added FR demo notebook

2 files changed, 44 insertions, 33 deletions

diff --git a/megapixels/app/processors/face_detector.py b/megapixels/app/processors/face_detector.py
index 593e9feb..3a90c557 100644
--- a/megapixels/app/processors/face_detector.py
+++ b/megapixels/app/processors/face_detector.py
@@ -24,15 +24,15 @@ class DetectorMTCNN:
     from mtcnn.mtcnn import MTCNN
     self.detector = MTCNN()
 
-  def detect(self, im, opt_size=(400,400), opt_conf_thresh=None, opt_pyramids=None, opt_largest=False):
+  def detect(self, im, size=(400,400), conf_thresh=None, pyramids=None, largest=False):
     '''Detects face using MTCNN and returns (list) of BBox
     :param im: (numpy.ndarray) image
     :returns list of BBox
     '''
     bboxes = []
-    #conf_thresh = self.conf_thresh if opt_conf_thresh is None else opt_conf_thresh
-    #pyramids = self.pyramids if opt_pyramids is None else opt_pyramids
-    dnn_size = self.dnn_size if opt_size is None else opt_size
+    #conf_thresh = self.conf_thresh if conf_thresh is None else conf_thresh
+    #pyramids = self.pyramids if pyramids is None else pyramids
+    dnn_size = self.dnn_size if size is None else size
 
     im = im_utils.resize(im, width=dnn_size[0], height=dnn_size[1])
     dim = im.shape[:2][::-1]
@@ -43,7 +43,7 @@ class DetectorMTCNN:
       bbox = BBox.from_xywh_dim(*rect, dim)
       bboxes.append(bbox)
 
-    if opt_largest and len(bboxes) > 1:
+    if largest and len(bboxes) > 1:
       # only keep largest
       bboxes.sort(key=operator.attrgetter('area'), reverse=True)
       bboxes = [bboxes[0]]
@@ -70,34 +70,33 @@ class DetectorDLIBCNN:
   pyramids = 0
   conf_thresh = 0.85
 
-  def __init__(self, opt_gpu=0):
+  def __init__(self, gpu=0):
     import dlib
     self.log = logger_utils.Logger.getLogger()
     cuda_visible_devices = os.getenv('CUDA_VISIBLE_DEVICES', '')
-    os.environ['CUDA_VISIBLE_DEVICES'] = str(opt_gpu)
+    os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu)
     self.log.info('load model: {}'.format(cfg.DIR_MODELS_DLIB_CNN))
     self.detector = dlib.cnn_face_detection_model_v1(cfg.DIR_MODELS_DLIB_CNN)
     os.environ['CUDA_VISIBLE_DEVICES'] = cuda_visible_devices  # reset
 
-  def detect(self, im, opt_size=None, opt_conf_thresh=None, opt_pyramids=None, opt_largest=False):
+  def detect(self, im, size=None, conf_thresh=None, pyramids=None, largest=False):
     bboxes = []
-    conf_thresh = self.conf_thresh if opt_conf_thresh is None else opt_conf_thresh
-    pyramids = self.pyramids if opt_pyramids is None else opt_pyramids
-    dnn_size = self.dnn_size if opt_size is None else opt_size
+    conf_thresh = self.conf_thresh if conf_thresh is None else conf_thresh
+    pyramids = self.pyramids if pyramids is None else pyramids
+    dnn_size = self.dnn_size if size is None else size
     # resize image
     im = im_utils.resize(im, width=dnn_size[0], height=dnn_size[1])
     dim = im.shape[:2][::-1]
     im = im_utils.bgr2rgb(im)  # convert to RGB for dlib
     # run detector
-    mmod_rects = self.detector(im, opt_pyramids)
+    mmod_rects = self.detector(im, pyramids)
     # sort results
     for mmod_rect in mmod_rects:
-      self.log.debug('conf: {}, this: {}'.format(conf_thresh, mmod_rect.confidence))
       if mmod_rect.confidence > conf_thresh:
         bbox = BBox.from_dlib_dim(mmod_rect.rect, dim)
         bboxes.append(bbox)
 
