Add Cog file to build Docker image

README.md

@@ -56,6 +56,8 @@ If you find this project useful, please star it. It is the greatest appreciation
 
 [Colab fo switching specific faces in multi-face videos](https://colab.research.google.com/github/neuralchen/SimSwap/blob/main/MultiSpecific.ipynb) 
 
+[Image face swapping demo & Docker image on Replicate](https://replicate.ai/neuralchen/simswap-image)
+
 Training: **coming soon**
 
 

cog.yaml

@@ -0,0 +1,20 @@
+build:
+  gpu: true
+  python_version: "3.8"
+  system_packages:
+    - "libgl1-mesa-glx"
+    - "libglib2.0-0"
+  python_packages:
+    - "imageio==2.9.0"
+    - "torch==1.8.0"
+    - "torchvision==0.9.0"
+    - "numpy==1.21.1"
+    - "insightface==0.2.1"
+    - "ipython==7.21.0"
+    - "Pillow==8.3.1"
+    - "opencv-python==4.5.3.56"
+    - "Fraction==1.5.1"
+    - "onnxruntime-gpu==1.8.1"
+    - "moviepy==1.0.3"
+
+predict: "predict.py:Predictor"

predict.py

@@ -0,0 +1,101 @@
+import cog
+import tempfile
+from pathlib import Path
+import argparse
+import cv2
+import torch
+from PIL import Image
+import torch.nn.functional as F
+from torchvision import transforms
+from models.models import create_model
+from options.test_options import TestOptions
+from util.reverse2original import reverse2wholeimage
+from util.norm import SpecificNorm
+from test_wholeimage_swapmulti import _totensor
+from insightface_func.face_detect_crop_multi import Face_detect_crop as Face_detect_crop_multi
+from insightface_func.face_detect_crop_single import Face_detect_crop as Face_detect_crop_single
+
+
+class Predictor(cog.Predictor):
+    def setup(self):
+        self.transformer_Arcface = transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+        ])
+
+    @cog.input("source", type=Path, help="source image")
+    @cog.input("target", type=Path, help="target image")
+    @cog.input("mode", type=str, options=['single', 'all'], default='all',
+               help="swap a single face (the one with highest confidence by face detection) or all faces in the target image")
+    def predict(self, source, target, mode='all'):
+
+        app = Face_detect_crop_multi(name='antelope', root='./insightface_func/models')
+
+        if mode == 'single':
+            app = Face_detect_crop_single(name='antelope', root='./insightface_func/models')
+
+        app.prepare(ctx_id=0, det_thresh=0.6, det_size=(640, 640))
+
+        options = TestOptions()
+        options.initialize()
+        opt = options.parser.parse_args(["--Arc_path", 'arcface_model/arcface_checkpoint.tar', "--pic_a_path", str(source),
+                                         "--pic_b_path", str(target), "--isTrain", False, "--no_simswaplogo"])
+
+        str_ids = opt.gpu_ids.split(',')
+        opt.gpu_ids = []
+        for str_id in str_ids:
+            id = int(str_id)
+            if id >= 0:
+                opt.gpu_ids.append(id)
+
+        # set gpu ids
+        if len(opt.gpu_ids) > 0:
+            torch.cuda.set_device(opt.gpu_ids[0])
+
+        torch.nn.Module.dump_patches = True
+        model = create_model(opt)
+        model.eval()
+
+        crop_size = 224
+        spNorm = SpecificNorm()
+
+        with torch.no_grad():
+            pic_a = opt.pic_a_path
+            img_a_whole = cv2.imread(pic_a)
+            img_a_align_crop, _ = app.get(img_a_whole, crop_size)
+            img_a_align_crop_pil = Image.fromarray(cv2.cvtColor(img_a_align_crop[0], cv2.COLOR_BGR2RGB))
+            img_a = self.transformer_Arcface(img_a_align_crop_pil)
+            img_id = img_a.view(-1, img_a.shape[0], img_a.shape[1], img_a.shape[2])
+
+            # convert numpy to tensor
+            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 = F.normalize(latend_id, p=2, dim=1)
+
+            ############## Forward Pass ######################
+
+            pic_b = opt.pic_b_path
+            img_b_whole = cv2.imread(pic_b)
+            img_b_align_crop_list, b_mat_list = app.get(img_b_whole, crop_size)
+
+            swap_result_list = []
+            b_align_crop_tenor_list = []
+
+            for b_align_crop in img_b_align_crop_list:
+                b_align_crop_tenor = _totensor(cv2.cvtColor(b_align_crop, cv2.COLOR_BGR2RGB))[None, ...].cuda()
+
+                swap_result = model(None, b_align_crop_tenor, latend_id, None, True)[0]
+                swap_result_list.append(swap_result)
+                b_align_crop_tenor_list.append(b_align_crop_tenor)
+
+            net = None
+
+            out_path = Path(tempfile.mkdtemp()) / "output.png"
+
+            reverse2wholeimage(b_align_crop_tenor_list, swap_result_list, b_mat_list, crop_size, img_b_whole, None,
+                               str(out_path), opt.no_simswaplogo,
+                               pasring_model=net, use_mask=opt.use_mask, norm=spNorm)
+            return out_path