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simswap_gui.py
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simswap_gui.py
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import os
import cv2
import glob
import uuid
import time
import shutil
import subprocess
import platform
import threading
import webbrowser
import numpy as np
import tkinter as tk
from tqdm import tqdm
from tkinter import ttk, messagebox
from PIL import Image, ImageTk, ImageOps, ImageDraw
import torch
import torch.nn as nn
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_multi import Face_detect_crop
from moviepy.editor import AudioFileClip, VideoFileClip
from moviepy.video.io.ffmpeg_tools import ffmpeg_extract_subclip
from moviepy.video.io.ImageSequenceClip import ImageSequenceClip
from util.norm import SpecificNorm
from parsing_model.model import BiSeNet
from util.add_watermark import watermark_image
from util.reverse2original import encode_segmentation_rgb, SoftErosion, postprocess
# pip install tkinterdnd2
from tkinterdnd2 import DND_FILES, TkinterDnD
######################################## DEFAULTS ########################################
CWD = os.path.curdir
OUTPUT_PATH = os.path.join(CWD, 'output', 'out.mp4')
ARC_PATH = os.path.join(CWD, 'arcface_model', 'arcface_checkpoint.tar')
TEMP_PATH = os.path.join(CWD, 'temp_results', 'sequence')
TRIM_PATH = os.path.join(CWD, 'temp_results', 'trims')
DET_THRESHOLD = 0.6
ICON_SIZE = (100, 100)
WINDOW_SIZE = (800, 450)
CROP_SIZE = 224
MASK_KERNEL = "40 40"
DET_SIZE = 640
DET_THRESHOLD = 0.6
USE_MASK = True
WATERMARK = False
THEME_STYLE = 'clam'
FFHQ = False
FACE_PART_IDS = "1 2 3 4 5 6 10 12 13"
######################################## DEFAULTS ########################################
# The codes are currently a huge mess.
# I don't have too much time to beautify code... So here is what it is.
# will clean things later
class SimSwap:
def _totensor(array):
tensor = torch.from_numpy(array)
img = tensor.transpose(0, 1).transpose(0, 2).contiguous()
return img.float().div(255)
def encode_segmentation_rgb(segmentation, no_neck=True, face_part_ids=[1, 2, 3, 4, 5, 6, 10, 12, 13]):
parse = segmentation
#face_part_ids = [1, 2, 3, 4, 5, 6, 10, 12, 13] if no_neck else [1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 13, 14]
mouth_id = 11
face_map = np.zeros([parse.shape[0], parse.shape[1]])
mouth_map = np.zeros([parse.shape[0], parse.shape[1]])
for valid_id in face_part_ids:
valid_index = np.where(parse==valid_id)
face_map[valid_index] = 255
valid_index = np.where(parse==mouth_id)
mouth_map[valid_index] = 255
return np.stack([face_map, mouth_map], axis=2)
def reverse2wholeimage(b_align_crop_tenor_list,swaped_imgs, mats, crop_size, oriimg, logoclass, save_path = '', \
no_simswaplogo = False,pasring_model =None,norm = None, use_mask = False, _kernel_size=(40,40)
, face_part_ids=[1, 2, 3, 4, 5, 6, 10, 12, 13]):
target_image_list = []
img_mask_list = []
if use_mask:
smooth_mask = SoftErosion(kernel_size=17, threshold=0.9, iterations=7).cuda()
else:
pass
for swaped_img, mat ,source_img in zip(swaped_imgs, mats,b_align_crop_tenor_list):
swaped_img = swaped_img.cpu().detach().numpy().transpose((1, 2, 0))
img_white = np.full((crop_size,crop_size), 255, dtype=float)
mat_rev = np.zeros([2,3])
div1 = mat[0][0]*mat[1][1]-mat[0][1]*mat[1][0]
mat_rev[0][0] = mat[1][1]/div1
mat_rev[0][1] = -mat[0][1]/div1
mat_rev[0][2] = -(mat[0][2]*mat[1][1]-mat[0][1]*mat[1][2])/div1
div2 = mat[0][1]*mat[1][0]-mat[0][0]*mat[1][1]
mat_rev[1][0] = mat[1][0]/div2
mat_rev[1][1] = -mat[0][0]/div2
mat_rev[1][2] = -(mat[0][2]*mat[1][0]-mat[0][0]*mat[1][2])/div2
orisize = (oriimg.shape[1], oriimg.shape[0])
if use_mask:
source_img_norm = norm(source_img)
source_img_512 = F.interpolate(source_img_norm,size=(512,512))
out = pasring_model(source_img_512)[0]
parsing = out.squeeze(0).detach().cpu().numpy().argmax(0)
vis_parsing_anno = parsing.copy().astype(np.uint8)
tgt_mask = SimSwap.encode_segmentation_rgb(vis_parsing_anno, face_part_ids=face_part_ids)
if tgt_mask.sum() >= 5000:
target_mask = cv2.resize(tgt_mask, (crop_size, crop_size))
target_image_parsing = postprocess(swaped_img, source_img[0].cpu().detach().numpy().transpose((1, 2, 0)), target_mask,smooth_mask)
target_image = cv2.warpAffine(target_image_parsing, mat_rev, orisize)
else:
target_image = cv2.warpAffine(swaped_img, mat_rev, orisize)[..., ::-1]
else:
target_image = cv2.warpAffine(swaped_img, mat_rev, orisize)
img_white = cv2.warpAffine(img_white, mat_rev, orisize)
img_white[img_white>20] =255
img_mask = img_white
kernel = np.ones(_kernel_size,np.uint8)
img_mask = cv2.erode(img_mask,kernel,iterations = 1)
kernel_size = (20, 20)
blur_size = tuple(2*i+1 for i in kernel_size)
img_mask = cv2.GaussianBlur(img_mask, blur_size, 0)
