Automatic colorization using deep neural networks. "Colorful Image Colorization." In ECCV, 2016.

Simple project webpage template. Originally used in Colorful Image Colorization. ECCV, 2016.

A deep learning approach to colorizing images

A Tensorflow implementation of ECCV2016 paper([Colorful Image Colorization](https://arxiv.org/pdf/1603.08511.pdf))

A from-scratch PyTorch implementation of "Colorful Image Colorization" by Zhang et al.

The most colorful echo command .................. Tags: color colors Color Colors colour colours shell Shell bash script hacker cool awesome unix linux mac osx swift rainbow image output print echo terminal Terminal console cli CLI

The pythorch implementation of Colorful Image Colorization. In ECCV, 2016.

This is a keras implementation of paper Colorful Image Colorization.

SHINE_color: MATLAB toolbox to control luminance of colorful images.

Keras复现论文"Colorful Image Colorization"

Colorize black and white (grayscale) image (or video) to colorful color with OpenCV (DNN module)

Reimplementation of caffe_traininglayers.py for paper Colorful Image Colorization.

No description available

Author: Xu Liu Date: 02/18/2019 This is a semi-auto labelling software for those who work on image labelling. Input: Click the "Open" button, open the "image" directory and select the first image to start. After this step, the left canvas will display the original image with 50% transparence colored label on it. And the right canvas will display the labelled image with totally black ground and colorful foreground (one color, one instance). All the images in the "image" directory are the outputs(aka. prediction) of the segmentation deep neural network and are located at the "masks" directory. Most parts of the right image are labelled correctly by the neural network, what we need to do is just to revise it slightly. Instructions: 1. Click your right mouse button on the right image to pick a color (the color is corresponding to the pixel where your cursor locates) that you wish to revise on the left image. If there are instances that have not been labelled, you can click your right mouse button on the circular color palatte to pick a different color. 2. Move your cursor to right image, click at the place where you wish to revise the label, keep press the left mouse button and move it can draw curve lines, which can revise a large part. 3. The silder on the top of the two images can control the pen (or brush) size, when you need to paint (revise) a large part, you can move the slider a bit right to get a larger pen size. On the other hand, just move it left can be helpful for revising small part. 4. Click "Save" button on the right can save the revised image (the right one) as a 3 channels, 8 unsigned bits PNG format file at the "output" directory, which is in the same directory as the "images" directory. 5. Click "Next" button then the two canvas can refresh and load the next pair: image and label. Then just repeat the above operations.

Colorize black and white (grayscale) image to colorful image with OpenCV and Deep Learning

No description available

Pytorch implementation of ECCV2016-"Colorful Image Colorization"

This is a PyTorch implementation of the paper Colorful Image Colorization

extracts the color glow from images or videos using K-Means clustering and Hasler and Süsstrunk's colorfulness metric.

implementation of the Colorful Image Colorization technique proposed by Richard Zhang, Phillip Isola, and Alexei A. Efros.

Colorful Image Colorization in tensorflow

jov.ai is an application that uses state of the art technologies like deep learning to colorize black and white jupiter images into colorful image also jovai can generate new jupiter images using StyleGAN.

A PyTorch implementation of the Colorful Image Colorization (Zhang et al., 2016) paper

MATLAB based CAM program to convert colorful images to machine code. Explicitly for laser-induced colorization processes.

