Archive for month: November, 2019

Original Source: https://www.hongkiat.com/blog/instagram-filters-on-web-images/

Many love using Instagram and the filters that come with the app, to make their photos more interesting and beautiful. So far though, the use of these filters are restricted to use inside the app….

Visit hongkiat.com for full content.

Original Source: https://www.sitepoint.com/keras-face-detection-recognition/?utm_source=rss

If you’re a regular user of Google Photos, you may have noticed how the application automatically extracts and groups faces of people from the photos that you back up to the cloud.

Face Recognition in the Google Photos web application

A photo application such as Google’s achieves this through the detection of faces of humans (and pets too!) in your photos and by then grouping similar faces together. Detection and then classification of faces in images is a common task in deep learning with neural networks.

In the first step of this tutorial, we’ll use a pre-trained MTCNN model in Keras to detect faces in images. Once we’ve extracted the faces from an image, we’ll compute a similarity score between these faces to find if they belong to the same person.

Prerequisites

Before you start with detecting and recognizing faces, you need to set up your development environment. First, you need to “read” images through Python before doing any processing on them. We’ll use the plotting library matplotlib to read and manipulate images. Install the latest version through the installer pip:

pip3 install matplotlib

To use any implementation of a CNN algorithm, you need to install keras. Download and install the latest version using the command below:

pip3 install keras

The algorithm that we’ll use for face detection is MTCNN (Multi-Task Convoluted Neural Networks), based on the paper Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks (Zhang et al., 2016). An implementation of the MTCNN algorithm for TensorFlow in Python3.4 is available as a package. Run the following command to install the package through pip:

pip3 install mtcnn

To compare faces after extracting them from images, we’ll use the VGGFace2 algorithm developed by the Visual Geometry Group at the University of Oxford. A TensorFlow-based Keras implementation of the VGG algorithm is available as a package for you to install:

pip3 install keras_vggface

While you may feel the need to build and train your own model, you’d need a huge training dataset and vast processing power. Since this tutorial focuses on the utility of these models, it uses existing, trained models by experts in the field.

Now that you’ve successfully installed the prerequisites, let’s jump right into the tutorial!

Step 1: Face Detection with the MTCNN Model

The objectives in this step are as follows:

retrieve images hosted externally to a local server
read images through matplotlib’s imread() function
detect and explore faces through the MTCNN algorithm
extract faces from an image.

1.1 Store External Images

You may often be doing an analysis from images hosted on external servers. For this example, we’ll use two images of Lee Iacocca, the father of the Mustang, hosted on the BBC and The Detroit News sites.

To temporarily store the images locally for our analysis, we’ll retrieve each from its URL and write it to a local file. Let’s define a function store_image for this purpose:

import urllib.request

def store_image(url, local_file_name):
with urllib.request.urlopen(url) as resource:
with open(local_file_name, ‘wb’) as f:
f.write(resource.read())

You can now simply call the function with the URL and the local file in which you’d like to store the image:

store_image(‘https://ichef.bbci.co.uk/news/320/cpsprodpb/5944/production/_107725822_55fd57ad-c509-4335-a7d2-bcc86e32be72.jpg’,
‘iacocca_1.jpg’)
store_image(‘https://www.gannett-cdn.com/presto/2019/07/03/PDTN/205798e7-9555-4245-99e1-fd300c50ce85-AP_080910055617.jpg?width=540&height=&fit=bounds&auto=webp’,
‘iacocca_2.jpg’)

After successfully retrieving the images, let’s detect faces in them.

1.2 Detect Faces in an Image

For this purpose, we’ll make two imports — matplotlib for reading images, and mtcnn for detecting faces within the images:

from matplotlib import pyplot as plt
from mtcnn.mtcnn import MTCNN

Use the imread() function to read an image:

image = plt.imread(‘iacocca_1.jpg’)

Next, initialize an MTCNN() object into the detector variable and use the .detect_faces() method to detect the faces in an image. Let’s see what it returns:

detector = MTCNN()

faces = detector.detect_faces(image)
for face in faces:
print(face)

For every face, a Python dictionary is returned, which contains three keys. The box key contains the boundary of the face within the image. It has four values: x- and y- coordinates of the top left vertex, width, and height of the rectangle containing the face. The other keys are confidence and keypoints. The keypoints key contains a dictionary containing the features of a face that were detected, along with their coordinates:

{‘box’: [160, 40, 35, 44], ‘confidence’: 0.9999798536300659, ‘keypoints’: {‘left_eye’: (172, 57), ‘right_eye’: (188, 57), ‘nose’: (182, 64), ‘mouth_left’: (173, 73), ‘mouth_right’: (187, 73)}}

1.3 Highlight Faces in an Image

Now that we’ve successfully detected a face, let’s draw a rectangle over it to highlight the face within the image to verify if the detection was correct.

