compatible with python3

src/core/detect.py → dface/core/detect.py

@@ -3,9 +3,9 @@ import time
 import numpy as np
 import torch
 from torch.autograd.variable import Variable
-from models import PNet,RNet,ONet
-import utils as utils
-import image_tools
+from dface.core.models import PNet,RNet,ONet
+import dface.core.utils as utils
+import dface.core.image_tools as image_tools
 
 
 def create_mtcnn_net(p_model_path=None, r_model_path=None, o_model_path=None, use_cuda=True):

src/core/resnet_inception_v2.py → dface/core/resnet_inception_v2.py

@@ -256,6 +256,7 @@ class InceptionResnetV2(nn.Module):
         self.conv2d_7b = BasicConv2d(2080, 1536, kernel_size=1, stride=1)
         self.avgpool_1a = nn.AvgPool2d(8, count_include_pad=False)
         self.classif = nn.Linear(1536, num_classes)
+        self.dropout = nn.Dropout(p=0.8)
 
     def forward(self, x):
         x = self.conv2d_1a(x)
@@ -275,5 +276,6 @@ class InceptionResnetV2(nn.Module):
         x = self.conv2d_7b(x)
         x = self.avgpool_1a(x)
         x = x.view(x.size(0), -1)
+        x = self.dropout(x)
         x = self.classif(x)
         return x

src/prepare_data/assemble_onet_imglist.py → dface/prepare_data/assemble_onet_imglist.py

@@ -1,5 +1,5 @@
 import os
-import config
+import dface.config as config
 import assemble as assemble
 
 

src/prepare_data/assemble_pnet_imglist.py → dface/prepare_data/assemble_pnet_imglist.py

@@ -1,5 +1,5 @@
 import os
-import config
+import dface.config as config
 import assemble as assemble
 
 

src/prepare_data/assemble_rnet_imglist.py → dface/prepare_data/assemble_rnet_imglist.py

@@ -1,5 +1,5 @@
 import os
-import config
+import dface.config as config
 import assemble as assemble
 
 

src/prepare_data/gen_Onet_train_data.py → dface/prepare_data/gen_Onet_train_data.py

@@ -2,15 +2,15 @@ import argparse
 
 import cv2
 import numpy as np
-from core.detect import MtcnnDetector,create_mtcnn_net
-from core.imagedb import ImageDB
-from core.image_reader import TestImageLoader
+from dface.core.detect import MtcnnDetector,create_mtcnn_net
+from dface.core.imagedb import ImageDB
+from dface.core.image_reader import TestImageLoader
 import time
 import os
 import cPickle
-from core.utils import convert_to_square,IoU
-import config
-import core.vision as vision
+from dface.core.utils import convert_to_square,IoU
+import dface.config
+import dface.core.vision as vision
 
 def gen_onet_data(data_dir, anno_file, pnet_model_file, rnet_model_file, prefix_path='', use_cuda=True, vis=False):
 

src/prepare_data/gen_Pnet_train_data.py → dface/prepare_data/gen_Pnet_train_data.py

@@ -3,10 +3,8 @@ import numpy as np
 import cv2
 import os
 import numpy.random as npr
-from core.utils import IoU
-import config
-
-
+from dface.core.utils import IoU
+import dface.config as config
 
 def gen_pnet_data(data_dir,anno_file):
 

src/prepare_data/gen_Rnet_train_data.py → dface/prepare_data/gen_Rnet_train_data.py

@@ -3,15 +3,15 @@ import argparse
 
 import cv2
 import numpy as np
-from core.detect import MtcnnDetector,create_mtcnn_net
-from core.imagedb import ImageDB
-from core.image_reader import TestImageLoader
+from dface.core.detect import MtcnnDetector,create_mtcnn_net
+from dface.core.imagedb import ImageDB
+from dface.core.image_reader import TestImageLoader
 import time
 import os
 import cPickle
-from core.utils import convert_to_square,IoU
-import config
-import core.vision as vision
+from dface.core.utils import convert_to_square,IoU
+import dface.config as config
+import dface.core.vision as vision
 
 def gen_rnet_data(data_dir, anno_file, pnet_model_file, prefix_path='', use_cuda=True, vis=False):
 

src/prepare_data/gen_landmark_12.py → dface/prepare_data/gen_landmark_12.py

@@ -5,8 +5,8 @@ import numpy as np
 import sys
 import numpy.random as npr
 import argparse
-import config
-import core.utils as utils
+import dface.config as config
+import dface.core.utils as utils
 
 
 def gen_data(anno_file, data_dir, prefix):

src/prepare_data/gen_landmark_24.py → dface/prepare_data/gen_landmark_24.py

@@ -6,8 +6,8 @@ import random
 import sys
 import numpy.random as npr
 import argparse
-import config
-import core.utils as utils
+import dface.config as config
+import dface.core.utils as utils
 
