CalvinBackup/neuralchen-SimSwap
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NNNNAI
2021-06-20 11:58:32 +08:00
parent 6e5ee6a3f6
commit b394ffdffd
4 changed files with 178 additions and 45 deletions
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options/test_options.py
+5 -3
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@@ -15,8 +15,10 @@ class TestOptions(BaseOptions):
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("--pic_a_path", type=str, default='./crop_224/gdg.jpg', help="People who provide identity information")
self.parser.add_argument("--pic_b_path", type=str, default='./crop_224/zrf.jpg', help="People who provide information other than their identity")
self.parser.add_argument("--video_path", type=str, default='./demo_file/mutil_people_1080p.mp4', help="path for the video to swap")
self.parser.add_argument("--temp_path", type=str, default='./temp_results', help="path to save temporarily images")
self.parser.add_argument("--output_path", type=str, default='./output/', help="results path")
self.isTrain = False
simswaplogo/simswaplogo.png
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test_one_image.py
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@@ -26,60 +26,60 @@ 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()
opt = TestOptions().parse()
start_epoch, epoch_iter = 1, 0
start_epoch, epoch_iter = 1, 0
torch.nn.Module.dump_patches = True
model = create_model(opt)
model.eval()
torch.nn.Module.dump_patches = True
model = create_model(opt)
model.eval()
pic_a = opt.pic_a_path
img_a = Image.open(pic_a).convert('RGB')
img_a = transformer_Arcface(img_a)
img_id = img_a.view(-1, img_a.shape[0], img_a.shape[1], img_a.shape[2])
pic_a = opt.pic_a_path
img_a = Image.open(pic_a).convert('RGB')
img_a = transformer_Arcface(img_a)
img_id = img_a.view(-1, img_a.shape[0], img_a.shape[1], img_a.shape[2])
pic_b = opt.pic_b_path
pic_b = opt.pic_b_path
img_b = Image.open(pic_b).convert('RGB')
img_b = transformer(img_b)
img_att = img_b.view(-1, img_b.shape[0], img_b.shape[1], img_b.shape[2])
img_b = Image.open(pic_b).convert('RGB')
img_b = transformer(img_b)
img_att = img_b.view(-1, img_b.shape[0], img_b.shape[1], img_b.shape[2])
# convert numpy to tensor
img_id = img_id.cuda()
img_att = img_att.cuda()
# convert numpy to tensor
img_id = img_id.cuda()
img_att = img_att.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')
#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 ######################
img_fake = model(img_id, img_att, latend_id, latend_id, True)
############## Forward Pass ######################
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)
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 = torch.cat([row1, row2, row3], dim=1).detach()
full = row3.detach()
full = full.permute(1, 2, 0)
output = full.to('cpu')
output = np.array(output)
output = output[..., ::-1]
#full = torch.cat([row1, row2, row3], dim=1).detach()
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*255
cv2.imwrite(opt.output_path + 'result.jpg',output)
cv2.imwrite(opt.output_path + 'result.jpg',output)
util/add_watermark.py
+131
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@@ -0,0 +1,131 @@
import cv2
import numpy as np
from PIL import Image
import math
import numpy as np
# import torch
# from torchvision import transforms
def rotate_image(image, angle, center = None, scale = 1.0):
(h, w) = image.shape[:2]
if center is None:
center = (w / 2, h / 2)
# Perform the rotation
M = cv2.getRotationMatrix2D(center, angle, scale)
rotated = cv2.warpAffine(image, M, (w, h))
return rotated
class watermark_image:
def __init__(self, logo_path, size=0.3, oritation="DR", margin=(5,20,20,100), angle=15, rgb_weight=(0,1,1.5), input_frame_shape=None) -> None:
logo_image = cv2.imread(logo_path, cv2.IMREAD_UNCHANGED)
h,w,c = logo_image.shape
if angle%360 != 0:
new_h = w*math.sin(angle/180*math.pi) + h*math.cos(angle/180*math.pi)
pad_h = int((new_h-h)//2)
padding = np.zeros((pad_h, w, c), dtype=np.uint8)
logo_image = cv2.vconcat([logo_image, padding])
logo_image = cv2.vconcat([padding, logo_image])
logo_image = rotate_image(logo_image, angle)
print(logo_image.shape)
self.logo_image = logo_image
if self.logo_image.shape[2] < 4:
print("No alpha channel found!")
