color-correction-asdfghjkl 0.0.1b1

help to do color correction on images based on color checker card classic 24 patch.

🎨 Color Correction

Note: The "asdfghjkl" is just a placeholder due to some naming difficulties.

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-asdfghjkl

🏋️‍♀️ How it works

⚡ How to use

# 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)

• Output evaluation 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,
      },
  }
  

• Sample output debug image (polynomial degree=2):

📈 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