Color Correction

Package formerly published as 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

pip install color-correction

🏋️‍♀️ How it works

⚡ How to use

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)

{
    "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,
    },
}

🔎 Reporting

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",
)

📈 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...)

📚 References

• Color Checker Classic 24 Patch Card
• Color Correction Tool ML
• Colour Science Python
• Fast and Robust Multiple ColorChecker Detection ()
• Automatic color correction with OpenCV and Python (PyImageSearch)
• ONNX-YOLOv8-Object-Detection
• yolov8-triton
• Streamlined Data Science Development: Organizing, Developing and Documenting Your Code