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Nov 21: [FIX] Bug Fixed 'face_recognized_attendance_login'

pull/351/merge
Cybrosys Technologies 6 months ago
parent
7953ba881f
commit
cbb465db08
28 changed files with 6773 additions and 56069 deletions
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  1. 9
      face_recognized_attendance_login/README.rst
  2. 1
      face_recognized_attendance_login/__init__.py
  3. 11
      face_recognized_attendance_login/__manifest__.py
  4. 127
      face_recognized_attendance_login/controllers/main.py
  5. 22619
      face_recognized_attendance_login/data/haarcascade_eye_tree_eyeglasses.xml
  6. 33314
      face_recognized_attendance_login/data/haarcascade_frontalface_default.xml
  7. 5
      face_recognized_attendance_login/doc/RELEASE_NOTES.md
  8. 1
      face_recognized_attendance_login/static/src/js/face-api.min.js
  9. 209
      face_recognized_attendance_login/static/src/js/face_recognition.js
  10. BIN
      face_recognized_attendance_login/static/src/js/weights/age_gender_model-shard1
  11. 618
      face_recognized_attendance_login/static/src/js/weights/age_gender_model-weights_manifest.json
  12. BIN
      face_recognized_attendance_login/static/src/js/weights/face_expression_model-shard1
  13. 606
      face_recognized_attendance_login/static/src/js/weights/face_expression_model-weights_manifest.json
  14. BIN
      face_recognized_attendance_login/static/src/js/weights/face_landmark_68_model-shard1
  15. 691
      face_recognized_attendance_login/static/src/js/weights/face_landmark_68_model-weights_manifest.json
  16. BIN
      face_recognized_attendance_login/static/src/js/weights/face_landmark_68_tiny_model-shard1
  17. 397
      face_recognized_attendance_login/static/src/js/weights/face_landmark_68_tiny_model-weights_manifest.json
  18. BIN
      face_recognized_attendance_login/static/src/js/weights/face_recognition_model-shard1
  19. 6
      face_recognized_attendance_login/static/src/js/weights/face_recognition_model-shard2
  20. 1462
      face_recognized_attendance_login/static/src/js/weights/face_recognition_model-weights_manifest.json
  21. BIN
      face_recognized_attendance_login/static/src/js/weights/mtcnn_model-shard1
  22. 402
      face_recognized_attendance_login/static/src/js/weights/mtcnn_model-weights_manifest.json
  23. BIN
      face_recognized_attendance_login/static/src/js/weights/ssd_mobilenetv1_model-shard1
  24. 137
      face_recognized_attendance_login/static/src/js/weights/ssd_mobilenetv1_model-shard2
  25. 1936
      face_recognized_attendance_login/static/src/js/weights/ssd_mobilenetv1_model-weights_manifest.json
  26. BIN
      face_recognized_attendance_login/static/src/js/weights/tiny_face_detector_model-shard1
  27. 273
      face_recognized_attendance_login/static/src/js/weights/tiny_face_detector_model-weights_manifest.json
  28. 18
      face_recognized_attendance_login/static/src/xml/face_recognition_template.xml

9
face_recognized_attendance_login/README.rst
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@ -8,14 +8,7 @@ Face Recognized Attendance Login
Configuration
=============
Opencv [version: 4.8.1.78]
numpy [version: 1.26.2]
Pillow [version: 9.0.1]
cmake [version: 3.27.7]
dlib [version: 19.24.2]
face-recognition [version: 1.2.3]
Please ensure the camera access also.
Please ensure the camera access.
Installation
============

1
face_recognized_attendance_login/__init__.py
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@ -20,4 +20,3 @@
#
#############################################################################
from . import controllers

11
face_recognized_attendance_login/__manifest__.py
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@ -21,7 +21,7 @@
#############################################################################
{
'name': 'Face Recognized Attendance Login',
'version': '17.0.1.0.0',
'version': '17.0.2.0.0',
'category': 'Human Resources',
'summary': """Mark the attendance of employee by recognizing their face""",
'description': """This module introduces a face recognition system in the
@ -32,9 +32,12 @@
'maintainer': 'Cybrosys Techno Solutions',
'website': "https://www.cybrosys.com",
'depends': ['base', 'mail', 'hr_attendance'],
'external_dependencies': {
'python': ['face-recognition', 'cmake', 'dlib', 'PIL',
'numpy', 'opencv-python'],
'assets': {
'hr_attendance.assets_public_attendance': [
'face_recognized_attendance_login/static/src/js/face-api.min.js',
'face_recognized_attendance_login/static/src/xml/face_recognition_template.xml',
'face_recognized_attendance_login/static/src/js/face_recognition.js',
]
},
'images': ['static/description/banner.jpg'],
'license': 'LGPL-3',

