Self-Distilled Internet Photos (SDIP) Dataset

Self-Distilled Internet Photos (SDIP) is a multi-domain image dataset. The dataset consists of Self-Distilled Flickr (SD-Flickr) and Self-Distilled LSUN (SD-LSUN) that were crawled from Flickr and LSUN dataset, respectively, and then curated using the method described in our Self-Distilled StyleGAN paper: Self-Distilled StyleGAN: Towards Generation from Internet PhotosRon Mokady, Michal Yarom, Omer Tov, Oran Lang, Daniel Cohen-Or, Tali Dekel, Michal Irani, Inbar Mosserihttps://arxiv.org/abs/2202.12211 Overview StyleGAN’s fascinating generative and editing abilities are limited to structurally aligned and well-curated datasets. It does […]

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Hough transform – A feature extraction method for detecting simple shapes such as circles, lines, etc in an image

Hough transform is a feature extraction method for detecting simple shapes such as circles, lines, etc in an image. Hough Transform and Hough Line Transform is implemented in OpenCV with two methods; the Standard Hough Transform and the Probabilistic Hough Line Transform. The “simple” characteristic is derived by the shape representation in terms of parameters. A “simple” shape will be only represented by a few parameters, for example a line can be represented by its slope and intercept, or a […]

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Crop your image to different resolutions without missing the subject

Set a focal point and “artdirector” crop your image to different resolutions without missing the subject. Ideal to create images for mixed or responsive media. Example Original Image (by me) Variants artdirector –focus-x 260 –focus-y 440 –height 600 –width 600 –zoom 0.0 –edge 3.0 example.jpeg test-1.jpeg artdirector –focus-x 260 –focus-y 440 –height 600 –width 300 –zoom 0.2 –edge 3.0 example.jpeg test-2.jpeg artdirector –focus-x 260 –focus-y 440 –height 600 –width 600 –zoom    

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Utilizing numpy to hold the image data, and to allow us to easily modify the image data

The goal of this project is to do some very simple image processing tasks, utilizing numpy to hold the image data, and to allow us to easily modify the image data. I utilize matplotlib and numpy. Part 1: Returns a new pattern that contains only the red, green or blue channel of the image based on 0 = red, 1 = green, 2 = blue. Part 2: Returns permutated image based on perm given. perm is a list of 0,1,2 […]

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Image Compressor with Python – GUI

]Image Compressor with Python – GUI 🙂 WARNING > Dont Copy ! – We Are SEE You WARNING > Any System ! | Exe File WARNING > use : python [BG]Image-Compressor.py www.mrd3f417.ir Step 1 = Select Your Image Step 2 = Select quality Step 3 = Enter new File name And Location Step 4 = And Compresed Image ! Discord : https://discordfa.com/servers/853585867070767135/join And Search The Google > MR.D3F417 MR.D3F417 > Bʟᴀᴄᴋ Gᴜᴀʀᴅ Dicord ALPHA : 𝙈𝙍.𝘿𝟑𝙁𝟒𝟏𝟕#8277 یک نرم افزار جدید […]

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Anonymize people in images and videos using yolov5-crowdhuman

Face Anonymizer Blur faces from image and video files in /input/ folder. Requirements yolov5-crowdhuman download crowdhuman_yolov5m.pt file in package folder.Download model trained on crowd human using yolov5(m) architeture.Download Link: YOLOv5m-crowd-human Useage python run.py –weights crowdhuman_yolov5m.pt –source input/ –headsPlace image and video files in /input/ folder.Result images will be in /output/ and videos with sound will be in /output/sound/ Demo Click image view Imgur video(note, demo is silent but videos in /output/sound contain audio) GitHub View Github    

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Final project in KAIST AI class

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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An image base contains 490 images for learning (400 cars and 90 boats), and another 21 images for testing

Données Une base d’images contient 490 images pour l’apprentissage (400 voitures et 90 bateaux), et encore 21 images pour fait des tests. Prétraitements Chaque image doit passer par la séquence des traitements suivantes. Conversion l’image au niveau de gris Binarisation (Noir et blanc) Redimensionnement (120*80) Conversion la matrice de l’image au vecteur Insertion se vecteur dans une matrice (images) Insertion dans un autre vecteur le nom de l’objet (Y). Le Classifier utilisé J’ai utilisé un classifier SVM de la bibliothèque […]

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