updates for batch processing

1 files changed, 25 insertions, 5 deletions

diff --git a/megapixels/app/processors/face_detector.py b/megapixels/app/processors/face_detector.py
index fbf91071..7b5310c5 100644
--- a/megapixels/app/processors/face_detector.py
+++ b/megapixels/app/processors/face_detector.py
@@ -69,6 +69,7 @@ class DetectorMTCNN_TF:
   # pip install mtcnn
 
   dnn_size = (300, 300)
+  conf_thresh = 0.9
 
   def __init__(self, size=(400,400), gpu=0):
     self.log = logger_utils.Logger.getLogger()
@@ -84,17 +85,33 @@ class DetectorMTCNN_TF:
     :param im: (numpy.ndarray) image
     :returns list of BBox
     '''
+
     bboxes = []
     dnn_size = self.dnn_size if size is None else size
+    conf_thresh = self.conf_thresh if conf_thresh is None else conf_thresh
 
     im = im_utils.resize(im, width=dnn_size[0], height=dnn_size[1])
     dim = im.shape[:2][::-1]
     dets = self.detector.detect_faces(im)
+    '''
+    {
+      'box': [4, 140, 14, 18], 
+      'confidence': 0.9588413834571838, 
+      'keypoints': {
+          'left_eye': (8, 147), 
+          'right_eye': (14, 146),
+          'nose': (12, 151),
+          'mouth_left': (9, 155),
+          'mouth_right': (14, 154)
+        }
+      }
+    '''
     for det in dets:
       rect = det['box']
-      #keypoints = det['keypoints']  # not using here. see 'face_landmarks.py'
-      bbox = BBox.from_xywh_dim(*rect, dim)
-      bboxes.append(bbox)
+      conf = det['confidence']
+      if conf > conf_thresh:
+        bbox = BBox.from_xywh_dim(*rect, dim)
+        bboxes.append(bbox)
 
     if largest and len(bboxes) > 1:
       # only keep largest
@@ -222,8 +239,11 @@ class DetectorCVDNN:
 
     bboxes = []
     for i in range(0, net_outputs.shape[2]):
-      conf = net_outputs[0, 0, i, 2]
-      if conf > conf_thresh:
+      conf = float(net_outputs[0, 0, i, 2])
+      # BUG: this face detector creates ghost face detections in stage-left from nose-bottom neck
+      # temp fix is to elminate ROI extending outside of frame
+      bounds = np.array(net_outputs[0, 0, i, 3:7])
+      if conf > conf_thresh and np.all(bounds < 1):
         rect_norm = net_outputs[0, 0, i, 3:7]
         bboxes.append(BBox(*rect_norm))