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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 face_recognition | |
| import cv2 | |
| import numpy as np | |
| initialize_login() | |
| initialize_level() | |
| LEVEL = 4 | |
| 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"]) | |
| # Get a reference to webcam #0 (the default one) | |
| video_capture = cv2.VideoCapture(0) | |
| def step4_page(): | |
| st.header("Trying It Out") | |
| st.info( | |
| "Now that we know how our face recognition application works, let's try it out!" | |
| ) | |
| face_encodings_dir = os.path.join(".sessions", get_login()["username"], "face_encodings") | |
| face_encodings = os.listdir(face_encodings_dir) | |
| known_face_encodings = [] | |
| known_face_names = [] | |
| if len(face_encodings) > 0: | |
| for i, face_encoding in enumerate(face_encodings): | |
| known_face_encoding = np.load(os.path.join(face_encodings_dir, face_encoding)) | |
| face_name = img.split(".")[0] | |
| known_face_encodings.append(known_face_encoding) | |
| known_face_names.append(face_name) | |
| while True: | |
| # Grab a single frame of video | |
| ret, frame = video_capture.read() | |
| # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses) | |
| rgb_frame = frame[:, :, ::-1] | |
| # Find all the faces and face encodings in the frame of video | |
| face_locations = face_recognition.face_locations(rgb_frame) | |
| face_encodings = face_recognition.face_encodings(rgb_frame, face_locations) | |
| # Loop through each face in this frame of video | |
| for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings): | |
| # See if the face is a match for the known face(s) | |
| matches = face_recognition.compare_faces(known_face_encodings, face_encoding) | |
| name = "Unknown" | |
| # If a match was found in known_face_encodings, just use the first one. | |
| # if True in matches: | |
| # first_match_index = matches.index(True) | |
| # name = known_face_names[first_match_index] | |
| # Or instead, use the known face with the smallest distance to the new face | |
| face_distances = face_recognition.face_distance(known_face_encodings, face_encoding) | |
| best_match_index = np.argmin(face_distances) | |
| if matches[best_match_index]: | |
| name = known_face_names[best_match_index] | |
| # Draw a box around the face | |
| cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2) | |
| # Draw a label with a name below the face | |
| cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED) | |
| font = cv2.FONT_HERSHEY_DUPLEX | |
| cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1) | |
| # Display the resulting image | |
| cv2.imshow('Video', frame) | |
| # Hit 'q' on the keyboard to quit! | |
| if cv2.waitKey(1) & 0xFF == ord('q'): | |
| break | |
| # Release handle to the webcam | |
| video_capture.release() | |
| cv2.destroyAllWindows() | |
| st.info("Click on the button below to complete this level!") | |
| if st.button("Complete Level"): | |
| complete_level(LEVEL) | |
| render_page(step4_page, LEVEL) | |