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import os
from os.path import join
from pathlib import Path
import cv2 as cv
import numpy as np
import dlib
import imutils
from app.utils import im_utils, logger_utils
from app.models.bbox import BBox
from app.settings import app_cfg as cfg
from app.settings import types
class RecognitionDLIB:
# https://github.com/davisking/dlib/blob/master/python_examples/face_recognition.py
# facerec.compute_face_descriptor(img, shape, 100, 0.25)
def __init__(self, opt_gpu=0):
self.log = logger_utils.Logger.getLogger()
if opt_gpu > 0:
cuda_visible_devices = os.getenv('CUDA_VISIBLE_DEVICES', '')
os.environ['CUDA_VISIBLE_DEVICES'] = str(opt_gpu)
self.predictor = dlib.shape_predictor(cfg.DIR_MODELS_DLIB_5PT)
self.facerec = dlib.face_recognition_model_v1(cfg.DIR_MODELS_DLIB_FACEREC_RESNET)
os.environ['CUDA_VISIBLE_DEVICES'] = cuda_visible_devices # reset GPU env
def vec(self, im, bbox, width=100,
jitters=cfg.DLIB_FACEREC_JITTERS, padding=cfg.DLIB_FACEREC_PADDING):
# Converts image and bbox into 128d vector
# scale the image so the face is always 100x100 pixels
scale = width / bbox.width
im = cv.resize(im, (scale, scale), interploation=cv.INTER_LANCZOS4)
bbox_dlib = bbox.to_dlib()
face_shape = self.predictor(im, bbox_dlib)
vec = self.facerec.compute_face_descriptor(im, face_shape, jitters, padding)
return vec
def similarity(self, query_enc, known_enc):
return np.linalg.norm(query_enc - known_enc, axis=1)
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