/bucket/img.jpg?w=200) for direct use from HTML. Source images can reside in blob storage, on another server, or on the filesystem. However, for production use, we recommend using Imageflow.NET Server, which is far more feature-rich and doesn't need a reverse proxy in front for security.
Open an issue to have us write example code for your use case. We believe in feedback-driven design, and streamlining real-world usage is the fastest way to a great product.
imageflow_tool examples --generate - creates an examples directory with JSON jobs and invocation scripts.
You can use command strings that are compatible with ImageResizer 4 querystrings:
imageflow_tool v1/querystring --in source.jpg --out thumb.jpg --command "width=50&height=50&mode=crop&format=jpg"
Or submit a JSON job file. JSON jobs can have multiple inputs and outputs, and can represent any kind of operation graph.
The following generates multiple sizes of an image from an example job file:
imageflow_tool v1/build --json examples/export_4_sizes/export_4_sizes.json --in waterhouse.jpg --out 1 waterhouse_w1600.jpg 2 waterhouse_w1200.jpg 3 waterhouse_w800.jpg 4 waterhouse_w400.jpg --response operation_result.json
By default, imageflow_tool prints a JSON response to stdout. You write this to disk with
--debug-package will create a .zip file to reproduce problematic behavior with both
v1/querystring. Please submit bug reports; we try to make it easy.
imageflow_server start --demo
Now you can edit images from HTML... and use srcset without headache.
<img src="http://localhost:39876/demo_images/u3.jpg?w=300" /> <img src="" srcset=" http://localhost:39876/demo_images/u3.jpg?w=300 300w http://localhost:39876/demo_images/u3.jpg?w=800 800w http://localhost:39876/demo_images/u3.jpg?w=1600 1600w" />
You'll want to mount various image source locations to prefixes. The
--mount command parses a colon (
:) delimited list of arguments. The first is the prefix you'll use in the URL (like
http://localhost:39876/prefix/. The second is the engine name. Remaining arguments are sent to the engine.
--mount "/proxyimg/:ir4_http:https:://myotherserver.com/imagefolder/"(note the double escaping of the colon)
You also may find that
imageflow_tool is quite fast enough for your needs.
You'll need more than just Rust to compile Imageflow, as it has a couple C dependencies.
git clone [email protected]:imazen/imageflow.git && cd imageflow)
If you are using
bash on any platform, you should be able to use
./build.sh clean- to clean
./build.sh test- run all tests
./build.sh debug- generate slow debug binaries
./build.sh release- generate release binaries
./build.sh install- install release binaries to
/usr/local(must run `./build.sh release first)
./build.sh uninstall- uninstall release binaries
build.sh places binaries in the
If you are on Windows, only run build commands in the window created by
You can also build using
cargo directly, although this will place binaries in
cargo test --all to test Imageflow in debug (slooow) mode
cargo build --package imageflow_abi --release to compile
cargo build --package imageflow_tool --release to compile
cargo build --package imageflow_server --release to compile
cargo build --all --release to compile everything in release mode
cargo doc --no-deps --all --release to generate documentation.
If you want to replicate the Imageflow CI environment:
git clone [email protected]:imazen/imageflow.git cd imageflow ./build_via_docker.sh debug
This will create caches within
~/.docker_imageflow_caches specific to the docker image used. Instances will be ephemeral; the only state will be in the caches.
The official Dockerfiles are also a great place to get more detailed environment setup steps, as we don't list steps for setting up:
(tested on Ubuntu 16.04 and 18.04.)
#Install Rust 1.41+ by running `curl https://sh.rustup.rs -sSf | sh -s -- -y --default-toolchain stable` #Ensure build tools are installed (git, curl, wget, gcc, g++, nasm, pkg-config, openssl, ca-certificates) `sudo apt-get install git wget curl build-essential pkg-config libssl-dev libpng-dev nasm `
brew install nasm pkg-config wget
sudo apt-get updateto update available packages.
curl https://sh.rustup.rs -sSf | sh -s -- -y --default-toolchain stable
sudo apt-get install git wget curl build-essential pkg-config libssl-dev libpng-dev nasm
ln -s /mnt/c/Users/[YourWindowsUserName]/Documents/imageflow ~/win_imageflow
Run As Administratorthe NASM 64-bit installer - it will not prompt.
stableas the default, and confirm adding it to
ci/wintools/SETUP_PATH.batto ensure that rust/cargo, nasm, git, and Git/mingw64/bin are all in
win_enter_env.batto start a sub-shell (edit it if you want a 32-bit build)
cmd.exe /c "ci\wintools\win_verify_tools.bat"to check tools are present.
First, read High Performance Images for context.
There are not many great textbooks on the subject. Here are some from my personal bookshelf. Between them (and Wikipedia) I was able to put together about 60% of the knowledge I needed; the rest I found by reading the source code to many popular image processing libraries.
I would start by reading Principles of Digital Image Processing: Core Algorithms front-to-back, then Digital Image Warping. Wikipedia is also a useful reference, although the relevant pages are not linked or categorized together - use specific search terms, like "bilinear interpolation" and "Lab color space".
I have found the source code for OpenCV, LibGD, FreeImage, Libvips, Pixman, Cairo, ImageMagick, stb_image, Skia, and FrameWave is very useful for understanding real-world implementations and considerations. Most textbooks assume an infinite plane, ignore off-by-one errors, floating-point limitations, color space accuracy, and operational symmetry within a bounded region. I cannot recommend any textbook as an accurate reference, only as a conceptual starting point. I made some notes regarding issues to be aware of when creating an imaging library.
Also, keep in mind that computer vision is very different from image creation. In computer vision, resampling accuracy matters very little, for example. But in image creation, you are serving images to photographers, people with far keener visual perception than the average developer. The images produced will be rendered side-by-side with other CSS and images, and the least significant bit of inaccuracy is quite visible. You are competing with Lightroom; with offline tools that produce visually perfect results. End-user software will be discarded if photographers feel it is corrupting their work.