 
 

src/prepare_data/gen_landmark_48.py → dface/prepare_data/gen_landmark_48.py

@@ -6,8 +6,8 @@ import random
 import sys
 import numpy.random as npr
 import argparse
-import config
-import core.utils as utils
+import dface.config as config
+import dface.core.utils as utils
 
 
 def gen_data(anno_file, data_dir, prefix):

src/prepare_data/gen_landmark_net_48.py → dface/prepare_data/gen_landmark_net_48.py

@@ -2,15 +2,15 @@ import argparse
 
 import cv2
 import numpy as np
-from core.detect import MtcnnDetector,create_mtcnn_net
-from core.imagedb import ImageDB
-from core.image_reader import TestImageLoader
+from dface.core.detect import MtcnnDetector,create_mtcnn_net
+from dface.core.imagedb import ImageDB
+from dface.core.image_reader import TestImageLoader
 import time
 import os
 import cPickle
-from core.utils import convert_to_square,IoU
-import config
-import core.vision as vision
+from dface.core.utils import convert_to_square,IoU
+import dface.config as config
+import dface.core.vision as vision
 
 def gen_landmark48_data(data_dir, anno_file, pnet_model_file, rnet_model_file, prefix_path='', use_cuda=True, vis=False):
 

src/prepare_data/widerface_annotation_gen/transform.py → dface/prepare_data/widerface_annotation_gen/transform.py

@@ -1,46 +1,46 @@
-from wider_loader import WIDER
-import cv2
-import time
-
-#wider face original images path
-path_to_image = '/idata/data/wider_face/WIDER_train/images'
-
-#matlab file path
-file_to_label = './wider_face_train.mat'
-
-#target file path
-target_file = './anno.txt'
-
-wider = WIDER(file_to_label, path_to_image)
-
-
-line_count = 0
-box_count = 0
-
-print 'start transforming....'
-t = time.time()
-
-with open(target_file, 'w+') as f:
-    # press ctrl-C to stop the process
-    for data in wider.next():
-        line = []
-        line.append(str(data.image_name))
-        line_count += 1
-        for i,box in enumerate(data.bboxes):
-            box_count += 1
-            for j,bvalue in enumerate(box):
-                line.append(str(bvalue))
-
-        line.append('\n')
-
-        line_str = ' '.join(line)
-        f.write(line_str)
-
-st = time.time()-t
-print 'end transforming'
-
-print 'spend time:%ld'%st
-print 'total line(images):%d'%line_count
-print 'total boxes(faces):%d'%box_count
-
-
+from wider_loader import WIDER
+import cv2
+import time
+
+#wider face original images path
+path_to_image = '/idata/data/wider_face/WIDER_train/images'
+
+#matlab file path
+file_to_label = './wider_face_train.mat'
+
+#target file path
+target_file = './anno.txt'
+
+wider = WIDER(file_to_label, path_to_image)
+
+
+line_count = 0
+box_count = 0
+
+print 'start transforming....'
+t = time.time()
+
+with open(target_file, 'w+') as f:
+    # press ctrl-C to stop the process
+    for data in wider.next():
+        line = []
+        line.append(str(data.image_name))
+        line_count += 1
+        for i,box in enumerate(data.bboxes):
+            box_count += 1
+            for j,bvalue in enumerate(box):
+                line.append(str(bvalue))
+
+        line.append('\n')
+
+        line_str = ' '.join(line)
+        f.write(line_str)
+
+st = time.time()-t
+print 'end transforming'
+
+print 'spend time:%ld'%st
+print 'total line(images):%d'%line_count
+print 'total boxes(faces):%d'%box_count
+
+

src/prepare_data/widerface_annotation_gen/wider_loader.py → dface/prepare_data/widerface_annotation_gen/wider_loader.py

