age gender dist

AgeGenderDist.egg-info/PKG-INFO

+Metadata-Version: 1.1
+Name: AgeGenderDist
+Version: 0.1.0
+Summary: A short description of the project
+Home-page: https://github.com/rentingting/AgeGenderDist
+Author: rentingting
+Author-email: Your address email (eq. you@example.com)
+License: BSD
+Download-URL: https://github.com/rentingting/AgeGenderDist/tarball/0.1.0
+Description: AgeGenderDist
+        ===============================
+        
+        version number: 0.1.0
+        author: rentingting
+        
+        Overview
+        --------
+        
+        A short description of the project
+        
+        Installation / Usage
+        --------------------
+        
+        To install use pip:
+        
+            $ pip install AgeGenderDist
+        
+        
+        Or clone the repo:
+        
+            $ git clone https://github.com/rentingting/AgeGenderDist.git
+            $ python setup.py install
+        
+        Contributing
+        ------------
+        
+        TBD
+        
+        Example
+        -------
+        
+        TBD
+Platform: UNKNOWN
+Classifier: Development Status :: 3 - Alpha
+Classifier: Intended Audience :: Developers
+Classifier: Programming Language :: Python :: 3

AgeGenderDist.egg-info/SOURCES.txt

+MANIFEST.in
+README.md
+requirements.txt
+setup.cfg
+setup.py
+AgeGenderDist/__init__.py
+AgeGenderDist/face_image.py
+AgeGenderDist/face_model.py
+AgeGenderDist/face_preprocess.py
+AgeGenderDist/gender.py
+AgeGenderDist/helper.py
+AgeGenderDist/mtcnn_detector.py
+AgeGenderDist/noise_sgd.py
+AgeGenderDist.egg-info/PKG-INFO
+AgeGenderDist.egg-info/SOURCES.txt
+AgeGenderDist.egg-info/dependency_links.txt
+AgeGenderDist.egg-info/requires.txt
+AgeGenderDist.egg-info/top_level.txt
+AgeGenderDist/model/model-0000.params
+AgeGenderDist/model/model-symbol.json
+AgeGenderDist/mtcnn-model/det1-0001.params
+AgeGenderDist/mtcnn-model/det1-symbol.json
+AgeGenderDist/mtcnn-model/det1.caffemodel
+AgeGenderDist/mtcnn-model/det1.prototxt
+AgeGenderDist/mtcnn-model/det2-0001.params
+AgeGenderDist/mtcnn-model/det2-symbol.json
+AgeGenderDist/mtcnn-model/det2.caffemodel
+AgeGenderDist/mtcnn-model/det2.prototxt
+AgeGenderDist/mtcnn-model/det3-0001.params
+AgeGenderDist/mtcnn-model/det3-symbol.json
+AgeGenderDist/mtcnn-model/det3.caffemodel
+AgeGenderDist/mtcnn-model/det3.prototxt
+AgeGenderDist/mtcnn-model/det4-0001.params
+AgeGenderDist/mtcnn-model/det4-symbol.json
+AgeGenderDist/mtcnn-model/det4.caffemodel
+AgeGenderDist/mtcnn-model/det4.prototxt
\ No newline at end of file

AgeGenderDist.egg-info/dependency_links.txt

+

AgeGenderDist.egg-info/requires.txt

+mxnet-cu100==1.4.1
+scipy==1.2.2
+scikit-learn==0.20.3
+opencv-python==4.1.0.25
+easydict==1.9
+scikit-image==0.14.3

AgeGenderDist.egg-info/top_level.txt

+AgeGenderDist

AgeGenderDist/:wq

+import AgeGenderDist.face_model
+import argparse
+import cv2
+import sys
+import numpy as np
+import datetime
+from AgeGenderDist.mtcnn_detector import MtcnnDetector
+import os
+import mxnet as mx
+import math
+
+sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'src', 'common'))
+
+parser = argparse.ArgumentParser(description='face model test')
+# general
+parser.add_argument('--image-size', default='112,112', help='')
+parser.add_argument('--image', default='Tom_Hanks_54745.png', help='')
+parser.add_argument('--model', default='model/model,0', help='path to load model.')
+parser.add_argument('--gpu', default=0, type=int, help='gpu id')
+parser.add_argument('--det', default=0, type=int, help='mtcnn option, 1 means using R+O, 0 means detect from begining')
+args = parser.parse_args()
+
+def get_age_gender_dist(img_src):
+   if args.gpu>=0:
+      ctx = mx.gpu(args.gpu)
+   else:
+      ctx = mx.cpu()
+   det_threshold = [0.6,0.7,0.8]
+   mtcnn_path = os.path.join(os.path.dirname(__file__), 'mtcnn-model')
+   if args.det==0:
+      detector = MtcnnDetector(model_folder=mtcnn_path, ctx=ctx, num_worker=1, accurate_landmark = True, threshold=det_threshold)
+   else:
+      detector = MtcnnDetector(model_folder=mtcnn_path, ctx=ctx, num_worker=1, accurate_landmark = True, threshold=[0.0,0.0,0.2])
+   ret = detector.detect_face(img_src, det_type = args.det)
+   if ret is None:
+      print('ret is none')
+   bbox, points = ret
+   points = points[0,:].reshape((2,5)).T
+   im=img_src.copy()
+   lf_eye=points[0]
+   rt_eye=points[1]
+   tmp=rt_eye-lf_eye
+   dist=math.hypot(tmp[0],tmp[1])
+  
+   model=face_model.FaceModel(args)
+   img=model.get_input(img_src)
+   gender,age=model.get_ga(img)
+    
+   return age,gender,dist
+
+def get_gender_age(img):
+    model=face_model.FaceModel(args)
+    img=model.get_input(img)
+    gender,age=model.get_ga(img)
+    return gender,age
+
+if __name__=='__main__':          
+   dirs= os.listdir(args.image)
+   for file in dirs:
+       print('file',file)
+       imgdir=os.path.join('testimg',file)
+       img_src = cv2.imread(imgdir)
+       gender,age=get_gender_age(img_src)
+       age,gender,dist=get_age_gender_dist(img_src)
+       print(age,gender,dist)
+       #print('gender:',gender)
+
+

