recognize_faces.py

import cv2
import streamlit as st
import face_recognition
import pandas as pd
import numpy as np
import pickle
import datetime
from pymongo import MongoClient


############################ Generate Encodings ######################


all_face_encodings = {}

neha_img = face_recognition.load_image_file("images/Neha_Koppikar/0.jpg")
all_face_encodings["Neha_Koppikar"] = face_recognition.face_encodings(neha_img)[0]

namrata_img = face_recognition.load_image_file("images/Namrata_Koppikar/0.jpg")
all_face_encodings["Namrata_Koppikar"] = face_recognition.face_encodings(namrata_img)[0]


with open('dataset_faces.dat', 'wb') as f:
    pickle.dump(all_face_encodings, f)


##############################################################



############################## Face-Recognition #############################

# CONSTANTS
#WEBCAMNUM = 2 # from videocapture_index_check.py
#PATH_DATA = 'data/DB.csv'
COLOR_DARK = (0, 0, 153)
#COLOR_WHITE = (255, 255, 255)
#COLS_INFO = ['name', 'description']
#COLS_ENCODE = [f'v{i}' for i in range(128)]

st.title("Webcam Face Recognition")
FRAME_WINDOW = st.image([])

# @st.cache
# def load_known_data():
#     DB = pd.read_csv(PATH_DATA)
#     return (
#         DB['name'].values, 
#         DB[COLS_ENCODE].values
#         )

@st.cache
def load_known_data():
    with open('dataset_faces.dat', 'rb') as f:
        all_face_encodings = pickle.load(f)

    # Grab the list of names and the list of encodings
    face_names = list(all_face_encodings.keys())
    face_encodings = np.array(list(all_face_encodings.values()))

    return (
        face_names,
        face_encodings
    )



# @st.cache
# def load_known_data():
#     objects = []
#     with (open("opencv-face-recognition/output/embeddings.pickle", "rb")) as openfile:
#         while True:
#             try:
#                 objects.append(pickle.load(openfile))
#             except EOFError:
#                 break
#     return(
#         objects[0]["names"],
#         objects[0]["embeddings"]
#     )


def capture_face(video_capture):
    # got 3 frames to auto adjust webcam light
    for i in range(3):
        video_capture.read()

    while(True):
        ret, frame = video_capture.read()
        FRAME_WINDOW.image(frame[:, :, ::-1])
        # face detection
        small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
        rgb_small_frame = small_frame[:, :, ::-1]
        face_locations = face_recognition.face_locations(rgb_small_frame)
        if len(face_locations) > 0:
            video_capture.release()
            return frame

def face_distance_to_conf(face_distance, face_match_threshold=0.6):
    if face_distance > face_match_threshold:
        range = (1.0 - face_match_threshold)
        linear_val = (1.0 - face_distance) / (range * 2.0)
        return linear_val
    else:
        range = face_match_threshold
        linear_val = 1.0 - (face_distance / (range * 2.0))
        return linear_val + ((1.0 - linear_val) * np.power((linear_val - 0.5) * 2, 0.2))

def recognize_frame(frame):
    # convert COLOR_BGR2RGB
    rgb_frame = frame[:, :, ::-1]
    face_locations = face_recognition.face_locations(rgb_frame)
    face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)
    for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
        # Draw a box around the face
        face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)

        best_match_index = np.argmin(face_distances)
        name = known_face_names[best_match_index]
        similarity = face_distance_to_conf(face_distances[best_match_index], 0.5)
        cv2.rectangle(frame, (left, top), (right, bottom), COLOR_DARK, 2)
        return name, similarity, frame[:, :, ::-1]

###############################################################################################################

################ Database - MongoDB #####################
@st.cache(hash_funcs={MongoClient: id})
def get_client():
    return MongoClient("mongodb://localhost:27017/")

# Connect to client
client = get_client()

# Connect to DB
visitor = client.get_database('visitor')

# Get the particular collection that contains the data
visit_records = visitor.register


########################### Main APP ####################################

# if __name__ == "__main__":
while(True):
    known_face_names, known_face_encodings = load_known_data()
    video_capture = cv2.VideoCapture(0)
    frame = capture_face(video_capture)
    name, similarity, frame = recognize_frame(frame)
    visitor_input = {'Name': name, 'Date': datetime.datetime.today().strftime("%d %B, %Y") , "Time": datetime.datetime.now().strftime("%H:%M:%S")}
    visit_records.insert_one(visitor_input)
    FRAME_WINDOW.image(frame)
    if similarity > 0.75:
        label = f"**{name}**: *{similarity:.2%} likely*"
        st.markdown(label)
        break
# press to restart the scripts
st.button('contunue......')