img_mask /= 255
img_mask = np.reshape(img_mask, [img_mask.shape[0],img_mask.shape[1],1])
if use_mask:
target_image = np.array(target_image, dtype=np.float) * 255
else:
target_image = np.array(target_image, dtype=np.float)[..., ::-1] * 255
img_mask_list.append(img_mask)
target_image_list.append(target_image)
img = np.array(oriimg, dtype=np.float)
for img_mask, target_image in zip(img_mask_list, target_image_list):
img = img_mask * target_image + (1-img_mask) * img
final_img = img.astype(np.uint8)
if not no_simswaplogo:
final_img = logoclass.apply_frames(final_img)
cv2.imwrite(save_path, final_img)
return final_img
def video_swap(video_path, id_vetor, swap_model, detect_model, save_path, temp_results_dir='./temp_results',
crop_size=224, no_simswaplogo = False,use_mask =False, self_obj=None, _kernel_size=(40, 40), face_part_ids=[1,2]):
video_forcheck = VideoFileClip(video_path)
if video_forcheck.audio is None:
no_audio = True
else:
no_audio = False
del video_forcheck
if not no_audio:
video_audio_clip = AudioFileClip(video_path)
video = cv2.VideoCapture(video_path)
logoclass = watermark_image('./simswaplogo/simswaplogo.png')
ret = True
frame_index = 0
frame_count = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
fps = video.get(cv2.CAP_PROP_FPS)
if os.path.exists(temp_results_dir):
shutil.rmtree(temp_results_dir)
spNorm =SpecificNorm()
if use_mask:
n_classes = 19
net = BiSeNet(n_classes=n_classes)
net.cuda()
save_pth = os.path.join('./parsing_model/checkpoint', '79999_iter.pth')
net.load_state_dict(torch.load(save_pth))
net.eval()
else:
net =None
for frame_index in tqdm(range(frame_count)):
if self_obj.stop_process:
return
ret, frame = video.read()
if ret:
detect_results = detect_model.get(frame,crop_size)
if detect_results is not None:
if not os.path.exists(temp_results_dir):
os.mkdir(temp_results_dir)
frame_align_crop_list = detect_results[0]
frame_mat_list = detect_results[1]
swap_result_list = []
frame_align_crop_tenor_list = []
for frame_align_crop in frame_align_crop_list:
frame_align_crop_tenor = SimSwap._totensor(cv2.cvtColor(frame_align_crop,cv2.COLOR_BGR2RGB))[None,...].cuda()
swap_result = swap_model(None, frame_align_crop_tenor, id_vetor, None, True)[0]
cv2.imwrite(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), frame)
swap_result_list.append(swap_result)
frame_align_crop_tenor_list.append(frame_align_crop_tenor)
final_img = SimSwap.reverse2wholeimage(frame_align_crop_tenor_list,swap_result_list, frame_mat_list, crop_size, frame, logoclass,\
os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)),no_simswaplogo,pasring_model =net,
use_mask=use_mask, norm = spNorm, _kernel_size=_kernel_size, face_part_ids=face_part_ids)
########################################### Two pass #####################################################################################
if self_obj.settings.two_pass.get():
self_obj.set_status(f"Performing two pass... ({int((frame_index/frame_count)*100)}% completed)")
detect_results = detect_model.get(final_img,crop_size)
if detect_results is not None:
if not os.path.exists(temp_results_dir):
os.mkdir(temp_results_dir)
frame_align_crop_list = detect_results[0]
frame_mat_list = detect_results[1]
swap_result_list = []
frame_align_crop_tenor_list = []
for frame_align_crop in frame_align_crop_list:
frame_align_crop_tenor = SimSwap._totensor(cv2.cvtColor(frame_align_crop,cv2.COLOR_BGR2RGB))[None,...].cuda()
swap_result = swap_model(None, frame_align_crop_tenor, id_vetor, None, True)[0]
cv2.imwrite(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), frame)
swap_result_list.append(swap_result)
frame_align_crop_tenor_list.append(frame_align_crop_tenor)
final_img = SimSwap.reverse2wholeimage(frame_align_crop_tenor_list,swap_result_list, frame_mat_list, crop_size, frame, logoclass,\
os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)),no_simswaplogo,pasring_model =net,
use_mask=use_mask, norm = spNorm, _kernel_size=_kernel_size, face_part_ids=face_part_ids)
########################################### Two pass #####################################################################################
try:
if self_obj.stop_process:
return
self_obj.video_player.update_display(image=final_img)
self_obj.set_status(f"Processing {frame_index} of {frame_count}... ({int((frame_index/frame_count)*100)}% completed)")
except: pass
else:
if not os.path.exists(temp_results_dir):
os.mkdir(temp_results_dir)
frame = frame.astype(np.uint8)
if not no_simswaplogo:
frame = logoclass.apply_frames(frame)
cv2.imwrite(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), frame)
self_obj.set_status(f"Skipping no face...({int((frame_index/frame_count)*100)}% completed)")
else:
break
video.release()
self_obj.set_status(f"Merging sequence...")
