Custom methodes for various data science, computer vision, and machine learning operations in python
Project description
JLpyUtils
Custom modules/classes/methods for various data science, computer vision, and machine learning operations in python
Dependancies
- General libraries distributed with Anaconda (pandas, numpy, sklearn, scipy, matplotlib, etc.)
- image/video analysis:
- cv2 (pip install opencv-python)
- ML_models sub-package dependancies:
- tensorflow or tensorflow-gpu
- dill
Installing & Importing
In CLI:
$ pip install -upgrade JLpyUtils
After this, the package can be imported into jupyter notebook or python in general via the comman:
import JLpyUtils
Modules Overview
There are several modules in this package:
JLpyUtils.summary_tables
JLpyUtils.plot
JLpyUtils.img
JLpyUtils.video
JLpyUtils.ML_models
JLpyUtils.summary_tables
and JLpyUtils.plot
probably aren't that useful for most people, so we won't go into detail on them here, but feel free to check them out if you're curious.
JLpyUtils.img
The JLpyUtils.img
module contains a number of functions related to image analysis, most of which wrap SciKit image functions in some way. The most interesting functions/classes are the JLpyUtils.img.auto_crop....
and JLpyUtils.img.decompose_video_to_img()
.
The auto_crop
class allows you to automatically crop an image using countours via the use_countours
method, which essentially wraps the function skimage.measure.find_contours
function. Alternatively, the use_edges
method provides cropping based on the skimage.feature.canny
function. Generally, I find the use_edges
runs faster and gives more intuitive autocropping results.
The decompose_video_to_img()
is fairly self explanatory and basically uses cv2 to pull out and save all the frames from a video.
JLpyUtils.video
...
JLpyUtils.kaggle
This module contains functions for interacting with kaggle. The simplest function is:
JLpyUtils.kaggle.competition_download_files(competition)
where competition
is the competition name, such as "home-credit-default-risk"
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