greetings , new Odoo user here
i have a project for face recognition and want to turn it into an Odoo (v12.0) module ,the code takes a picture (test.jpg) from a folder and compare it to other ones and displays the result in a new windows , but i want to try and upload an image to my custom module then read that image from the database and compare it to pictures either on my hard-drive (or on the odoo database )
(the face_trigger function is linked to a button that will trigger the whole process)
how can i read the image from the database and use it as input for the face detection program.
my code is shown below , any help will be appreciated
# -*- coding: utf-8 *-
from odoo import models, fields, _, api
import face_recognition as fr
import os
import cv2
import face_recognition
import numpy as np
class HospitalPatient(models.Model):
_name = 'hospital.patient'
_description = 'Patient Record'
patient_name = fields.Char(string='Name', required='True')
patient_age = fields.Integer('Age')
notes = fields.Text(string='Notes')
image = fields.Binary(string='Image',required='True')
@api.multi
def face_trigger(self):
def get_encoded_faces():
"""
looks through the faces folder and encodes all
the faces
:return: dict of (name, image encoded)
"""
encoded = {}
for dirpath, dnames, fnames in os.walk("./faces"):
for f in fnames:
if f.endswith(".jpg") or f.endswith(".png"):
face = fr.load_image_file("faces/" + f)
encoding = fr.face_encodings(face)[0]
encoded[f.split(".")[0]] = encoding
return encoded
def unknown_image_encoded(img):
face = fr.load_image_file("faces/" + img)
encoding = fr.face_encodings(face)[0]
return encoding
def classify_face(im):
"""
will find all of the faces in a given image and label
them if it knows what they are
:param im: str of file path
:return: list of face names
"""
faces = get_encoded_faces()
faces_encoded = list(faces.values())
known_face_names = list(faces.keys())
img = cv2.imread(im, 1)
# img = cv2.resize(img, (0, 0), fx=0.5, fy=0.5)
# img = img[:,:,::-1]
face_locations = face_recognition.face_locations(img)
unknown_face_encodings = face_recognition.face_encodings(img, face_locations)
face_names = []
for face_encoding in unknown_face_encodings:
# See if the face is a match for the known face(s)
matches = face_recognition.compare_faces(faces_encoded, face_encoding)
name = "Unknown"
# use the known face with the smallest distance to the new face
face_distances = face_recognition.face_distance(faces_encoded, face_encoding)
best_match_index = np.argmin(face_distances)
if matches[best_match_index]:
name = known_face_names[best_match_index]
face_names.append(name)
for (top, right, bottom, left), name in zip(face_locations, face_names):
# Draw a box around the face
cv2.rectangle(img, (left - 20, top - 20), (right + 20, bottom + 20), (255, 0, 0), 2)
# Draw a label with a name below the face
cv2.rectangle(img, (left - 20, bottom - 15), (right + 20, bottom + 20), (255, 0, 0), cv2.FILLED)
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(img, name, (left - 20, bottom + 15), font, 1.0, (255, 255, 255), 2)
# Display the resulting image
while True:
cv2.imshow('Video', img)
if cv2.waitKey(1) & 0xFF == ord('q'):
return face_names
print(classify_face("test.jpg"))