path = os.path.join(temp_results_dir,'*.jpg')
image_filenames = sorted(glob.glob(path))
clips = ImageSequenceClip(image_filenames,fps = fps)
if not no_audio:
clips = clips.set_audio(video_audio_clip)
clips.write_videofile(save_path,audio_codec='aac')
def video_target_swap(video_path, id_vetor,specific_person_id_nonorm,id_thres, swap_model,
detect_model, save_path, temp_results_dir='./temp_results',
crop_size=224, no_simswaplogo = False,use_mask =False, self_obj=None, _kernel_size=(40, 40), face_part_ids=[1,2]):
video_forcheck = VideoFileClip(video_path)
if video_forcheck.audio is None:
no_audio = True
else:
no_audio = False
del video_forcheck
if not no_audio:
video_audio_clip = AudioFileClip(video_path)
video = cv2.VideoCapture(video_path)
logoclass = watermark_image('./simswaplogo/simswaplogo.png')
ret = True
frame_index = 0
frame_count = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
fps = video.get(cv2.CAP_PROP_FPS)
if os.path.exists(temp_results_dir):
shutil.rmtree(temp_results_dir)
spNorm =SpecificNorm()
mse = torch.nn.MSELoss().cuda()
if use_mask:
n_classes = 19
net = BiSeNet(n_classes=n_classes)
net.cuda()
save_pth = os.path.join('./parsing_model/checkpoint', '79999_iter.pth')
net.load_state_dict(torch.load(save_pth))
net.eval()
else:
net =None
for frame_index in tqdm(range(frame_count)):
if self_obj.stop_process:
return
ret, frame = video.read()
if ret:
detect_results = detect_model.get(frame,crop_size)
if detect_results is not None:
if not os.path.exists(temp_results_dir):
os.mkdir(temp_results_dir)
frame_align_crop_list = detect_results[0]
frame_mat_list = detect_results[1]
id_compare_values = []
frame_align_crop_tenor_list = []
for frame_align_crop in frame_align_crop_list:
frame_align_crop_tenor = SimSwap._totensor(cv2.cvtColor(frame_align_crop,cv2.COLOR_BGR2RGB))[None,...].cuda()
frame_align_crop_tenor_arcnorm = spNorm(frame_align_crop_tenor)
frame_align_crop_tenor_arcnorm_downsample = F.interpolate(frame_align_crop_tenor_arcnorm, size=(112,112))
frame_align_crop_crop_id_nonorm = swap_model.netArc(frame_align_crop_tenor_arcnorm_downsample)
id_compare_values.append(mse(frame_align_crop_crop_id_nonorm,specific_person_id_nonorm).detach().cpu().numpy())
frame_align_crop_tenor_list.append(frame_align_crop_tenor)
id_compare_values_array = np.array(id_compare_values)
min_index = np.argmin(id_compare_values_array)
min_value = id_compare_values_array[min_index]
if min_value < id_thres:
swap_result = swap_model(None, frame_align_crop_tenor_list[min_index], id_vetor, None, True)[0]
final_img = SimSwap.reverse2wholeimage([frame_align_crop_tenor_list[min_index]], [swap_result], [frame_mat_list[min_index]], crop_size, frame, logoclass,\
os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)),no_simswaplogo,pasring_model =net,
use_mask= use_mask, norm = spNorm, _kernel_size=_kernel_size, face_part_ids=face_part_ids)
########################################### Two pass #####################################################################################
if self_obj.settings.two_pass.get():
self_obj.set_status(f"Performing two pass... ({int((frame_index/frame_count)*100)}% completed)")
detect_results = detect_model.get(final_img,crop_size)
if detect_results is not None:
if not os.path.exists(temp_results_dir):
os.mkdir(temp_results_dir)
frame_align_crop_list = detect_results[0]
frame_mat_list = detect_results[1]
swap_result_list = []
frame_align_crop_tenor_list = []
for frame_align_crop in frame_align_crop_list:
frame_align_crop_tenor = SimSwap._totensor(cv2.cvtColor(frame_align_crop,cv2.COLOR_BGR2RGB))[None,...].cuda()
swap_result = swap_model(None, frame_align_crop_tenor, id_vetor, None, True)[0]
cv2.imwrite(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), frame)
swap_result_list.append(swap_result)
frame_align_crop_tenor_list.append(frame_align_crop_tenor)
final_img = SimSwap.reverse2wholeimage(frame_align_crop_tenor_list,swap_result_list, frame_mat_list, crop_size, frame, logoclass,\
os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)),no_simswaplogo,pasring_model =net,
use_mask=use_mask, norm = spNorm, _kernel_size=_kernel_size, face_part_ids=face_part_ids)
########################################### Two pass #####################################################################################
try:
if self_obj.stop_process:
return
self_obj.video_player.update_display(image=final_img)
self_obj.set_status(f"Processing {frame_index} of {frame_count}... ({int((frame_index/frame_count)*100)}% completed)")
except: pass
else:
if not os.path.exists(temp_results_dir):
os.mkdir(temp_results_dir)
frame = frame.astype(np.uint8)
if not no_simswaplogo:
frame = logoclass.apply_frames(frame)
cv2.imwrite(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), frame)
else:
if not os.path.exists(temp_results_dir):
os.mkdir(temp_results_dir)
frame = frame.astype(np.uint8)
if not no_simswaplogo:
frame = logoclass.apply_frames(frame)
cv2.imwrite(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), frame)
self_obj.set_status(f"Skipping no face...({int((frame_index/frame_count)*100)}% completed)")
else:
break
video.release()
self_obj.set_status(f"Merging sequence...")
