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How to Build a Facial Recognition App (A Step-by-Step Guide & Tutorial) using Python
Facial recognition is a technology that can identify or verify a person's identity from a digital image or a video frame. It is widely used in various applications, such as security, biometrics, law enforcement, entertainment, and social media. In this tutorial, we will show you how to build a simple facial recognition app using Python and the Face Recognition and Face Detection API by Lambda Labs. This API is a convenient tool that enables you to integrate computer vision into your web and mobile applications.

What You Will Learn
By the end of this tutorial, you will be able to:

• Set up a Python environment and install the required packages

• Use the Face Recognition and Face Detection API to detect and recognize faces in images

• Build a Flask web app that allows users to upload images and see the results of facial recognition

• Deploy your app to Heroku and test it online

Prerequisites
To follow this tutorial, you will need:

• A basic knowledge of Python and Flask

• A free developer account on RapidAPI

• A subscription to the Face Recognition and Face Detection API

• A Heroku account and the Heroku CLI installed

Step 1: Set Up Your Python Environment and Install the Required Packages
The first step is to create a virtual environment for your project and install the required packages. You can use any Python version you prefer, but we recommend using Python 3.7 or higher. To create a virtual environment, you can use the venv module:

python -m venv env

This will create a folder called env in your current directory, where the virtual environment files will be stored. To activate the virtual environment, you can run the following command:

source env/bin/activate

This will change your prompt to indicate that you are in the virtual environment. To deactivate the virtual environment, you can run the following command:

deactivate

Next, you need to install the required packages for this project. You can use the pip tool to install them from the requirements.txt file:

pip install -r requirements.txt

The requirements.txt file contains the following packages:

• flask: A lightweight web framework for Python

• requests: A library for making HTTP requests in Python

• pillow: A library for image processing in Python

• numpy: A library for scientific computing in Python

• opencv-python: A library for computer vision in Python

You can also install these packages manually by running the pip install command for each one of them.

Step 2: Use the Face Recognition and Face Detection API to Detect and Recognize Faces in Images
The next step is to use the Face Recognition and Face Detection API to detect and recognize faces in images. This API provides two main endpoints: detect and recognize. The detect endpoint takes an image URL or a base64-encoded image as input and returns a JSON object with the coordinates of the faces and the landmarks (such as eyes, nose, and mouth) in the image. The recognize endpoint takes an image URL or a base64-encoded image and an album name and key as input and returns a JSON object with the names and confidence scores of the recognized faces in the image. The album name and key are used to identify the collection of images that you want to use for training the face recognition model. You can create and manage your albums using the Album Management endpoints of the API.

To use the Face Recognition and Face Detection API, you need to sign up for a free developer account on RapidAPI and subscribe to the API. You can find the instructions on how to do that in this blog post. After subscribing, you will get an API key that you need to pass as a header in your requests. You can also test the API endpoints using the RapidAPI interface.

To use the API in Python, you can use the requests library to make HTTP requests. Here is an example of how to use the detect endpoint to detect faces in an image:

import requests

# The image URL or base64-encoded image



# The API endpoint
url = "https://lambda-face-recognition.p.rapidapi.com/..."

# The API headers
headers = {
"x-rapidapi-key": "your-api-key",
"x-rapidapi-host": "lambda-face-recognition.p.rapidapi.com",
"content-type": "application/json"
}

# The API parameters
payload = {
"image": image
}

# Make the request
response = requests.post(url, headers=headers, json=payload)

# Print the response
print(response.json())

This will print something like this:

{
"status": "success",
"photos": [
{


"width": 640,
"height": 480,
"tags": [
{
"eye_left": {
"y": 163,
"x": 212
},
"center": {
"y": 175,
"x": 230
},
"eye_right": {
"y": 163,
"x": 248
},
"height": 87,
"width": 87,
"mouth_left": {
"y": 200,
"x": 214
},
"mouth_right": {
"y": 200,
"x": 245
},
"nose": {
"y": 182,
"x": 230
},
"tid": "TEMP_F@_b0a9f4c0-0c8f-11eb-9f6d-0f2e1c4f0a0f",
"attributes": {
"face": {
"value": "true",
"confidence": 99
}
}
}
]
}
]
}

The response contains the URL, width, and height of the image, and an array of tags, each representing a detected face. Each tag contains the coordinates of the eyes, nose, mouth, and center of the face, as well as the height and width of the face. It also contains a temporary ID (tid) and an attribute indicating that the tag is a face with a high confidence score.

Here is an example of how to use the recognize endpoint to recognize faces in an image:

import requests

# The image URL or base64-encoded image



# The album name and key
album = "my-album"
albumkey = "my-album-key"

# The API endpoint
url = "https://lambda-face-recognition.p.rapidapi.com/..."

# The API headers
headers = {
"x-rapidapi-key": "your-api-key",
"x-rapidapi-host": "lambda-face-recognition.p.rapidapi.com",
"content-type": "application/json"
}

# The API parameters
payload = {
"image": image,
"album": album,
"albumkey": albumkey
}

# Make the request
response = requests.post(url, headers=headers, json=payload)

# Print the response
print(response.json())

This will print something like this:

{
"status": "success",
"photos": [
{


"width": 640,
"height": 480,
"tags": [
{
"eye_left": {
"y": 163,
"x": 212
},
"center": {
"y": 175,
"x": 230
},
"eye_right": {
"y": 163,
"x": 248
},
"height": 87,
"width": 87,
"mouth_left": {
"y": 200,
"x": 214
},
"mouth_right": {
"y": 200,
"x": 245
},
"nose": {
"y": 182,
"x": 230
},
"tid": "TEMP_F@_b0a9f4c0-0c8f-11eb-9f6d-0f2e1c4f0a0f",
"attributes": {
"face": {
"value": "true",
"confidence": 99
}
},
"uids": [
{
"confidence": 99,
"prediction": "Alice"
}
]
}
]
}
]
}

The response is similar to the one from the detect endpoint, but it also contains an array of uids, each representing a recognized face. Each uid contains the confidence score and the prediction of the name of the person. In this example, the API recognized the face