def loaddata(): video_index = random.randint(0, len(videos) - 1) video = videos[video_index] img_index = random.randint(N, lengths[video_index] - N) input_img = np.zeros((SIZE, SIZE, 3 * N + 1), dtype='uint8') for i in range(0, N): # print('./dataset/'+video+'/mosaic/output_'+'%05d'%(img_index+i-int(N/2))+'.png') img = cv2.imread('./dataset/' + video + '/mosaic/output_' + '%05d' % (img_index + i - int(N / 2)) + '.png') img = impro.resize(img, SIZE) input_img[:, :, i * 3:(i + 1) * 3] = img mask = cv2.imread( './dataset/' + video + '/mask/output_' + '%05d' % (img_index) + '.png', 0) mask = impro.resize(mask, 256) mask = impro.mask_threshold(mask, 15, 128) input_img[:, :, -1] = mask input_img = data.im2tensor(input_img, bgr2rgb=False, use_gpu=opt.use_gpu, use_transform=False) ground_true = cv2.imread('./dataset/' + video + '/ori/output_' + '%05d' % (img_index) + '.png') ground_true = impro.resize(ground_true, SIZE) # ground_true = im2tensor(ground_true,use_gpu) ground_true = data.im2tensor(ground_true, bgr2rgb=False, use_gpu=opt.use_gpu, use_transform=False) return input_img, ground_true
def run_styletransfer(opt, net, img): if opt.output_size != 0: if 'resize' in opt.preprocess and 'resize_scale_width' not in opt.preprocess: img = impro.resize(img, opt.output_size) elif 'resize_scale_width' in opt.preprocess: img = cv2.resize(img, (opt.output_size, opt.output_size)) img = img[0:4 * int(img.shape[0] / 4), 0:4 * int(img.shape[1] / 4), :] if 'edges' in opt.preprocess: if opt.canny > 100: canny_low = opt.canny - 50 canny_high = np.clip(opt.canny + 50, 0, 255) elif opt.canny < 50: canny_low = np.clip(opt.canny - 25, 0, 255) canny_high = opt.canny + 25 else: canny_low = opt.canny - int(opt.canny / 2) canny_high = opt.canny + int(opt.canny / 2) img = cv2.Canny(img, opt.canny - 50, opt.canny + 50) if opt.only_edges: return img img = data.im2tensor(img, use_gpu=opt.use_gpu, gray=True, use_transform=False, is0_1=False) else: img = data.im2tensor(img, use_gpu=opt.use_gpu) img = net(img) img = data.tensor2im(img) return img
def loaddata(video_index): videoname = videonames[video_index] img_index = random.randint( int(N / 2) + 1, lengths[video_index] - int(N / 2) - 1) input_img = np.zeros((opt.loadsize, opt.loadsize, 3 * N + 1), dtype='uint8') # this frame this_mask = impro.imread(os.path.join(opt.dataset, videoname, 'mask', '%05d' % (img_index) + '.png'), 'gray', loadsize=opt.loadsize) input_img[:, :, -1] = this_mask #print(os.path.join(opt.dataset,videoname,'origin_image','%05d'%(img_index)+'.jpg')) ground_true = impro.imread(os.path.join(opt.dataset, videoname, 'origin_image', '%05d' % (img_index) + '.jpg'), loadsize=opt.loadsize) mosaic_size, mod, rect_rat, father = mosaic.get_random_parameter( ground_true, this_mask) # merge other frame for i in range(0, N): img = impro.imread(os.path.join( opt.dataset, videoname, 'origin_image', '%05d' % (img_index + i - int(N / 2)) + '.jpg'), loadsize=opt.loadsize) mask = impro.imread(os.path.join( opt.dataset, videoname, 'mask', '%05d' % (img_index + i - int(N / 2)) + '.png'), 'gray', loadsize=opt.loadsize) img_mosaic = mosaic.addmosaic_base(img, mask, mosaic_size, model=mod, rect_rat=rect_rat, father=father) input_img[:, :, i * 3:(i + 1) * 3] = img_mosaic # to tensor input_img, ground_true = data.random_transform_video( input_img, ground_true, opt.finesize, N) input_img = data.im2tensor(input_img, bgr2rgb=False, use_gpu=opt.use_gpu, use_transform=False, is0_1=False) ground_true = data.im2tensor(ground_true, bgr2rgb=False, use_gpu=opt.use_gpu, use_transform=False, is0_1=False) return input_img, ground_true
