import face_recognition
import cv2
import numpy as np
import pandas as pd
import datetime
from datetime import timedelta

#Melhoria:
# Tirar nova foto a pessoas que já tiraram foto mas continuam a não ser reconhecidas

# This is a demo of running face recognition on live video from your webcam. It's a little more complicated than the
# other example, but it includes some basic performance tweaks to make things run a lot faster:
#   1. Process each video frame at 1/4 resolution (though still display it at full resolution)
#   2. Only detect faces in every other frame of video.

# PLEASE NOTE: This example requires OpenCV (the `cv2` library) to be installed only to read from your webcam.
# OpenCV is *not* required to use the face_recognition library. It's only required if you want to run this
# specific demo. If you have trouble installing it, try any of the other demos that don't require it instead.

# Vars globais


# Criar Dataframe para guardar registos de entrada e saida
time_df = pd.DataFrame(columns = ["nome", "data"])

# Get a reference to webcam #0 (the default one)
video_capture = cv2.VideoCapture(0)

# Load a sample picture and learn how to recognize it.
pai_image = face_recognition.load_image_file("saved_img.jpg")
pai_face_encoding = face_recognition.face_encodings(pai_image)[0]
eu_image = face_recognition.load_image_file("eu.jpg")
eu_face_encoding = face_recognition.face_encodings(eu_image)[0]
bea_image = face_recognition.load_image_file("bea.jpg")
bea_face_encoding = face_recognition.face_encodings(bea_image)[0]

# Create arrays of known face encodings and their names
known_face_encodings = [
    pai_face_encoding,
    eu_face_encoding,
    bea_face_encoding
]
known_face_names = [
    "<NAME>",
    "<NAME>",
    "<NAME>"
]

# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True

df = pd.DataFrame()

while True:
    # Grab a single frame of video
    ret, frame = video_capture.read()

    # Resize frame of video to 1/4 size for faster face recognition processing
    small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)

    # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
    rgb_small_frame = small_frame[:, :, ::-1]

    # Only process every other frame of video to save time
    if process_this_frame:
        # Find all the faces and face encodings in the current frame of video
        face_locations = face_recognition.face_locations(rgb_small_frame)
        face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)

        face_names = []
        for face_encoding in 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)


            # Se houve match
            if matches[best_match_index]:
                name = known_face_names[best_match_index]
                now = datetime.datetime.now()
                # Contar 1 minuto desde que entrou até poder ser detetado outra vez
                threshold = timedelta(seconds = 10)
                final_time = now + threshold
                # Adicionar aqui código para guardar registo da pessoa
                # Se o DF está vazio (sem registos, faz primeiro registo)
                if time_df.empty:
                    now = datetime.datetime.now()

                    aux_df = pd.DataFrame({"nome": name, "data": now, "registo": "entrada", "ok_time": final_time}, index = [0])
                    time_df = time_df.append(aux_df)
                    print(time_df)

                else:
                    # ir ao dataframe buscar ultimo registo para aquele nome
                    # Se esse registo for para a mesma hora/dia, quer dizer que é repetido
                    # Mudar para um timer de 30 minutos (threads?)
                    auxiliar_df = time_df[time_df.nome == name]
                    # Primeiro registo de cada pessoa - sempre "entrada"
                    if auxiliar_df.empty:
                        aux_df = pd.DataFrame({"nome": name, "data": now, "registo": "entrada",
                                               "ok_time": final_time, "horas_trabalho": 0}, index = [0])

                        time_df = time_df.append(aux_df)
                        entrada_saida_flag = "entrada"
                        print(time_df)
                    else:
                        ultimo_registo_df = auxiliar_df.iloc[-1]
                        entrada_saida_flag = ultimo_registo_df.registo
                        # Se ainda não passou o threshold dessa pessoa
                        if (ultimo_registo_df.nome == name) & (ultimo_registo_df.ok_time >= now):
                            break
                        else:
                            # se o último registo foi uma entrada
                            if entrada_saida_flag == "entrada":

