Metadata-Version: 2.1
Name: maskilayer
Version: 1.0.5
Summary: Python tool to composite two images using multiple mask images
Home-page: https://github.com/twardoch/maskilayer
Author: Adam Twardoch
Author-email: adam+github@twardoch.com
License: Apache-2.0
Project-URL: Source, https://github.com/twardoch/maskilayer
Project-URL: Tracker, https://github.com/twardoch/maskilayer/issues
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Multimedia :: Graphics
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.11
Description-Content-Type: text/markdown; charset=UTF-8; variant=GFM
License-File: LICENSE.txt
Requires-Dist: numpy
Requires-Dist: Pillow
Requires-Dist: aiofiles
Requires-Dist: fire
Requires-Dist: rich
Provides-Extra: testing
Requires-Dist: setuptools; extra == "testing"
Requires-Dist: pytest; extra == "testing"
Requires-Dist: pytest-cov; extra == "testing"

# maskilayer

`maskilayer` is a Python tool for compositing two images using one or more mask images.

- Composite two images (background + overlay) using one or more mask images
- Supply positive masks (overlay is preferred in bright areas of the mask) or negative masks (overlay is preferred in dark mask areas)
- Normalize masks with adjustable levels (0-5)

## 1. Installation

Install `maskilayer` using pip:

```bash
pip install --upgrade maskilayer
```

or the development version in a specific Python:

```bash
python3.11 -m pip install --upgrade git+https://github.com/twardoch/maskilayer
```

## 2. Rationale

### 2.1. Selective image sharpening

Combine a sharpened image with its original version using a segmentation mask:

1. Process an original image with an automatic sharpening method.
2. Use a segmentation model to generate a segmentation mask that isolates a specific subject.
3. Use `maskilayer` to composite:
   - background: original image
   - overlay: sharpened version
   - mask: segmentation mask

`maskilayer` will save a result, which will be a composite image where only the subject is sharpened, while the rest remains as in the original.

### 2.2. Creative upscaling with depth based compositing

Blend two differently upscaled versions of an image using a depth mask:

1. Upscale an image using a conservative upscaler like [Codeformer](https://replicate.com/sczhou/codeformer) to get predictable details for the background areas of the image. Supply the conservative upscale as background to `maskilayer`.
2. Upscale the same image using a creative upscaler like [Ultimate SD Upscale](https://replicate.com/fewjative/ultimate-sd-upscale) to get additional details for foreground (subject) areas of the image. Supply the creative upscale as overlay (compositing image) to `maskilayer`.
3. Generate a depth mask using [Depth Anything](https://replicate.com/cjwbw/depth-anything) or [Midas](https://replicate.com/cjwbw/midas) (where the far areas are dark, and the close areas are bright). Supply the result as the (positive) mask to `maskilayer`. Or use a model like [Marigold](https://replicate.com/adirik/marigold) (where the close subjects are dark), and supply the resulting mask as inverted (negative) mask.
4. Use `maskilayer` to composite:
   - background: conservative upscale
   - overlay: creative upscale
   - mask: depth mask (inverted mask if close areas are dark)

`maskilayer` will save a result, which will be a composite image with creative details for close subjects, and more conservative rendering for distant areas.

## 3. Usage

### 3.1. Command Line Interface

#### 3.1.1. Basic usage

```bash
maskilayer -b background.png -c overlay.png -o output.png
```

#### 3.1.2. Selective image sharpening example

```bash
maskilayer --back original.png --comp sharpened.png --out sharpened_subject.png --masks segmentation_mask.png --norm 3 --verbose
```

#### 3.1.3. Creative upscaling with depth based compositing example

```bash
maskilayer --back conservative_upscale.png --comp creative_upscale.png --out composite_upscale.png --masks "depth_mask1.png;depth_mask2.png" --imasks "inverted_depth_mask3.png" --norm 2 --verbose
```