AgeGenderDist/face_image.py

+
+from easydict import EasyDict as edict
+import os
+import json
+import numpy as np
+
+
+def load_property(data_dir):
+  prop = edict()
+  for line in open(os.path.join(data_dir, 'property')):
+    vec = line.strip().split(',')
+    assert len(vec)==3
+    prop.num_classes = int(vec[0])
+    prop.image_size = [int(vec[1]), int(vec[2])]
+  return prop
+
+
+
+def get_dataset_webface(input_dir):
+  clean_list_file = input_dir+"_clean_list.txt"
+  ret = []
+  for line in open(clean_list_file, 'r'):
+    vec = line.strip().split()
+    assert len(vec)==2
+    fimage = edict()
+    fimage.id = vec[0].replace("\\", '/')
+    fimage.classname = vec[1]
+    fimage.image_path = os.path.join(input_dir, fimage.id)
+    ret.append(fimage)
+  return ret
+
+def get_dataset_celeb(input_dir):
+  clean_list_file = input_dir+"_clean_list.txt"
+  ret = []
+  dir2label = {}
+  for line in open(clean_list_file, 'r'):
+    line = line.strip()
+    if not line.startswith('./m.'):
+      continue
+    line = line[2:]
+    vec = line.split('/')
+    assert len(vec)==2
+    if vec[0] in dir2label:
+      label = dir2label[vec[0]]
+    else:
+      label = len(dir2label)
+      dir2label[vec[0]] = label
+
+    fimage = edict()
+    fimage.id = line
+    fimage.classname = str(label)
+    fimage.image_path = os.path.join(input_dir, fimage.id)
+    ret.append(fimage)
+  return ret
+
+def _get_dataset_celeb(input_dir):
+  list_file = input_dir+"_original_list.txt"
+  ret = []
+  for line in open(list_file, 'r'):
+    vec = line.strip().split()
+    assert len(vec)==2
+    fimage = edict()
+    fimage.id = vec[0]
+    fimage.classname = vec[1]
+    fimage.image_path = os.path.join(input_dir, fimage.id)
+    ret.append(fimage)
+  return ret
+
+def get_dataset_facescrub(input_dir):
+  ret = []
+  label = 0
+  person_names = []
+  for person_name in os.listdir(input_dir):
+    person_names.append(person_name)
+  person_names = sorted(person_names)
+  for person_name in person_names:
+    subdir = os.path.join(input_dir, person_name)
+    if not os.path.isdir(subdir):
+      continue
+    for _img in os.listdir(subdir):
+      fimage = edict()
+      fimage.id = os.path.join(person_name, _img)
+      fimage.classname = str(label)
+      fimage.image_path = os.path.join(subdir, _img)
+      fimage.landmark = None
+      fimage.bbox = None
+      ret.append(fimage)
+    label += 1
+  return ret
+
+def get_dataset_megaface(input_dir):
+  ret = []
+  label = 0
+  for prefixdir in os.listdir(input_dir):
+    _prefixdir = os.path.join(input_dir, prefixdir)
+    for subdir in os.listdir(_prefixdir):
+      _subdir = os.path.join(_prefixdir, subdir)
+      if not os.path.isdir(_subdir):
+        continue
+      for img in os.listdir(_subdir):
+        if not img.endswith('.jpg.jpg') and img.endswith('.jpg'):
+          fimage = edict()
+          fimage.id = os.path.join(prefixdir, subdir, img)
+          fimage.classname = str(label)
+          fimage.image_path = os.path.join(_subdir, img)
+          json_file = fimage.image_path+".json"
+          data = None
+          fimage.bbox = None
+          fimage.landmark = None
+          if os.path.exists(json_file):
+            with open(json_file, 'r') as f:
+              data = f.read()
+              data = json.loads(data)
+            assert data is not None
+            if 'bounding_box' in data:
+              fimage.bbox = np.zeros( (4,), dtype=np.float32 )
+              bb = data['bounding_box']
+              fimage.bbox[0] = bb['x']
+              fimage.bbox[1] = bb['y']
+              fimage.bbox[2] = bb['x']+bb['width']
+              fimage.bbox[3] = bb['y']+bb['height']
+              #print('bb')
+            if 'landmarks' in data:
+              landmarks = data['landmarks']
+              if '1' in landmarks and '0' in landmarks and '2' in landmarks:
+                fimage.landmark = np.zeros( (3,2), dtype=np.float32 )
+                fimage.landmark[0][0] = landmarks['1']['x']
+                fimage.landmark[0][1] = landmarks['1']['y']
+                fimage.landmark[1][0] = landmarks['0']['x']
+                fimage.landmark[1][1] = landmarks['0']['y']
+                fimage.landmark[2][0] = landmarks['2']['x']
+                fimage.landmark[2][1] = landmarks['2']['y']
+              #print('lm')
+
+          ret.append(fimage)
+      label+=1
+  return ret
+
+def get_dataset_fgnet(input_dir):
+  ret = []
+  label = 0
+  for subdir in os.listdir(input_dir):
+    _subdir = os.path.join(input_dir, subdir)
+    if not os.path.isdir(_subdir):
+      continue
+    for img in os.listdir(_subdir):
+      if img.endswith('.JPG'):
+        fimage = edict()
+        fimage.id = os.path.join(_subdir, img)
+        fimage.classname = str(label)
+        fimage.image_path = os.path.join(_subdir, img)
+        json_file = fimage.image_path+".json"
+        data = None
+        fimage.bbox = None
+        fimage.landmark = None
+        if os.path.exists(json_file):
+          with open(json_file, 'r') as f:
+            data = f.read()
+            data = json.loads(data)
+          assert data is not None
+          if 'bounding_box' in data:
+            fimage.bbox = np.zeros( (4,), dtype=np.float32 )
+            bb = data['bounding_box']
+            fimage.bbox[0] = bb['x']
+            fimage.bbox[1] = bb['y']
+            fimage.bbox[2] = bb['x']+bb['width']
+            fimage.bbox[3] = bb['y']+bb['height']
+            #print('bb')
+          if 'landmarks' in data:
+            landmarks = data['landmarks']
+            if '1' in landmarks and '0' in landmarks and '2' in landmarks:
+              fimage.landmark = np.zeros( (3,2), dtype=np.float32 )
+              fimage.landmark[0][0] = landmarks['1']['x']
+              fimage.landmark[0][1] = landmarks['1']['y']
+              fimage.landmark[1][0] = landmarks['0']['x']
+              fimage.landmark[1][1] = landmarks['0']['y']
+              fimage.landmark[2][0] = landmarks['2']['x']
+              fimage.landmark[2][1] = landmarks['2']['y']
+            #print('lm')
+
+        #fimage.landmark = None
+        ret.append(fimage)
+    label+=1
+  return ret
+
+def get_dataset_ytf(input_dir):
+  ret = []
+  label = 0
+  person_names = []
+  for person_name in os.listdir(input_dir):
+    person_names.append(person_name)
+  person_names = sorted(person_names)
+  for person_name in person_names:
+    _subdir = os.path.join(input_dir, person_name)
+    if not os.path.isdir(_subdir):
+      continue
+    for _subdir2 in os.listdir(_subdir):
+      _subdir2 = os.path.join(_subdir, _subdir2)
+      if not os.path.isdir(_subdir2):
+        continue
+      _ret = []
+      for img in os.listdir(_subdir2):
+        fimage = edict()
+        fimage.id = os.path.join(_subdir2, img)
+        fimage.classname = str(label)
+        fimage.image_path = os.path.join(_subdir2, img)
+        fimage.bbox = None
+        fimage.landmark = None
+        _ret.append(fimage)
+      ret += _ret
+    label+=1
+  return ret
+
+def get_dataset_clfw(input_dir):
+  ret = []
+  label = 0
+  for img in os.listdir(input_dir):
+    fimage = edict()
+    fimage.id = img
+    fimage.classname = str(0)
+    fimage.image_path = os.path.join(input_dir, img)
+    fimage.bbox = None
+    fimage.landmark = None
+    ret.append(fimage)
+  return ret
+
+def get_dataset_common(input_dir, min_images = 1):
+  ret = []
+  label = 0
+  person_names = []
+  for person_name in os.listdir(input_dir):
+    person_names.append(person_name)
+  person_names = sorted(person_names)
+  for person_name in person_names:
+    _subdir = os.path.join(input_dir, person_name)
+    if not os.path.isdir(_subdir):
+      continue
+    _ret = []
+    for img in os.listdir(_subdir):
+      fimage = edict()
+      fimage.id = os.path.join(person_name, img)
+      fimage.classname = str(label)
+      fimage.image_path = os.path.join(_subdir, img)
+      fimage.bbox = None
+      fimage.landmark = None
+      _ret.append(fimage)
+    if len(_ret)>=min_images:
+      ret += _ret
+      label+=1
+  return ret
+
+def get_dataset(name, input_dir):
+  if name=='webface' or name=='lfw' or name=='vgg':
+    return get_dataset_common(input_dir)
+  if name=='celeb':
+    return get_dataset_celeb(input_dir)
+  if name=='facescrub':
+    return get_dataset_facescrub(input_dir)
+  if name=='megaface':
+    return get_dataset_megaface(input_dir)
+  if name=='fgnet':
+    return get_dataset_fgnet(input_dir)
+  if name=='ytf':
+    return get_dataset_ytf(input_dir)
+  if name=='clfw':
+    return get_dataset_clfw(input_dir)
+  return None
+
+

