compatible with python3

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

@@ -3,9 +3,9 @@
 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):
@@ -623,7 +623,7 @@ def detect_face(self,img):
 
             t3 = time.time() - t
             t = time.time()
-            print "time cost " + '{:.3f}'.format(t1+t2+t3) + '  pnet {:.3f}  rnet {:.3f}  onet {:.3f}'.format(t1, t2, t3)
+            print("time cost " + '{:.3f}'.format(t1+t2+t3) + '  pnet {:.3f}  rnet {:.3f}  onet {:.3f}'.format(t1, t2, t3))
 
         return boxes_align, landmark_align
 

src/core/imagedb.py → dface/core/imagedb.py

@@ -137,7 +137,7 @@ def append_flipped_images(self, imdb):
         imdb: dict
             image database with flipped image annotations added
         """
-        print 'append flipped images to imdb', len(imdb)
+        print('append flipped images to imdb', len(imdb))
         for i in range(len(imdb)):
             imdb_ = imdb[i]
             m_bbox = imdb_['bbox_target'].copy()

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

@@ -1,6 +1,6 @@
 import os
-import config
-import assemble as assemble
+import dface.config as config
+import dface.prepare_data.assemble as assemble
 
 
 if __name__ == '__main__':
@@ -22,4 +22,4 @@
     imglist_file = os.path.join(anno_dir, imglist_filename)
 
     chose_count = assemble.assemble_data(imglist_file ,anno_list)
-    print "PNet train annotation result file path:%s" % imglist_file
+    print("PNet train annotation result file path:%s" % imglist_file)

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

@@ -1,6 +1,6 @@
 import os
-import config
-import assemble as assemble
+import dface.config as config
+import dface.prepare_data.assemble as assemble
 
 
 if __name__ == '__main__':
@@ -22,4 +22,4 @@
     imglist_file = os.path.join(anno_dir, imglist_filename)
 
     chose_count = assemble.assemble_data(imglist_file ,anno_list)
-    print "PNet train annotation result file path:%s" % imglist_file
+    print("PNet train annotation result file path:%s" % imglist_file)

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

@@ -1,6 +1,6 @@
 import os
-import config
-import assemble as assemble
+import dface.config as config
+import dface.prepare_data.assemble as assemble
 
 
 if __name__ == '__main__':
@@ -22,4 +22,4 @@
     imglist_file = os.path.join(anno_dir, imglist_filename)
 
     chose_count = assemble.assemble_data(imglist_file ,anno_list)
-    print "PNet train annotation result file path:%s" % imglist_file
+    print("PNet train annotation result file path:%s" % imglist_file)

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

@@ -8,9 +8,9 @@
 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_onet_data(data_dir, anno_file, pnet_model_file, rnet_model_file, prefix_path='', use_cuda=True, vis=False):
 
@@ -27,7 +27,7 @@ def gen_onet_data(data_dir, anno_file, pnet_model_file, rnet_model_file, prefix_
 
     for databatch in image_reader:
         if batch_idx % 100 == 0:
-            print "%d images done" % batch_idx
+            print("%d images done" % batch_idx)
         im = databatch
 
         t = time.time()
@@ -89,7 +89,7 @@ def gen_onet_sample_data(data_dir,anno_file,det_boxs_file):
     im_idx_list = list()
     gt_boxes_list = list()
     num_of_images = len(annotations)
-    print "processing %d images in total" % num_of_images
+    print("processing %d images in total" % num_of_images)
 
     for annotation in annotations:
         annotation = annotation.strip().split(' ')
@@ -112,7 +112,7 @@ def gen_onet_sample_data(data_dir,anno_file,det_boxs_file):
     det_handle = open(det_boxs_file, 'r')
 
     det_boxes = cPickle.load(det_handle)
-    print len(det_boxes), num_of_images
+    print(len(det_boxes), num_of_images)
     assert len(det_boxes) == num_of_images, "incorrect detections or ground truths"
 
     # index of neg, pos and part face, used as their image names
@@ -122,7 +122,7 @@ def gen_onet_sample_data(data_dir,anno_file,det_boxs_file):
     image_done = 0
     for im_idx, dets, gts in zip(im_idx_list, det_boxes, gt_boxes_list):
         if image_done % 100 == 0:
-            print "%d images done" % image_done
+            print("%d images done" % image_done)
         image_done += 1
 
         if dets.shape[0] == 0:

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

@@ -3,8 +3,8 @@
 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
 
 
 
@@ -34,7 +34,7 @@ def gen_pnet_data(data_dir,anno_file):
         annotations = f.readlines()
 
     num = len(annotations)
-    print "%d pics in total" % num
+    print("%d pics in total" % num)
     p_idx = 0
     n_idx = 0
     d_idx = 0
@@ -48,7 +48,7 @@ def gen_pnet_data(data_dir,anno_file):
         img = cv2.imread(im_path)
         idx += 1
         if idx % 100 == 0:
-            print idx, "images done"
+            print(idx, "images done")
 
         height, width, channel = img.shape
 
@@ -147,7 +147,7 @@ def gen_pnet_data(data_dir,anno_file):
                     cv2.imwrite(save_file, resized_im)
                     d_idx += 1
             box_idx += 1
-            print "%s images done, pos: %s part: %s neg: %s"%(idx, p_idx, d_idx, n_idx)
+            print("%s images done, pos: %s part: %s neg: %s"%(idx, p_idx, d_idx, n_idx))
 
     f1.close()
     f2.close()

