https://github.com/agfianf/color-correction
help to do color correction on images based on color checker card classic 24 patch.
https://github.com/agfianf/color-correction
color-calibration color-correction computer-vision image-manipulation image-processing
Last synced: about 1 year ago
JSON representation
help to do color correction on images based on color checker card classic 24 patch.
- Host: GitHub
- URL: https://github.com/agfianf/color-correction
- Owner: agfianf
- Created: 2025-01-22T06:41:43.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-26T00:57:45.000Z (about 1 year ago)
- Last Synced: 2025-04-07T12:16:19.588Z (about 1 year ago)
- Topics: color-calibration, color-correction, computer-vision, image-manipulation, image-processing
- Language: Python
- Homepage: https://agfianf.github.io/color-correction/
- Size: 4.34 MB
- Stars: 6
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
Awesome Lists containing this project
README
# Color Correction
[](https://badge.fury.io/py/color-correction)
[](https://pypistats.org/packages/color-correction)
[](https://badge.fury.io/py/color-correction)
[](https://colab.research.google.com/drive/146SXHHihMmGLzaTSwdBXncVr3SU_I-Dm?usp=sharing)
[](https://agfianf.github.io/color-correction/)
> _Package formerly published as [`color-correction-asdfghjkl`](https://pypi.org/project/color-correction-asdfghjkl/) on PyPI. The name has been simplified for better accessibility and professional recognition._
This package is designed to perform color correction on images using the Color Checker Classic 24 Patch card. It provides a robust solution for ensuring accurate color representation in your images.
## 📦 Installation
```bash
pip install color-correction
```
## 🏋️♀️ How it works
## ⚡ How to use
```python
from color_correction import ColorCorrection
# Step 1: Define the path to the input image
image_path = "asset/images/cc-19.png"
# Step 2: Load the input image
input_image = cv2.imread(image_path)
# Step 3: Initialize the color correction model with specified parameters
color_corrector = ColorCorrection(
detection_model="yolov8",
detection_conf_th=0.25,
correction_model="polynomial", # "least_squares", "affine_reg", "linear_reg"
degree=3, # for polynomial correction model
use_gpu=True,
)
# Step 4: Extract color patches from the input image
# you can set reference patches from another image (image has color checker card)
# or use the default D50
# color_corrector.set_reference_patches(image=None, debug=True)
color_corrector.set_input_patches(image=input_image, debug=True)
color_corrector.fit()
corrected_image = color_corrector.predict(
input_image=input_image,
debug=True,
debug_output_dir="zzz",
)
# Step 5: Evaluate the color correction results
eval_result = color_corrector.calc_color_diff_patches()
print(eval_result)
```
Sample Evaluation Output
```json
{
"initial": {
"min": 2.254003059526461,
"max": 13.461066402633447,
"mean": 8.3072755187654,
"std": 3.123962754767539,
},
"corrected": {
"min": 0.30910031798755183,
"max": 5.422311999126372,
"mean": 1.4965478752947827,
"std": 1.2915738724958112,
},
"delta": {
"min": 1.9449027415389093,
"max": 8.038754403507074,
"mean": 6.810727643470616,
"std": 1.8323888822717276,
},
}
```
Sample Output Debug Image
## 🔎 Reporting
```python
import cv2
from color_correction import ColorCorrectionAnalyzer
# input_image_path = "assets/cc-19.png"
input_image_path = "assets/cc-1.jpg"
report = ColorCorrectionAnalyzer(
list_correction_methods=[
("least_squares", {}),
("linear_reg", {}),
("affine_reg", {}),
("polynomial", {"degree": 2}),
("polynomial", {"degree": 3}),
# ("polynomial", {"degree": 4}),
# ("polynomial", {"degree": 5}),
],
list_detection_methods=[
("yolov8", {"detection_conf_th": 0.25}),
],
)
report.run(
input_image=cv2.imread(input_image_path),
reference_image=None,
output_dir="report-output",
)
```
Sample Report Output
## 📈 Benefits
- **Consistency**: Ensure uniform color correction across multiple images.
- **Accuracy**: Leverage the color correction matrix for precise color adjustments.
- **Flexibility**: Adaptable for various image sets with different color profiles.
## 🤸 TODO
- [ ] Add Loggers
- [x] Add detection MCC:CCheckerDetector from opencv
- [ ] Add Segmentation Color Checker using YOLOv11 ONNX
- [ ] Improve validation preprocessing (e.g., auto-match-orientation CC)
- [ ] Add more analysis and evaluation metrics (Still thinking...)
## 📚 References
- [Color Checker Classic 24 Patch Card](https://www.xrite.com/categories/calibration-profiling/colorchecker-classic)
- [Color Correction Tool ML](https://github.com/collinswakholi/ML_ColorCorrection_tool/tree/Pip_package)
- [Colour Science Python](https://www.colour-science.org/colour-checker-detection/)
- [Fast and Robust Multiple ColorChecker Detection ()](https://github.com/pedrodiamel/colorchecker-detection)
- [Automatic color correction with OpenCV and Python (PyImageSearch)](https://pyimagesearch.com/2021/02/15/automatic-color-correction-with-opencv-and-python/)
- [ONNX-YOLOv8-Object-Detection](https://github.com/ibaiGorordo/ONNX-YOLOv8-Object-Detection)
- [yolov8-triton](https://github.com/omarabid59/yolov8-triton/tree/main)
- [Streamlined Data Science Development: Organizing, Developing and Documenting Your Code](https://medium.com/henkel-data-and-analytics/streamlined-data-science-development-organizing-developing-and-documenting-your-code-bfd69e3ef4fb)