Shaun Berryman sberryman

Compiling DeepMatching's GPU version on Ubuntu 16.10

DeepMatching is an algorithm that finds corresponding points in two images. Its GPU implementation was written for Fedora 21, which makes things a bit more difficult if you want to run it on an Ubuntu system. This document contains step-by-step instructions on how to get DeepMatching running on Ubuntu 16.10. I only tested it with Ubuntu 16.10, just let me know if it works with previous versions too.

To compile the GPU version you first need to compile the Caffe version that is included that comes with the DeepMatching files. Newer versions of Caffe won't work because Caffe changed the structure of its header files.

Compiling Caffe

Before compiling Caffe we need to make sure all its dependencies are installed. From the installation guide for Ubuntu 16.04/15.10:

sudo apt-get install build-essential cmake git pkg-config

name	caffemodel	caffemodel_url	license	sha1	caffe_commit
Vanilla CNN Model	vanillaCNN.caffemodel	https://github.com/ishay2b/VanillaCNN/blob/master/ZOO/vanillaCNN.caffemodel?raw=true	unrestricted	b5e34ce75d078025e07452cb47e65d198fe27912	9c9f94e18a8909580a6b94c44dbb1e46f0ee8eb8

Implementation of the Vanilla CNN described in the paper: Yue Wu and Tal Hassner, "Facial Landmark Detection with Tweaked Convolutional Neural Networks", arXiv preprint arXiv:1511.04031, 12 Nov. 2015. See project page for more information about this project.

Gil Levi and Tal Hassner, Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns

Convolutional neural networks for emotion classification from facial images as described in the following work:

Gil Levi and Tal Hassner, Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns, Proc. ACM International Conference on Multimodal Interaction (ICMI), Seattle, Nov. 2015

Project page: http://www.openu.ac.il/home/hassner/projects/cnn_emotions/

If you find our models useful, please add suitable reference to our paper in your work.

Age and Gender Classification using Convolutional Neural Networks

name: Age Classification CNN

caffemodel: age_net.caffemodel

caffemodel_url: https://github.com/GilLevi/AgeGenderDeepLearning/raw/master/models/age_net.caffemodel

Demo of multipart form/file uploading with hapi.js.

Usage

npm install
npm run setup
npm run server

Then ...

	"""
	stable diffusion dreaming
	creates hypnotic moving videos by smoothly walking randomly through the sample space

	example way to run this script:

	$ python stablediffusionwalk.py --prompt "blueberry spaghetti" --name blueberry

	to stitch together the images, e.g.:
	$ ffmpeg -r 10 -f image2 -s 512x512 -i blueberry/frame%06d.jpg -vcodec libx264 -crf 10 -pix_fmt yuv420p blueberry.mp4

	import subprocess as sp
	import cv2
	import numpy as np
	from PIL import Image
	import tensorflow as tf

	ffmpeg_cmd_1 = ["./ffmpeg", "-y",
	"-hwaccel", "nvdec",
	"-c:v", "h264_cuvid",
	"-vsync", "0",

	import torch
	import torchvision

	class VGGPerceptualLoss(torch.nn.Module):
	def __init__(self, resize=True):
	super(VGGPerceptualLoss, self).__init__()
	blocks = []
	blocks.append(torchvision.models.vgg16(pretrained=True).features[:4].eval())
	blocks.append(torchvision.models.vgg16(pretrained=True).features[4:9].eval())
	blocks.append(torchvision.models.vgg16(pretrained=True).features[9:16].eval())

	//
	// debounce-throttle.swift
	//
	// Created by Simon Ljungberg on 19/12/16.
	// License: MIT
	//

	import Foundation

	extension TimeInterval {

	// app/server/synchers/base.js
	define(['lazoSyncher'], function (LazoSyncher) {

	'use strict';

	// set up mongo connection here; not sure of best practices for establishing connections
	// https://www.npmjs.org/package/mongodb

	return LazoSyncer.extend({