Author: Deep Learner

PDF

Uses PyPDF3 for reading and writing PDF files written in python

pystitcher pystitcher stitches your PDF files together, generating nice customizable bookmarks for you using a declarative input in the form of a markdown file. It is written in pure python and uses PyPDF3 for reading and writing PDF files. pystitcher is a command line tool, with very few cli options: usage: pystitcher [-h] [–version] [-v] [–cleanup | –no-cleanup] spine.md output.pdf Stitch PDF files together positional arguments: spine.md Input markdown file output.pdf Output PDF file optional arguments: -h, –help show this […]

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Machine Learning

Polygon Yolov5 with adjustments to enable polygon prediction boxes

Polygon-Yolov5 This repository is based on Ultralytics/yolov5, with adjustments to enable polygon prediction boxes. Section I. Description The codes are based on Ultralytics/yolov5, and several functions are added and modified to enable polygon prediction boxes. The modifications compared with Ultralytics/yolov5 and their brief descriptions are summarized below: data/polygon_ucas.yaml : Exemplar UCAS-AOD dataset to test the effects of polygon boxes data/images/UCAS-AOD : For the inference of polygon-yolov5s-ucas.pt models/common.py :3.1. class Polygon_NMS : Non-Maximum Suppression (NMS) module for Polygon Boxes3.2. class Polygon_AutoShape […]

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Images

A synthesized dataset for Object Shadow Generation with python

Object-Shadow-Generation-Dataset-DESOBA Object Shadow Generation is to deal with the shadow inconsistency between the foreground object and the background in a composite image, that is, generating shadow for the foreground object according to background information, to make the composite image more realistic. Our dataset DESOBA is a synthesized dataset for Object Shadow Generation. We build our dataset on the basis of Shadow-OBject Association dataset SOBA, which collects real-world images in complex scenes and provides annotated masks for object-shadow pairs. Based on […]

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AWS

EKS CDK Quick Start in Python

quickstart-eks-cdk-python This Quick Start is a reference architecture and implementation of how you can use the Cloud Development Kit (CDK) to orchestrate the Elastic Kubernetes Serivce (EKS) to quickly deploy a more complete and “production ready” Kubernetes environment on AWS. What does this Quick Start create for you: An appropriate VPC (/22 CDIR w/1024 IPs by default – though you can edit this in cluster-bootstrap/cdk.json) with public and private subnets across three availability zones. Alternatively, just flip create_new_vpc to False […]

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Transformer

Chasing Sparsity in Vision Transformers: An End-to-End Exploration

SViTE [Preprint] “Chasing Sparsity in Vision Transformers: An End-to-End Exploration” by Tianlong Chen, Yu Cheng, Zhe Gan, Lu Yuan, Lei Zhang, Zhangyang Wang Extensive results on ImageNet with diverse ViT backbones validate the effectiveness of our proposals which obtain significantly reduced computational cost and almost unimpaired generalization. Perhaps most surprisingly, we find that the proposed sparse (co-)training can even improve the ViT accuracy rather than compromising it, making sparsity a tantalizing “free lunch”. For example, our sparsified DeiT-Small at (5%, […]

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Graph

A text-based visual programming language inspired by Unreal Engine blueprint function graphs

Box Box is a text-based visual programming language inspired by Unreal Engine blueprint function graphs. $ cat factorial.box ┌─ƒ(Factorial)───┐ ┌─[Branch]─────┐ ┌─[Set]─┐ │ ►┼─────────────────────┼► True ►┼───────────────────────┼► ►┼─────────┐ ┌─[For Loop]───────────┐ ┌───────┐ │ n ○┼──┐ ┌──┼○ False ►┼──┐ ┌──────────┐ ┌───┼○ │ └─────────┼► Loop body ►┼───────────────────┼► │ └────────────────┘ │ ┌────────┐ │ │ │ │ │ result ○┼──┘ ┌─┼○ │ │ │ ┌──────────┐ ┌────┼○ *= │ ┌────┐ └────┼○ >= ○┼─┘ └──────────────┘ │ └──────────┘ │ └───────┘ ┌────┐ │ │ │ result ○┼─┘ ┌─┼○ │ │ 1 […]

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Assistant

Dahua Camera and Doorbell Home Assistant Integration

Home Assistant Dahua Integration The Dahua Home Assistant integration allows you to integrate your Dahua cameras and doorbells in Home Assistant. It’s also confirmed to work with some Lorex cameras. Supports motion events, alarm events (and others), enabling/disabling motion detection, switches for infrared, illuminator (white light), security lights (red/blue flashers), sirens, doorbell button press events, and more. Also exposes several services to enable/disable motion detection or set the text overlay on the video. NOTE: Using the switch to turn on/off […]

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HTTP

An awesome library that records and replays HTTP interactions for unit tests

Cornell: record & replay mock server When your application integrates with multiple web based services, end-to-end testing is crucial before deploying to production. Mocking is often a tedious task, it becomes even more tiresome when working with multiple APIs from multiple vendors. vcrpy is an awesome library that records and replays HTTP interactions for unit tests. Its output is saved to reusable “cassette” files. By wrapping vcrpy with Flask, Cornell provides a lightweight record and replay server that can be […]

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Data Analysis

Manage your exceptions in Python like a PRO

tryceratops Manage your exceptions in Python like a PRO. Installation and usage Installation pip install tryceratops Usage tryceratops [filename or dir…] You can enable experimental analyzers by running: tryceratops –experimental [filename or dir…] You can ignore specific violations by using: –ignore TCXXX repeatedly: tryceratops –ignore TC201 –ignore TC202 [filename or dir…] You can exclude dirs by using: –exclude dir/path repeatedly: tryceratops –exclude tests –exclude .venv [filename or dir…] Violations All violations and its descriptions can be found in docs. Ignoring […]

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Images

A suite of image generation models that produce images from simple random processes

Learning to See by Looking at Noise In this work, we investigate a suite of image generation models that produce images from simple random processes. These are then used as training data for a visual representation learner with a contrastive loss. We study two types of noise processes, statistical image models and deep generative models under different random initializations. Requirements This version of code has been tested with Python 3.7.7 and pytorch 1.6.0. Other versions of pytorch are likely to […]

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