What you see above is the gif of a driver subject from test data with the estimated facial keypoints.

Evaluation of driving performance is very important to reduce road accident rate. There are a lot of public data related to the driver's faces, including some from the Kaggle competition by State Farm Distracted Driver Detection

In this blog, I will show how to extract image (.png) from video recorded using iphone.

Preparation¶

I recorded a 10 minutes and 21 second video using my iphone 6s. In my current directory, I have IMG7367.MOV file with:

• Size: 639.3 MB
• Dimensions: 1280 × 720

You need cv2¶

If you do not have cv2, please make sure to pip install it as:

In this blog post, I will try to create a deep learning model that can color a gray scale image. I follow this great blog post Colorizing B&W; Photos with Neural Networks.

I will consider two example data to train a model:

• Flickr8K data
• Hunter x Hunter anime data

Flickr8K data is a famous public data in computer vision community, and it was also previously analyzed in my blog. The downloading process is described at Develop an image captioning deep learning model using Flickr 8K data

In this blog post, you will learn color spaces that are often used in image processing problems. More specifically, after reading the blog, you will be familiar with using

• skimage.color.rgb2lab
• skimage.color.lab2rgb
• keras.preprocessing.image.load_img
• keras.preprocessing.image.img_to_array
• matplotlib.pyplot.imshow

Let's first load two example images using keras.preprocessing.image.load_img. The list dir_data contains the path to two jpg images.

In this blog post, I describe how I download a lot of images from Google images. I followed pyimagesearch's blog post so please give credits to his blog. His method has two steps:

• Step 1: The first step is to gather URL links of the images that appear in Google Images when you enter a query. pyimagesearch's blog post

Once you create a cool deep learning application, next step is to deploy it so that anyone all over the world can use your cool application. In this blog and the next blog, I will explore simple ways to deploy deep learning models into a public cloud platform. This blog will focus on the model development phase.

Image captioning is an interesting problem, where you can learn both computer vision techniques and natural language processing techniques. In this blog post, I will follow How to Develop a Deep Learning Photo Caption Generator from Scratch and create an image caption generation model using Flicker 8K data. This model takes a single image as input and output the caption to this image.

In the previous post, titled Extract weights from Keras's LSTM and calcualte hidden and cell states, I discussed LSTM model. In this blog post, I would like to discuss the stateful flag in Keras's recurrent model.

If you google "stateful LSTM" or "stateful RNN", google shows many blog posts discussing and puzzling about this notorious parameter, e.g.:

In this blog post, I will review the famous long short-term memory (LSTM) model and try to understand how it is implemented in Keras. If you know nothing about recurrent deep learning model, please read my previous post about recurrent neural network. If you know reccurent neural network (RNN) but not LSTM, you should first read Colah's great blog post