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| import streamlit as st | |
| 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 json | |
| initialize_login() | |
| initialize_level() | |
| LEVEL = 3 | |
| def infer(image): | |
| time.sleep(1) | |
| output = query(image) | |
| cols = st.columns(2) | |
| cols[0].image(image, use_column_width=True) | |
| with cols[1]: | |
| for item in output: | |
| st.progress(item["score"], text=item["label"]) | |
| def step3_page(): | |
| st.header("Feature encoding") | |
| st.markdown( | |
| """ | |
| ### What is Face Encodings? | |
| 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://1.bp.blogspot.com/-Cehw4NbX2L8/X1-DJeQgtmI/AAAAAAAAKeE/fA55q9EXsgQLU0trbgX_vJkiFMwR927yQCLcBGAsYHQ/s853/final.gif", | |
| use_column_width=True, | |
| ) | |
| st.markdown( | |
| """ | |
| 3. **Finding Patterns**: Our teacher starts to notice patterns in the pictures and the emotions we assigned to them. They might see that smiling faces usually mean happiness, or that certain features are common in sad expressions. These patterns help the teacher understand how emotions are expressed on people's faces. | |
| """ | |
| ) | |
| st.image( | |
| "https://miro.medium.com/v2/resize:fit:1358/1*KoHwRNZGrVrhdbye3BDEew.png", | |
| 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 the face database you have created earlier!" | |
| ) | |
| img_dir = os.path.join(".sessions", get_login()["username"], "known_faces") | |
| images = os.listdir(img_dir) | |
| if len(images) > 0: | |
| st.subheader("Lets see your available images in your known-face database.") | |
| cols = st.columns(len(images)) | |
| for i, img in enumerate(images): | |
| face_name = img.split("_")[0] | |
| 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 encodings...") | |
| if len(images) > 0: | |
| for i, img in enumerate(images): | |
| with open(os.path.join(img_dir, img), "rb") as f: | |
| face_image = f.read() | |
| my_face_encoding = face_recognition.face_encodings(face_image)[0] | |
| my_bar.progress(int((i + 1) / len(images) * 100), text="Generating encodings...") | |
| face_encodings_dict[img] = my_face_encoding | |
| 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) | |