Feature/workspace deps

Cargo.toml

@@ -1,7 +1,29 @@
 [workspace]
 members = ["infur", "infur-test-gen", "ff-video", "image-ext"]
+default-members = ["infur"]
 resolver = "2"
 
+[workspace.package]
+version = "0.2.0"
+edition = "2021"
+description = "ONNX model inference on video and images"
+authors = ["Alexander Hirner"]
+license = "MIT"
+keywords = ["ONNX", "Neural Networks", "Inference", "Segmentation", "GUI", "Prediction", "Video"]
+readme = "README.md"
+repository = "https://github.com/ahirner/infur"
+homepage = "https://github.com/ahirner/infur"
+
+[workspace.dependencies]
+image = "0.24"
+fast_image_resize = { version = "1" }
+# need onnxruntime .14 for 0-dim input tolerance (not in .13),
+# then furthermore need master to resolve ndarray with tract-core
+onnxruntime = { git = "https://github.com/nbigaouette/onnxruntime-rs" }
+serde = { version = "1", features = ["derive"] }
+thiserror = "1"
+tracing = "0.1"
+
 [profile.dev.package]
 image-ext = { opt-level = 3 }
 backtrace = { opt-level = 3 }

ff-video/Cargo.toml

@@ -1,11 +1,10 @@
 [package]
 name = "ff-video"
-version = "0.1.0"
-edition = "2021"
-
-# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
+description = "Slim image decoding with ffmpeg."
+version.workspace = true
+edition.workspace = true
 
 [dependencies]
 image-ext = { path = "../image-ext" }
-thiserror = "1"
-tracing = "0.1"
+thiserror.workspace = true
+tracing.workspace = true

image-ext/Cargo.toml

@@ -1,8 +1,8 @@
 [package]
 name = "image-ext"
-version = "0.1.0"
-edition = "2021"
 description = "Extend image crate with BGR pixel type"
+version.workspace = true
+edition.workspace = true
 
 [dependencies]
-image = "0.24"
+image.workspace = true

infur-test-gen/Cargo.toml

@@ -1,8 +1,8 @@
 [package]
 name = "infur-test-gen"
-version = "0.1.0"
-edition = "2021"
 description = "generate synthetic test media"
+version.workspace = true
+edition.workspace = true
 
 [build-dependencies]
 ureq = "2"

infur/Cargo.toml

@@ -1,31 +1,23 @@
 [package]
-authors = ["Alexander Hirner <[email protected]>"]
-license = "MIT"
 name = "infur"
-version = "0.1.0"
-edition = "2021"
-description = "ONNX model inference on video and images"
-keywords = ["ONNX", "Neural Networks", "Inference", "Segmentation", "GUI", "Prediction", "Video"]
-repository = "https://github.com/ahirner/infur"
-readme = "README.md"
-homepage = "https://github.com/ahirner/infur"
+description.workspace = true
+version.workspace = true
+edition.workspace = true
 
 [features]
 default = ["persistence"]
 persistence = ["eframe/persistence"]
 
 [dependencies]
-thiserror = "1"
-stable-eyre = "0.2"
-tracing = "0.1"
-tracing-subscriber = { version = "0.3", features = ["ansi", "env-filter", "fmt"], default-features = false }
-eframe = { version = "0.19", features = ["wgpu", "default_fonts"], default-features = false}
-serde = { version = "1", features = ["derive"] }
-fast_image_resize = { version = "1" }
-# need onnxruntime .14 for 0-dim input tolerance (not in .13),
-# then furthermore need master to resolve ndarray with tract-core...
-onnxruntime = { git = "https://github.com/nbigaouette/onnxruntime-rs" }
+fast_image_resize.workspace = true
+onnxruntime.workspace = true
+thiserror.workspace = true
+tracing.workspace = true
+serde.workspace = true
 once_cell = "1"
+eframe = { version = "0.19", features = ["wgpu", "default_fonts"], default-features = false }
+tracing-subscriber = { version = "0.3", features = ["ansi", "env-filter", "fmt"], default-features = false }
+stable-eyre = "0.2"
 image-ext = { path = "../image-ext" }
 ff-video = { path = "../ff-video" }
 

infur/src/gui.rs

@@ -319,7 +319,7 @@ impl eframe::App for InFur {
                 // occupy max width with constant aspect ratio
                 let max_width = ui.available_width();
                 let [w, h] = tex_frame.handle.size();
-                let w_scale = max_width as f32 / w as f32;
+                let w_scale = max_width / w as f32;
                 let (w, h) = (w as f32 * w_scale, h as f32 * w_scale);
                 ui.image(&tex_frame.handle, [w, h]);
                 // prop decoded image underneath