Starting Web Development Bootcamp – any advice?

Hey everyone! I hope you’re all doing well. I’ve joined this subreddit in consideration of some recent career pursuits to work toward learning how to code and up-skill on my web development expertise.Quick background – my experience is between academic administration and technology. I have my Associates, Bachelors, and a Masters in Higher Education – currently 27 and working for a technical startup. Through my career history both in college and post grad – I’ve done quite a bit of technical work and operations for both university and startups which has included management of websites/knowledge base sites.I’ve had very intermediate experience coding HTML, Java, and CSS – I’ve worked closely with development teams on code updates, learning how to set up redirects, and have picked up general bits of knowledge along the way. I essentially understand the foundations of certain code languages and have used these (since MySpace days!) to either do small coding for companies/school, Wordpress projects, or explaining code/use cases for articles I’ve written respectively in my current role.For the last year, I’ve really been exploring an opportunity to invest in that will benefit in the long term. I love designing user experiences, am super passionate for content, and always found coding fascinating – I joked if I had the patience and brain to be a Computer Science or engineering major I could’ve pursued (advanced math is not my strongest so I didn’t go this route).I can truly see myself building on this experience in the next few months-year with the right discipline, practice, and would want to bring this type of experience into my current/future career work opportunities and use it for personal web projects.I’ve signed up for this self learn program on Udemy (love their website): https://www.udemy.com/course/the-web-developer-bootcamp/?utm_source=email-ProMy questions are:Is it possible to learn web development and be successful in it without a formalized degree?What has your experience been like working or pursuing web development?Is there any specific or major learning curve for someone whose not strongest in mathematics (I can manage if needed/sort out algorithms) but am generally curious if this will inhibit me?What are your personal pros and cons?I’m really looking for some general insight as I start this! I snagged the course for relatively cheap so hoping the return on investment to learn web development was a wise purchase.Thank you to everyone and anyone who takes the time to connect here. I really appreciate it.

An Introduction to Animation in Web Design

Animation is not just for cartoons anymore. From full-screen moving images to small hover effects, touches of animation are popping up everywhere. Animation is trendy, fun, and user-friendly. And the obstacles to using animation have started to fall. With most users on high-speed connections and the ease of creating anything from simple movements or a…

Scenarios Where WordPress May Not Be the Best Option

There are a number of reasons why WordPress has the biggest market share among content management systems (CMS). For some developers, it’s the massive ecosystem of available themes and plugins that draws them in. Others may cherish the opportunity to create their own custom addons. The most common thread here is flexibility. WordPress is capable…

Face Detection and Recognition with Keras

Face Detection and Recognition with Keras – SitePointSkip to main contentFree JavaScript Book!Write powerful, clean and maintainable JavaScript.RRP $11.95 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):

image = plt.imread(image_path)
plt.imshow(image)

ax = plt.gca()

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)

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.

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)):

image = plt.imread(image_path)
detector = MTCNN()
faces = detector.detect_faces(image)

face_images = []

for face in faces:

x1, y1, width, height = face[‘box’]
x2, y2 = x1 + width, y1 + height

face_boundary = image[y1:y2, x1:x2]

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’)

plt.imshow(extracted_face[0])
plt.show()

Here’s how the extracted face looks from the first image.

Extracted and resized face from first image

Step 2: Face Recognition with VGGFace2 Model
In this section, let’s first test the model on the two images of Lee Iacocca that we’ve retrieved. Then, we’ll move on to compare faces from images of the starting eleven of the Chelsea football team in 2018 and 2019. You’ll then be able to assess if the algorithm identifies faces of common players between the images.
2.1 Compare Two Faces
In this section, you need to import three modules: VGGFace to prepare the extracted faces to be used in the face recognition models, and the cosine function from SciPy to compute the distance between two faces:
from keras_vggface.utils import preprocess_input
from keras_vggface.vggface import VGGFace
from scipy.spatial.distance import cosine

Let’s define a function that takes the extracted faces as inputs and returns the computed model scores. The model returns a vector, which represents the features of a face:
def get_model_scores(faces):
samples = asarray(faces, ‘float32′)

samples = preprocess_input(samples, version=2)

model = VGGFace(model=’resnet50′,
include_top=False,
input_shape=(224, 224, 3),
pooling=’avg’)

return model.predict(samples)

faces = [extract_face_from_image(image_path)
for image_path in [‘iacocca_1.jpg’, ‘iacocca_2.jpg’]]

model_scores = get_model_scores(faces)

Since the model scores for each face are vectors, we need to find the similarity between the scores of two faces. We can typically use a Euclidean or Cosine function to calculate the similarity.
Vector representation of faces is suited to the cosine similarity. Here’s a detailed comparison between cosine and Euclidean distances with an example.
The cosine() function computes the cosine distance between two vectors. The lower this number, the better match your faces are. In our case, we’ll put the threshold at a distance of 0.4. This threshold is debatable and will vary with your use case. You should set this threshold based on case studies on your dataset:
if cosine(model_scores[0], model_scores[1])

I created a website for sharing devtools and other resources for web development.

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