Script for mutil-specifc face swapping for image

test_wholeimage_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.reverse2original import reverse2wholeimage
+import os
+from util.add_watermark import watermark_image
+import torch.nn as nn
+from util.norm import SpecificNorm
+import glob
+
+def lcm(a, b): return abs(a * b) / fractions.gcd(a, b) if a and b else 0
+
+transformer_Arcface = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+def _totensor(array):
+    tensor = torch.from_numpy(array)
+    img = tensor.transpose(0, 1).transpose(0, 2).contiguous()
+    return img.float().div(255)
+
+def _toarctensor(array):
+    tensor = torch.from_numpy(array)
+    img = tensor.transpose(0, 1).transpose(0, 2).contiguous()
+    return img.float().div(255)
+
+if __name__ == '__main__':
+    opt = TestOptions().parse()
+
+    start_epoch, epoch_iter = 1, 0
+    crop_size = 224
+
+    mutilsepcific_dir = opt.mutilsepcific_dir
+
+    torch.nn.Module.dump_patches = True
+    logoclass = watermark_image('./simswaplogo/simswaplogo.png')
+    model = create_model(opt)
+    model.eval()
+    mse = torch.nn.MSELoss().cuda()
+
+    spNorm =SpecificNorm()
+
+
+    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 !!!"
+
+    ############## 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)
+    # detect_results = None
+    swap_result_list = []
+
+    id_compare_values = [] 
+    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()
+
+        b_align_crop_tenor_arcnorm = spNorm(b_align_crop_tenor)
+        b_align_crop_tenor_arcnorm_downsample = F.interpolate(b_align_crop_tenor_arcnorm, scale_factor=0.5)
+        b_align_crop_id_nonorm = model.netArc(b_align_crop_tenor_arcnorm_downsample)
+
+        id_compare_values.append([])
+        for source_specific_id_nonorm_tmp in source_specific_id_nonorm_list:
+            id_compare_values[-1].append(mse(b_align_crop_id_nonorm,source_specific_id_nonorm_tmp).detach().cpu().numpy())
+        b_align_crop_tenor_list.append(b_align_crop_tenor)
+
+    id_compare_values_array = np.array(id_compare_values).transpose(1,0)
+    min_indexs = np.argmin(id_compare_values_array,axis=0)
+    min_value = np.min(id_compare_values_array,axis=0)
+
+    swap_result_list = [] 
+    swap_result_matrix_list = []
+
+    for tmp_index, min_index in enumerate(min_indexs):
+        if min_value[tmp_index] < opt.id_thres:
+            swap_result = model(None, b_align_crop_tenor_list[tmp_index], target_id_norm_list[min_index], None, True)[0]
+            swap_result_list.append(swap_result)
+            swap_result_matrix_list.append(b_mat_list[tmp_index])
+        else:
+            pass
+
+    if len(swap_result_list) !=0:
+    
+        reverse2wholeimage(swap_result_list, swap_result_matrix_list, crop_size, img_b_whole, logoclass, os.path.join(opt.output_path, 'result_whole_swap_mutilspecific.jpg'), opt.no_simswaplogo)
+
+        print(' ')
+
+        print('************ Done ! ************')
+    
+    else:
+        print('The people you specified are not found on the picture: {}'.format(pic_b))