diff options
Diffstat (limited to 'megapixels/app/processors/face_age_gender.py')
| -rw-r--r-- | megapixels/app/processors/face_age_gender.py | 20 |
1 files changed, 12 insertions, 8 deletions
diff --git a/megapixels/app/processors/face_age_gender.py b/megapixels/app/processors/face_age_gender.py index 95efa8fc..66c51fa8 100644 --- a/megapixels/app/processors/face_age_gender.py +++ b/megapixels/app/processors/face_age_gender.py @@ -32,19 +32,21 @@ class _FaceAgeGender: ''' dnn_size = (224,224) - dnn_mean = (104.0, 177.0, 123.0) + dnn_mean = (104.0, 177.0, 123.0) # ? + # authors used imagenet mean + #dnn_mean = [103.939, 116.779, 123.68] ages = np.arange(0, 101).reshape(101, 1) + padding = 0.4 def __init__(self, fp_prototxt, fp_model): self.log = logger_utils.Logger.getLogger() self.net = cv.dnn.readNetFromCaffe(fp_prototxt, fp_model) - def _preprocess(self, im, bbox_dim): + def _preprocess(self, im, bbox_norm): # isolate face ROI, expand bbox by 40% according to authors # https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/ dim = im.shape[:2][::-1] - bbox_dim_exp = bbox_dim.expand_dim( int(0.4*bbox_dim.width), dim) - roi = bbox_dim_exp.to_xyxy() + roi = bbox_norm.expand(self.padding).to_dim(dim).to_xyxy() im_face_crop = im[roi[1]:roi[3], roi[0]:roi[2]] # isolate face roi # resize for blob @@ -52,6 +54,7 @@ class _FaceAgeGender: blob = cv.dnn.blobFromImage(im_resized, 1.0, self.dnn_size, self.dnn_mean) return blob + class FaceGender(_FaceAgeGender): # use "apparent" age models @@ -61,17 +64,18 @@ class FaceGender(_FaceAgeGender): def __init__(self): super().__init__(self.fp_prototxt, self.fp_model) - def predict(self, im, bbox_dim): + def predict(self, im, bbox_norm): '''Predicts gender from face crop :param im: (numpy.ndarray) BGR image :param bbox_dim: (BBox) dimensioned :returns (dict) with scores for male and female ''' - im_blob = self._preprocess(im, bbox_dim) + im_blob = self._preprocess(im, bbox_norm) self.net.setInput(im_blob) preds = self.net.forward()[0] return {'f': preds[0], 'm': preds[1]} + class FaceAgeApparent(_FaceAgeGender): # use "apparent" age models @@ -81,13 +85,13 @@ class FaceAgeApparent(_FaceAgeGender): def __init__(self): super().__init__(self.fp_prototxt, self.fp_model) - def predict(self, im, bbox_dim): + def predict(self, im, bbox_norm): '''Predicts apparent age from face crop :param im: (numpy.ndarray) BGR image :param bbox_dim: (BBox) dimensioned :returns (float) predicted age ''' - im_blob = self._preprocess(im, bbox_dim) + im_blob = self._preprocess(im, bbox_norm) self.net.setInput(im_blob) preds = self.net.forward()[0] age = preds.dot(self.ages).flatten()[0] |