This advanced image processing framework provides sophisticated analysis of electrode degradation patterns through computer vision techniques. The solution implements a tailored algorithmic pipeline that identifies, segments, and quantifies multiple degradation parameters with high precision, enabling detailed morphological assessment without requiring deep learning components.
The framework leverages classical computer vision algorithms augmented by strategic parameter optimization to achieve high-fidelity segmentation of electrode surface features. Rather than implementing computationally intensive neural networks, the solution utilizes a deterministic approach with binary mask processing, contour detection, and region-of-interest (ROI) analysis—delivering reliable, reproducible results while maintaining computational efficiency.
The system implements a sophisticated parameter differentiation scheme through chromatic encoding, assigning specific color channels to distinct electrode states:
- Covered regions (Green)
- Uncovered regions (Blue)
- Delamination zones (Red)
- Substrate areas (Yellow)
- Overall electrode boundaries (Purple)
This encoding enables intuitive visual interpretation while maintaining algorithmic separation for quantitative analysis.
The framework's core functionality derives from its advanced spatial segmentation capabilities, processing mask files to extract morphologically significant regions. The contour detection algorithm implements connectivity-based boundary tracing to identify degradation interfaces with sub-pixel precision.
The solution integrates with ImageJ-compatible ROI definitions, transforming vector data into numerically labeled boundary representations. Sophisticated centroid calculation through spatial moment analysis enables precise region identification and measurement.
A key innovation is the implementation of adaptive calibration, where pixel measurements are systematically converted to physical units (micrometers) through reference measurements. This approach accounts for image-specific variations, ensuring consistent dimensional analysis across datasets.
The solution incorporates a robust file discovery and integration system that handles diverse input formats, including:
- Binary mask images for region identification
- ROI vector sets for precise boundary definition
- CSV measurement data for calibration reference
- Original images for visualization context
Perimetric quantification is achieved through arcLength algorithms applied to vectorized contours. These measurements undergo calibration transformation through pixel-to-micrometer conversion factors derived from reference data, ensuring metrological traceability.
The framework generates comprehensive visual outputs with overlaid contours, numeric region labeling, and detailed measurement legends. This visualization approach maintains scientific accuracy while enabling intuitive interpretation of complex degradation patterns.
This electrode analysis framework represents a sophisticated image processing solution that achieves high-precision morphological analysis through optimized computer vision techniques. The implementation demonstrates how targeted algorithmic approaches can deliver robust scientific measurement without requiring deep learning components, providing an efficient and reliable tool for electrode degradation assessment.
- install uv, it's for managing dependencies
- afterwards run
- uv venv && uv sync
- run the programs as needed
- results are stored in the directory Results and one csv with all image parameters measurements are inside it
- unncessary zips can be deleted but make sure to have some zip, organize.py works on them
- Image in their respective folder
- Run image-sort.py on the same level where all the classes are defined, it will organize all the images related data in their respective named folder
- organize.py
- keep on the same level as the images zip
- enter the zip names in the code
- run it and it will provide clean structure for main code that draws contour
- conflict-ren
- conflicting naming scheme resolve
- put in the folder and provide what needs to adjusted
- conflict-ren-without-overlay
- conflicting naming scheme resolve ditching the overlay word from the names.
- put in the folder and provide what needs to adjusted
- main.py
- all the actions is here to draw contour and label with roiset and find the measurements
- currently since the original images are missing it's working on Delamination overlay image on each Stack_Frontview since it has the least overlay
├── Stack_Frontview01_Covered.csv
├── Stack_Frontview01_Covered.jpg
├── Stack_Frontview01_Covered_Mask.jpg
├── Stack_Frontview01_Covered_RoiSet.zip
├── Stack_Frontview01_Covered.tif
├── Stack_Frontview01_Delamination.csv
├── Stack_Frontview01_Delamination.jpg
├── Stack_Frontview01_Delamination_Mask.jpg
├── Stack_Frontview01_Delamination_RoiSet.zip
├── Stack_Frontview01_Original.jpg
├── Stack_Frontview01_Substrate.csv
├── Stack_Frontview01_Substrate.jpg
├── Stack_Frontview01_Substrate_Mask.jpg
├── Stack_Frontview01_Substrate_RoiSet.zip
├── Stack_Frontview01_Uncovered.csv
├── Stack_Frontview01_Uncovered.jpg
├── Stack_Frontview01_Uncovered_Mask.jpg
└── Stack_Frontview01_Uncovered_RoiSet.zip
- make sure the directory contain the all parameters image labeled correctly
- make sure the main.py is runnning on the same level as the images directory
├── main.py
├── Stack_Frontview01
├── Stack_Frontview02
├── Stack_Frontview03
├── Stack_Frontview04
├── Stack_Frontview05
├── Stack_Frontview06
├── Stack_Frontview07
├── Stack_Frontview08
├── Stack_Frontview09
├── Stack_Frontview10
├── Stack_Frontview11
└── Stack_Frontview12
- make sure that all dependencies are installed, if you are using uv then run uv sync, if it's any other tool for dependency management then look online for it's dependencies installation methos