127
face_recognized_attendance_login/controllers/main.py
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@ -19,21 +19,6 @@
# If not, see <http://www.gnu.org/licenses/>.
#
#############################################################################
import base64
import PIL
import cmake
import cv2
import dlib
import face_recognition
from io import BytesIO
import numpy
import numpy as np
import os
from PIL import Image
import time
from odoo.exceptions import AccessError
from odoo.http import request
from odoo.addons.hr_attendance.controllers.main import HrAttendance
from odoo import _, http
@ -42,112 +27,8 @@ from odoo import _, http
class HrAttendances(HrAttendance):
"""Controllers Overrides to add the Face detection feature"""
@http.route('/hr_attendance/attendance_employee_data', type="json",
@http.route('/get_image', type="json",
auth="public")
def employee_attendance_data(self, token, employee_id):
"""In this Code section the face detection is added to
employee_attendance_data"""
company = self._get_company(token)
employee_pic = request.env['hr.employee'].sudo().browse(
employee_id).image_1920
sub_folder = os.path.abspath(os.path.dirname(__file__))
project_folder = os.path.abspath(os.path.join(sub_folder, os.pardir))
eye_cascade_path = os.path.join(project_folder, 'data',
'haarcascade_eye_tree_eyeglasses.xml')
face_cascade_path = os.path.join(project_folder, 'data',
'haarcascade_frontalface_default.xml')
face_cascade = cv2.CascadeClassifier(face_cascade_path)
eye_cascade = cv2.CascadeClassifier(eye_cascade_path)
binary_data = base64.b64decode(employee_pic)
image_bytes = BytesIO(binary_data)
pil_image = Image.open(image_bytes)
np_image = np.array(pil_image)
img = cv2.cvtColor(np_image, cv2.COLOR_BGR2RGB)
# Extract features from the referenced eye(s)
orb = cv2.ORB_create()
referenced_key_points, referenced_descriptors = orb.detectAndCompute(
img, None)
encoded_face = face_recognition.face_encodings(img)
start_time = time.time()
camera_time = 0
face_recognized = 0
eyes_match_fail_index = 0
eyes_match_index = 0
cap = cv2.VideoCapture(0)
ret, frame = cap.read()
while ret and camera_time < 9:
ret, frame = cap.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.bilateralFilter(gray, 5, 1, 1)
faces = face_cascade.detectMultiScale(gray, 1.3, 5,
minSize=(200, 200))
if len(faces) == 1:
for (x, y, w, h) in faces:
frame = cv2.rectangle(frame, (x, y), (x + w, y + h),
(0, 255, 0), 2)
eyes = eye_cascade.detectMultiScale(gray, scaleFactor=1.3,
minNeighbors=5)
current_key_points, current_descriptors = orb.detectAndCompute(
gray, None)
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
matches = bf.match(referenced_descriptors,
current_descriptors)
good_matches = [m for m in matches if m.distance < 70]
if len(good_matches) >= 5:
eyes_match_index += 1
else:
eyes_match_fail_index += 1
if len(eyes) == 0:
img_frame = cv2.resize(frame, (0, 0), None, 0.25, 0.25)
img_frame = cv2.cvtColor(img_frame, cv2.COLOR_BGR2RGB)
face_current_frame = face_recognition.face_locations(
img_frame)
encode_current_frame = face_recognition.face_encodings(
img_frame, face_current_frame)
for encode_face, face_loc in zip(encode_current_frame,
face_current_frame):
face_matches = face_recognition.compare_faces(
encoded_face, encode_face)
face_distance = face_recognition.face_distance(
encoded_face, encode_face)
match_index = np.argmin(face_distance)
elapsed_time = time.time() - start_time
if face_matches[
match_index] and eyes_match_index > eyes_match_fail_index:
face_recognized = 1
if elapsed_time > 6:
time.sleep(1)
else:
face_recognized = 0
# cv2.imshow('frame', frame)
# the imshow is removed from here because it is not needed anymore
# Also it is making error while running the code second time.
cv2.waitKey(0)
else:
# Reset the counters and related variables when no face is
# detected
camera_time += 1
eyes_match_index = 0
eyes_match_fail_index = 0
cap.release()
cv2.destroyAllWindows()
camera_time = 0
if company and face_recognized != 1:
raise AccessError(
_("Sorry, Can't recognize you. Please try again"))
else:
employee = request.env['hr.employee'].sudo().browse(employee_id)
if employee.company_id == company:
return self._get_employee_info_response(employee)
cv2.waitKey(0)
return {}
def get_image(self,employee_id):
image = request.env['hr.employee'].sudo().browse(employee_id).image_1920
return image

