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FaceIdentification/Project.py

+import cv2
+import numpy as np
+import face_recognition
+import os
+from datetime import datetime
+
+path = 'ImagesAttendance'
+images = []
+classNames = []
+myList = os.listdir(path)
+# print(myList)
+
+for cls in myList:
+    curImg = cv2.imread(f'{path}/{cls}')
+    images.append(curImg)
+    classNames.append(os.path.splitext(cls)[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 unknown_image_encoded(images):
+    """
+    encode a face given the file name
+    """
+    face = face_recognition.load_image_file("faces/" + images)
+    encoding = face_recognition.face_encodings(face)[0]
+
+    return encoding
+
+
+# def markAttendance(name):
+#     with open('Attendance.csv', 'r+') as f:
+#         myDataList = f.readlines()
+#         print(myDataList)
+#         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}')
+
+
+encodeListKnown = findEncodings(images)
+print('Encoding Complete')
+
+cap = cv2.VideoCapture(0)
+
+while True:
+    success, img = cap.read()
+    imgS = cv2.resize(img, (0, 0), None, 0.25, 0.25)
+    imgS = cv2.cvtColor(imgS, cv2.COLOR_BGR2RGB)
+
+    facesCurFrame = face_recognition.face_locations(imgS)
+    encodesCurFrame = face_recognition.face_encodings(imgS, facesCurFrame)
+    face_locations = face_recognition.face_locations(imgS)
+    unknown_face_encodings = face_recognition.face_encodings(imgS, face_locations)
+
+
+    for face_encoding in unknown_face_encodings:
+        # See if the face is a match for the known face(s)
+        matches = face_recognition.compare_faces(encodeListKnown, face_encoding)
+        name = "Unknown"
+
+        # use the known face with the smallest distance to the new face
+        face_distances = face_recognition.face_distance(encodeListKnown, face_encoding)
+        best_match_index = np.argmin(face_distances)
+        if matches[best_match_index]:
+            name = encodeListKnown[best_match_index]
+
+    for encodeFace, faceLoc in zip(encodesCurFrame, facesCurFrame):
+        matches = face_recognition.compare_faces(encodeListKnown, encodeFace)
+        faceDis = face_recognition.face_distance(encodeListKnown, encodeFace)
+        # print(faceDis)
+        matchIndex = np.argmin(faceDis)
+
+        if matches[matchIndex]:
+            name = classNames[matchIndex].upper()
+            # print(name)
+            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)
+
+    cv2.imshow('Webcam', img)
+    cv2.waitKey(1)
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