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

@@ -3,15 +3,15 @@
 
 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):
 
@@ -28,7 +28,7 @@ def gen_rnet_data(data_dir, anno_file, pnet_model_file, prefix_path='', use_cuda
 
     for databatch in image_reader:
         if batch_idx % 100 == 0:
-            print "%d images done" % batch_idx
+            print("%d images done" % batch_idx)
         im = databatch
 
         t = time.time()
@@ -85,7 +85,7 @@ def gen_rnet_sample_data(data_dir,anno_file,det_boxs_file):
     im_idx_list = list()
     gt_boxes_list = list()
     num_of_images = len(annotations)
-    print "processing %d images in total" % num_of_images
+    print("processing %d images in total" % num_of_images)
 
     for annotation in annotations:
         annotation = annotation.strip().split(' ')
@@ -118,7 +118,7 @@ def gen_rnet_sample_data(data_dir,anno_file,det_boxs_file):
     image_done = 0
     for im_idx, dets, gts in zip(im_idx_list, det_boxes, gt_boxes_list):
         if image_done % 100 == 0:
-            print "%d images done" % image_done
+            print("%d images done" % image_done)
         image_done += 1
 
         if dets.shape[0] == 0:

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

@@ -5,8 +5,8 @@
 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):
@@ -34,7 +34,7 @@ def gen_data(anno_file, data_dir, prefix):
         annotations = f2.readlines()
 
     num = len(annotations)
-    print "%d pics in total" % num
+    print("%d pics in total" % num)
 
     l_idx =0
     idx = 0
@@ -68,7 +68,7 @@ def gen_data(anno_file, data_dir, prefix):
 
         idx = idx + 1
         if idx % 100 == 0:
-            print "%d images done, landmark images: %d"%(idx,l_idx)
+            print("%d images done, landmark images: %d"%(idx,l_idx))
 
         x1, y1, x2, y2 = gt_box
 

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

@@ -6,8 +6,8 @@
 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
 
 
 
@@ -35,7 +35,7 @@ def gen_data(anno_file, data_dir, prefix):
         annotations = f2.readlines()
 
     num = len(annotations)
-    print "%d total images" % num
+    print("%d total images" % num)
 
     l_idx =0
     idx = 0
@@ -67,7 +67,7 @@ def gen_data(anno_file, data_dir, prefix):
 
         idx = idx + 1
         if idx % 100 == 0:
-            print "%d images done, landmark images: %d"%(idx,l_idx)
+            print("%d images done, landmark images: %d"%(idx,l_idx))
 
         x1, y1, x2, y2 = gt_box
 

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

@@ -6,8 +6,8 @@
 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):
@@ -34,7 +34,7 @@ def gen_data(anno_file, data_dir, prefix):
         annotations = f2.readlines()
 
     num = len(annotations)
-    print "%d total images" % num
+    print("%d total images" % num)
 
     l_idx =0
     idx = 0
@@ -66,7 +66,7 @@ def gen_data(anno_file, data_dir, prefix):
 
         idx = idx + 1
         if idx % 100 == 0:
-            print "%d images done, landmark images: %d"%(idx,l_idx)
+            print("%d images done, landmark images: %d"%(idx,l_idx))
 
         x1, y1, x2, y2 = gt_box
 

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

@@ -8,9 +8,9 @@
 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):
 
@@ -27,7 +27,7 @@ def gen_landmark48_data(data_dir, anno_file, pnet_model_file, rnet_model_file, p
 
     for databatch in image_reader:
         if batch_idx % 100 == 0:
-            print "%d images done" % batch_idx
+            print("%d images done" % batch_idx)
         im = databatch
 
 
@@ -91,7 +91,7 @@ def gen_sample_data(data_dir, anno_file, det_boxs_file, prefix_path =''):
     gt_boxes_list = list()
     gt_landmark_list = list()
     num_of_images = len(annotations)
-    print "processing %d images in total" % num_of_images
+    print("processing %d images in total" % num_of_images)
 
     for annotation in annotations:
         annotation = annotation.strip().split(' ')
@@ -117,15 +117,15 @@ def gen_sample_data(data_dir, anno_file, det_boxs_file, prefix_path =''):
     det_handle = open(det_boxs_file, 'r')
 
     det_boxes = cPickle.load(det_handle)
-    print len(det_boxes), num_of_images
+    print(len(det_boxes), num_of_images)
     assert len(det_boxes) == num_of_images, "incorrect detections or ground truths"
 
     # index of neg, pos and part face, used as their image names
     p_idx = 0
     image_done = 0
     for im_idx, dets, gts, landmark in zip(im_idx_list, det_boxes, gt_boxes_list, gt_landmark_list):
         if image_done % 100 == 0:
-            print "%d images done" % image_done
+            print("%d images done" % image_done)
         image_done += 1
 
         if dets.shape[0] == 0: