shape: &[usize]) -> Self { | ||
| // Self(Array::zeros(Dim(IxDynImpl::from(shape.to_vec())))) | ||
| // } | ||
|
|
||
| // pub fn data(&self) -> &Array<f32, IxDyn> { | ||
| // &self.0 | ||
| // } | ||
|
|
||
| // pub fn shape(&self) -> &[usize] { | ||
| // self.0.shape() | ||
| // } | ||
|
|
||
| // pub fn dims(&self) -> &[usize] { | ||
| // self.0.shape() | ||
| // } | ||
|
|
||
| // pub fn normalize(mut self, min_: f32, max_: f32) -> Self { | ||
| // self.0 = (self.0 - min_) / (max_ - min_); | ||
| // self | ||
| // } | ||
|
|
||
| // pub fn standardize(mut self, mean: &[f32], std: &[f32]) -> Self { | ||
| // // let shape = self.0.shape(); | ||
| // let mean = Array::from_shape_vec((1, mean.len(), 1, 1), mean.to_vec()).unwrap(); | ||
| // let std = Array::from_shape_vec((1, std.len(), 1, 1), std.to_vec()).unwrap(); | ||
| // self.0 = (self.0 - mean) / std; | ||
| // self | ||
| // } | ||
|
|
||
| // pub fn into_image() { | ||
| // todo!() | ||
| // } | ||
| // } |
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