path = os.path.join(temp_results_dir,'*.jpg')
image_filenames = sorted(glob.glob(path))
clips = ImageSequenceClip(image_filenames,fps = fps)
if not no_audio:
clips = clips.set_audio(video_audio_clip)
clips.write_videofile(save_path,audio_codec='aac')
def video_swap_multispecific(video_path, target_id_norm_list,source_specific_id_nonorm_list,id_thres, swap_model,
detect_model, save_path, temp_results_dir='./temp_results', crop_size=224, no_simswaplogo = False,use_mask =False,
self_obj=None, _kernel_size=(40, 40), face_part_ids=[1,2]):
video_forcheck = VideoFileClip(video_path)
if video_forcheck.audio is None:
no_audio = True
else:
no_audio = False
del video_forcheck
if not no_audio:
video_audio_clip = AudioFileClip(video_path)
video = cv2.VideoCapture(video_path)
logoclass = watermark_image('./simswaplogo/simswaplogo.png')
ret = True
frame_index = 0
frame_count = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
fps = video.get(cv2.CAP_PROP_FPS)
if os.path.exists(temp_results_dir):
shutil.rmtree(temp_results_dir)
spNorm =SpecificNorm()
mse = torch.nn.MSELoss().cuda()
if use_mask:
n_classes = 19
net = BiSeNet(n_classes=n_classes)
net.cuda()
save_pth = os.path.join('./parsing_model/checkpoint', '79999_iter.pth')
net.load_state_dict(torch.load(save_pth))
net.eval()
else:
net =None
for frame_index in tqdm(range(frame_count)):
ret, frame = video.read()
if ret:
detect_results = detect_model.get(frame,crop_size)
if detect_results is not None:
if not os.path.exists(temp_results_dir):
os.mkdir(temp_results_dir)
frame_align_crop_list = detect_results[0]
frame_mat_list = detect_results[1]
id_compare_values = []
frame_align_crop_tenor_list = []
for frame_align_crop in frame_align_crop_list:
frame_align_crop_tenor = SimSwap._totensor(cv2.cvtColor(frame_align_crop,cv2.COLOR_BGR2RGB))[None,...].cuda()
frame_align_crop_tenor_arcnorm = spNorm(frame_align_crop_tenor)
frame_align_crop_tenor_arcnorm_downsample = F.interpolate(frame_align_crop_tenor_arcnorm, size=(112,112))
frame_align_crop_crop_id_nonorm = swap_model.netArc(frame_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(frame_align_crop_crop_id_nonorm,source_specific_id_nonorm_tmp).detach().cpu().numpy())
frame_align_crop_tenor_list.append(frame_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 = []
swap_result_ori_pic_list = []
for tmp_index, min_index in enumerate(min_indexs):
if min_value[tmp_index] < id_thres:
swap_result = swap_model(None, frame_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(frame_mat_list[tmp_index])
swap_result_ori_pic_list.append(frame_align_crop_tenor_list[tmp_index])
else:
pass
if len(swap_result_list) !=0:
final_img = SimSwap.reverse2wholeimage(swap_result_ori_pic_list,swap_result_list, swap_result_matrix_list, crop_size, frame, logoclass,\
os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)),no_simswaplogo,pasring_model =net,use_mask=use_mask,
norm = spNorm, _kernel_size=_kernel_size, face_part_ids=face_part_ids)
########################################################## Two Pass ####################################################################
if self_obj.settings.two_pass.get():
self_obj.set_status(f"Performing two pass... ({int((frame_index/frame_count)*100)}% completed)")
detect_results = detect_model.get(final_img,crop_size)
if detect_results is not None:
if not os.path.exists(temp_results_dir):
os.mkdir(temp_results_dir)
frame_align_crop_list = detect_results[0]
frame_mat_list = detect_results[1]
id_compare_values = []
frame_align_crop_tenor_list = []
for frame_align_crop in frame_align_crop_list:
frame_align_crop_tenor = SimSwap._totensor(cv2.cvtColor(frame_align_crop,cv2.COLOR_BGR2RGB))[None,...].cuda()
frame_align_crop_tenor_arcnorm = spNorm(frame_align_crop_tenor)
frame_align_crop_tenor_arcnorm_downsample = F.interpolate(frame_align_crop_tenor_arcnorm, size=(112,112))
frame_align_crop_crop_id_nonorm = swap_model.netArc(frame_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(frame_align_crop_crop_id_nonorm,source_specific_id_nonorm_tmp).detach().cpu().numpy())
frame_align_crop_tenor_list.append(frame_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 = []
swap_result_ori_pic_list = []
for tmp_index, min_index in enumerate(min_indexs):
if min_value[tmp_index] < id_thres:
swap_result = swap_model(None, frame_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(frame_mat_list[tmp_index])
swap_result_ori_pic_list.append(frame_align_crop_tenor_list[tmp_index])
else:
pass
if len(swap_result_list) !=0:
