Deep Learning, NLP, NMT, AI, ML
About The Project What it does: Download the source video from a user inputted Tiktok URL. 📙 Add audio to the Tiktok video from a folder of your audio files. 🎵 Crop the video to fit the aspect ratio of an instagram post (square by default but you can change this!) 📐 Output the edited video to a folder of your choice. 📩 Who should use this? Of course anyone is free to download and use it, but this program […]
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A graphical user interface calendar with python In this project I used tkinter module If you dont have tkinter module you can install it by this … > Write this code in your terminal! Also I used calendar module , which is an built in module for python. With this program, you can know the date of the days of each year that you want. GitHub View Github
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Description HOG (Histograms of Oriented Gradients) Algorithm is an algorithm aiming to realize object segmentation (edge detection) on images before CNN models are widely used. Reference Navneet Dalal and Bill Triggs, Histograms of Oriented Gradients, CVPR 2005 Getting Started Clone this repository git clone https://github.com/LeoTheBestCoder/HOG_implementation.git Install related libraries pip install opencv-python pip install numpy pip install matplotlib Put your input image under same directory and modify line 10 in image.py (include filename extension) Run the script Demostration Input Image
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HTTP graph database built in Python 3. Reference Format References are strings in the format:{[email protected]} Authentication Currently, there is no authentication system. I’m working to implement one. Query depth Currently, there is no query depth. I’m working to implement it. HTTP API Query node by reference GET /{[email protected]}Response 200 with JSON body Query nodes by group GET /@GROUPResponse 200 with JSON body Insert node with reference PUT /{[email protected]}Request must have JSON bodyResponse 201 with Location header Insert node
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YouTube Playlist: https://www.youtube.com/playlist?list=PL9nZZVP3OGOAx2S75YdBkrIbVpiSL5oc5 Installation pip install -r requirements.txt Python Packages Discord.py: https://discordpy.readthedocs.io/en/latest/intro.html Pillow: https://pillow.readthedocs.io/en/stable/installation.html Python-dotenv: https://pypi.org/project/python-dotenv/ Requests: https://pypi.org/project/requests/ Dateutil: https://pypi.org/project/python-dateutil/ Tabulate: https://pypi.org/project/tabulate/ Matplotlib: https://matplotlib.org/stable/users/installing.html Pandas: https://pandas.pydata.org/docs/getting_started/index.html Black: https://black.readthedocs.io/en/stable/installation_and_usage.html Documentations Discord: https://discord.com/developers/docs/intro Discord.py: https://discordpy.readthedocs.io/en/latest/index.html Pillow: https://pillow.readthedocs.io/en/stable/index.html GitHub View Github
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See config/ for configuration information. This workflow takes as input a set of protein sequences. It clusters them and thenfunctionally annotates the clusters’ representatives using Eggnog DB, and picks those without KO annotationstocontinue the process. These “hypothetical proteins” get aligned by hhblits against Uniclust30 and then against PDB70. Testing Decompress selected_seqs_by_size.tar.gz and use that path in the config file (already set). To see the commands being executed (-p) without an actual execution of the workflow, use -n.-r prints the “reason” […]
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Library for Russian imprecise rhymes generation. Quick Start Generate rhymes by any given rhyme scheme (aabb, abab, aaccbb, etc …): from tom_rhymer.rhymer import Rhymer rhymer = Rhymer.load() for rhyme in rhymer.get_rhymes_by_scheme(‘abab’): print(str(rhyme)) # предоставленными # отличите # доставлена # ограничительных Generate rhymes word by word:
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MLP-Mixer based Multi-modal image-text retrieval Image: Original image is cropped with 16 x 16 patch size without overlap. Then, it is reshaped to (batch, (hxw), (patch x patch x channel)). Text: Also, original text is tokenized and embedded with BERT-based approach (BERT-base-uncased). Data processing: When we train our model, we randomly samples(50 %) reports to make the matched- and un-matched image-text set.Basically, matched and un-matched set is classified with label information using chexpert labeler, we consider unmatched set when randomly […]
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