Loading repository data…
ArbelHaiman / repository
An implementation of blob detector using LoG filter with python and opencv.
A transparent discovery signal based on current public GitHub metadata.
This score does not audit code, security, maintainers, documentation quality, or suitability. Verify the repository and its current documentation before adoption.
An implementation of blob detector using LoG filter with python and opencv. The code is enabling the user to choose the following parameters:
dark, bright, or both- paremeter 'type'.
parameter 'levels'.
paraemter 'sigma'.
parameter 'k'.
The last 3 parameters determine the size of blobs that the agorithm will detect.
parameter 'threshold'.
parameter 'overlap_thresh'.
In the main part at the beginning, the user can decide the all the parameter values, except for the type. In the bottom of the file, there is the call for the main function. at this call, the user can choose the type of the blobs.
I used common images used in computer vision courses. The names can be, and should be changed, according to the images you want to apply the algorithm on.
Selected from shared topics, language and repository description—not editorial ratings.
ShangtongZhang /
Python Implementation of Reinforcement Learning: An Introduction
92/100 healthharvardnlp /
An annotated implementation of the Transformer paper.
93/100 healthbrython-dev /
Brython (Browser Python) is an implementation of Python 3 running in the browser
84/100 healthpython-jsonschema /
An implementation of the JSON Schema specification for Python
90/100 healthpy-why /
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
76/100 healthjaymody /
An unnecessarily tiny implementation of GPT-2 in NumPy.
90/100 health