final_img = SimSwap.reverse2wholeimage(swap_result_ori_pic_list,swap_result_list, swap_result_matrix_list, crop_size, frame, logoclass,\
os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)),no_simswaplogo,pasring_model =net,use_mask=use_mask,
norm = spNorm, _kernel_size=_kernel_size, face_part_ids=face_part_ids)
########################################################## Two Pass ####################################################################
if self_obj.stop_process:
return
self_obj.video_player.update_display(image=final_img)
self_obj.set_status(f"Processing {frame_index} of {frame_count}... ({int((frame_index/frame_count)*100)}% completed)")
else:
if not os.path.exists(temp_results_dir):
os.mkdir(temp_results_dir)
frame = frame.astype(np.uint8)
if not no_simswaplogo:
frame = logoclass.apply_frames(frame)
cv2.imwrite(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), frame)
else:
if not os.path.exists(temp_results_dir):
os.mkdir(temp_results_dir)
frame = frame.astype(np.uint8)
if not no_simswaplogo:
frame = logoclass.apply_frames(frame)
cv2.imwrite(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), frame)
self_obj.set_status(f"Skipping no face...({int((frame_index/frame_count)*100)}% completed)")
else:
break
video.release()
self_obj.set_status(f"Merging sequence...")
path = os.path.join(temp_results_dir,'*.jpg')
image_filenames = sorted(glob.glob(path))
clips = ImageSequenceClip(image_filenames,fps = fps)
if not no_audio:
clips = clips.set_audio(video_audio_clip)
clips.write_videofile(save_path,audio_codec='aac')
def runSwap(srcImg, dstImg, vidPath, settings, swap_all=False, self_obj=None):
transformer = transforms.Compose([transforms.ToTensor(),])
transformer_Arcface = transforms.Compose([transforms.ToTensor(),transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
opt = TestOptions().parse()
opt.Arc_path = settings.arc_path.get()
opt.temp_path = settings.temp_path.get()
opt.use_mask = settings.use_mask.get()
opt.output_path = settings.out_path.get()
opt.video_path = vidPath
opt.id_thres = 0.03
opt.no_simswaplogo = not settings.simswap_logo.get()
start_epoch, epoch_iter = 1, 0
opt.crop_size = int(settings.crop_size.get())
crop_size = opt.crop_size
det_size = int(settings.det_size.get())
_kernel_size = tuple(map(int, settings.kernel_size.get().split(' ')))
face_part_ids = list(map(int, settings.face_part_ids.get().split(' ')))
mode = 'None'
if settings.ffhq.get():
mode = 'ffhq'
torch.nn.Module.dump_patches = True
if crop_size == 512:
opt.which_epoch = 550000
opt.name = '512'
#mode = 'ffhq'
#else:
# mode = 'None'
model = create_model(opt)
model.eval()
app = Face_detect_crop(name='antelope', root='./insightface_func/models')
app.prepare(ctx_id= 0, det_thresh=settings.det_thresh.get(), det_size=(det_size,det_size), mode=mode)
with torch.no_grad():
img_a_whole = srcImg
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])
img_id = img_id.cuda()
img_id_downsample = F.interpolate(img_id, size=(112,112))
latend_id = model.netArc(img_id_downsample)
latend_id = F.normalize(latend_id, p=2, dim=1)
if swap_all:
img_id_downsample = F.interpolate(img_id, size=(112,112))
latend_id = model.netArc(img_id_downsample)
latend_id = F.normalize(latend_id, p=2, dim=1)
SimSwap.video_swap(
opt.video_path,
latend_id, model,
app,
opt.output_path,
temp_results_dir=opt.temp_path,\
no_simswaplogo=opt.no_simswaplogo,
use_mask=opt.use_mask,
self_obj=self_obj,
_kernel_size=_kernel_size,
face_part_ids=face_part_ids)
else:
specific_person_whole = dstImg
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])
specific_person = specific_person.cuda()
specific_person_downsample = F.interpolate(specific_person, size=(112,112))
specific_person_id_nonorm = model.netArc(specific_person_downsample)
SimSwap.video_target_swap(
opt.video_path,
latend_id,
specific_person_id_nonorm,
opt.id_thres,
model,
app,
opt.output_path,
temp_results_dir=opt.temp_path,
no_simswaplogo=opt.no_simswaplogo,
use_mask=opt.use_mask,
crop_size=crop_size,
self_obj=self_obj,
_kernel_size=_kernel_size,
face_part_ids=face_part_ids)
def swap_multi_specific(src_dst_dict, vidPath, settings, self_obj=None):
transformer = transforms.Compose([transforms.ToTensor(),])
transformer_Arcface = transforms.Compose([transforms.ToTensor(),transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
opt = TestOptions().parse()
opt.Arc_path = settings.arc_path.get()
opt.temp_path = settings.temp_path.get()
opt.use_mask = settings.use_mask.get()
opt.output_path = settings.out_path.get()
opt.video_path = vidPath
opt.id_thres = 0.03