AgeGenderDist/face_model.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from scipy import misc
+import sys
+import os
+import argparse
+#import tensorflow as tf
+import numpy as np
+import mxnet as mx
+import random
+import cv2
+import sklearn
+from sklearn.decomposition import PCA
+from time import sleep
+from easydict import EasyDict as edict
+from AgeGenderDist.mtcnn_detector import MtcnnDetector
+sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'src', 'common'))
+import AgeGenderDist.face_image
+import AgeGenderDist.face_preprocess
+
+
+def do_flip(data):
+  for idx in range(data.shape[0]):
+    data[idx,:,:] = np.fliplr(data[idx,:,:])
+
+def get_model(ctx, image_size, model_str, layer):
+  _vec = model_str.split(',')
+  assert len(_vec)==2
+  prefix = _vec[0]
+  epoch = int(_vec[1])
+  print('loading',prefix, epoch)
+  sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch)
+  all_layers = sym.get_internals()
+  sym = all_layers[layer+'_output']
+  model = mx.mod.Module(symbol=sym, context=ctx, label_names = None)
+  #model.bind(data_shapes=[('data', (args.batch_size, 3, image_size[0], image_size[1]))], label_shapes=[('softmax_label', (args.batch_size,))])
+  model.bind(data_shapes=[('data', (1, 3, image_size[0], image_size[1]))])
+  model.set_params(arg_params, aux_params)
+  return model
+
+class FaceModel:
+  def __init__(self, args):
+    self.args = args
+    if args.gpu>=0:
+      ctx = mx.gpu(args.gpu)
+    else:
+      ctx = mx.cpu()
+    _vec = args.image_size.split(',')
+    assert len(_vec)==2
+    image_size = (int(_vec[0]), int(_vec[1]))
+    self.model = None
+    if len(args.model)>0:
+      self.model = get_model(ctx, image_size, args.model, 'fc1')
+
+    self.det_minsize = 50
+    self.det_threshold = [0.6,0.7,0.8]
+    #self.det_factor = 0.9
+    self.image_size = image_size
+    mtcnn_path = os.path.join(os.path.dirname(__file__), 'mtcnn-model')
+    if args.det==0:
+      detector = MtcnnDetector(model_folder=mtcnn_path, ctx=ctx, num_worker=1, accurate_landmark = True, threshold=self.det_threshold)
+    else:
+      detector = MtcnnDetector(model_folder=mtcnn_path, ctx=ctx, num_worker=1, accurate_landmark = True, threshold=[0.0,0.0,0.2])
+    self.detector = detector
+
+
+  def get_input(self, face_img):
+    ret = self.detector.detect_face(face_img, det_type = self.args.det)
+    #print('ret',ret)
+    if ret is None:
+      return None
+    bbox, points = ret
+    if bbox.shape[0]==0:
+      return None
+    bbox = bbox[0,0:4]
+    points = points[0,:].reshape((2,5)).T
+    #print(bbox)
+    #print(points)
+    nimg = face_preprocess.preprocess(face_img, bbox, points, image_size='112,112')
+    nimg = cv2.cvtColor(nimg, cv2.COLOR_BGR2RGB)
+    aligned = np.transpose(nimg, (2,0,1))
+    input_blob = np.expand_dims(aligned, axis=0)
+    data = mx.nd.array(input_blob)
+    db = mx.io.DataBatch(data=(data,))
+    return db
+
+
+  def get_ga(self, data):
+    self.model.forward(data, is_train=False)
+    ret = self.model.get_outputs()[0].asnumpy()
+    g = ret[:,0:2].flatten()
+    gender = np.argmax(g)
+    a = ret[:,2:202].reshape( (100,2) )
+    a = np.argmax(a, axis=1)
+    age = int(sum(a))
+
+    return gender, age
+

