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GANimation/test.py

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+import os
+import argparse
+import glob
+import cv2
+from utils import face_utils
+from utils import cv_utils
+import face_recognition
+from PIL import Image
+import torchvision.transforms as transforms
+import torch
+import pickle
+import numpy as np
+from models.models import ModelsFactory
+from options.test_options import TestOptions
+
+class MorphFacesInTheWild:
+    def __init__(self, opt):
+        self._opt = opt
+        self._model = ModelsFactory.get_by_name(self._opt.model, self._opt)
+        self._model.set_eval()
+        self._transform = transforms.Compose([transforms.ToTensor(),
+                                              transforms.Normalize(mean=[0.5, 0.5, 0.5],
+                                                                   std=[0.5, 0.5, 0.5])
+                                              ])
+
+    def morph_file(self, img_path, expresion):
+        img = cv_utils.read_cv2_img(img_path)
+        morphed_img = self._img_morph(img, expresion)
+        output_name = '%s_out.png' % os.path.basename(img_path)
+        self._save_img(morphed_img, output_name)
+
+    def _img_morph(self, img, expresion):
+        bbs = face_recognition.face_locations(img)
+        if len(bbs) > 0:
+            y, right, bottom, x = bbs[0]
+            bb = x, y, (right - x), (bottom - y)
+            face = face_utils.crop_face_with_bb(img, bb)
+            face = face_utils.resize_face(face)
+        else:
+            face = face_utils.resize_face(img)
+
+        morphed_face = self._morph_face(face, expresion)
+
+        return morphed_face
+
+    def _morph_face(self, face, expresion):
+        face = torch.unsqueeze(self._transform(Image.fromarray(face)), 0)
+        expresion = torch.unsqueeze(torch.from_numpy(expresion/5.0), 0)
+        test_batch = {'real_img': face, 'real_cond': expresion, 'desired_cond': expresion, 'sample_id': torch.FloatTensor(), 'real_img_path': []}
+        self._model.set_input(test_batch)
+        imgs, _ = self._model.forward(keep_data_for_visuals=False, return_estimates=True)
+        return imgs['concat']
+
+    def _save_img(self, img, filename):
+        filepath = os.path.join(self._opt.output_dir, filename)
+        img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+        cv2.imwrite(filepath, img)
+
+
+def main():
+    opt = TestOptions().parse()
+    if not os.path.isdir(opt.output_dir):
+        os.makedirs(opt.output_dir)
+
+    morph = MorphFacesInTheWild(opt)
+
+    image_path = opt.input_path
+    expression = np.random.uniform(0, 1, opt.cond_nc)
+    morph.morph_file(image_path, expression)
+
+
+
+if __name__ == '__main__':
+    main()