Custom methodes for various data science, computer vision, and machine learning operations in python
JLpy Utils Package
Custom modules/classes/methods for various data science, computer vision, and machine learning operations in python
- 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
Installing & Importing
$ pip install -upgrade JLpy_utils_package
After this, the package can be imported into jupyter notebook or python in general via the comman:
import JLpy_utils_package as JLutils
There are several modules in this package:
JLutils.summary_tables JLutils.plot JLutils.img JLutils.video JLutils.ML_models
JLutils.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.
JLutils.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
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.
decompose_video_to_img() is fairly self explanatory and basically uses cv2 to pull out and save all the frames from a video.
This module contains functions for interacting with kaggle. The simplest function is:
competition is the competition name, such as "home-credit-default-risk"
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