AgeGenderDist/face_preprocess.py

+
+import cv2
+import numpy as np
+from skimage import transform as trans
+
+def parse_lst_line(line):
+  vec = line.strip().split("\t")
+  assert len(vec)>=3
+  aligned = int(vec[0])
+  image_path = vec[1]
+  label = int(vec[2])
+  bbox = None
+  landmark = None
+  #print(vec)
+  if len(vec)>3:
+    bbox = np.zeros( (4,), dtype=np.int32)
+    for i in xrange(3,7):
+      bbox[i-3] = int(vec[i])
+    landmark = None
+    if len(vec)>7:
+      _l = []
+      for i in xrange(7,17):
+        _l.append(float(vec[i]))
+      landmark = np.array(_l).reshape( (2,5) ).T
+  #print(aligned)
+  return image_path, label, bbox, landmark, aligned
+
+
+
+
+def read_image(img_path, **kwargs):
+  mode = kwargs.get('mode', 'rgb')
+  layout = kwargs.get('layout', 'HWC')
+  if mode=='gray':
+    img = cv2.imread(img_path, cv2.CV_LOAD_IMAGE_GRAYSCALE)
+  else:
+    img = cv2.imread(img_path, cv2.CV_LOAD_IMAGE_COLOR)
+    if mode=='rgb':
+      #print('to rgb')
+      img = img[...,::-1]
+    if layout=='CHW':
+      img = np.transpose(img, (2,0,1))
+  return img
+
+
+def preprocess(img, bbox=None, landmark=None, **kwargs):
+  if isinstance(img, str):
+    img = read_image(img, **kwargs)
+  M = None
+  image_size = []
+  str_image_size = kwargs.get('image_size', '')
+  if len(str_image_size)>0:
+    image_size = [int(x) for x in str_image_size.split(',')]
+    if len(image_size)==1:
+      image_size = [image_size[0], image_size[0]]
+    assert len(image_size)==2
+    assert image_size[0]==112
+    assert image_size[0]==112 or image_size[1]==96
+  if landmark is not None:
+    assert len(image_size)==2
+    src = np.array([
+      [30.2946, 51.6963],
+      [65.5318, 51.5014],
+      [48.0252, 71.7366],
+      [33.5493, 92.3655],
+      [62.7299, 92.2041] ], dtype=np.float32 )
+    if image_size[1]==112:
+      src[:,0] += 8.0
+    dst = landmark.astype(np.float32)
+
+    tform = trans.SimilarityTransform()
+    tform.estimate(dst, src)
+    M = tform.params[0:2,:]
+    #M = cv2.estimateRigidTransform( dst.reshape(1,5,2), src.reshape(1,5,2), False)
+
+  if M is None:
+    if bbox is None: #use center crop
+      det = np.zeros(4, dtype=np.int32)
+      det[0] = int(img.shape[1]*0.0625)
+      det[1] = int(img.shape[0]*0.0625)
+      det[2] = img.shape[1] - det[0]
+      det[3] = img.shape[0] - det[1]
+    else:
+      det = bbox
+    margin = kwargs.get('margin', 44)
+    bb = np.zeros(4, dtype=np.int32)
+    bb[0] = np.maximum(det[0]-margin/2, 0)
+    bb[1] = np.maximum(det[1]-margin/2, 0)
+    bb[2] = np.minimum(det[2]+margin/2, img.shape[1])
+    bb[3] = np.minimum(det[3]+margin/2, img.shape[0])
+    ret = img[bb[1]:bb[3],bb[0]:bb[2],:]
+    if len(image_size)>0:
+      ret = cv2.resize(ret, (image_size[1], image_size[0]))
+    return ret 
+  else: #do align using landmark
+    assert len(image_size)==2
+
+    #src = src[0:3,:]
+    #dst = dst[0:3,:]
+
+
+    #print(src.shape, dst.shape)
+    #print(src)
+    #print(dst)
+    #print(M)
+    warped = cv2.warpAffine(img,M,(image_size[1],image_size[0]), borderValue = 0.0)
+
+    #tform3 = trans.ProjectiveTransform()
+    #tform3.estimate(src, dst)
+    #warped = trans.warp(img, tform3, output_shape=_shape)
+    return warped
+
+

