From 5813d5270d4629fa0b68ff1c0af8b9bfc3a817e4 Mon Sep 17 00:00:00 2001 From: NNNNAI <844294823@qq.com> Date: Sun, 20 Jun 2021 11:59:26 +0800 Subject: [PATCH] Script for image face swaping Script for image face swaping --- test_wholeimage_swapmutil.py | 82 +++++++++++++++++++++++++++++++++++ test_wholeimage_swapsingle.py | 82 +++++++++++++++++++++++++++++++++++ 2 files changed, 164 insertions(+) create mode 100644 test_wholeimage_swapmutil.py create mode 100644 test_wholeimage_swapsingle.py diff --git a/test_wholeimage_swapmutil.py b/test_wholeimage_swapmutil.py new file mode 100644 index 0000000..0f9b063 --- /dev/null +++ b/test_wholeimage_swapmutil.py @@ -0,0 +1,82 @@ + +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 + +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) + +if __name__ == '__main__': + opt = TestOptions().parse() + + start_epoch, epoch_iter = 1, 0 + crop_size = 224 + + torch.nn.Module.dump_patches = True + logoclass = watermark_image('./simswaplogo/simswaplogo.png') + model = create_model(opt) + model.eval() + + + app = Face_detect_crop(name='antelope', root='./insightface_func/models') + app.prepare(ctx_id= 0, det_thresh=0.8, det_size=(640,640)) + + 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 = 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 = latend_id.detach().to('cpu') + latend_id = latend_id/np.linalg.norm(latend_id,axis=1,keepdims=True) + latend_id = latend_id.to('cuda') + + + ############## Forward Pass ###################### + + pic_b = opt.pic_b_path + img_b_whole = cv2.imread(pic_b) + img_b_align_crop, b_mat = app.get(img_b_whole,crop_size) + + img_b_align_crop_list, b_mat_list = app.get(img_b_whole,crop_size) + # detect_results = None + swap_result_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) + + reverse2wholeimage(swap_result_list, b_mat_list, crop_size, img_b_whole, logoclass, os.path.join(opt.output_path, 'result_whole_swapmutil.jpg')) + print(' ') + + print('************ Done ! ************') diff --git a/test_wholeimage_swapsingle.py b/test_wholeimage_swapsingle.py new file mode 100644 index 0000000..8ac51d3 --- /dev/null +++ b/test_wholeimage_swapsingle.py @@ -0,0 +1,82 @@ + +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_single import Face_detect_crop +from util.reverse2original import reverse2wholeimage +import os +from util.add_watermark import watermark_image + +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) +if __name__ == '__main__': + opt = TestOptions().parse() + + start_epoch, epoch_iter = 1, 0 + crop_size = 224 + + torch.nn.Module.dump_patches = True + logoclass = watermark_image('./simswaplogo/simswaplogo.png') + model = create_model(opt) + model.eval() + + + app = Face_detect_crop(name='antelope', root='./insightface_func/models') + app.prepare(ctx_id= 0, det_thresh=0.8, det_size=(640,640)) + + 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 = 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 = latend_id.detach().to('cpu') + latend_id = latend_id/np.linalg.norm(latend_id,axis=1,keepdims=True) + latend_id = latend_id.to('cuda') + + + ############## Forward Pass ###################### + + pic_b = opt.pic_b_path + img_b_whole = cv2.imread(pic_b) + img_b_align_crop, b_mat = app.get(img_b_whole,crop_size) + + img_b_align_crop_list, b_mat_list = app.get(img_b_whole,crop_size) + # detect_results = None + swap_result_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) + + reverse2wholeimage(swap_result_list, b_mat_list, crop_size, img_b_whole, logoclass, os.path.join(opt.output_path, 'result_whole_swapsingle.jpg')) + + print(' ') + + print('************ Done ! ************')