upload

.vscode/settings.json

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+{
+    "git.ignoreLimitWarning": true
+}

AttendanceRecord.csv

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+NAME, TIME
+
+
+BERTOLDAS,00:54:15

face.py

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+import numpy as np
+import face_recognition as fr
+import cv2
+
+video_capture = cv2.VideoCapture(0)
+
+bertas_image = fr.load_image_file("Images/bertas.jpg")
+bertas_face_encoding = fr.face_encodings(bertas_image)[0]
+
+known_face_encodings = [bertas_face_encoding]
+known_face_names = ["bertas"]
+
+while True:
+    ret, frame = video_capture.read()
+
+    rgb_frame = frame[:, :, ::-1]
+
+    face_locations = fr.face_locations(rgb_frame)
+    face_encodings = fr.face_encodings(rgb_frame, face_locations)
+
+    for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
+
+        matches = fr.compare_faces(known_face_encodings, face_encoding)
+
+        name = "unknown"
+        face_distances = fr.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]
+
+        cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
+
+        cv2.rectangle(frame, (left, bottom - 35),
+                      (right, bottom), (0, 0, 255), cv2.FILLED)
+        font = cv2.FONT_HERSHEY_SIMPLEX
+        cv2.putText(frame, name, (left + 6, bottom - 6),
+                    font, 1.0, (255, 255, 255), 1)
+
+    cv2.imshow('webcam', frame)
+
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+
+video_capture.release()
+cv2.destroyAllWindows()
+
+
+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
+
+    encodeListKnown = findEncodings(images)
+
+
+print('Encoding Complete')

test.py

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+import face_recognition as fc
+import os
+import numpy as np
+import cv2
+from datetime import datetime
+
+path = 'Photos'
+photos = []
+NameClass = []
+ImgList = os.listdir(path)
+
+for cl in ImgList:
+    curImg = cv2.imread(f'{path}/{cl}')
+    photos.append(curImg)
+    NameClass.append(os.path.splitext(cl)[0])
+
+
+def EncodingsFind(images):
+    ListEncodes = []
+    for img in photos:
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        encode = fc.face_encodings(img)[0]
+        ListEncodes.append(encode)
+    return ListEncodes
+
+
+def Attendance(name):
+    with open('AttendanceRecord.csv', 'r+') as f:
+        DataList = f.readlines()
+        nameList = []
+        for line in DataList:
+            gap = line.split(',')
+            nameList.append(gap[0])
+        if name not in nameList:
+            time = datetime.now()
+            dtString = time.strftime('%H:%M:%S')
+            f.writelines(f'\n{name},{dtString}')
+
+
+ListEncodesKnown = EncodingsFind(photos)
+
+capture = cv2.VideoCapture(0)
+
+while True:
+    success, img = capture.read()
+    imgS = cv2.resize(img, (0, 0), None, 0.25, 0.25)
+    imgS = cv2.cvtColor(imgS, cv2.COLOR_BGR2RGB)
+
+    facesFrame = fc.face_locations(imgS)
+    encodesCurFrame = fc.face_encodings(imgS, facesFrame)
+
+    for encodeFace, faceLoc in zip(encodesCurFrame, facesFrame):
+        match = fc.compare_faces(ListEncodesKnown, encodeFace)
+        Distance = fc.face_distance(ListEncodesKnown, encodeFace)
+
+        matchIndex = np.argmin(Distance)
+
+        if match[matchIndex]:
+            name = NameClass[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)
+
+            Attendance(name)
+
+    cv2.imshow('webcam', img)
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break