#### 3.1.4. CLI documentation

```text
NAME
    maskilayer - Composite two images using mask(s).

SYNOPSIS
    maskilayer <flags>

DESCRIPTION
    Composite two images using mask(s).

FLAGS
    -b, --back=BACK
        Type: str
        Default: ''
        layer 0 (background image path)
    -c, --comp=COMP
        Type: str
        Default: ''
        layer 1 (overlay image path that will be composited via masks)
    -o, --out=OUT
        Type: str
        Default: ''
        output composite image
    -s, --smask=SMASK
        Type: str
        Default: ''
        path to save the final mask (optional)
    -m, --masks=MASKS
        Type: Optional
        Default: None
        ;-separated mask image paths (optional)
    -i, --imasks=IMASKS
        Type: Optional
        Default: None
        ;-separated negative mask image paths (optional)
    -n, --norm=NORM
        Type: int
        Default: 0
        perform mask normalization with level 0-5
    -v, --verbose=VERBOSE
        Type: bool
        Default: False
        print additional output
    -f, --fast=FAST
        Type: bool
        Default: False
        save fast but larger files
```

### 3.2. Python API

#### 3.2.1. Basic usage

```python
from pathlib import Path
from maskilayer import comp_images

comp_images(
    background=Path("background.png"),
    overlay=Path("overlay.png"),
    output=Path("output.png")
)
```

#### 3.2.2. Selective image sharpening example

```python
from pathlib import Path
from maskilayer import comp_images

comp_images(
    background=Path("original.png"),
    overlay=Path("sharpened.png"),
    output=Path("sharpened_subject.png"),
    masks=[Path("segmentation_mask.png")],
    normalize_level=3,
    verbose=True
)
```

#### 3.2.3. Creative upscaling with depth based compositing example

```python
from pathlib import Path
from maskilayer import comp_images

comp_images(
    background=Path("conservative_upscale.png"),
    overlay=Path("creative_upscale.png"),
    output=Path("composite_upscale.png"),
    masks=[Path("depth_mask1.png"), Path("depth_mask2.png")],
    invert_masks=[Path("inverted_depth_mask3.png")],
    normalize_level=2,
    verbose=True
)
```

## 4. Mask handling

- If you supply multiple masks, `maskilayer` averages them for the final composition.
- `maskilayer` always converts the mask images to grayscale.
- If you supply a normalization level, `maskilayer` will adjust the mask contrast:
  - Level 0 uses masks as-is
  - Level 1 stretches grayscale range to full black-white spectrum
  - Levels 2-5 progressively increase contrast for more abrupt transitions between bright and dark

### 4.1. Tips for handling multiple mask paths

#### 4.1.1. In CLI

- Use semicolons (`;`) to separate multiple mask paths (you also may use commas):
  ```bash
  maskilayer --masks mask1.png;mask2.png;mask3.png
  ```
- For inverted masks, use the `--imasks` flag:
  ```bash
  maskilayer --imasks inverted_mask1.png;inverted_mask2.png
  ```
- You can use both positive and negative masks in the same command:
  ```bash
  maskilayer --masks positive_mask.png --imasks negative_mask.png
  ```

#### 4.1.2. In Python

- Use lists to provide multiple mask paths:
  ```python
  masks=[Path("mask1.png"), Path("mask2.png"), Path("mask3.png")]
  ```
- For inverted masks, use the `invert_masks` parameter:
  ```python
  invert_masks=[Path("inverted_mask1.png"), Path("inverted_mask2.png")]
  ```
- You can use both positive and negative masks in the same function call:
  ```python
  comp_images(
      ...,
      masks=[Path("positive_mask.png")],
      invert_masks=[Path("negative_mask.png")]
  )
  ```

## 5. License

- Idea & Copyright (c) 2024 Adam Twardoch
- [Python code](https://github.com/twardoch/maskilayer) written with assistance from OpenAI GPT-4o and Anthropic Claude 3
- Licensed under the [Apache License 2.0](./LICENSE.txt)