opt.no_simswaplogo = not settings.simswap_logo.get()
start_epoch, epoch_iter = 1, 0
opt.crop_size = int(settings.crop_size.get())
crop_size = opt.crop_size
det_size = int(settings.det_size.get())
_kernel_size = tuple(map(int, settings.kernel_size.get().split(' ')))
face_part_ids = list(map(int, settings.face_part_ids.get().split(' ')))
mode = 'None'
if settings.ffhq.get():
mode = 'ffhq'
torch.nn.Module.dump_patches = True
if crop_size == 512:
opt.which_epoch = 550000
opt.name = '512'
#mode = 'ffhq'
#else:
# mode = 'None'
model = create_model(opt)
model.eval()
app = Face_detect_crop(name='antelope', root='./insightface_func/models')
app.prepare(ctx_id= 0, det_thresh=settings.det_thresh.get(), det_size=(det_size,det_size), mode=mode)
with torch.no_grad():
source_specific_id_nonorm_list = []
target_id_norm_list = []
for key in src_dst_dict:
_src = src_dst_dict[key][1]
_dst = src_dst_dict[key][0]
specific_person_align_crop, _ = app.get(_src, 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])
specific_person = specific_person.cuda()
specific_person_downsample = F.interpolate(specific_person, size=(112,112))
specific_person_id_nonorm = model.netArc(specific_person_downsample)
source_specific_id_nonorm_list.append(specific_person_id_nonorm.clone())
img_a_align_crop, _ = app.get(_dst,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])
img_id = img_id.cuda()
img_id_downsample = F.interpolate(img_id, size=(112,112))
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)
SimSwap.video_swap_multispecific(
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,
use_mask=opt.use_mask,
crop_size=crop_size,
self_obj=self_obj,
_kernel_size=_kernel_size,
face_part_ids=face_part_ids)
class Utils:
def remove_files(folder):
for filename in os.listdir(folder):
file_path = os.path.join(folder, filename)
try:
if os.path.isfile(file_path) or os.path.islink(file_path):
os.unlink(file_path)
elif os.path.isdir(file_path):
shutil.rmtree(file_path)
except Exception as e:
print('Failed to delete %s. Reason: %s' % (file_path, e))
def pil_create_txt_image(size, text, tk_image=True):
img = Image.new('RGB', size)
img_draw = ImageDraw.Draw(img)
w, h = img_draw.textsize(text)
img_draw.text(((size[0]-w)/2,(size[1]-h)/2), text, fill="white")
if tk:
return Utils.pil_to_tkImage(img)
return img
def get_error_image(size):
img = np.ones((size[1], size[0], 3), dtype=np.uint8)
img[:] = (125, 0, 0)
return img
def make_preview_image(img, expected_size=(500, 600), scale_to_fit=False):
if scale_to_fit:
base_width = expected_size[0]
wpercent = (base_width/float(img.size[0]))
hsize = int((float(img.size[1])*float(wpercent)))
img = img.resize((base_width,hsize), Image.ANTIALIAS)
img.thumbnail((expected_size[0], expected_size[1]))
delta_width = expected_size[0] - img.size[0]
delta_height = expected_size[1] - img.size[1]
pad_width = delta_width // 2
pad_height = delta_height // 2
padding = (pad_width, pad_height, delta_width - pad_width, delta_height - pad_height)
new_img = ImageOps.expand(img, padding)
return new_img
def cv2_to_tkImage(img, size=72):
b,g,r = cv2.split(img)
img = cv2.merge((r,g,b))
im = Image.fromarray(img)
im = im.resize((size,size), resample=Image.LANCZOS)
return Utils.pil_to_tkImage(im)
def cv2_to_pil(img):
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
return Image.fromarray(img)
def pil_to_tkImage(img):
return ImageTk.PhotoImage(img)
class Spinbox(ttk.Entry):
def __init__(self, master=None, **kw):
ttk.Entry.__init__(self, master, "ttk::spinbox", **kw)
def set(self, value):
self.tk.call(self._w, "set", value)
class ImageCanvas:
def __init__(self, window, size, default_text="..."):
self.size = size
self.image = Utils.pil_create_txt_image(size, default_text, tk_image=False)
self.topx, self.topy, self.botx, self.boty = 0, 0, 0, 0
self.rect_id = None
self.canvas = tk.Canvas(window, width=self.image.width(), height=self.image.height(),
borderwidth=0, highlightthickness=0)
self.canvas_image = self.canvas.create_image(0, 0, image=self.image, anchor=tk.NW)
self.canvas.img = self.image
self.canvas.pack(side="left", fill="both", expand="yes")
self.rect_id = self.canvas.create_rectangle(self.topx, self.topy, self.topx, self.topy,
dash=(2,2), fill='', outline='white')
self.canvas.bind('<Button-1>', self.get_mouse_posn)
self.canvas.bind('<B1-Motion>', self.update_sel_rect)
def get_mouse_posn(self, event):
self.topx, self.topy = event.x, event.y
def update_sel_rect(self, event, fixed=False):
self.botx, self.boty = event.x, event.y
if fixed:
maxb = max(self.botx, self.boty)
