<div align="center">
<!-- image logo -->
<img src="assets/logo-v1.png" alt="Color Correction Logo" width="150"/>
# Color Correction
<br>
[](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/)
</div>
> _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)
```
<details>
<summary>Sample Evaluation Output</summary>
```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,
},
}
```
</details>
<details>
<summary>Sample Output Debug Image</summary>
</details>
## 🔎 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",
)
```
<details>
<summary>Sample Report Output</summary>
</details>
## 📈 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
- [ ] 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...)
<!-- write reference -->
## 📚 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)
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"description": "\n<div align=\"center\">\n<!-- image logo -->\n<img src=\"assets/logo-v1.png\" alt=\"Color Correction Logo\" width=\"150\"/>\n\n# Color Correction\n\n<br>\n\n[](https://badge.fury.io/py/color-correction)\n[](https://pypistats.org/packages/color-correction)\n[](https://badge.fury.io/py/color-correction)\n[](https://colab.research.google.com/drive/146SXHHihMmGLzaTSwdBXncVr3SU_I-Dm?usp=sharing)\n[](https://agfianf.github.io/color-correction/)\n\n</div>\n\n> _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._\n\nThis 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.\n\n## \ud83d\udce6 Installation\n\n```bash\npip install color-correction\n```\n\n## \ud83c\udfcb\ufe0f\u200d\u2640\ufe0f How it works\n\n\n\n## \u26a1 How to use\n\n```python\nfrom color_correction import ColorCorrection\n\n# Step 1: Define the path to the input image\nimage_path = \"asset/images/cc-19.png\"\n\n# Step 2: Load the input image\ninput_image = cv2.imread(image_path)\n\n# Step 3: Initialize the color correction model with specified parameters\ncolor_corrector = ColorCorrection(\n detection_model=\"yolov8\",\n detection_conf_th=0.25,\n correction_model=\"polynomial\", # \"least_squares\", \"affine_reg\", \"linear_reg\"\n degree=3, # for polynomial correction model\n use_gpu=True,\n)\n\n# Step 4: Extract color patches from the input image\n# you can set reference patches from another image (image has color checker card)\n# or use the default D50\n# color_corrector.set_reference_patches(image=None, debug=True)\ncolor_corrector.set_input_patches(image=input_image, debug=True)\ncolor_corrector.fit()\ncorrected_image = color_corrector.predict(\n input_image=input_image,\n debug=True,\n debug_output_dir=\"zzz\",\n)\n\n# Step 5: Evaluate the color correction results\neval_result = color_corrector.calc_color_diff_patches()\nprint(eval_result)\n```\n\n<details>\n<summary>Sample Evaluation Output</summary>\n\n```json\n{\n \"initial\": {\n \"min\": 2.254003059526461,\n \"max\": 13.461066402633447,\n \"mean\": 8.3072755187654,\n \"std\": 3.123962754767539,\n },\n \"corrected\": {\n \"min\": 0.30910031798755183,\n \"max\": 5.422311999126372,\n \"mean\": 1.4965478752947827,\n \"std\": 1.2915738724958112,\n },\n \"delta\": {\n \"min\": 1.9449027415389093,\n \"max\": 8.038754403507074,\n \"mean\": 6.810727643470616,\n \"std\": 1.8323888822717276,\n },\n}\n```\n</details>\n\n<details>\n<summary>Sample Output Debug Image</summary>\n\n\n\n</details>\n\n## \ud83d\udd0e Reporting\n```python\nimport cv2\n\nfrom color_correction import ColorCorrectionAnalyzer\n\n# input_image_path = \"assets/cc-19.png\"\ninput_image_path = \"assets/cc-1.jpg\"\n\nreport = ColorCorrectionAnalyzer(\n list_correction_methods=[\n (\"least_squares\", {}),\n (\"linear_reg\", {}),\n (\"affine_reg\", {}),\n (\"polynomial\", {\"degree\": 2}),\n (\"polynomial\", {\"degree\": 3}),\n # (\"polynomial\", {\"degree\": 4}),\n # (\"polynomial\", {\"degree\": 5}),\n ],\n list_detection_methods=[\n (\"yolov8\", {\"detection_conf_th\": 0.25}),\n ],\n)\nreport.run(\n input_image=cv2.imread(input_image_path),\n reference_image=None,\n output_dir=\"report-output\",\n)\n```\n<details>\n<summary>Sample Report Output</summary>\n\n\n</details>\n\n## \ud83d\udcc8 Benefits\n- **Consistency**: Ensure uniform color correction across multiple images.\n- **Accuracy**: Leverage the color correction matrix for precise color adjustments.\n- **Flexibility**: Adaptable for various image sets with different color profiles.\n\n\n## \ud83e\udd38 TODO\n- [ ] Add Loggers\n- [ ] Add detection MCC:CCheckerDetector from opencv\n- [ ] Add Segmentation Color Checker using YOLOv11 ONNX\n- [ ] Improve validation preprocessing (e.g., auto-match-orientation CC)\n- [ ] Add more analysis and evaluation metrics (Still thinking...)\n\n<!-- write reference -->\n\n## \ud83d\udcda References\n- [Color Checker Classic 24 Patch Card](https://www.xrite.com/categories/calibration-profiling/colorchecker-classic)\n- [Color Correction Tool ML](https://github.com/collinswakholi/ML_ColorCorrection_tool/tree/Pip_package)\n- [Colour Science Python](https://www.colour-science.org/colour-checker-detection/)\n- [Fast and Robust Multiple ColorChecker Detection ()](https://github.com/pedrodiamel/colorchecker-detection)\n- [Automatic color correction with OpenCV and Python (PyImageSearch)](https://pyimagesearch.com/2021/02/15/automatic-color-correction-with-opencv-and-python/)\n- [ONNX-YOLOv8-Object-Detection](https://github.com/ibaiGorordo/ONNX-YOLOv8-Object-Detection)\n- [yolov8-triton](https://github.com/omarabid59/yolov8-triton/tree/main)\n- [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)\n",
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