Finish tensor compatibility for MTCNN (#117)

models/mtcnn.py

@@ -248,7 +248,11 @@ def forward(self, img, save_path=None, return_prob=False):
 
         # Determine if a batch or single image was passed
         batch_mode = True
-        if not isinstance(img, (list, tuple)) and not (isinstance(img, np.ndarray) and len(img.shape) == 4):
+        if (
+            not isinstance(img, (list, tuple)) and
+            not (isinstance(img, np.ndarray) and len(img.shape) == 4) and
+            not (isinstance(img, torch.Tensor) and len(img.shape) == 4)
+        ):
             img = [img]
             batch_boxes = [batch_boxes]
             batch_probs = [batch_probs]
@@ -373,7 +377,11 @@ def detect(self, img, landmarks=False):
         probs = np.array(probs)
         points = np.array(points)
 
-        if not isinstance(img, (list, tuple)) and not (isinstance(img, np.ndarray) and len(img.shape) == 4):
+        if (
+            not isinstance(img, (list, tuple)) and 
+            not (isinstance(img, np.ndarray) and len(img.shape) == 4) and
+            not (isinstance(img, torch.Tensor) and len(img.shape) == 4)
+        ):
             boxes = boxes[0]
             probs = probs[0]
             points = points[0]
@@ -388,6 +396,7 @@ def fixed_image_standardization(image_tensor):
     processed_tensor = (image_tensor - 127.5) / 128.0
     return processed_tensor
 
+
 def prewhiten(x):
     mean = x.mean()
     std = x.std()

models/utils/detect_face.py

@@ -308,11 +308,18 @@ def imresample(img, sz):
 
 def crop_resize(img, box, image_size):
     if isinstance(img, np.ndarray):
+        img = img[box[1]:box[3], box[0]:box[2]]
         out = cv2.resize(
-            img[box[1]:box[3], box[0]:box[2]],
+            img,
             (image_size, image_size),
             interpolation=cv2.INTER_AREA
         ).copy()
+    elif isinstance(img, torch.Tensor):
+        img = img[box[1]:box[3], box[0]:box[2]]
+        out = imresample(
+            img.permute(2, 0, 1).unsqueeze(0).float(),
+            (image_size, image_size)
+        ).byte().squeeze(0).permute(1, 2, 0)
     else:
         out = img.crop(box).copy().resize((image_size, image_size), Image.BILINEAR)
     return out
@@ -326,7 +333,7 @@ def save_img(img, path):
 
 
 def get_size(img):
-    if isinstance(img, np.ndarray):
+    if isinstance(img, (np.ndarray, torch.Tensor)):
         return img.shape[1::-1]
     else:
         return img.size

setup.py

@@ -1,7 +1,7 @@
 import setuptools, os
 
 PACKAGE_NAME = 'facenet-pytorch'
-VERSION = '2.3.1'
+VERSION = '2.4.1'
 AUTHOR = 'Tim Esler'
 EMAIL = '[email protected]'
 DESCRIPTION = 'Pretrained Pytorch face detection and recognition models'