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Script for mutil-specific face swapping for video

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  1. 94 0
      test_video_swap_mutilspecific.py

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test_video_swap_mutilspecific.py

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+
+import cv2
+import torch
+import fractions
+import numpy as np
+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 insightface_func.face_detect_crop_mutil import Face_detect_crop
+from util.videoswap_mutilspecific import video_swap
+import os
+import glob
+
+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])
+#     ])
+
+
+if __name__ == '__main__':
+    opt = TestOptions().parse()
+    pic_specific = opt.pic_specific_path
+    start_epoch, epoch_iter = 1, 0
+    crop_size = 224
+
+    mutilsepcific_dir = opt.mutilsepcific_dir
+    torch.nn.Module.dump_patches = True
+    model = create_model(opt)
+    model.eval()
+
+
+    app = Face_detect_crop(name='antelope', root='./insightface_func/models')
+    app.prepare(ctx_id= 0, det_thresh=0.6, det_size=(640,640))
+
+    # The specific person to be swapped(source)
+
+    source_specific_id_nonorm_list = []
+    source_path = os.path.join(mutilsepcific_dir,'source','*')
+    source_specific_images_path = sorted(glob.glob(source_path))
+
+    for source_specific_image_path in source_specific_images_path:
+        specific_person_whole = cv2.imread(source_specific_image_path)
+        specific_person_align_crop, _ = app.get(specific_person_whole,crop_size)
+        specific_person_align_crop_pil = Image.fromarray(cv2.cvtColor(specific_person_align_crop[0],cv2.COLOR_BGR2RGB)) 
+        specific_person = transformer_Arcface(specific_person_align_crop_pil)
+        specific_person = specific_person.view(-1, specific_person.shape[0], specific_person.shape[1], specific_person.shape[2])
+        # convert numpy to tensor
+        specific_person = specific_person.cuda()
+        #create latent id
+        specific_person_downsample = F.interpolate(specific_person, scale_factor=0.5)
+        specific_person_id_nonorm = model.netArc(specific_person_downsample)
+        source_specific_id_nonorm_list.append(specific_person_id_nonorm.clone())
+
+
+    # The person who provides id information (list)
+    target_id_norm_list = []
+    target_path = os.path.join(mutilsepcific_dir,'target','*')
+    target_images_path = sorted(glob.glob(target_path))
+
+    for target_image_path in target_images_path:
+        img_a_whole = cv2.imread(target_image_path)
+        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 = 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)
+        target_id_norm_list.append(latend_id.clone())
+
+    assert len(target_id_norm_list) == len(source_specific_id_nonorm_list), "The number of images in source and target directory must be same !!!"
+
+
+
+    video_swap(opt.video_path, target_id_norm_list,source_specific_id_nonorm_list, opt.id_thres, \
+        model, app, opt.output_path,temp_results_dir=opt.temp_path,no_simswaplogo=opt.no_simswaplogo)
+