# Tableau Homework - Citi Bike Analytics ### Before You Begin * This assignment will be saved to your tableau public account rather than github. * If you haven't already, be sure to create a tableau public account [here](https://public.tableau.com/s/). * The free tier of tableau only lets you save to their public server. This means that each time you save your file it will be uploaded to your tableau public profile. * You are able to load and continue working on the same workbook. * When you are finished with your assignment, you will turn in the URL to your tableau public workbook along with any additional files used for your analysis. ## Background  Congratulations on your new job! As the new lead analyst for the [New York Citi Bike](https://en.wikipedia.org/wiki/Citi_Bike) Program, you are now responsible for overseeing the largest bike sharing program in the United States. In your new role, you will be expected to generate regular reports for city officials looking to publicize and improve the city program. Since 2013, the Citi Bike Program has implemented a robust infrastructure for collecting data on the program's utilization. Through the team's efforts, each month bike data is collected, organized, and made public on the [Citi Bike Data](https://www.citibikenyc.com/system-data) webpage. However, while the data has been regularly updated, the team has yet to implement a dashboard or sophisticated reporting process. City officials have a number of questions on the program, so your first task on the job is to build a set of data reports to provide the answers. ## Task **Your task in this assignment is to aggregate the data found in the Citi Bike Trip History Logs and find two unexpected phenomena.** **Design 2-5 visualizations for each discovered phenomena (4-10 total). You may work with a timespan of your choosing. Optionally, you may merge multiple datasets from different periods.** **The following are some questions you may wish to tackle. Do not limit yourself to these questions; they are suggestions for a starting point. Be creative!** * How many trips have been recorded total during the chosen period? * By what percentage has total ridership grown? * How has the proportion of short-term customers and annual subscribers changed? * What are the peak hours in which bikes are used during summer months? * What are the peak hours in which bikes are used during winter months? * Today, what are the top 10 stations in the city for starting a journey? (Based on data, why do you hypothesize these are the top locations?) * Today, what are the top 10 stations in the city for ending a journey? (Based on data, why?) * Today, what are the bottom 10 stations in the city for starting a journey? (Based on data, why?) * Today, what are the bottom 10 stations in the city for ending a journey (Based on data, why?) * Today, what is the gender breakdown of active participants (Male v. Female)? * How effective has gender outreach been in increasing female ridership over the timespan? * How does the average trip duration change by age? * What is the average distance in miles that a bike is ridden? * Which bikes (by ID) are most likely due for repair or inspection in the timespan? * How variable is the utilization by bike ID? **Next, as a chronic over-achiever:** * Use your visualizations (does not have to be all of them) to design a dashboard for each phenomena. * The dashboards should be accompanied with an analysis explaining why the phenomena may be occuring. **City officials would also like to see one of the following visualizations:** * **Basic:** A static map that plots all bike stations with a visual indication of the most popular locations to start and end a journey with zip code data overlaid on top. * **Advanced:** A dynamic map that shows how each station's popularity changes over time (by month and year). Again, with zip code data overlaid on the map. * The map you choose should also be accompanied by a write-up unveiling any trends that were noticed during your analysis. **Finally, create your final presentation** * Create a Tableau story that brings together the visualizations, requested maps, and dashboards. * This is what will be presented to the officials, so be sure to make it professional, logical, and visually appealing. ## Considerations Remember, the people reading your analysis will **NOT** be data analysts. Your audience will be city officials, public administrators, and heads of New York City departments. Your data and analysis needs to be presented in a way that is focused, concise, easy-to-understand, and visually compelling. Your visualizations should be colorful enough to be included in press releases, and your analysis should be thoughtful enough for dictating programmatic changes. ## Submission Your final submission should include: * A link to your Tableau Public workbook that includes: * 4-10 Total "Phenomenon" Visualizations * 2 Dashboards * 1 City Official Map * 1 Story * A text or markdown file with your analysis on the phenomenons you uncovered from the data. ## Assessment Your final product will be assessed on the following metrics: * Analytic Rigor * Readability * Visual Attraction ## Hints * You may need to get creative in how you combine each of the CSV files. Don't just assume Tableau is the right tool for the job. At this point, you have a wealth of technical skills and research abilities. Dig for an approach that works and just go with it. * Don't just assume the CSV format hasn't changed since 2013. Subtle changes to the formats in any of your columns can blockade your analysis. Ensure your data is consistent and clean throughout your analysis. (Hint: Start and End Time change at some point in the history logs). * Consider building your visualizations with small extracts of the data (i.e. single files) before attempting to import the whole thing. What you will find is that importing all 20+ million records of data will create performance issues quickly. Welcome to "Big Data." * While utilizing all of the data may seem like a nice power play, consider the time-course in making your analysis. Is data from 2013 the most relevant for making bike replacement decisions today? Probably not. Don't let overwhelming data fool you. Ground your analysis in common sense. * Remember, data alone doesn't "answer" anything. You will need to accompany your data visualizations with clear and directed answers and analysis. * As is often the case, your clients are asking for a LOT of answers. Be considerate about their need-to-know and the importance of not "cramming in everything". Of course, answer each question, but do so in a way that is organized and presentable. * Since this is a project for the city, spend the appropriate time thinking through decisions on color schemes, fonts, and visual story-telling. The Citi Bike program has a clear visual footprint. As a suggestion, look for ways to have your data visualizations match their aesthetic tones. * Pay attention to labels. What exactly is "time duration"? What's the value of "age of birth"? You will almost certainly need calculated fields to get what you need. * Keep a close eye for obvious outliers or false data. Not everyone who signs up for the program is answering honestly. * In answering the question of "why" a phenomenon is occurring, consider adding other pieces of information on socioeconomic or other geographic data. Tableau has a map "layer" feature that you may find handy. * Don't be afraid to manipulate your data and play with settings in Tableau. Tableau is meant to be explored. We haven't covered all that you need -- so you will need to keep an eye out for new tricks. * Treat this as a serious endeavor! This is an opportunity to show future employers that you have what it takes to be a top-notch analyst. * Good luck! ### Copyright Data Boot Camp (C) 2019. All Rights Reserved.

Pytorch implementation of the paper Colorful Image Colorization https://arxiv.org/abs/1603.08511

No description available

Colorful Image Colorization

PaddlePaddle implementation of ECCV2016-"Colorful Image Colorization"

PyTorch Implementation for the Image Colorization Paper "Colorful Image Colorization"