1 files changed, 0 insertions, 68 deletions
diff --git a/megapixels/app/processors/face_recognition.py b/megapixels/app/processors/face_recognition.py
deleted file mode 100644
index 76f00aa1..00000000
--- a/megapixels/app/processors/face_recognition.py
+++ /dev/null
@@ -1,68 +0,0 @@
-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, gpu=0):
-    self.log = logger_utils.Logger.getLogger()
-    
-    if gpu > -1:
-      cuda_visible_devices = os.getenv('CUDA_VISIBLE_DEVICES', '')
-      os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu)
-    
-    self.predictor = dlib.shape_predictor(cfg.DIR_MODELS_DLIB_5PT)
-    self.facerec = dlib.face_recognition_model_v1(cfg.DIR_MODELS_DLIB_FACEREC_RESNET)
-    
-    if gpu > -1:
-      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
-    :param im: (numpy.ndarray) BGR image
-    :param bbox: (BBox)
-    '''
-    # scale the image so the face is always 100x100 pixels
-
-    #self.log.debug('compute scale')
-    scale = width / bbox.width
-    #im = cv.resize(im, (scale, scale), cv.INTER_LANCZOS4)
-    #self.log.debug('resize')
-    cv.resize(im, None, fx=scale, fy=scale, interpolation=cv.INTER_LANCZOS4) 
-    #self.log.debug('to dlib')
-    bbox_dlib = bbox.to_dlib()
-    #self.log.debug('precitor')
-    face_shape = self.predictor(im, bbox_dlib)
-    # vec = self.facerec.compute_face_descriptor(im, face_shape, jitters, padding)
-    #self.log.debug('vec')
-    vec = self.facerec.compute_face_descriptor(im, face_shape, jitters)
-    #vec = self.facerec.compute_face_descriptor(im, face_shape)
-    return vec
-
-  def flatten(self, vec):
-    '''Converts 128D vector into a flattened list for CSV
-    :param points: (list) a feature vector as list of floats
-    :returns dict item for each point (eg {'d1':0.28442156, 'd1': 0.1868632})
-    '''
-    vec_flat = {}
-    for idx, val in enumerate(vec, 1):
-      vec_flat[f'd{idx}'] = val
-    return vec_flat
-
-  def similarity(self, query_enc, known_enc):
-    return np.linalg.norm(query_enc - known_enc, axis=1)