self.logo_image = self.__addAlpha__(self.logo_image) #add alpha channel
self.size = size
self.oritation = oritation
self.margin = margin
self.ori_shape = self.logo_image.shape
self.resized = False
self.rgb_weight = rgb_weight
self.logo_image[:, :, 2] = self.logo_image[:, :, 2]*self.rgb_weight[0]
self.logo_image[:, :, 1] = self.logo_image[:, :, 1]*self.rgb_weight[1]
self.logo_image[:, :, 0] = self.logo_image[:, :, 0]*self.rgb_weight[2]
if input_frame_shape is not None:
if input_frame_shape[0] > input_frame_shape[1]:
logo_h = input_frame_shape[0] * self.size
ratio = logo_h / self.ori_shape[0]
logo_w = int(ratio * self.ori_shape[1])
logo_h = int(logo_h)
else:
logo_w = input_frame_shape[1] * self.size
ratio = logo_w / self.ori_shape[1]
logo_h = int(ratio * self.ori_shape[0])
logo_w = int(logo_w)
size = (logo_w, logo_h)
self.logo_image = cv2.resize(self.logo_image, size, interpolation = cv2.INTER_CUBIC)
self.resized = True
if oritation == "UL":
self.coor_h = self.margin[1]
self.coor_w = self.margin[0]
elif oritation == "UR":
self.coor_h = self.margin[1]
self.coor_w = input_frame_shape[1] - (logo_w + self.margin[2])
elif oritation == "DL":
self.coor_h = input_frame_shape[0] - (logo_h + self.margin[1])
self.coor_w = self.margin[0]
else:
self.coor_h = input_frame_shape[0] - (logo_h + self.margin[1])
self.coor_w = input_frame_shape[1] - (logo_w + self.margin[2])
self.logo_w = logo_w
self.logo_h = logo_h
self.mask = self.logo_image[:,:,3]
self.mask = cv2.bitwise_not(self.mask//255)
def apply_frames(self, frame):
if not self.resized:
shape = frame.shape
if shape[0] > shape[1]:
logo_h = shape[0] * self.size
ratio = logo_h / self.ori_shape[0]
logo_w = int(ratio * self.ori_shape[1])
logo_h = int(logo_h)
else:
logo_w = shape[1] * self.size
ratio = logo_w / self.ori_shape[1]
logo_h = int(ratio * self.ori_shape[0])
logo_w = int(logo_w)
size = (logo_w, logo_h)
self.logo_image = cv2.resize(self.logo_image, size, interpolation = cv2.INTER_CUBIC)
self.resized = True
if self.oritation == "UL":
self.coor_h = self.margin[1]
self.coor_w = self.margin[0]
elif self.oritation == "UR":
self.coor_h = self.margin[1]
self.coor_w = shape[1] - (logo_w + self.margin[2])
elif self.oritation == "DL":
self.coor_h = shape[0] - (logo_h + self.margin[1])
self.coor_w = self.margin[0]
else:
self.coor_h = shape[0] - (logo_h + self.margin[1])
self.coor_w = shape[1] - (logo_w + self.margin[2])
self.logo_w = logo_w
self.logo_h = logo_h
self.mask = self.logo_image[:,:,3]
self.mask = cv2.bitwise_not(self.mask//255)
original_frame = frame[self.coor_h:(self.coor_h+self.logo_h), self.coor_w:(self.coor_w+self.logo_w),:]
blending_logo = cv2.add(self.logo_image[:,:,0:3],original_frame,mask = self.mask)
frame[self.coor_h:(self.coor_h+self.logo_h), self.coor_w:(self.coor_w+self.logo_w),:] = blending_logo
return frame
def __addAlpha__(self, image):
shape = image.shape
alpha_channel = np.ones((shape[0],shape[1],1),np.uint8)*255
return np.concatenate((image,alpha_channel),2)