AgeGenderDist/gender.py

+import AgeGenderDist.face_model
+import argparse
+import cv2
+import sys
+import numpy as np
+import datetime
+from AgeGenderDist.mtcnn_detector import MtcnnDetector
+import os
+import mxnet as mx
+import math
+
+sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'src', 'common'))
+
+parser = argparse.ArgumentParser(description='face model test')
+# general
+parser.add_argument('--image-size', default='112,112', help='')
+parser.add_argument('--image', default='Tom_Hanks_54745.png', help='')
+parser.add_argument('--model', default='model/model,0', help='path to load model.')
+parser.add_argument('--gpu', default=0, type=int, help='gpu id')
+parser.add_argument('--det', default=0, type=int, help='mtcnn option, 1 means using R+O, 0 means detect from begining')
+args = parser.parse_args()
+
+def get_age_gender_dist(img_src):
+   if args.gpu>=0:
+      ctx = mx.gpu(args.gpu)
+   else:
+      ctx = mx.cpu()
+   det_threshold = [0.6,0.7,0.8]
+   mtcnn_path = os.path.join(os.path.dirname(__file__), 'mtcnn-model')
+   if args.det==0:
+      detector = MtcnnDetector(model_folder=mtcnn_path, ctx=ctx, num_worker=1, accurate_landmark = True, threshold=det_threshold)
+   else:
+      detector = MtcnnDetector(model_folder=mtcnn_path, ctx=ctx, num_worker=1, accurate_landmark = True, threshold=[0.0,0.0,0.2])
+   ret = detector.detect_face(img_src, det_type = args.det)
+   if ret is None:
+      print('ret is none')
+   bbox, points = ret
+   points = points[0,:].reshape((2,5)).T
+   im=img_src.copy()
+   lf_eye=points[0]
+   rt_eye=points[1]
+   tmp=rt_eye-lf_eye
+   dist=math.hypot(tmp[0],tmp[1])
+  
+   model=face_model.FaceModel(args)
+   img=model.get_input(img_src)
+   gender,age=model.get_ga(img)
+    
+   return age,gender,dist
+
+def get_gender_age(img):
+    model=face_model.FaceModel(args)
+    img=model.get_input(img)
+    gender,age=model.get_ga(img)
+    return gender,age
+
+if __name__=='__main__':          
+   dirs= os.listdir(args.image)
+   for file in dirs:
+       print('file',file)
+       imgdir=os.path.join('testimg',file)
+       img_src = cv2.imread(imgdir)
+       gender,age=get_gender_age(img_src)
+       age,gender,dist=get_age_gender_dist(img_src)
+       print(age,gender,dist)
+       #print('gender:',gender)
+
+

AgeGenderDist/helper.py

+# coding: utf-8
+# YuanYang
+import math
+import cv2
+import numpy as np
+
+
+def nms(boxes, overlap_threshold, mode='Union'):
+    """
+        non max suppression
+
+    Parameters:
+    ----------
+        box: numpy array n x 5
+            input bbox array
+        overlap_threshold: float number
+            threshold of overlap
+        mode: float number
+            how to compute overlap ratio, 'Union' or 'Min'
+    Returns:
+    -------
+        index array of the selected bbox
+    """
+    # if there are no boxes, return an empty list
+    if len(boxes) == 0:
+        return []
+
+    # if the bounding boxes integers, convert them to floats
+    if boxes.dtype.kind == "i":
+        boxes = boxes.astype("float")
+
+    # initialize the list of picked indexes
+    pick = []
+
+    # grab the coordinates of the bounding boxes
+    x1, y1, x2, y2, score = [boxes[:, i] for i in range(5)]
+
+    area = (x2 - x1 + 1) * (y2 - y1 + 1)
+    idxs = np.argsort(score)
+
+    # keep looping while some indexes still remain in the indexes list
+    while len(idxs) > 0:
+        # grab the last index in the indexes list and add the index value to the list of picked indexes
+        last = len(idxs) - 1
+        i = idxs[last]
+        pick.append(i)
+
+        xx1 = np.maximum(x1[i], x1[idxs[:last]])
+        yy1 = np.maximum(y1[i], y1[idxs[:last]])
+        xx2 = np.minimum(x2[i], x2[idxs[:last]])
+        yy2 = np.minimum(y2[i], y2[idxs[:last]])
+
+        # compute the width and height of the bounding box
+        w = np.maximum(0, xx2 - xx1 + 1)
+        h = np.maximum(0, yy2 - yy1 + 1)
+
+        inter = w * h
+        if mode == 'Min':
+            overlap = inter / np.minimum(area[i], area[idxs[:last]])
+        else:
+            overlap = inter / (area[i] + area[idxs[:last]] - inter)
+
+        # delete all indexes from the index list that have
+        idxs = np.delete(idxs, np.concatenate(([last],
+                                               np.where(overlap > overlap_threshold)[0])))
+
+    return pick
+
+def adjust_input(in_data):
+    """
+        adjust the input from (h, w, c) to ( 1, c, h, w) for network input
+
+    Parameters:
+    ----------
+        in_data: numpy array of shape (h, w, c)
+            input data
+    Returns:
+    -------
+        out_data: numpy array of shape (1, c, h, w)
+            reshaped array
+    """
+    if in_data.dtype is not np.dtype('float32'):
+        out_data = in_data.astype(np.float32)
+    else:
+        out_data = in_data
+
+    out_data = out_data.transpose((2,0,1))
+    out_data = np.expand_dims(out_data, 0)
+    out_data = (out_data - 127.5)*0.0078125
+    return out_data
+
+def generate_bbox(map, reg, scale, threshold):
+     """
+         generate bbox from feature map
+     Parameters:
+     ----------
+         map: numpy array , n x m x 1
+             detect score for each position
+         reg: numpy array , n x m x 4
+             bbox
+         scale: float number
+             scale of this detection
+         threshold: float number
+             detect threshold
+     Returns:
+     -------
+         bbox array
+     """
+     stride = 2
+     cellsize = 12
+
+     t_index = np.where(map>threshold)
+
+     # find nothing
+     if t_index[0].size == 0:
+         return np.array([])
+
+     dx1, dy1, dx2, dy2 = [reg[0, i, t_index[0], t_index[1]] for i in range(4)]
+
+     reg = np.array([dx1, dy1, dx2, dy2])
+     score = map[t_index[0], t_index[1]]
+     boundingbox = np.vstack([np.round((stride*t_index[1]+1)/scale),
+                              np.round((stride*t_index[0]+1)/scale),
+                              np.round((stride*t_index[1]+1+cellsize)/scale),
+                              np.round((stride*t_index[0]+1+cellsize)/scale),
+                              score,
+                              reg])
+
+     return boundingbox.T
+
+
+def detect_first_stage(img, net, scale, threshold):
+    """
+        run PNet for first stage
+    
+    Parameters:
+    ----------
+        img: numpy array, bgr order
+            input image
+        scale: float number
+            how much should the input image scale
+        net: PNet
+            worker
+    Returns:
+    -------
+        total_boxes : bboxes
+    """
+    height, width, _ = img.shape
+    hs = int(math.ceil(height * scale))
+    ws = int(math.ceil(width * scale))
+    
+    im_data = cv2.resize(img, (ws,hs))
+    
+    # adjust for the network input
+    input_buf = adjust_input(im_data)
+    output = net.predict(input_buf)
+    boxes = generate_bbox(output[1][0,1,:,:], output[0], scale, threshold)
+
+    if boxes.size == 0:
+        return None
+
+    # nms
+    pick = nms(boxes[:,0:5], 0.5, mode='Union')
+    boxes = boxes[pick]
+    return boxes
+
+def detect_first_stage_warpper( args ):
+    return detect_first_stage(*args)

