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| import cv2 | |
| import numpy as np | |
| import face_recognition | |
| import os | |
| from datetime import datetime | |
| import gradio as gr | |
| def faceEncodings(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 Attandance(text,video,image): | |
| names=[] | |
| path = text | |
| images = [] | |
| personNames = [] | |
| myList = os.listdir(path) | |
| unkownEncodings=[] | |
| print(myList) | |
| for cu_img in myList: | |
| current_Img = cv2.imread(f'{path}/{cu_img}') | |
| images.append(current_Img) | |
| personNames.append(os.path.splitext(cu_img)[0]) | |
| print(personNames) | |
| encodeListKnown = faceEncodings(images) | |
| print('All Encodings Complete!!!') | |
| if video is not None: | |
| cap = cv2.VideoCapture(video) | |
| index=1 | |
| while True: | |
| try: | |
| ret, frame = cap.read() | |
| #faces = cv2.resize(frame, (0, 0), None, 0.25, 0.25) | |
| faces = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) | |
| facesCurrentFrame = face_recognition.face_locations(faces) | |
| encodesCurrentFrame = face_recognition.face_encodings(faces, facesCurrentFrame) | |
| for encodeFace, faceLoc in zip(encodesCurrentFrame, facesCurrentFrame): | |
| matches = face_recognition.compare_faces(encodeListKnown, encodeFace) | |
| faceDis = face_recognition.face_distance(encodeListKnown, encodeFace) | |
| # print(faceDis) | |
| matchIndex = np.argmin(faceDis) | |
| if matches[matchIndex]: | |
| name = personNames[matchIndex].upper() | |
| if names.count(name) == 0: | |
| names.append(name) | |
| cv2.waitKey(1) | |
| except: | |
| break | |
| return ' '.join(names) | |
| else: | |
| try: | |
| #faces = cv2.resize(frame, (0, 0), None, 0.25, 0.25) | |
| faces = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) | |
| facesCurrentFrame = face_recognition.face_locations(faces) | |
| encodesCurrentFrame = face_recognition.face_encodings(faces, facesCurrentFrame) | |
| for encodeFace, faceLoc in zip(encodesCurrentFrame, facesCurrentFrame): | |
| matches = face_recognition.compare_faces(encodeListKnown, encodeFace) | |
| faceDis = face_recognition.face_distance(encodeListKnown, encodeFace) | |
| # print(faceDis) | |
| matchIndex = np.argmin(faceDis) | |
| if matches[matchIndex]: | |
| name = personNames[matchIndex].upper() | |
| if names.count(name) == 0: | |
| names.append(name) | |
| cv2.waitKey(1) | |
| except: | |
| break | |
| return ' '.join(names) | |
| demo=gr.Interface(fn=Attandance, | |
| inputs=["text","video","image"], | |
| outputs="text", | |
| title="Face Attendance", | |
| ) | |
| demo.launch(debug=True) | |