Practical overview
When it fits
Use it when you want to guide a local image colorization result with your own point-based color choices and can install the documented local software dependencies.
Who it is for
- Photographers and image editors who want to steer colorization with selected color hints.
- Developers or researchers exploring interactive deep-learning image colorization through Python demos.
Documented uses
- Add color to a black-and-white image while guiding selected areas with chosen hues.
- Adjust, move or remove color hints and save the resulting colorized image with the input color values.
- Explore the project’s separate global-hints example for histogram transfer.
Requirements & limits
The repository is public and lists an MIT license. The software is presented as a local installation; no account or pricing details are stated.
- The main README gives Linux or macOS as general prerequisites; a separate Windows branch offers an installation guide for the PyTorch model.
- The notebook demo does not require Qt, while the full GUI requires Qt5 and QDarkStyle.
- The README identifies Caffe as the official model and PyTorch as a reimplementation using converted weights.
- The full setup lists OpenCV, scikit-learn and scikit-image, and calls for a CPU or NVIDIA GPU with CUDA and cuDNN.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Prepare the repository and reference model
Set up the documented project files and download the reference model before choosing a demo.
Before you start
- Linux or macOS, as specified in the general prerequisites.
- Caffe or PyTorch, plus the listed Python image-processing libraries: OpenCV, scikit-learn and scikit-image.
Steps
Clone the repository and change into its directory.
Run the model-fetch script: the command below.
bash ./models/fetch_models.shInstall Caffe or PyTorch and the required Python libraries.
For the full GUI, also install Qt5 and QDarkStyle; the barebones notebook demo does not require Qt.
Expected outcome
The repository and reference model are prepared for the documented notebook or GUI workflow.
Guide limitations
- The instructions do not establish that this setup has been tested on a particular machine.
- The general prerequisites name Linux or macOS; the README points to a separate Windows guide for the PyTorch model.
- This covers setup only; it does not claim that a demo has been run.
Guide color hints in the graphical demo
Use the documented GUI controls to place and adjust color hints for an image.
Before you start
- A prepared repository and reference model.
- Caffe or PyTorch backend; Qt5 and QDarkStyle for the full GUI.
- A local image file to load.
Steps
Start the GUI with the command below, supplying the documented GPU and backend arguments.
python ideepcolor.py --gpu [GPU_ID] --backend [CAFFE OR PYTORCH]Load an image, then left-click on the input pad to add hint points.
Select a point and choose a recommended color or a color from the ab gamut; drag points to move them, right-click to remove them, or use the mouse wheel to change patch size.
Save the result using the GUI’s save control.
Expected outcome
The GUI saves the colorized result in the directory containing the image file, along with the user-input ab values.
Guide limitations
- The README documents the interaction and save behavior but does not guarantee output quality for a particular image.
- The example command uses placeholders for GPU ID and backend; use values appropriate to the local setup.
- This guide reflects repository instructions, not a human execution test.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 16 October 2026
- Original website title
- junyanz/interactive-deep-colorization · Official homepage title · retained in its source language