support test video

options/test_options.py

@@ -1,22 +1,23 @@
-from .base_options import BaseOptions
-
-class TestOptions(BaseOptions):
-    def initialize(self):
-        BaseOptions.initialize(self)
-        self.parser.add_argument('--ntest', type=int, default=float("inf"), help='# of test examples.')
-        self.parser.add_argument('--results_dir', type=str, default='./results/', help='saves results here.')
-        self.parser.add_argument('--aspect_ratio', type=float, default=1.0, help='aspect ratio of result images')
-        self.parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc')
-        self.parser.add_argument('--which_epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model')
-        self.parser.add_argument('--how_many', type=int, default=50, help='how many test images to run')       
-        self.parser.add_argument('--cluster_path', type=str, default='features_clustered_010.npy', help='the path for clustered results of encoded features')
-        self.parser.add_argument('--use_encoded_image', action='store_true', help='if specified, encode the real image to get the feature map')
-        self.parser.add_argument("--export_onnx", type=str, help="export ONNX model to a given file")
-        self.parser.add_argument("--engine", type=str, help="run serialized TRT engine")
-        self.parser.add_argument("--onnx", type=str, help="run ONNX model via TRT")        
-        self.parser.add_argument("--Arc_path", type=str, default='models/BEST_checkpoint.tar', help="run ONNX model via TRT")
-        self.parser.add_argument("--pic_a_path", type=str, default='crop_224/gdg.jpg', help="people a")
-        self.parser.add_argument("--pic_b_path", type=str, default='crop_224/zrf.jpg', help="people b")
-        self.parser.add_argument("--output_path", type=str, default='output/', help="people b")
-
-        self.isTrain = False
+from .base_options import BaseOptions
+
+class TestOptions(BaseOptions):
+    def initialize(self):
+        BaseOptions.initialize(self)
+        self.parser.add_argument('--ntest', type=int, default=float("inf"), help='# of test examples.')
+        self.parser.add_argument('--results_dir', type=str, default='./results/', help='saves results here.')
+        self.parser.add_argument('--aspect_ratio', type=float, default=1.0, help='aspect ratio of result images')
+        self.parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc')
+        self.parser.add_argument('--which_epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model')
+        self.parser.add_argument('--how_many', type=int, default=50, help='how many test images to run')       
+        self.parser.add_argument('--cluster_path', type=str, default='features_clustered_010.npy', help='the path for clustered results of encoded features')
+        self.parser.add_argument('--use_encoded_image', action='store_true', help='if specified, encode the real image to get the feature map')
+        self.parser.add_argument("--export_onnx", type=str, help="export ONNX model to a given file")
+        self.parser.add_argument("--engine", type=str, help="run serialized TRT engine")
+        self.parser.add_argument("--onnx", type=str, help="run ONNX model via TRT")        
+        self.parser.add_argument("--Arc_path", type=str, default='models/BEST_checkpoint.tar', help="run ONNX model via TRT")
+        self.parser.add_argument("--pic_a_path", type=str, default='crop_224/gdg.jpg', help="people a")
+        self.parser.add_argument("--pic_b_path", type=str, default='crop_224/zrf.jpg', help="people b")
+        self.parser.add_argument("--output_path", type=str, default='output/', help="people b")
+        self.parser.add_argument("--video_path",type=str,help="people b video")
+
+        self.isTrain = False

test_video.py

@@ -0,0 +1,95 @@
+
+import cv2
+import torch
+import fractions
+import numpy as np
+import torch.nn.functional as F
+from torchvision import transforms
+from models.models import create_model
+from options.test_options import TestOptions
+
+
+def lcm(a, b): return abs(a * b) / fractions.gcd(a, b) if a and b else 0
+
+transformer = transforms.Compose([
+        transforms.ToTensor(),
+        #transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+transformer_Arcface = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+detransformer = transforms.Compose([
+        transforms.Normalize([0, 0, 0], [1/0.229, 1/0.224, 1/0.225]),
+        transforms.Normalize([-0.485, -0.456, -0.406], [1, 1, 1])
+    ])
+
+opt = TestOptions().parse()
+
+start_epoch, epoch_iter = 1, 0
+
+torch.nn.Module.dump_patches = True
+model = create_model(opt)
+model.eval()
+
+def img_b_atte(img_b):
+    img_b = transformer(img_b)
+    img_att = img_b.view(-1, img_b.shape[0], img_b.shape[1], img_b.shape[2])
+    img_att = img_att.cuda()
+    return img_att
+
+def swap(img_id,img_att,latend_id):
+    img_fake = model(img_id, img_att, latend_id, latend_id, True)
+    for i in range(img_id.shape[0]):
+        if i == 0:
+            row1 = img_id[i]
+            row2 = img_att[i]
+            row3 = img_fake[i]
+        else:
+            row1 = torch.cat([row1, img_id[i]], dim=2)
+            row2 = torch.cat([row2, img_att[i]], dim=2)
+            row3 = torch.cat([row3, img_fake[i]], dim=2)
+
+    full = row3.detach()
+    full = full.permute(1, 2, 0)
+    output = full.to('cpu')
+    output = np.array(output)
+    output = output[..., ::-1]
+    output = output*255
+    output=output.astype(np.uint8)
+    return output
+
+pic_a = opt.pic_a_path
+img_a=cv2.imread(pic_a)
+img_a=cv2.cvtColor(img_a,cv2.COLOR_BGR2RGB)
+h,w,_=img_a.shape
+if w!=224 or h!=224:
+    img_a=cv2.resize(img_a,(224,224))
+img_a = transformer_Arcface(img_a)
+img_id = img_a.view(-1, img_a.shape[0], img_a.shape[1], img_a.shape[2])
+img_id=img_id.cuda()
+
+#create latent id
+img_id_downsample = F.interpolate(img_id, scale_factor=0.5)
+latend_id = model.netArc(img_id_downsample)
+latend_id = latend_id.detach().to('cpu')
+latend_id = latend_id/np.linalg.norm(latend_id)
+latend_id = latend_id.to('cuda')
+
+cap=cv2.VideoCapture(opt.video_path)
+
+while cap.isOpened():
+    _,img_b=cap.read()
+    if img_b is None:
+        break
+    h,w,_=img_b.shape
+    if w!=224 or h!=224:
+        img_b=cv2.resize(img_b,(224,224))
+    img_b=cv2.cvtColor(img_b,cv2.COLOR_BGR2RGB)
+    img_att=img_b_atte(img_b)
+    img_fake=swap(img_id,img_att,latend_id)
+    cv2.imshow("swap",img_fake)
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break