AgeGenderDist/mtcnn-model/det1-symbol.json

+{
+  "nodes": [
+    {
+      "op": "null", 
+      "param": {}, 
+      "name": "data", 
+      "inputs": [], 
+      "backward_source_id": -1
+    }, 
+    {
+      "op": "null", 
+      "param": {}, 
+      "name": "conv1_weight", 
+      "inputs": [], 
+      "backward_source_id": -1
+    }, 
+    {
+      "op": "null", 
+      "param": {}, 
+      "name": "conv1_bias", 
+      "inputs": [], 
+      "backward_source_id": -1
+    }, 
+    {
+      "op": "Convolution", 
+      "param": {
+        "cudnn_off": "False", 
+        "cudnn_tune": "off", 
+        "dilate": "(1,1)", 
+        "kernel": "(3,3)", 
+        "no_bias": "False", 
+        "num_filter": "10", 
+        "num_group": "1", 
+        "pad": "(0,0)", 
+        "stride": "(1,1)", 
+        "workspace": "1024"
+      }, 
+      "name": "conv1", 
+      "inputs": [[0, 0], [1, 0], [2, 0]], 
+      "backward_source_id": -1
+    }, 
+    {
+      "op": "null", 
+      "param": {}, 
+      "name": "prelu1_gamma", 
+      "inputs": [], 
+      "backward_source_id": -1
+    }, 
+    {
+      "op": "LeakyReLU", 
+      "param": {
+        "act_type": "prelu", 
+        "lower_bound": "0.125", 
+        "slope": "0.25", 
+        "upper_bound": "0.334"
+      }, 
+      "name": "prelu1", 
+      "inputs": [[3, 0], [4, 0]], 
+      "backward_source_id": -1
+    }, 
+    {
+      "op": "Pooling", 
+      "param": {
+        "global_pool": "False", 
+        "kernel": "(2,2)", 
+        "pad": "(0,0)", 
+        "pool_type": "max", 
+        "pooling_convention": "full", 
+        "stride": "(2,2)"
+      }, 
+      "name": "pool1", 
+      "inputs": [[5, 0]], 
+      "backward_source_id": -1
+    }, 
+    {
+      "op": "null", 
+      "param": {}, 
+      "name": "conv2_weight", 
+      "inputs": [], 
+      "backward_source_id": -1
+    }, 
+    {
+      "op": "null", 
+      "param": {}, 
+      "name": "conv2_bias", 
+      "inputs": [], 
+      "backward_source_id": -1
+    }, 
+    {
+      "op": "Convolution", 
+      "param": {
+        "cudnn_off": "False", 
+        "cudnn_tune": "off", 
+        "dilate": "(1,1)", 
+        "kernel": "(3,3)", 
+        "no_bias": "False", 
+        "num_filter": "16", 
+        "num_group": "1", 
+        "pad": "(0,0)", 
+        "stride": "(1,1)", 
+        "workspace": "1024"
+      }, 
+      "name": "conv2", 
+      "inputs": [[6, 0], [7, 0], [8, 0]], 
+      "backward_source_id": -1
+    }, 
+    {
+      "op": "null", 
+      "param": {}, 
+      "name": "prelu2_gamma", 
+      "inputs": [], 
+      "backward_source_id": -1
+    }, 
+    {
+      "op": "LeakyReLU", 
+      "param": {
+        "act_type": "prelu", 
+        "lower_bound": "0.125", 
+        "slope": "0.25", 
+        "upper_bound": "0.334"
+      }, 
+      "name": "prelu2", 
+      "inputs": [[9, 0], [10, 0]], 
+      "backward_source_id": -1
+    }, 
+    {
+      "op": "null", 
+      "param": {}, 
+      "name": "conv3_weight", 
+      "inputs": [], 
+      "backward_source_id": -1
+    }, 
+    {
+      "op": "null", 
+      "param": {}, 
+      "name": "conv3_bias", 
+      "inputs": [], 
+      "backward_source_id": -1
+    }, 
+    {
+      "op": "Convolution", 
+      "param": {
+        "cudnn_off": "False", 
+        "cudnn_tune": "off", 
+        "dilate": "(1,1)", 
+        "kernel": "(3,3)", 
+        "no_bias": "False", 
+        "num_filter": "32", 
+        "num_group": "1", 
+        "pad": "(0,0)", 
+        "stride": "(1,1)", 
+        "workspace": "1024"
+      }, 
+      "name": "conv3", 
+      "inputs": [[11, 0], [12, 0], [13, 0]], 
+      "backward_source_id": -1
+    }, 
+    {
+      "op": "null", 
+      "param": {}, 
+      "name": "prelu3_gamma", 
+      "inputs": [], 
+      "backward_source_id": -1
+    }, 
+    {
+      "op": "LeakyReLU", 
+      "param": {
+        "act_type": "prelu", 
+        "lower_bound": "0.125", 
+        "slope": "0.25", 
+        "upper_bound": "0.334"
+      }, 
+      "name": "prelu3", 
+      "inputs": [[14, 0], [15, 0]], 
+      "backward_source_id": -1
+    }, 
+    {
+      "op": "null", 
+      "param": {}, 
+      "name": "conv4_2_weight", 
+      "inputs": [], 
+      "backward_source_id": -1
+    }, 
+    {
+      "op": "null", 
+      "param": {}, 
+      "name": "conv4_2_bias", 
+      "inputs": [], 
+      "backward_source_id": -1
+    }, 
+    {
+      "op": "Convolution", 
+      "param": {
+        "cudnn_off": "False", 
+        "cudnn_tune": "off", 
+        "dilate": "(1,1)", 
+        "kernel": "(1,1)", 
+        "no_bias": "False", 
+        "num_filter": "4", 
+        "num_group": "1", 
+        "pad": "(0,0)", 
+        "stride": "(1,1)", 
+        "workspace": "1024"
+      }, 
+      "name": "conv4_2", 
+      "inputs": [[16, 0], [17, 0], [18, 0]], 
+      "backward_source_id": -1
+    }, 
+    {
+      "op": "null", 
+      "param": {}, 
+      "name": "conv4_1_weight", 
+      "inputs": [], 
+      "backward_source_id": -1
+    }, 
+    {
+      "op": "null", 
+      "param": {}, 
+      "name": "conv4_1_bias", 
+      "inputs": [], 
+      "backward_source_id": -1
+    }, 
+    {
+      "op": "Convolution", 
+      "param": {
+        "cudnn_off": "False", 
+        "cudnn_tune": "off", 
+        "dilate": "(1,1)", 
+        "kernel": "(1,1)", 
+        "no_bias": "False", 
+        "num_filter": "2", 
+        "num_group": "1", 
+        "pad": "(0,0)", 
+        "stride": "(1,1)", 
+        "workspace": "1024"
+      }, 
+      "name": "conv4_1", 
+      "inputs": [[16, 0], [20, 0], [21, 0]], 
+      "backward_source_id": -1
+    }, 
+    {
+      "op": "SoftmaxActivation", 
+      "param": {"mode": "channel"}, 
+      "name": "prob1", 
+      "inputs": [[22, 0]], 
+      "backward_source_id": -1
+    }
+  ], 
+  "arg_nodes": [
+    0, 
+    1, 
+    2, 
+    4, 
+    7, 
+    8, 
+    10, 
+    12, 
+    13, 
+    15, 
+    17, 
+    18, 
+    20, 
+    21
+  ], 
+  "heads": [[19, 0], [23, 0]]
+}
\ No newline at end of file