                                aux_df = pd.DataFrame({"nome": name, "data": now, "registo": "saida",
                                                       "ok_time": final_time, "horas_trabalho": datetime.datetime.now() - ultimo_registo_df.data}, index=[0])

                            else:

                                aux_df = pd.DataFrame({"nome": name, "data": now,
                                                       "registo": "entrada", "ok_time": final_time, "horas_trabalho": 0}, index = [0])

                            time_df = time_df.append(aux_df)

                            print(time_df)

            face_names.append(name)

    process_this_frame = not process_this_frame


    # Display the results
    for (top, right, bottom, left), name in zip(face_locations, face_names):
        # Scale back up face locations since the frame we detected in was scaled to 1/4 size
        top *= 4
        right *= 4
        bottom *= 4
        left *= 4

        # 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'):
        #print(time_df)
        for emp in known_face_names:
            aux = time_df[time_df.nome == emp]
            hr_emp = pd.to_timedelta(aux.horas_trabalho)
            print(emp)
            print(hr_emp)

        time_df = time_df.drop(columns=["ok_time"], axis = 1)

        time_df.to_csv("/Users/miguelroque/Documents/Startup/FaceDetect-master/bd_registos.csv")
        break

# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()
import face_recognition
import cv2
import numpy as np
import pandas as pd
import datetime
from datetime import timedelta

#Melhoria:
# Tirar nova foto a pessoas que já tiraram foto mas continuam a não ser reconhecidas

# This is a demo of running face recognition on live video from your webcam. It's a little more complicated than the
# other example, but it includes some basic performance tweaks to make things run a lot faster:
#   1. Process each video frame at 1/4 resolution (though still display it at full resolution)
#   2. Only detect faces in every other frame of video.

# PLEASE NOTE: This example requires OpenCV (the `cv2` library) to be installed only to read from your webcam.
# OpenCV is *not* required to use the face_recognition library. It's only required if you want to run this
# specific demo. If you have trouble installing it, try any of the other demos that don't require it instead.

# Vars globais


# Criar Dataframe para guardar registos de entrada e saida
time_df = pd.DataFrame(columns = ["nome", "data"])

# Get a reference to webcam #0 (the default one)
video_capture = cv2.VideoCapture(0)

# Load a sample picture and learn how to recognize it.
pai_image = face_recognition.load_image_file("saved_img.jpg")
pai_face_encoding = face_recognition.face_encodings(pai_image)[0]
eu_image = face_recognition.load_image_file("eu.jpg")
eu_face_encoding = face_recognition.face_encodings(eu_image)[0]
bea_image = face_recognition.load_image_file("bea.jpg")
bea_face_encoding = face_recognition.face_encodings(bea_image)[0]

# Create arrays of known face encodings and their names
known_face_encodings = [
    pai_face_encoding,
    eu_face_encoding,
    bea_face_encoding
]
known_face_names = [
    "<NAME>",
    "<NAME>",
    "<NAME>"
]

# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True

df = pd.DataFrame()

while True:
    # Grab a single frame of video
    ret, frame = video_capture.read()

    # Resize frame of video to 1/4 size for faster face recognition processing
    small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)

    # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
    rgb_small_frame = small_frame[:, :, ::-1]

    # Only process every other frame of video to save time
    if process_this_frame:
        # Find all the faces and face encodings in the current frame of video
        face_locations = face_recognition.face_locations(rgb_small_frame)
        face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)

        face_names = []
        for face_encoding in 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)


            # Se houve match
            if matches[best_match_index]:
                name = known_face_names[best_match_index]
                now = datetime.datetime.now()
                # Contar 1 minuto desde que entrou até poder ser detetado outra vez
                threshold = timedelta(seconds = 10)
                final_time = now + threshold
                # Adicionar aqui código para guardar registo da pessoa
                # Se o DF está vazio (sem registos, faz primeiro registo)
                if time_df.empty:
                    now = datetime.datetime.now()

                    aux_df = pd.DataFrame({"nome": name, "data": now, "registo": "entrada", "ok_time": final_time}, index = [0])
                    time_df = time_df.append(aux_df)
                    print(time_df)

                else:
                    # ir ao dataframe buscar ultimo registo para aquele nome
                    # Se esse registo for para a mesma hora/dia, quer dizer que é repetido
                    # Mudar para um timer de 30 minutos (threads?)
                    auxiliar_df = time_df[time_df.nome == name]
                    # Primeiro registo de cada pessoa - sempre "entrada"
                    if auxiliar_df.empty:
                        aux_df = pd.DataFrame({"nome": name, "data": now, "registo": "entrada",
                                               "ok_time": final_time, "horas_trabalho": 0}, index = [0])

