🖼️ Image Processing CLI Tool

Overview

This is a command-line Python tool for performing a wide range of image processing operations. The tool supports:

• Elementary image transformations (e.g., brightness, contrast, negative)
• Geometric transformations (e.g., flip, scale)
• Noise filtering and smoothing
• Image comparison and similarity metrics
• Histogram and power transformations
• Image statistics and characteristics
• Convolution-based filters
• Morphological transformations
• Fourier transform and frequency domain filtering

All functionality is exposed via CLI arguments, making it easy to chain into scripts or integrate with other tools.

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🛠️ How to Use

All commands follow the format:

python imageprocessing.py [OPERATION FLAGS] --input=INPUT_PATH --output=OUTPUT_PATH [OPTIONS]

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🧪 Example Commands

🧱 Elementary Operations

Apply basic image enhancements and transformations:

python imageprocessing.py --negative --input=data/lenac.bmp --output=data/negative-lenac.bmp
python imageprocessing.py --brightness --strength=100 --input=data/lenac.bmp --output=data/bright-lenac.bmp
python imageprocessing.py --contrast --strength=3 --input=data/lenac.bmp --output=data/contrast-lenac.bmp

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🧭 Geometric Operations

Manipulate image orientation and scale:

python imageprocessing.py --hflip --input=data/lenac.bmp --output=data/hflip-lenac.bmp
python imageprocessing.py --vflip --input=data/lenac.bmp --output=data/vflip-lenac.bmp
python imageprocessing.py --dflip --input=data/lenac.bmp --output=data/dflip-lenac.bmp
python imageprocessing.py --enlarge --proportion=1.5 --input=data/interpolation_test.bmp --output=data/large-lenac.bmp
python imageprocessing.py --shrink --proportion=0.6 --input=data/lenac.bmp --output=data/shrink-lenac.bmp

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🔇 Noise Filtering

Reduce noise using different filtering techniques:

python imageprocessing.py --median --input=data/lenac_normal3.bmp --output=data/lenac_normal3-median.bmp
python imageprocessing.py --gmean --input=data/lenac_normal3.bmp --output=data/lenac_normal3-gmean.bmp

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🧮 Image Similarity Metrics

Compare images to calculate distortion or similarity:

python imageprocessing.py --mse --input=data/lenac_normal3-median.bmp --reference=data/lenac.bmp
python imageprocessing.py --pmse --input=data/lenac_normal3-median.bmp --reference=data/lenac.bmp
python imageprocessing.py --snr --input=data/lenac_normal3-median.bmp --reference=data/lenac.bmp
python imageprocessing.py --psnr --input=data/lenac_normal3-median.bmp --reference=data/lenac.bmp
python imageprocessing.py --md --input=data/lenac_normal3-median.bmp --reference=data/lenac.bmp

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📊 Histogram Analysis

Visualize pixel intensity distributions:

python imageprocessing.py --histogram --input=data/lena.bmp --output=data/hist/h-lena.bmp
python imageprocessing.py --hpower --input=data/lena.bmp --output=data/hist/lena-hpower.bmp
python imageprocessing.py --histogram --input=data/hist/lena-hpower.bmp --output=data/hist/h-lena-hpower.bmp

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📈 Statistical Characteristics

Extract image-level statistics:

python imageprocessing.py --cmean --input=data/lena.bmp
python imageprocessing.py --cvariance --input=data/lena.bmp
python imageprocessing.py --cstdev --input=data/lena.bmp
python imageprocessing.py --cvarcoi --input=data/lena.bmp
python imageprocessing.py --casyco --input=data/lena.bmp
python imageprocessing.py --cflatco --input=data/lena.bmp
python imageprocessing.py --cvarcoii --input=data/lena.bmp
python imageprocessing.py --centropy --input=data/lena.bmp

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🧾 Convolution Filters

Apply edge enhancement and other spatial domain filters:

python imageprocessing.py --sedgesharp --input=data/lena.bmp --output=data/conv/lena-sharp.bmp --kernel=2
python imageprocessing.py --orosenfeld --input=data/lena.bmp --output=data/conv/lena-rosenfeld.bmp --P=2

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🧬 Morphological Operations

Process binary or grayscale images for shape analysis:

python imageprocessing.py --dilation --input=data/lenabw.bmp --output=data/morphological/dilation.bmp
python imageprocessing.py --erosion --input=data/lenabw.bmp --output=data/morphological/erosion.bmp
python imageprocessing.py --opening --input=data/morphological/test.bmp --output=data/morphological/opening.bmp
python imageprocessing.py --closing --input=data/morphological/test.bmp --output=data/morphological/closing.bmp
python imageprocessing.py --hmt --input=data/morphological/test.bmp --output=data/morphological/hmt.bmp
python imageprocessing.py --m3 --input=data/boatbw.bmp --output=data/morphological/m3.bmp --p=280,500
python imageprocessing.py --regions --input=data/camera.bmp --output=data/morphological/regions.bmp --sthreshold=10 --seeds=100

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🔁 Frequency Domain Operations (DFT/FFT)

Perform transformations and frequency-based filtering:

python imageprocessing.py --dft-test --input=data/fourier/test-dft.bmp --output=data/fourier/dft.bmp
python imageprocessing.py --fft-test --input=data/fourier/test-dft.bmp --output=data/fourier/fft.bmp
python imageprocessing.py --fft --input=data/fourier/test-dft.bmp --output=data/fourier/fft.bmp

🎚️ Frequency Filters

Apply filters based on frequency bands:

python imageprocessing.py --low-pass --input=data/lena.bmp --output=data/fourier/lena.bmp --band=16
python imageprocessing.py --high-pass --input=data/lena.bmp --output=data/fourier/lena.bmp --band=16
python imageprocessing.py --band-pass --input=data/lena.bmp --output=data/fourier/lena.bmp --band-min=8 --band-max=128
python imageprocessing.py --band-cut --input=data/lena.bmp --output=data/fourier/lena.bmp --band-min=32 --band-max=128

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🩺 Mask and Phase Filtering

Use masks and phase manipulation:

python imageprocessing.py --filter --input=data/fourier/f5/F5test1.png --mask=data/fourier/f5/F5mask1.png --output=data/fourier/f5/F5test1-filtered.bmp
python imageprocessing.py --phase-filter --input=data/lena.bmp --output=data/fourier/lena.bmp --k=100 --l=256

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📝 Notes

• This tool is designed to work primarily with .bmp and .png images.
• Output paths must include filenames with appropriate extensions (e.g., .bmp, .png).
• All options are case-sensitive.

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✅ Requirements

• Python 3.x
• Libraries: Pillow, NumPy, and others depending on your features

You can install dependencies with:

pip install -r requirements.txt

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🧾 License

This project is licensed under the MIT License.