python webapp test meidiapipe csv flask pillow opencv

[directory]

face_recognition_app/
│
├── app.py
├── known_faces/
│   ├── jiwon_han.jpg
│   └── jaewook_song.jpg
├── static/
│   └── styles.css
├── templates/
│   ├── index.html
│   └── result.html
└── requirements.txt

 

[app.py]

from flask import Flask, request, render_template, redirect

import cv2

import mediapipe as mp

import numpy as np

import os

import csv

from datetime import datetime

app = Flask(__name__)

# Fake database for demo purposes

user_data = {

    'Jiwon Han': '123-456-7890',

    'Jaewook Song': '098-765-4321'

}

# Initialize MediaPipe Face Detection

mp_face_detection = mp.solutions.face_detection

face_detection = mp_face_detection.FaceDetection(min_detection_confidence=0.2)

mp_drawing = mp.solutions.drawing_utils

# Load known face images and encodings

known_face_encodings = []

known_face_names = []

def load_known_faces():

    global known_face_encodings, known_face_names

    for name in user_data.keys():

        image_path = os.path.join('known_faces', f'{name.lower().replace(" ", "_")}.jpg')

        if os.path.exists(image_path):

            image = cv2.imread(image_path)

            if image is None:

                print(f"Error reading image file: {image_path}")

                continue

            rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

            results = face_detection.process(rgb_image)

            if results.detections:

                for detection in results.detections:

                    bboxC = detection.location_data.relative_bounding_box

                    h, w, _ = image.shape

                    x, y, width, height = int(bboxC.xmin * w), int(bboxC.ymin * h), int(bboxC.width * w), int(bboxC.height * h)

                    face = image[y:y+height, x:x+width]

                    if face.size == 0:

                        print(f"Detected face has zero size: {image_path}")

                        continue

                    face_rgb = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)

                    face_encoding = face_rgb.flatten()  # Flatten the face for demonstration

                    known_face_encodings.append(face_encoding)

                    known_face_names.append(name)

load_known_faces()

def save_to_csv(data):

    csv_file_path = 'uploads.csv'

    file_exists = os.path.isfile(csv_file_path)

    with open(csv_file_path, mode='a', newline='') as file:

        writer = csv.writer(file)

        if not file_exists:

            writer.writerow(['Timestamp', 'Image Filename', 'Name', 'Phone Number'])

        writer.writerow(data)

@app.route('/')

def index():

    return render_template('index.html')

@app.route('/upload', methods=['POST'])

def upload():

    if 'file' not in request.files:

        return redirect(request.url)

    file = request.files['file']

    if file.filename == '':

        return redirect(request.url)

    if file:

        file_path = os.path.join('static', file.filename)

        file.save(file_path)

        # Load and process the uploaded image

        uploaded_image = cv2.imread(file_path)

        if uploaded_image is None:

            return f"Error reading uploaded image file: {file_path}"

        rgb_image = cv2.cvtColor(uploaded_image, cv2.COLOR_BGR2RGB)

        results = face_detection.process(rgb_image)

        name = 'Unknown'

        phone_number = 'Not found'

        # Check if the face matches any known faces

        if results.detections:

            for detection in results.detections:

                bboxC = detection.location_data.relative_bounding_box

                h, w, _ = uploaded_image.shape

                x, y, width, height = int(bboxC.xmin * w), int(bboxC.ymin * h), int(bboxC.width * w), int(bboxC.height * h)

                face = uploaded_image[y:y+height, x:x+width]

                if face.size == 0:

                    return "Detected face has zero size"

                face_rgb = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)

                face_encoding = face_rgb.flatten()  # Flatten the face for demonstration

               

                # Compare encodings

                for known_encoding, known_name in zip(known_face_encodings, known_face_names):

                    if len(face_encoding) == len(known_encoding):

                        if np.allclose(face_encoding, known_encoding):

                            name = known_name

                            phone_number = user_data.get(name, 'Not found')

                            break

        # Save the result to a CSV file

        save_to_csv([datetime.now().strftime('%Y-%m-%d %H:%M:%S'), file.filename, name, phone_number])

        return render_template('result.html', name=name, phone_number=phone_number, image_url=file_path)

if __name__ == '__main__':

    app.run(debug=True)

 

[styles.css]

body {

    font-family: Arial, sans-serif;

    margin: 20px;

    text-align: center;

}

h1 {

    color: #333;

}

img {

    border: 1px solid #ddd;

    border-radius: 4px;

    padding: 5px;

}

 

[index.html]

<!DOCTYPE html>

<html>

<head>

    <title>Face Matching Web App</title>

    <link rel="stylesheet" type="text/css" href="{{ url_for('static', filename='styles.css') }}">

</head>

<body>

    <h1>Upload a Photo</h1>

    <form action="/upload" method="post" enctype="multipart/form-data">

        <input type="file" name="file" accept="image/*">

        <input type="submit" value="Upload">

    </form>

</body>

</html>

 

[result.html]

<!DOCTYPE html>

<html>

<head>

    <title>Face Matching Result</title>

    <link rel="stylesheet" type="text/css" href="{{ url_for('static', filename='styles.css') }}">

</head>

<body>

    <h1>Result</h1>

    <img src="{{ image_url }}" alt="Uploaded Image" style="width: 300px;">

    <p>Name: {{ name }}</p>

    <p>Phone Number: {{ phone_number }}</p>

    <a href="/">Upload another photo</a>

</body>

</html>

 

[operation]

pip install -r requirement.txt

python app.py