maxb -= max(self.topx, self.topy)
self.botx, self.boty = self.topx + maxb, self.topy + maxb
self.canvas.coords(self.rect_id, self.topx, self.topy, self.botx, self.boty)
def update_canvas(self, image):
self.image = Utils.make_preview_image(image, expected_size=self.size, scale_to_fit=True)
image = Utils.pil_to_tkImage(self.image)
self.canvas.itemconfig(self.canvas_image, image=image)
self.canvas.configure(width=image.width(), height=image.height())
self.canvas.img = image
def get_roi(self):
x1, y1, x2, y2 = self.topx, self.topy, self.botx, self.boty
if x1 > x2:
x1, x2 = x2, x1
if y1 > y2:
y1, y2 = y2, y1
return [x1, y1, x2, y2]
class VideoPlayer:
def __init__(self, frame, size=(300, 400)):
self.monitor_frame = ttk.Frame(frame)
self.monitor_frame.pack(side="top", expand=True, padx=10)
self.monitor_size = size
self.pause = True
# monitor
self.monitor_img = Utils.ImageCanvas(self.monitor_frame, self.monitor_size, default_text="Simswap GUI")
self.controller_frame = ttk.Frame(frame)
self.controller_frame.pack(side="top", expand=True, padx=10)
# slider
self.slider = ttk.Scale(self.controller_frame, length=size[0])
self.slider.pack(side="top", pady=5, expand=True)
# buttons and controls
self.sub_controller_frame = ttk.Frame(self.controller_frame)
self.sub_controller_frame.pack(side="top", anchor=tk.CENTER, pady=5, expand=True)
self.start_frame_spin = Utils.Spinbox(self.sub_controller_frame, width=5, from_=1)
self.stop_frame_spin = Utils.Spinbox(self.sub_controller_frame, width=5, from_=1)
self.forward_btn = ttk.Button(self.sub_controller_frame, text=">>", style='btn.TButton')
self.bacward_btn = ttk.Button(self.sub_controller_frame, text="<<", style='btn.TButton')
self.play_btn = ttk.Button(self.sub_controller_frame, text="Play", style='btn.TButton')
self.pause_btn = ttk.Button(self.sub_controller_frame, text="Pause", style='btn.TButton')
self.trim_start_btn = ttk.Button(self.sub_controller_frame, text=u"[--", style='btn.TButton')
self.trim_stop_btn = ttk.Button(self.sub_controller_frame, text="--]", style='btn.TButton')
self.current_frame_label = ttk.Label(self.sub_controller_frame, text="0001", font='Helvetica 16 bold')
self.start_frame_spin.grid(column=0, row=0, padx=(0,10))
self.trim_start_btn.grid(column=1, row=0, padx=2)
self.bacward_btn.grid(column=2, row=0, padx=2)
self.pause_btn.grid(column=3, row=0, padx=2)
self.current_frame_label.grid(column=4, row=0)
self.play_btn.grid(column=5, row=0, padx=2)
self.forward_btn.grid(column=6, row=0, padx=2)
self.trim_stop_btn.grid(column=7, row=0, padx=2)
self.stop_frame_spin.grid(column=8, row=0, padx=(10,0))
self.current_frame = tk.IntVar(value=1)
self.start_frame = tk.IntVar(value=1)
self.stop_frame = tk.IntVar(value=100)
self.cap = None
self.set_monitor_variables()
self.set_monitor_functions()
self.reader_available = False
def interrupt_play(self):
self.pause = True
self.play_btn.config(state='normal')
def set_max_frame(self, value):
self.start_frame_spin.config(to=value)
self.stop_frame_spin.config(to=value)
def set_monitor_variables(self):
self.slider.config(variable=self.current_frame)
self.slider.config(from_=self.start_frame.get())
self.slider.config(to=self.stop_frame.get())
self.start_frame_spin.config(textvariable=self.start_frame, to=1000)
self.stop_frame_spin.config(textvariable=self.stop_frame, to=1000)
def slider_func(self, *args):
self.slider.config(from_=self.start_frame.get())
self.slider.config(to=self.stop_frame.get())
frame = str(max(self.current_frame.get(),1))
self.current_frame_label.config(text=frame.zfill(4))
if self.pause:
self.update_display()
def trim_start_func(self, *args):
self.start_frame_spin.set(self.current_frame.get())
self.slider_func()
def trim_stop_func(self, *args):
self.stop_frame_spin.set(self.current_frame.get())
self.slider_func()
def set_monitor_functions(self):
self.slider.config(command=self.slider_func)
self.trim_start_btn.config(command=self.trim_start_func)
self.trim_stop_btn.config(command=self.trim_stop_func)
self.play_btn.config(command=lambda: threading.Thread(target=self.play_video).start())
self.pause_btn.config(command=self.interrupt_play)
self.forward_btn.config(command=lambda: self.slider.set(self.current_frame.get() + 1))
self.bacward_btn.config(command=lambda: self.slider.set(self.current_frame.get() - 1))
def reset_monitor_controls(self):
self.start_frame.set(1)
self.current_frame.set(1)
self.start_frame_spin.config(from_=1, to=99999)
self.stop_frame_spin.config(from_=1, to=99999)
self.slider.config(from_=1, to=99999)
