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* Create haralick_descriptors * Working on creating Unit Testing for Haralick Descriptors module * Type hinting for Haralick descriptors * Fixed docstrings, unit testing and formatting choices * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fixed line size formatting * Added final doctests * Changed main callable * Updated requirements.txt * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update computer_vision/haralick_descriptors.py No! What if the Kernel is empty? Example: >>> kernel = np.zeros((1)) >>> kernel or np.ones((3, 3)) array([[1., 1., 1.], [1., 1., 1.], [1., 1., 1.]]) Co-authored-by: Christian Clauss <cclauss@me.com> * Undone wrong commit * Update haralick_descriptors.py * Apply suggestions from code review * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix ruff errors in haralick_descriptors.py * Add type hint to haralick_descriptors.py to fix ruff error * Update haralick_descriptors.py * Update haralick_descriptors.py * Update haralick_descriptors.py * Update haralick_descriptors.py * Try to fix mypy errors in haralick_descriptors.py * Update haralick_descriptors.py * Fix type hint in haralick_descriptors.py --------- Co-authored-by: Rafael Zimmer <rzimmerdev@monmo.localdomain> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Christian Clauss <cclauss@me.com> Co-authored-by: Tianyi Zheng <tianyizheng02@gmail.com> |
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| .. | ||
| __init__.py | ||
| cnn_classification.py | ||
| flip_augmentation.py | ||
| haralick_descriptors.py | ||
| harris_corner.py | ||
| horn_schunck.py | ||
| mean_threshold.py | ||
| mosaic_augmentation.py | ||
| pooling_functions.py | ||
| README.md | ||
Computer Vision
Computer vision is a field of computer science that works on enabling computers to see, identify and process images in the same way that human does, and provide appropriate output.
It is like imparting human intelligence and instincts to a computer. Image processing and computer vision are a little different from each other. Image processing means applying some algorithms for transforming image from one form to the other like smoothing, contrasting, stretching, etc.
While computer vision comes from modelling image processing using the techniques of machine learning, computer vision applies machine learning to recognize patterns for interpretation of images (much like the process of visual reasoning of human vision).