add dat

.gitattributes

@@ -32,4 +32,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.dat filter=lfs diff=lfs merge=lfs -text

app.py

@@ -16,12 +16,15 @@ for year in years:
     G, w_avg = utils.load_stylegan2(f"pretrained_models/{year}.pkl", device)
     orig_models[year] = { "G": G.eval()}
     
-transform = transforms.Compose([
-        transforms.Resize((256, 256)),
-        transforms.ToTensor(),
-        transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
 
-# Download human-readable labels for ImageNet.
+def run_alignment(image_path,idx=None):
+    import dlib
+    from align_all_parallel import align_face
+    predictor = dlib.shape_predictor("pretrained_models/shape_predictor_68_face_landmarks.dat")
+    aligned_image = align_face(filepath=image_path, predictor=predictor, idx=idx) 
+    print("Aligned image has shape: {}".format(aligned_image.size))
+    return aligned_image 
+
 def predict(inp):
   #with torch.no_grad():     
   return inp