self.slider.set(1)
def read_video(self, path="video.mp4"):
try:
if self.cap is not None:
self.cap.release()
self.input_path = path
self.cap = cv2.VideoCapture(path)
self.input_fps = self.cap.get(cv2.CAP_PROP_FPS)
self.total_frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))
self.reader_available = True
except Exception as e:
print(e)
self.reader_available = False
def get_video_frame(self, frame=1):
if frame <= 0 or not self.reader_available:
return Utils.get_error_image(self.monitor_size)
self.cap.set(1, frame - 1)
_, image = self.cap.read()
return image
def get_current_frame(self):
return self.get_video_frame(self.current_frame.get())
def update_display(self, image=None):
try:
if image is None:
frame = self.current_frame.get()
image = self.get_video_frame(frame=frame)
pil_image = Utils.cv2_to_pil(image)
self.monitor_img.update_canvas(pil_image)
except: pass
def open_video(self, path):
self.reset_monitor_controls()
self.read_video(path)
self.start_frame.set(1)
self.current_frame.set(1)
self.stop_frame.set(self.total_frames)
self.set_max_frame(self.total_frames)
self.update_display()
def get_video_data(self):
if not self.reader_available:
return
video = {"frame":self.get_current_frame(),
"fps": self.input_fps,
"start": self.start_frame.get(),
"end": self.stop_frame.get()}
return video
def play_video(self):
self.pause = False
if not self.reader_available:
return
self.play_btn.config(state='disabled')
current = self.current_frame.get()
self.cap.set(1, current-1)
_fps = self.input_fps
while(self.cap.isOpened()):
ret, frame = self.cap.read()
if ret == True and self.pause == False and current <= self.stop_frame.get():
self.slider.set(current)
current += 1
self.update_display(image=frame)
if cv2.waitKey(int((1/int(_fps))*1000)) & 0xFF == ord('q'):
break
else:
break
self.interrupt_play()
# _start = self.current_frame.get()
# _end = self.stop_frame.get()
# _fps = max(1, self.cap.get(cv2.CAP_PROP_FPS)) * 10
# print("fps=", _fps)
# for i in range(_start, _end):
# if self.pause:
# break
# self.slider.set(i)
# self.update_display(image=self.get_video_frame(frame=i))
# time.sleep(1/_fps)
# self.interrupt_play()
class MenuBar:
def __init__(self, root):
self.menu_bar = tk.Menu(root)
self.filemenu = tk.Menu(self.menu_bar, tearoff=0)
self.menu_bar.add_cascade(label="File", menu=self.filemenu)
self.editmenu = tk.Menu(self.menu_bar, tearoff=0)
self.menu_bar.add_cascade(label="Edit", menu=self.editmenu)
helpmenu = tk.Menu(self.menu_bar, tearoff=0)
url = "https://github.com/harisreedhar/SimSwap-GUI"
helpmenu.add_command(label="Report", command=lambda: webbrowser.open(url, new=1))
info = "Unofficial GUI implementaion of simswap"
helpmenu.add_command(label="About", command=lambda: messagebox.showinfo("About", info))
self.menu_bar.add_cascade(label="Help", menu=helpmenu)
root.config(menu=self.menu_bar)
def set_menubar_functions(self, functions):
self.filemenu.add_command(label="Import video", command=functions.get("open video"))
self.filemenu.add_command(label="Import source image", command=functions.get("import source"))
self.filemenu.add_command(label="Import target image", command=functions.get("import target"))
self.filemenu.add_separator()
#self.filemenu.add_command(label="Open output directory", command=functions.get("open out dir"))
self.filemenu.add_command(label="Clear temp path", command=functions.get("clear temp"))
self.filemenu.add_command(label="Clear trim path", command=functions.get("clear trim"))
self.filemenu.add_separator()
self.filemenu.add_command(label="Quit", command=functions.get("quit"))
self.editmenu.add_command(label="Reset trim", command=functions.get("reset trim"))
self.editmenu.add_command(label="Trim & reload", command=functions.get("trim & reload"))
class StatusBar:
def __init__(self, root):
self.status = tk.Label(root, text="...", bd=1, relief=tk.RIDGE, anchor=tk.W)
self.status.pack(side=tk.BOTTOM, fill=tk.X)
class SettingsWindow:
def __init__(self, root, size):
_width = 50
_width_2 = 10
settings_frame = ttk.Frame(root)
settings_frame.pack(side="top", fill="both", padx=5, pady=5, expand=True)
self.out_path = tk.StringVar()
self.out_path.set(OUTPUT_PATH)
out_path_label = ttk.Label(settings_frame, text="Output video path")
self.out_path_entry = ttk.Entry(settings_frame, width= _width, textvariable=self.out_path)
self.arc_path = tk.StringVar()
self.arc_path.set(ARC_PATH)
arc_path_label = ttk.Label(settings_frame, text="ArcFace path")
self.arc_path_entry = ttk.Entry(settings_frame, width= _width, textvariable=self.arc_path)