AgeGenderDist/mtcnn-model/det1.prototxt

+name: "PNet"
+input: "data"
+input_dim: 1
+input_dim: 3
+input_dim: 12
+input_dim: 12
+
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 10
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "PReLU1"
+  type: "PReLU"
+  bottom: "conv1"
+  top: "conv1"
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "conv1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 16
+    kernel_size: 3
+    stride: 1
+     weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "PReLU2"
+  type: "PReLU"
+  bottom: "conv2"
+  top: "conv2"
+}
+
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "conv2"
+  top: "conv3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 32
+    kernel_size: 3
+    stride: 1
+     weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+	  type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "PReLU3"
+  type: "PReLU"
+  bottom: "conv3"
+  top: "conv3"
+}
+
+
+layer {
+  name: "conv4-1"
+  type: "Convolution"
+  bottom: "conv3"
+  top: "conv4-1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 2
+    kernel_size: 1
+    stride: 1
+     weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  name: "conv4-2"
+  type: "Convolution"
+  bottom: "conv3"
+  top: "conv4-2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 4
+    kernel_size: 1
+    stride: 1
+     weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "prob1"
+  type: "Softmax"
+  bottom: "conv4-1"
+  top: "prob1"
+}

AgeGenderDist/mtcnn-model/det2-symbol.json

+{
+  "nodes": [
+    {
+      "op": "null", 
+      "param": {}, 
+      "name": "data", 
+      "inputs": [], 
+      "backward_source_id": -1
+    }, 
+    {
+      "op": "null", 
+      "param": {}, 
+      "name": "conv1_weight", 
+      "inputs": [], 
+      "backward_source_id": -1
+    }, 
+    {
+      "op": "null", 
+      "param": {}, 
+      "name": "conv1_bias", 
+      "inputs": [], 
+      "backward_source_id": -1
+    }, 
+    {
+      "op": "Convolution", 
+      "param": {
+        "cudnn_off": "False", 
+        "cudnn_tune": "off", 
+        "dilate": "(1,1)", 
+        "kernel": "(3,3)", 
+        "no_bias": "False", 
+        "num_filter": "28", 
+        "num_group": "1", 
+        "pad": "(0,0)", 
+        "stride": "(1,1)", 
+        "workspace": "1024"
+      }, 
+      "name": "conv1", 
+      "inputs": [[0, 0], [1, 0], [2, 0]], 
+      "backward_source_id": -1
+    }, 
+    {
+      "op": "null", 
+      "param": {}, 
+      "name": "prelu1_gamma", 
+      "inputs": [], 
+      "backward_source_id": -1
+    }, 
+    {
+      "op": "LeakyReLU", 
+      "param": {
+        "act_type": "prelu", 
+        "lower_bound": "0.125", 
+        "slope": "0.25", 
+        "upper_bound": "0.334"
+      }, 
+      "name": "prelu1", 
+      "inputs": [[3, 0], [4, 0]], 
+      "backward_source_id": -1
+    }, 
+    {
+      "op": "Pooling", 
+      "param": {
+        "global_pool": "False", 
+        "kernel": "(3,3)", 
+        "pad": "(0,0)", 
+        "pool_type": "max", 
+        "pooling_convention": "full", 
+        "stride": "(2,2)"
+      }, 
+      "name": "pool1", 
+      "inputs": [[5, 0]], 
+      "backward_source_id": -1
+    }, 
+    {
+      "op": "null", 
+      "param": {}, 
+      "name": "conv2_weight", 
+      "inputs": [], 
+      "backward_source_id": -1
+    }, 
+    {
+      "op": "null", 
+      "param": {}, 
+      "name": "conv2_bias", 
+      "inputs": [], 
+      "backward_source_id": -1
+    }, 
+    {
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+    {
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+      "inputs": [[20, 0], [21, 0]], 
+      "backward_source_id": -1
+    }, 
+    {
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+      "param": {}, 
+      "name": "conv5_2_weight", 
+      "inputs": [], 
+      "backward_source_id": -1
+    }, 
+    {
+      "op": "null", 
+      "param": {}, 
+      "name": "conv5_2_bias", 
+      "inputs": [], 
+      "backward_source_id": -1
+    }, 
+    {
+      "op": "FullyConnected", 
+      "param": {
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+        "num_hidden": "4"
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+      "name": "conv5_2", 
+      "inputs": [[22, 0], [23, 0], [24, 0]], 
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+    }, 
+    {
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+      "inputs": [], 
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+    {
+      "op": "null", 
+      "param": {}, 
+      "name": "conv5_1_bias", 
+      "inputs": [], 
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+    }, 
+    {
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+      "inputs": [[22, 0], [26, 0], [27, 0]], 
+      "backward_source_id": -1
+    }, 
+    {
+      "op": "null", 
+      "param": {}, 
+      "name": "prob1_label", 
+      "inputs": [], 
+      "backward_source_id": -1
+    }, 
+    {
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+      "param": {
+        "grad_scale": "1", 
+        "ignore_label": "-1", 
+        "multi_output": "False", 
+        "normalization": "null", 
+        "use_ignore": "False"
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+      "inputs": [[28, 0], [29, 0]], 
+      "backward_source_id": -1
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+  ], 
+  "arg_nodes": [
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+    24, 
+    26, 
+    27, 
+    29
+  ], 
+  "heads": [[25, 0], [30, 0]]
+}
\ No newline at end of file

