131 lines
5.0 KiB
Python
131 lines
5.0 KiB
Python
import os
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import cv2
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import glob
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import torch
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import shutil
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import numpy as np
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from tqdm import tqdm
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from util.reverse2original import reverse2wholeimage
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import moviepy.editor as mp
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from moviepy.editor import AudioFileClip, VideoFileClip
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from moviepy.video.io.ImageSequenceClip import ImageSequenceClip
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import time
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from util.add_watermark import watermark_image
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from util.norm import SpecificNorm
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import torch.nn.functional as F
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from parsing_model.model import BiSeNet
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def _totensor(array):
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tensor = torch.from_numpy(array)
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img = tensor.transpose(0, 1).transpose(0, 2).contiguous()
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return img.float().div(255)
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def video_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):
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video_forcheck = VideoFileClip(video_path)
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if video_forcheck.audio is None:
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no_audio = True
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else:
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no_audio = False
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del video_forcheck
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if not no_audio:
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video_audio_clip = AudioFileClip(video_path)
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video = cv2.VideoCapture(video_path)
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logoclass = watermark_image('./simswaplogo/simswaplogo.png')
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ret = True
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frame_index = 0
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frame_count = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
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# video_WIDTH = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
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# video_HEIGHT = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
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fps = video.get(cv2.CAP_PROP_FPS)
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if os.path.exists(temp_results_dir):
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shutil.rmtree(temp_results_dir)
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spNorm =SpecificNorm()
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mse = torch.nn.MSELoss().cuda()
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if use_mask:
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n_classes = 19
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net = BiSeNet(n_classes=n_classes)
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net.cuda()
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save_pth = os.path.join('./parsing_model/checkpoint', '79999_iter.pth')
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net.load_state_dict(torch.load(save_pth))
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net.eval()
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else:
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net =None
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# while ret:
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for frame_index in tqdm(range(frame_count)):
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ret, frame = video.read()
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if ret:
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detect_results = detect_model.get(frame,crop_size)
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if detect_results is not None:
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# print(frame_index)
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if not os.path.exists(temp_results_dir):
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os.mkdir(temp_results_dir)
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frame_align_crop_list = detect_results[0]
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frame_mat_list = detect_results[1]
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id_compare_values = []
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frame_align_crop_tenor_list = []
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for frame_align_crop in frame_align_crop_list:
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# BGR TO RGB
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# frame_align_crop_RGB = frame_align_crop[...,::-1]
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frame_align_crop_tenor = _totensor(cv2.cvtColor(frame_align_crop,cv2.COLOR_BGR2RGB))[None,...].cuda()
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frame_align_crop_tenor_arcnorm = spNorm(frame_align_crop_tenor)
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frame_align_crop_tenor_arcnorm_downsample = F.interpolate(frame_align_crop_tenor_arcnorm, size=(112,112))
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frame_align_crop_crop_id_nonorm = swap_model.netArc(frame_align_crop_tenor_arcnorm_downsample)
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id_compare_values.append(mse(frame_align_crop_crop_id_nonorm,specific_person_id_nonorm).detach().cpu().numpy())
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frame_align_crop_tenor_list.append(frame_align_crop_tenor)
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id_compare_values_array = np.array(id_compare_values)
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min_index = np.argmin(id_compare_values_array)
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min_value = id_compare_values_array[min_index]
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if min_value < id_thres:
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swap_result = swap_model(None, frame_align_crop_tenor_list[min_index], id_vetor, None, True)[0]
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reverse2wholeimage([frame_align_crop_tenor_list[min_index]], [swap_result], [frame_mat_list[min_index]], crop_size, frame, logoclass,\
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os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)),no_simswaplogo,pasring_model =net,use_mask= use_mask, norm = spNorm)
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else:
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if not os.path.exists(temp_results_dir):
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os.mkdir(temp_results_dir)
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frame = frame.astype(np.uint8)
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if not no_simswaplogo:
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frame = logoclass.apply_frames(frame)
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cv2.imwrite(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), frame)
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else:
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if not os.path.exists(temp_results_dir):
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os.mkdir(temp_results_dir)
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frame = frame.astype(np.uint8)
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if not no_simswaplogo:
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frame = logoclass.apply_frames(frame)
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cv2.imwrite(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), frame)
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else:
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break
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video.release()
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# image_filename_list = []
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path = os.path.join(temp_results_dir,'*.jpg')
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image_filenames = sorted(glob.glob(path))
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clips = ImageSequenceClip(image_filenames,fps = fps)
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if not no_audio:
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clips = clips.set_audio(video_audio_clip)
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clips.write_videofile(save_path,audio_codec='aac')
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