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face_recognized_attendance_login/data/haarcascade_eye_tree_eyeglasses.xml
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face_recognized_attendance_login/data/haarcascade_frontalface_default.xml
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5
face_recognized_attendance_login/doc/RELEASE_NOTES.md
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@ -3,3 +3,8 @@
#### Version 17.0.1.0.0
##### ADD
- Initial Commit for Face Recognized Attendance Login
#### 19.11.2024
#### Version 17.0.2.0.0
##### ADD
- Update

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face_recognized_attendance_login/static/src/js/face-api.min.js
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209
face_recognized_attendance_login/static/src/js/face_recognition.js
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@ -0,0 +1,209 @@
/** @odoo-module **/
import kiosk from "@hr_attendance/public_kiosk/public_kiosk_app";
import { patch } from "@web/core/utils/patch";
import { useService } from "@web/core/utils/hooks";
import { useRef, useState } from "@odoo/owl";
import { _t } from "@web/core/l10n/translation";
const MODEL_URL = '/face_recognized_attendance_login/static/src/js/weights';
faceapi.nets.ssdMobilenetv1.loadFromUri(MODEL_URL)
faceapi.nets.faceLandmark68Net.loadFromUri(MODEL_URL)
faceapi.nets.faceRecognitionNet.loadFromUri(MODEL_URL)
faceapi.nets.tinyFaceDetector.load(MODEL_URL),
faceapi.nets.faceLandmark68TinyNet.load(MODEL_URL),
faceapi.nets.faceExpressionNet.load(MODEL_URL),
faceapi.nets.ageGenderNet.load(MODEL_URL)
patch(kiosk.kioskAttendanceApp.prototype, {
setup() {
super.setup(...arguments);
this.orm = useService("orm");
this.employee_image = useRef("employee_image");
this.video = useRef("video");
this.notification = useService("notification");
this.state.employee = false;
this.state.verifiedEmployeeId = null
this.isRecognitionActive = false;
this.currentStream = null;
this.faceMatcher = null;
this.noMatchCount = 0;
},
async loadImage(employeeId) {
var image = await this.rpc("/get_image", {
employee_id: employeeId
})
this.have_image = image
const employee_image = this.employee_image.el
employee_image.src = "data:image/jpeg;base64," + image
this.currentVerificationId = employeeId;
},
async startWebcam() {
const video = this.video.el;
if (video) {
video.srcObject = null;
video.style.display = 'block';
}
this.isRecognitionActive = true;
this.state.employee = false;
this.noMatchCount = 0; // Reset no match counter
this.faceMatcher = null; // Reset faceMatcher
try {
const stream = await navigator.mediaDevices.getUserMedia({
video: true,
audio: false
});
this.currentStream = stream;
video.srcObject = stream;
await new Promise(resolve => {
video.onloadedmetadata = resolve;
});
await this.faceRecognition(video);
} catch (error) {
console.error("Error starting webcam:", error);
this.isRecognitionActive = false;
this.notification.add(_t("Your browser does not support camera access. Please try a different browser."), {
title : "Access Denied !",
type: "danger",
});
}
},
async getLabeledFaceDescriptions() {
const employee_image = this.employee_image.el;
const detections = await faceapi
.detectSingleFace(employee_image)
.withFaceLandmarks()
.withFaceExpressions()
.withFaceDescriptor();
return detections;
},
stopRecognition(video, canvas) {
this.isRecognitionActive = false;
if (this.currentStream) {
this.currentStream.getTracks().forEach(track => track.stop());
this.currentStream = null;
}
if (video) {
video.srcObject = null;
video.style.display = 'none';
}
if (canvas && canvas.parentNode) {
canvas.remove();
}
const modal = document.getElementById('WebCamModal');
if (modal) {
modal.style.display = 'none';
}
this.faceMatcher = null;
this.noMatchCount = 0;
},
async faceRecognition(video) {
if (!this.isRecognitionActive) return;
if (!this.faceMatcher) {
const labeledFaceDescriptors = await this.getLabeledFaceDescriptions();
if (labeledFaceDescriptors && labeledFaceDescriptors.descriptor) {
this.faceMatcher = new faceapi.FaceMatcher([labeledFaceDescriptors.descriptor]);
} else {
console.error("Could not get face descriptor from reference image");
this.notification.add(_t("Failed to initialize face recognition, Please upload a new, properly formatted image."), {
type: "danger",
title: "Image detection failed!",
});
this.stopRecognition(video);
return;
}
}
const canvas = faceapi.createCanvasFromMedia(video);
document.body.append(canvas);
canvas.style.display = 'none'
const displaySize = { width: video.videoWidth, height: video.videoHeight };
faceapi.matchDimensions(canvas, displaySize);
const processFrame = async () => {
if (!this.isRecognitionActive) return;
try {
const detections = await faceapi
.detectAllFaces(video)
.withFaceLandmarks()
.withFaceExpressions()
.withFaceDescriptors();
if (detections.length === 0) {
if (this.isRecognitionActive) {
requestAnimationFrame(processFrame);
}
return;
}
for (const detection of detections) {
const match = this.faceMatcher.findBestMatch(detection.descriptor);
if (match._distance < 0.4) {
this.state.employee = true;
this.state.verifiedEmployeeId = this.currentVerificationId;
this.stopRecognition(video, canvas);
return;
} else {
this.noMatchCount++;
if (this.noMatchCount >= 3) {
this.notification.add(_t("Sorry, cannot recognize you"), {
title:"Recognition Failed ! ",
type: "danger",
});
this.stopRecognition(video, canvas);
return;
}
}
}
if (this.isRecognitionActive) {
requestAnimationFrame(processFrame);
}
} catch (error) {
console.error("Face recognition error:", error);
this.stopRecognition(video, canvas);
}
};
processFrame();
},
async onManualSelection(employeeId, enteredPin) {
if (this.isRecognitionActive) {
this.stopRecognition(this.video.el);
}
await this.loadImage(employeeId);
if (this.have_image) {
const modal = document.getElementById('WebCamModal');
if (modal) {
modal.style.display = 'block';
}
await this.startWebcam();
const checkInterval = setInterval(() => {
if (this.state.employee && this.state.verifiedEmployeeId === employeeId) {
clearInterval(checkInterval);
this.rpc("manual_selection", {
token: this.props.token,
employee_id: employeeId,
pin_code: enteredPin,
}).then(result => {
if (result && result.attendance) {
this.employeeData = result;
this.switchDisplay("greet");
} else {
if (enteredPin) {
this.notification.add(_t("Wrong Pin"), {
type: "danger",
});
}
}
});
}
}, 500);
} else {
await this.popup.add(ErrorPopup, {
title: _t("Authentication failed"),
body: _t("Selected cashier has no image."),
});
location.reload();
}
},
});