-    if opt_largest and len(bboxes) > 1:
+    if largest and len(bboxes) > 1:
       # only keep largest
       bboxes.sort(key=operator.attrgetter('area'), reverse=True)
       bboxes = [bboxes[0]]
@@ -116,25 +115,24 @@ class DetectorDLIBHOG:
     self.log = logger_utils.Logger.getLogger()
     self.detector = dlib.get_frontal_face_detector()
 
-  def detect(self, im, opt_size=None, opt_conf_thresh=None, opt_pyramids=0, opt_largest=False):
-    conf_thresh = self.conf_thresh if opt_conf_thresh is None else opt_conf_thresh
-    dnn_size = self.size if opt_size is None else opt_size
-    pyramids = self.pyramids if opt_pyramids is None else opt_pyramids
+  def detect(self, im, size=None, conf_thresh=None, pyramids=0, largest=False):
+    conf_thresh = self.conf_thresh if conf_thresh is None else conf_thresh
+    dnn_size = self.size if size is None else size
+    pyramids = self.pyramids if pyramids is None else pyramids
     
-    im = im_utils.resize(im, width=opt_size[0], height=opt_size[1])
+    im = im_utils.resize(im, width=dnn_size[0], height=dnn_size[1])
     dim = im.shape[:2][::-1]
     im = im_utils.bgr2rgb(im)  # ?
     hog_results = self.detector.run(im, pyramids)
     
     bboxes = []
     if len(hog_results[0]) > 0:
-      self.log.debug(hog_results)
       for rect, score, direction in zip(*hog_results):
         if score > conf_thresh:
           bbox = BBox.from_dlib_dim(rect, dim)
           bboxes.append(bbox)
     
-    if opt_largest and len(bboxes) > 1:
+    if largest and len(bboxes) > 1:
       # only keep largest
       bboxes.sort(key=operator.attrgetter('area'), reverse=True)
       bboxes = [bboxes[0]]
@@ -157,10 +155,10 @@ class DetectorCVDNN:
     self.net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
     self.net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU)
 
-  def detect(self, im, opt_size=None, opt_conf_thresh=None, opt_largest=False, opt_pyramids=None):
+  def detect(self, im, size=None, conf_thresh=None, largest=False, pyramids=None):
     """Detects faces and returns (list) of (BBox)"""
-    conf_thresh = self.conf_thresh if opt_conf_thresh is None else opt_conf_thresh
-    dnn_size = self.size if opt_size is None else opt_size
+    conf_thresh = self.conf_thresh if conf_thresh is None else conf_thresh
+    dnn_size = self.size if size is None else size
     im = cv.resize(im, dnn_size)
     blob = cv.dnn.blobFromImage(im, self.dnn_scale, dnn_size, self.dnn_mean)
     self.net.setInput(blob)
@@ -173,7 +171,7 @@ class DetectorCVDNN:
         rect_norm = net_outputs[0, 0, i, 3:7]
         bboxes.append(BBox(*rect_norm))
     
-    if opt_largest and len(bboxes) > 1:
+    if largest and len(bboxes) > 1:
       # only keep largest
       bboxes.sort(key=operator.attrgetter('area'), reverse=True)
       bboxes = [bboxes[0]]
diff --git a/megapixels/app/processors/face_recognition.py b/megapixels/app/processors/face_recognition.py
index 9c3a301d..e0b9f752 100644
--- a/megapixels/app/processors/face_recognition.py
+++ b/megapixels/app/processors/face_recognition.py
@@ -17,25 +17,38 @@ 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):
+  def __init__(self, gpu=0):
     self.log = logger_utils.Logger.getLogger()
-    if opt_gpu > 0:
+    
+    if gpu > -1:
       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'] = 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):
+      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
 
+    #self.log.debug('compute scale')
     scale = width / bbox.width
-    im = cv.resize(im, (scale, scale), interploation=cv.INTER_LANCZOS4)
+    #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)
+    # 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