To draw a rectangle, import the Rectangle object from matplotlib.patches:

from matplotlib.patches import Rectangle

Let’s define a function highlight_faces to first display the image and then draw rectangles over faces that were detected. First, read the image through imread() and plot it through imshow(). For each face that was detected, draw a rectangle using the Rectangle() class.

Finally, display the image and the rectangles using the .show() method. If you’re using Jupyter notebooks, you may use the %matplotlib inline magic command to show plots inline:

def highlight_faces(image_path, faces):
# display image
image = plt.imread(image_path)
plt.imshow(image)

ax = plt.gca()

# for each face, draw a rectangle based on coordinates
for face in faces:
x, y, width, height = face[‘box’]
face_border = Rectangle((x, y), width, height,
fill=False, color=’red’)
ax.add_patch(face_border)
plt.show()

Let’s now display the image and the detected face using the highlight_faces() function:

highlight_faces(‘iacocca_1.jpg’, faces)

Detected face in an image of Lee Iacocca. Source: BBC

Let’s display the second image and the face(s) detected in it:

image = plt.imread(‘iacocca_2.jpg’)
faces = detector.detect_faces(image)

highlight_faces(‘iacocca_2.jpg’, faces)

The Detroit News

In these two images, you can see that the MTCNN algorithm correctly detects faces. Let’s now extract this face from the image to perform further analysis on it.

1.4 Extract Face for Further Analysis

At this point, you know the coordinates of the faces from the detector. Extracting the faces is a fairly easy task using list indices. However, the VGGFace2 algorithm that we use needs the faces to be resized to 224 x 224 pixels. We’ll use the PIL library to resize the images.

The function extract_face_from_image() extracts all faces from an image:

from numpy import asarray
from PIL import Image

def extract_face_from_image(image_path, required_size=(224, 224)):
# load image and detect faces
image = plt.imread(image_path)
detector = MTCNN()
faces = detector.detect_faces(image)

face_images = []

for face in faces:
# extract the bounding box from the requested face
x1, y1, width, height = face[‘box’]
x2, y2 = x1 + width, y1 + height

# extract the face
face_boundary = image[y1:y2, x1:x2]

# resize pixels to the model size
face_image = Image.fromarray(face_boundary)
face_image = face_image.resize(required_size)
face_array = asarray(face_image)
face_images.append(face_array)

return face_images

extracted_face = extract_face_from_image(‘iacocca_1.jpg’)

# Display the first face from the extracted faces
plt.imshow(extracted_face[0])
plt.show()

Here is how the extracted face looks from the first image.

Extracted and resized face from first image

The post Face Detection and Recognition with Keras appeared first on SitePoint.

Original Source: https://www.sitepoint.com/detox-react-native-testing-automation/?utm_source=rss

Detox is an end-to-end testing and automation framework that runs on a device or a simulator, just like an actual end user.

Software development demands fast responses to user and/or market needs. This fast development cycle can result (sooner or later) in parts of a project being broken, especially when the project grows so large. Developers get overwhelmed with all the technical complexities of the project, and even the business people start to find it hard to keep track of all scenarios the product caters for.

In this scenario, there’s a need for software to keep on top of the project and allow us to deploy with confidence. But why end-to-end testing? Aren’t unit testing and integration testing enough? And why bother with the complexity that comes with end-to-end testing?

First of all, the complexity issue has been tackled by most of the end-to-end frameworks, to the extent that some tools (whether free, paid or limited) allow us to record the test as a user, then replay it and generate the necessary code. Of course, that doesn’t cover the full range of scenarios that you’d be able to address programmatically, but it’s still a very handy feature.

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End-to-end Integration and Unit Testing

End-to-end testing versus integration testing versus unit testing: I always find the word “versus” drives people to take camps — as if it’s a war between good and evil. That drives us to take camps instead of learning from each other and understanding the why instead of the how. The examples are countless: Angular versus React, React versus Angular versus Vue, and even more, React versus Angular versus Vue versus Svelte. Each camp trash talks the other.

jQuery made me a better developer by taking advantage of the facade pattern $('') to tame the wild DOM beast and keep my mind on the task at hand. Angular made me a better developer by taking advantage of componentizing the reusable parts into directives that can be composed (v1). React made me a better developer by taking advantage of functional programming, immutability, identity reference comparison, and the level of composability that I don’t find in other frameworks. Vue made me a better developer by taking advantage of reactive programming and the push model. I could go on and on, but I’m just trying to demonstrate the point that we need to concentrate more on the why: why this tool was created in the first place, what problems it solves, and whether there are other ways of solving the same problems.