                        time_df = time_df.append(aux_df)
                        entrada_saida_flag = "entrada"
                        print(time_df)
                    else:
                        ultimo_registo_df = auxiliar_df.iloc[-1]
                        entrada_saida_flag = ultimo_registo_df.registo
                        # Se ainda não passou o threshold dessa pessoa
                        if (ultimo_registo_df.nome == name) & (ultimo_registo_df.ok_time >= now):
                            break
                        else:
                            # se o último registo foi uma entrada
                            if entrada_saida_flag == "entrada":

                                aux_df = pd.DataFrame({"nome": name, "data": now, "registo": "saida",
                                                       "ok_time": final_time, "horas_trabalho": datetime.datetime.now() - ultimo_registo_df.data}, index=[0])

                            else:

                                aux_df = pd.DataFrame({"nome": name, "data": now,
                                                       "registo": "entrada", "ok_time": final_time, "horas_trabalho": 0}, index = [0])

                            time_df = time_df.append(aux_df)

                            print(time_df)

            face_names.append(name)

    process_this_frame = not process_this_frame


    # Display the results
    for (top, right, bottom, left), name in zip(face_locations, face_names):
        # Scale back up face locations since the frame we detected in was scaled to 1/4 size
        top *= 4
        right *= 4
        bottom *= 4
        left *= 4

        # 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'):
        #print(time_df)
        for emp in known_face_names:
            aux = time_df[time_df.nome == emp]
            hr_emp = pd.to_timedelta(aux.horas_trabalho)
            print(emp)
            print(hr_emp)

        time_df = time_df.drop(columns=["ok_time"], axis = 1)

        time_df.to_csv("/Users/miguelroque/Documents/Startup/FaceDetect-master/bd_registos.csv")
        break

# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()
#Melhoria: # Tirar nova foto a pessoas que já tiraram foto mas continuam a não ser reconhecidas # This is a demo of running face recognition on live video from your webcam. It's a little more complicated than the # other example, but it includes some basic performance tweaks to make things run a lot faster: #   1. Process each video frame at 1/4 resolution (though still display it at full resolution) #   2. Only detect faces in every other frame of video. # PLEASE NOTE: This example requires OpenCV (the `cv2` library) to be installed only to read from your webcam. # OpenCV is *not* required to use the face_recognition library. It's only required if you want to run this # specific demo. If you have trouble installing it, try any of the other demos that don't require it instead. # Vars globais # Criar Dataframe para guardar registos de entrada e saida # Get a reference to webcam #0 (the default one) # Load a sample picture and learn how to recognize it. # Create arrays of known face encodings and their names # Initialize some variables # Grab a single frame of video # Resize frame of video to 1/4 size for faster face recognition processing # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses) # Only process every other frame of video to save time # Find all the faces and face encodings in the current frame of video # See if the face is a match for the known face(s) # # 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 # Se houve match # Contar 1 minuto desde que entrou até poder ser detetado outra vez # Adicionar aqui código para guardar registo da pessoa # Se o DF está vazio (sem registos, faz primeiro registo) # ir ao dataframe buscar ultimo registo para aquele nome # Se esse registo for para a mesma hora/dia, quer dizer que é repetido # Mudar para um timer de 30 minutos (threads?) # Primeiro registo de cada pessoa - sempre "entrada" # Se ainda não passou o threshold dessa pessoa # se o último registo foi uma entrada # Display the results # Scale back up face locations since the frame we detected in was scaled to 1/4 size # Draw a box around the face # Draw a label with a name below the face # Display the resulting image # Hit 'q' on the keyboard to quit! #print(time_df) # Release handle to the webcam