@@ -1,42 +1,42 @@
-import h5py
-import os
-
-
-class DATA(object):
-    def __init__(self, image_name, bboxes):
-        self.image_name = image_name
-        self.bboxes = bboxes
-
-
-class WIDER(object):
-    def __init__(self, file_to_label, path_to_image):
-        self.file_to_label = file_to_label
-        self.path_to_image = path_to_image
-
-        self.f = h5py.File(file_to_label, 'r')
-        self.event_list = self.f.get('event_list')
-        self.file_list = self.f.get('file_list')
-        self.face_bbx_list = self.f.get('face_bbx_list')
-
-    def next(self):
-
-        for event_idx, event in enumerate(self.event_list.value[0]):
-            directory = self.f[event].value.tostring().decode('utf-16')
-            for im_idx, im in enumerate(
-                    self.f[self.file_list.value[0][event_idx]].value[0]):
-
-                im_name = self.f[im].value.tostring().decode('utf-16')
-                face_bbx = self.f[self.f[self.face_bbx_list.value
-                                  [0][event_idx]].value[0][im_idx]].value
-
-                bboxes = []
-
-                for i in range(face_bbx.shape[1]):
-                    xmin = int(face_bbx[0][i])
-                    ymin = int(face_bbx[1][i])
-                    xmax = int(face_bbx[0][i] + face_bbx[2][i])
-                    ymax = int(face_bbx[1][i] + face_bbx[3][i])
-                    bboxes.append((xmin, ymin, xmax, ymax))
-
-                yield DATA(os.path.join(self.path_to_image, directory,
-                           im_name + '.jpg'), bboxes)
+import h5py
+import os
+
+
+class DATA(object):
+    def __init__(self, image_name, bboxes):
+        self.image_name = image_name
+        self.bboxes = bboxes
+
+
+class WIDER(object):
+    def __init__(self, file_to_label, path_to_image):
+        self.file_to_label = file_to_label
+        self.path_to_image = path_to_image
+
+        self.f = h5py.File(file_to_label, 'r')
+        self.event_list = self.f.get('event_list')
+        self.file_list = self.f.get('file_list')
+        self.face_bbx_list = self.f.get('face_bbx_list')
+
+    def next(self):
+
+        for event_idx, event in enumerate(self.event_list.value[0]):
+            directory = self.f[event].value.tostring().decode('utf-16')
+            for im_idx, im in enumerate(
+                    self.f[self.file_list.value[0][event_idx]].value[0]):
+
+                im_name = self.f[im].value.tostring().decode('utf-16')
+                face_bbx = self.f[self.f[self.face_bbx_list.value
+                                  [0][event_idx]].value[0][im_idx]].value
+
+                bboxes = []
+
+                for i in range(face_bbx.shape[1]):
+                    xmin = int(face_bbx[0][i])
+                    ymin = int(face_bbx[1][i])
+                    xmax = int(face_bbx[0][i] + face_bbx[2][i])
+                    ymax = int(face_bbx[1][i] + face_bbx[3][i])
+                    bboxes.append((xmin, ymin, xmax, ymax))
+
+                yield DATA(os.path.join(self.path_to_image, directory,
+                           im_name + '.jpg'), bboxes)

src/train_net/train.py → dface/train_net/train.py

@@ -1,10 +1,10 @@
-from core.image_reader import TrainImageReader
+from dface.core.image_reader import TrainImageReader
 import datetime
 import os
-from core.models import PNet,RNet,ONet,LossFn
+from dface.core.models import PNet,RNet,ONet,LossFn
 import torch
 from torch.autograd import Variable
-import core.image_tools as image_tools
+import dface.core.image_tools as image_tools
 
 
 

src/train_net/train_o_net.py → dface/train_net/train_o_net.py

@@ -1,8 +1,8 @@
 import argparse
 import sys
-from core.imagedb import ImageDB
+from dface.core.imagedb import ImageDB
 import train as train
-import config
+import dface.config as config
 import os
 
 

src/train_net/train_p_net.py → dface/train_net/train_p_net.py

@@ -1,8 +1,8 @@
 import argparse
 import sys
-from core.imagedb import ImageDB
+from dface.core.imagedb import ImageDB
 from train import train_pnet
-import config
+import dface.config
 import os
 
 

src/train_net/train_r_net.py → dface/train_net/train_r_net.py

@@ -1,8 +1,8 @@
 import argparse
 import sys
-from core.imagedb import ImageDB
+from dface.core.imagedb import ImageDB
 import train as train
-import config
+import dface.config
 import os
 
 

test_image.py

@@ -1,7 +1,6 @@
 import cv2
-from core.detect import create_mtcnn_net, MtcnnDetector
-import core.vision as vision
-
+from dface.core.detect import create_mtcnn_net, MtcnnDetector
+from dface.core import vision