Upload facereco.py

facereco.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import cv2
+import face_recognition
+import os
+import sys
+from pathlib import Path
+from datetime import datetime
+
+st.title('Face RECOGNITION')
+
+index = st.sidebar.selectbox(
+    'Toma lista',
+    (0, 1, 2)
+)
+lista = ["/Users/hectorgonzalez/Documents/CLOUD/streamlit/Video/Josue.mp4",
+         "/Users/hectorgonzalez/Documents/CLOUD/streamlit/Video/rudy.mp4", "/Users/hectorgonzalez/Documents/CLOUD/streamlit/Video/video.mp4"]
+
+
+st.write(f'You selected: {lista[index]}')
+
+path = "ImagesAttendance"
+images = []
+classNames = []
+myList = os.listdir(path)
+print(myList)
+for cl in myList:
+    curImg = cv2.imread(f'{path}/{cl}')
+    images.append(curImg)
+    classNames.append(os.path.splitext(cl)[0])
+print(classNames)
+
+
+def findEncodings(images):
+    encodeList = []
+    for img in images:
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        encode = face_recognition.face_encodings(img)[0]
+        encodeList.append(encode)
+
+    return encodeList
+
+
+def markAttendance(name):
+    with open('Attendance.csv', 'r+') as f:
+        myDataList = f.readlines()
+        nameList = []
+        for line in myDataList:
+            entry = line.split(',')
+            nameList.append(entry[0])
+        if name not in nameList:
+            now = datetime.now()
+            dtString = now.strftime('%H:%M:%S')
+            f.writelines(f'\n{name},{dtString}, {now}')
+
+
+encodeListKnown = findEncodings(images)
+print('Encoding Complete')
+
+# Videos sections
+# Rudys one /Users/hectorgonzalez/Documents/CLOUD/streamlit/Video/vid.mp4
+
+videoLoaded = (
+    lista[index])
+
+video_file = open(
+    videoLoaded, 'rb')
+video_bytes = video_file.read()
+st.video(video_bytes)
+
+cap = cv2.VideoCapture(videoLoaded)
+
+while True:
+    success, img = cap.read()
+    if success == False:
+        print("No image")
+        break
+
+    imgS = cv2.resize(img, (0, 0), None, 0.25, 0.25)
+    #imgS = cv2.cvtColor(imgS, cv2.COLOR_BGR2GRAY)
+
+    facesCurFrame = face_recognition.face_locations(imgS)
+    encodesCurFrame = face_recognition.face_encodings(imgS, facesCurFrame)
+
+    for encodeFace, faceLoc in zip(encodesCurFrame, facesCurFrame):
+        matches = face_recognition.compare_faces(
+            encodeListKnown, encodeFace)
+        faceDis = face_recognition.face_distance(
+            encodeListKnown, encodeFace)
+        matchIndex = np.argmin(faceDis)
+
+    if matches[matchIndex]:
+        name = classNames[matchIndex].upper()
+        y1, x2, y2, x1 = faceLoc
+        y1, x2, y2, x1 = y1*4, x2*4, y2*4, x1*4
+        cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
+        cv2.rectangle(img, (x1, y2-35), (x2, y2), (0, 255, 0), cv2.FILLED)
+        cv2.putText(img, name, (x1+6, y2-6),
+                    cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2)
+        markAttendance(name)
+        print(name)
+        st.error(f"Lista de alumnos {classNames}", icon="🚨")
+        st.success(name, icon="✅")
+
+    cv2.imshow('Webcam', img)
+    cv2.waitKey()