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face_recognized_attendance_login/static/src/js/weights/age_gender_model-shard1
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618
face_recognized_attendance_login/static/src/js/weights/age_gender_model-weights_manifest.json
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@ -0,0 +1,618 @@
[
{
"weights": [
{
"name": "entry_flow/conv_in/filters",
"shape": [
3,
3,
3,
32
],
"dtype": "float32",
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"scale": 0.005431825039433498,
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}
},
{
"name": "entry_flow/conv_in/bias",
"shape": [
32
],
"dtype": "float32"
},
{
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face_recognized_attendance_login/static/src/xml/face_recognition_template.xml
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<?xml version="1.0" encoding="UTF-8"?>
<templates xml:space="preserve">
<t t-inherit="hr_attendance.public_kiosk_app" t-inherit-mode="extension">
<xpath expr="." position="inside">
<div id="WebCamModal"
style="display: block;pointer-events: none; position: fixed; z-index: 9999; top: 50%; left: 30%; transform: translate(-50%, -50%); width: 50px; background-color: transparent">
<div class="container">
<video id="video" width="500" height="500" autoplay=""
t-ref="video"
muted=""
style="display: block;margin-top: 213px; margin-left: 150px;"/>
<img id="employee_image" style="visibility:hidden;height:400px;width:400px"
t-ref="employee_image"/>
</div>
</div>
</xpath>
</t>
</templates>
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