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testing_webcam_flask.py
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testing_webcam_flask.py
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import sys
import argparse
import cv2
from libfaceid.detector import FaceDetectorModels, FaceDetector
from libfaceid.encoder import FaceEncoderModels, FaceEncoder
# Use flask for web app
from flask import Flask, render_template, Response
app = Flask(__name__)
# Set the input directories
INPUT_DIR_DATASET = "datasets"
INPUT_DIR_MODEL_DETECTION = "models/detection/"
INPUT_DIR_MODEL_ENCODING = "models/encoding/"
INPUT_DIR_MODEL_TRAINING = "models/training/"
INPUT_DIR_MODEL_ESTIMATION = "models/estimation/"
# Set width and height
RESOLUTION_QVGA = (320, 240)
RESOLUTION_VGA = (640, 480)
RESOLUTION_HD = (1280, 720)
RESOLUTION_FULLHD = (1920, 1080)
def cam_init(cam_index, width, height):
cap = cv2.VideoCapture(cam_index)
if sys.version_info < (3, 0):
cap.set(cv2.cv.CV_CAP_PROP_FPS, 30)
cap.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH, width)
cap.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, height)
else:
cap.set(cv2.CAP_PROP_FPS, 30)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, width)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, height)
return cap
def label_face(frame, face_rect, face_id, confidence):
(x, y, w, h) = face_rect
cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 255, 255), 1)
if face_id is not None:
cv2.putText(frame, "{} {:.2f}%".format(face_id, confidence),
(x+5,y+h-5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1, cv2.LINE_AA)
def process_facerecognition():
cam_index = 0
cam_resolution = RESOLUTION_QVGA
model_detector=FaceDetectorModels.HAARCASCADE
# model_detector=FaceDetectorModels.DLIBHOG
# model_detector=FaceDetectorModels.DLIBCNN
# model_detector=FaceDetectorModels.SSDRESNET
# model_detector=FaceDetectorModels.MTCNN
# model_detector=FaceDetectorModels.FACENET
model_recognizer=FaceEncoderModels.LBPH
# model_recognizer=FaceEncoderModels.OPENFACE
# model_recognizer=FaceEncoderModels.DLIBRESNET
# model_recognizer=FaceEncoderModels.FACENET
# Initialize the camera
camera = cam_init(cam_index, cam_resolution[0], cam_resolution[1])
try:
# Initialize face detection
face_detector = FaceDetector(model=model_detector, path=INPUT_DIR_MODEL_DETECTION)
# Initialize face recognizer
face_encoder = FaceEncoder(model=model_recognizer, path=INPUT_DIR_MODEL_ENCODING, path_training=INPUT_DIR_MODEL_TRAINING, training=False)
except:
face_encoder = None
print("Warning, check if models and trained dataset models exists!")
face_id, confidence = (None, 0)
while (True):
# Capture frame from webcam
ret, frame = camera.read()
if frame is None:
print("Error, check if camera is connected!")
break
# Detect and identify faces in the frame
faces = face_detector.detect(frame)
for (index, face) in enumerate(faces):
(x, y, w, h) = face
# Indentify face based on trained dataset (note: should run facial_recognition_training.py)
if face_encoder is not None:
face_id, confidence = face_encoder.identify(frame, (x, y, w, h))
# Set text and bounding box on face
label_face(frame, (x, y, w, h), face_id, confidence)
# Process 1 face only
break
# Display updated frame to web app
yield (b'--frame\r\nContent-Type: image/jpeg\r\n\r\n' + cv2.imencode('.jpg', frame)[1].tobytes() + b'\r\n\r\n')
# Release the camera
camera.release()
cv2.destroyAllWindows()
# Initialize for web app
@app.route('/')
def index():
return render_template('web_app_flask.html')
# Entry point for web app
@app.route('/video_viewer')
def video_viewer():
return Response(process_facerecognition(), mimetype='multipart/x-mixed-replace; boundary=frame')
if __name__ == '__main__':
print("\n\nNote: Open browser and type http://127.0.0.1:5000/ or http://ip_address:5000/ \n\n")
# Run flask for web app
app.run(host='0.0.0.0', threaded=True, debug=True)