fix minor issue in cropping faces

fawkes/__init__.py

@@ -4,7 +4,7 @@
 # @Link    : https://www.shawnshan.com/
 
 
-__version__ = '1.0.1'
+__version__ = '1.0.2'
 
 from .differentiator import FawkesMaskGeneration
 from .protection import main, Fawkes

fawkes/align_face.py

@@ -8,14 +8,13 @@ def to_rgb(img):
     ret[:, :, 0] = ret[:, :, 1] = ret[:, :, 2] = img
     return ret
 
-def aligner():
-    return MTCNN()
 
-def align(orig_img, aligner, margin=0.8, detect_multiple_faces=True):
+def aligner():
+    return MTCNN(min_face_size=30)
+
+
+def align(orig_img, aligner):
     """ run MTCNN face detector """
-    minsize = 20  # minimum size of face
-    threshold = [0.6, 0.7, 0.7]  # three steps's threshold
-    factor = 0.709  # scale factor
 
     if orig_img.ndim < 2:
         return None
@@ -23,45 +22,57 @@ def align(orig_img, aligner, margin=0.8, detect_multiple_faces=True):
         orig_img = to_rgb(orig_img)
     orig_img = orig_img[:, :, 0:3]
 
-    bounding_boxes = aligner.detect_faces(orig_img)
+    detect_results = aligner.detect_faces(orig_img)
-    nrof_faces= len(bounding_boxes)
-
-    if nrof_faces > 0:
-        det = bounding_boxes[0]['box']
-        det_arr = []
-        img_size = np.asarray(orig_img.shape)[0:2]
-        if nrof_faces > 1:
-            margin = margin / 1.5
-            if detect_multiple_faces:
-                for i in range(nrof_faces):
-                    det_arr.append(np.squeeze(bounding_boxes[i]['box']))
-            else:
-                bounding_box_size = (det[1] + det[3])
-                img_center = img_size / 2
-                offsets = np.vstack([(det[0] + det[2]) / 2 - img_center[1],
-                                     (det[1] + det[3]) / 2 - img_center[0]])
-                offset_dist_squared = np.sum(np.power(offsets, 2.0), 0)
-                index = np.argmax(bounding_box_size - offset_dist_squared * 2.0)  # some extra weight on the centering
-                det_arr.append(det[index, :])
-        else:
-            det_arr.append(np.squeeze(det))
-
     cropped_arr = []
     bounding_boxes_arr = []
-        for i, det in enumerate(det_arr):
+    for dic in detect_results:
-            det = np.squeeze(det)
+        if dic['confidence'] < 0.9:
-            bb = np.zeros(4, dtype=np.int32)
+            continue
-            # add in margin
+        x, y, width, height = dic['box']
-            marg1 = int((det[2] - det[0]) * margin)
+        if width < 30 or height < 30:
-            marg2 = int((det[3] - det[1]) * margin)
+            continue
-
+        bb = [y, x, y + height, x + width]
-            bb[0] = max(det[0] - marg1/2, 0)
+        cropped = orig_img[bb[0]:bb[2], bb[1]:bb[3], :]
-            bb[1] = max(det[1] - marg2/2, 0)
+        cropped_arr.append(np.copy(cropped))
-            bb[2] = min(det[0] + det[2] + marg1/2, img_size[0])
+        bounding_boxes_arr.append(bb)
-            bb[3] = min(det[1] + det[3] + marg2/2, img_size[1])
-            cropped = orig_img[bb[0]:bb[2], bb[1]: bb[3],:]
-            cropped_arr.append(cropped)
-            bounding_boxes_arr.append([bb[0], bb[1], bb[2], bb[3]])
     return cropped_arr, bounding_boxes_arr
-    else:
+
-        return None
+    # if nrof_faces > 0:
+    #     det = bounding_boxes[0]['box']
+    #     det_arr = []
+    #     img_size = np.asarray(orig_img.shape)[0:2]
+    #     if nrof_faces > 1:
+    #         margin = margin / 1.5
+    #         if detect_multiple_faces:
+    #             for i in range(nrof_faces):
+    #                 det_arr.append(np.squeeze(bounding_boxes[i]['box']))
+    #         else:
+    #             bounding_box_size = (det[1] + det[3])
+    #             img_center = img_size / 2
+    #             offsets = np.vstack([(det[0] + det[2]) / 2 - img_center[1],
+    #                                  (det[1] + det[3]) / 2 - img_center[0]])
+    #             offset_dist_squared = np.sum(np.power(offsets, 2.0), 0)
+    #             index = np.argmax(bounding_box_size - offset_dist_squared * 2.0)  # some extra weight on the centering
+    #             det_arr.append(det[index, :])
+    #     else:
+    #         det_arr.append(np.squeeze(det))
+    #
+    #     cropped_arr = []
+    #     bounding_boxes_arr = []
+    #     for i, det in enumerate(det_arr):
+    #         det = np.squeeze(det)
+    #         bb = np.zeros(4, dtype=np.int32)
+    #         # add in margin
+    #         marg1 = int((det[2] - det[0]) * margin)
+    #         marg2 = int((det[3] - det[1]) * margin)
+    #
+    #         bb[0] = max(det[0] - marg1 / 2, 0)
+    #         bb[1] = max(det[1] - marg2 / 2, 0)
+    #         bb[2] = min(det[0] + det[2] + marg1 / 2, img_size[0])
+    #         bb[3] = min(det[1] + det[3] + marg2 / 2, img_size[1])
+    #         cropped = orig_img[bb[0]:bb[2], bb[1]: bb[3], :]
+    #         cropped_arr.append(cropped)
+    #         bounding_boxes_arr.append([bb[0], bb[1], bb[2], bb[3]])
+    #     return cropped_arr, bounding_boxes_arr
+    # else:
+    #     return None

fawkes/protection.py

@@ -57,14 +57,14 @@ class Fawkes(object):
 
     def mode2param(self, mode):
         if mode == 'low':
-            th = 0.0045
+            th = 0.005
-            max_step = 25
+            max_step = 30
             lr = 25
             extractors = ["extractor_2"]
 
         elif mode == 'mid':
             th = 0.012
-            max_step = 50
+            max_step = 75
             lr = 20
             extractors = ["extractor_0", "extractor_2"]
 

fawkes/utils.py

@@ -145,13 +145,8 @@ class Faces(object):
             p = image_paths[i]
             self.org_faces.append(cur_img)
 
-            if eval_local:
-                margin = 0
-            else:
-                margin = 0.7
-
             if not no_align:
-                align_img = align(cur_img, self.aligner, margin=margin)
+                align_img = align(cur_img, self.aligner)
                 if align_img is None:
                     print("Find 0 face(s) in {}".format(p.split("/")[-1]))
                     self.images_without_face.append(i)
@@ -221,6 +216,7 @@ class Faces(object):
             org_shape = self.cropped_faces_shape[i]
 
             old_square_shape = max([org_shape[0], org_shape[1]])
+
             cur_protected = resize(cur_protected, (old_square_shape, old_square_shape))
             cur_original = resize(cur_original, (old_square_shape, old_square_shape))