As You Go Up, You Gain More Confidence

As you go more on the spectrum of simulating the user journey, you have to do more work to simulate the user interaction with the product. But on the other hand, you get the most confidence because you’re testing the real product that the user interacts with. So, you catch all the issues—whether it’s a styling issue that could cause a whole section or a whole interaction process to be invisible or non interactive, a content issue, a UI issue, an API issue, a server issue, or a database issue. You get all of this covered, which gives you the most confidence.

Why Detox?

We discussed the benefit of end-to-end testing to begin with and its value in providing the most confidence when deploying new features or fixing issues. But why Detox in particular? At the time of writing, it’s the most popular library for end-to-end testing in React Native and the one that has the most active community. On top of that, it’s the one React Native recommends in its documentation.

The Detox testing philosophy is “gray-box testing”. Gray-box testing is testing where the framework knows about the internals of the product it’s testing.In other words, it knows it’s in React Native and knows how to start up the application as a child of the Detox process and how to reload it if needed after each test. So each test result is independent of the others.

Prerequisites

macOS High Sierra 10.13 or above
Xcode 10.1 or above

Homebrew:

/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

Node 8.3.0 or above:

brew update && brew install node

Apple Simulator Utilities: brew tap wix/brew and brew install applesimutils

Detox CLI 10.0.7 or above:

npm install -g detox-cli

See the Result in Action

First, let’s clone a very interesting open-source React Native project for the sake of learning, then add Detox to it:

git clone https://github.com/ahmedam55/movie-swiper-detox-testing.git
cd movie-swiper-detox-testing
npm install
react-native run-ios

Create an account on The Movie DB website to be able to test all the application scenarios. Then add your username and password in .env file with usernamePlaceholder and passwordPlaceholder respectively:

isTesting=true
username=usernamePlaceholder
password=passwordPlaceholder

After that, you can now run the tests:

detox test

Note that I had to fork this repo from the original one as there were a lot of breaking changes between detox-cli, detox, and the project libraries. Use the following steps as a basis for what to do:

Migrate it completely to latest React Native project.
Update all the libraries to fix issues faced by Detox when testing.
Toggle animations and infinite timers if the environment is testing.
Add the test suite package.

Setup for New Projects
Add Detox to Our Dependencies

Go to your project’s root directory and add Detox:

npm install detox –save-dev

Configure Detox

Open the package.json file and add the following right after the project name config. Be sure to replace movieSwiper in the iOS config with the name of your app. Here we’re telling Detox where to find the binary app and the command to build it. (This is optional. We can always execute react-native run-ios instead.) Also choose which type of simulator: ios.simulator, ios.none, android.emulator, or android.attached. And choose which device to test on:

{
"name": "movie-swiper-detox-testing",

// add these:
"detox": {
"configurations": {
"ios.sim.debug": {
"binaryPath": "ios/build/movieSwiper/Build/Products/Debug-iphonesimulator/movieSwiper.app",
"build": "xcodebuild -project ios/movieSwiper.xcodeproj -scheme movieSwiper -configuration Debug -sdk iphonesimulator -derivedDataPath ios/build",
"type": "ios.simulator",
"name": "iPhone 7 Plus"
}
}
}
}

Here’s a breakdown of what the config above does:

Execute react-native run-ios to create the binary app.
Search for the binary app at the root of the project: find . -name "*.app".
Put the result in the build directory.

Before firing up the test suite, make sure the device name you specified is available (for example, iPhone 7). You can do that from the terminal by executing the following:

xcrun simctl list

Here’s what it looks like:

Now that weve added Detox to our project and told it which simulator to start the application with, we need a test runner to manage the assertions and the reporting—whether it’s on the terminal or otherwise.

Detox supports both Jest and Mocha. We’ll go with Jest, as it has bigger community and bigger feature set. In addition to that, it supports parallel test execution, which could be handy to speed up the end-to-end tests as they grow in number.

Adding Jest to Dev Dependencies

Execute the following to install Jest:

npm install jest jest-cli –save-dev

The post React Native End-to-end Testing and Automation with Detox appeared first on SitePoint.

Original Source: http://feedproxy.google.com/~r/abduzeedo/~3/nc8FXFPvgKk/beautiful-industrial-design-kuro-modular-shelving-system

Beautiful Industrial Design for KUR!O Modular Shelving System

abduzeedoNov 06, 2019

Markus Hofko shared an amazing industrial design project for a modular shelving system called KUR!O. The product is still in development but the idea is quite simple, you can create different configurations with a limited amount of pieces and that’s the beauty of the whole thing, the simplicity and beautiful colors. Take a look below and let us know what you think. 

For more information about the KUR!O modular shelving system check out https://von-morgen.de

Industrial Design