AgeGenderDist/mtcnn-model/det2.prototxt

+name: "RNet"
+input: "data"
+input_dim: 1
+input_dim: 3
+input_dim: 24
+input_dim: 24
+
+
+##########################
+######################
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 28
+    kernel_size: 3
+    stride: 1
+     weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "prelu1"
+  type: "PReLU"
+  bottom: "conv1"
+  top: "conv1"
+  propagate_down: true
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "conv1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 48
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "prelu2"
+  type: "PReLU"
+  bottom: "conv2"
+  top: "conv2"
+  propagate_down: true
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "conv2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+####################################
+
+##################################
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 2
+    stride: 1
+    weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "prelu3"
+  type: "PReLU"
+  bottom: "conv3"
+  top: "conv3"
+  propagate_down: true
+}
+###############################
+
+###############################
+
+layer {
+  name: "conv4"
+  type: "InnerProduct"
+  bottom: "conv3"
+  top: "conv4"
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 128
+    weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "prelu4"
+  type: "PReLU"
+  bottom: "conv4"
+  top: "conv4"
+}
+
+layer {
+  name: "conv5-1"
+  type: "InnerProduct"
+  bottom: "conv4"
+  top: "conv5-1"
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 2
+    #kernel_size: 1
+    #stride: 1
+    weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv5-2"
+  type: "InnerProduct"
+  bottom: "conv4"
+  top: "conv5-2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  inner_product_param {
+    num_output: 4
+    #kernel_size: 1
+    #stride: 1
+     weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "prob1"
+  type: "Softmax"
+  bottom: "conv5-1"
+  top: "prob1"
+}
\ No newline at end of file

AgeGenderDist/mtcnn-model/det3-symbol.json

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+    2, 
+    4, 
+    7, 
+    8, 
+    10, 
+    13, 
+    14, 
+    16, 
+    19, 
+    20, 
+    22, 
+    24, 
+    25, 
+    27, 
+    29, 
+    30, 
+    32, 
+    33, 
+    35, 
+    36, 
+    38
+  ], 
+  "heads": [[31, 0], [34, 0], [39, 0]]
+}
\ No newline at end of file

AgeGenderDist/mtcnn-model/det3.prototxt

+name: "ONet"
+input: "data"
+input_dim: 1
+input_dim: 3
+input_dim: 48
+input_dim: 48
+##################################
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  convolution_param {
+    num_output: 32
+    kernel_size: 3
+    stride: 1
+     weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "prelu1"
+  type: "PReLU"
+  bottom: "conv1"
+  top: "conv1"
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "conv1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 3
+    stride: 1
+     weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  name: "prelu2"
+  type: "PReLU"
+  bottom: "conv2"
+  top: "conv2"
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "conv2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  convolution_param {
+	num_output: 64
+	kernel_size: 3
+    weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "prelu3"
+  type: "PReLU"
+  bottom: "conv3"
+  top: "conv3"
+}
+layer {
+  name: "pool3"
+  type: "Pooling"
+  bottom: "conv3"
+  top: "pool3"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv4"
+  type: "Convolution"
+  bottom: "pool3"
+  top: "conv4"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  convolution_param {
+	num_output: 128
+	kernel_size: 2
+    weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "prelu4"
+  type: "PReLU"
+  bottom: "conv4"
+  top: "conv4"
+}
+
+
+layer {
+  name: "conv5"
+  type: "InnerProduct"
+  bottom: "conv4"
+  top: "conv5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  inner_product_param {
+	#kernel_size: 3
+	num_output: 256
+    weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  name: "drop5"
+  type: "Dropout"
+  bottom: "conv5"
+  top: "conv5"
+  dropout_param {
+    dropout_ratio: 0.25
+  }
+}
+layer {
+  name: "prelu5"
+  type: "PReLU"
+  bottom: "conv5"
+  top: "conv5"
+}
+
+
+layer {
+  name: "conv6-1"
+  type: "InnerProduct"
+  bottom: "conv5"
+  top: "conv6-1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  inner_product_param {
+    #kernel_size: 1
+	num_output: 2
+    weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv6-2"
+  type: "InnerProduct"
+  bottom: "conv5"
+  top: "conv6-2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  inner_product_param {
+  	#kernel_size: 1
+	num_output: 4
+    weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv6-3"
+  type: "InnerProduct"
+  bottom: "conv5"
+  top: "conv6-3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  inner_product_param {
+  	#kernel_size: 1
+	num_output: 10
+    weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "prob1"
+  type: "Softmax"
+  bottom: "conv6-1"
+  top: "prob1"
+}