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| import streamlit as st | |
| from PIL import Image | |
| from utils.levels import complete_level, render_page, initialize_level | |
| from utils.login import get_login, initialize_login | |
| from utils.inference import query | |
| import os | |
| import time | |
| import face_recognition | |
| import json | |
| import numpy as np | |
| initialize_login() | |
| initialize_level() | |
| LEVEL = 3 | |
| def step3_page(): | |
| st.header("Feature encoding") | |
| st.markdown( | |
| """ | |
| ### What is Face Encoding? | |
| In face recognition, face encodings are numerical representations of facial features that are used to uniquely identify individuals. | |
| These encodings are obtained by extracting relevant facial information from an input image or video frame. | |
| Face encodings are typically computed using deep learning models, such as Convolutional Neural Networks (CNNs), | |
| that are trained on large datasets of labeled faces. During the training process, these models learn to recognize patterns and extract discriminative features from facial images. | |
| """ | |
| ) | |
| st.image( | |
| "https://miro.medium.com/v2/resize:fit:720/format:webp/1*V_wNVR0QvLQ7JZyUwMTv8w.jpeg", | |
| use_column_width=True, | |
| ) | |
| st.markdown( | |
| """ | |
| Once the face encodings are obtained, they can be stored in a database or used for face recognition tasks. | |
| During face recognition, the encodings of input faces are compared to the stored encodings to determine if a match exists. | |
| Various similarity metrics, such as Euclidean distance or cosine similarity, can be utilized to measure the similarity between | |
| face encodings and determine potential matches. | |
| """ | |
| ) | |
| st.info( | |
| "Now it's your turn to create face encodings to each of the faces in the known-face database that you have created in the previous step!" | |
| ) | |
| img_dir = os.path.join(".sessions", get_login()["username"], "known_faces") | |
| face_encodings_dir = os.path.join(".sessions", get_login()["username"], "face_encodings") | |
| os.makedirs(face_encodings_dir, exist_ok=True) | |
| images = os.listdir(img_dir) | |
| if len(images) > 0: | |
| st.subheader("Let's see your saved faces in your known-face database.") | |
| cols = st.columns(len(images)) | |
| for i, img in enumerate(images): | |
| face_name = img.split("_")[1] | |
| cols[i].image(os.path.join(img_dir, img), use_column_width=True) | |
| cols[i].write(face_name) | |
| st.subheader("Lets create face encodings for the known-faces.") | |
| # face_encodings_dict = {} | |
| if st.button("Create Face Encodings"): | |
| my_bar = st.progress(0, text="Generating face encodings...") | |
| if len(images) > 0: | |
| for i, img in enumerate(images): | |
| face_image = face_recognition.load_image_file(os.path.join(img_dir, img)) | |
| my_face_encoding = face_recognition.face_encodings(face_image) | |
| my_bar.progress(int((i + 1) / len(images) * 100), text="Generating face encodings...") | |
| np.save(os.path.join(face_encodings_dir, img.split("_")[1]+".npy"), my_face_encoding) | |
| # face_encodings_dict[img.split("_")[1]] = my_face_encoding.tolist() | |
| my_bar.progress(100, text="Successfully encoded all the known faces!") | |
| st.success("Face encoding completed successfully!") | |
| # with open(os.path.join(".sessions", get_login()["username"], "face_encodings.json"), "w") as write_file: | |
| # json.dump(face_encodings_dict, write_file, indent=4) | |
| complete_level(LEVEL) | |
| else: | |
| my_bar.empty() | |
| st.error("You have not taken any images yet! Do the previous steps first!") | |
| render_page(step3_page, LEVEL) | |