def run_pix2pix(img, net, opt): if opt.netG == 'HD': img = impro.resize(img, 512) else: img = impro.resize(img, 128) img = data.im2tensor(img, use_gpu=opt.use_gpu) img_fake = net(img) img_fake = data.tensor2im(img_fake) return img_fake
def run_segment(img, net, size=360, use_gpu=0): img = impro.resize(img, size) img = data.im2tensor(img, use_gpu=use_gpu, bgr2rgb=False, use_transform=False, is0_1=True) mask = net(img) mask = data.tensor2im(mask, gray=True, rgb2bgr=False, is0_1=True) return mask
def cleanmosaic_video_fusion(opt,netG,netM): path = opt.media_path N = 25 INPUT_SIZE = 128 fps,imagepaths = video_init(opt,path) positions = [] # get position for i,imagepath in enumerate(imagepaths,1): img_origin = impro.imread(os.path.join('./tmp/video2image',imagepath)) # x,y,size = runmodel.get_mosaic_position(img_origin,net_mosaic_pos,opt)[:3] x,y,size,mask = runmodel.get_mosaic_position(img_origin,netM,opt) cv2.imwrite(os.path.join('./tmp/mosaic_mask',imagepath), mask) positions.append([x,y,size]) print('\r','Find mosaic location:'+str(i)+'/'+str(len(imagepaths)),util.get_bar(100*i/len(imagepaths),num=40),end='') print('\nOptimize mosaic locations...') positions =np.array(positions) for i in range(3):positions[:,i] = filt.medfilt(positions[:,i],opt.medfilt_num) # clean mosaic for i,imagepath in enumerate(imagepaths,0): x,y,size = positions[i][0],positions[i][1],positions[i][2] img_origin = impro.imread(os.path.join('./tmp/video2image',imagepath)) mask = cv2.imread(os.path.join('./tmp/mosaic_mask',imagepath),0) if size==0: cv2.imwrite(os.path.join('./tmp/replace_mosaic',imagepath),img_origin) else: mosaic_input = np.zeros((INPUT_SIZE,INPUT_SIZE,3*N+1), dtype='uint8') for j in range(0,N): img = impro.imread(os.path.join('./tmp/video2image',imagepaths[np.clip(i+j-12,0,len(imagepaths)-1)])) img = img[y-size:y+size,x-size:x+size] img = impro.resize(img,INPUT_SIZE) mosaic_input[:,:,j*3:(j+1)*3] = img mask = impro.resize(mask,np.min(img_origin.shape[:2])) mask = mask[y-size:y+size,x-size:x+size] mask = impro.resize(mask, INPUT_SIZE) mosaic_input[:,:,-1] = mask mosaic_input = data.im2tensor(mosaic_input,bgr2rgb=False,use_gpu=opt.use_gpu,use_transform = False,is0_1 = False) unmosaic_pred = netG(mosaic_input) #unmosaic_pred = (unmosaic_pred.cpu().detach().numpy()*255)[0] #img_fake = unmosaic_pred.transpose((1, 2, 0)) img_fake = data.tensor2im(unmosaic_pred,rgb2bgr = False ,is0_1 = False) img_result = impro.replace_mosaic(img_origin,img_fake,x,y,size,opt.no_feather) cv2.imwrite(os.path.join('./tmp/replace_mosaic',imagepath),img_result) print('\r','Clean Mosaic:'+str(i+1)+'/'+str(len(imagepaths)),util.get_bar(100*i/len(imagepaths),num=40),end='') print() ffmpeg.image2video( fps, './tmp/replace_mosaic/output_%05d.'+opt.tempimage_type, './tmp/voice_tmp.mp3', os.path.join(opt.result_dir,os.path.splitext(os.path.basename(path))[0]+'_clean.mp4'))
def cleanmosaic_video_fusion(opt,netG,netM): path = opt.media_path N = 25 if 'HD' in os.path.basename(opt.model_path): INPUT_SIZE = 256 else: INPUT_SIZE = 128 fps,imagepaths,height,width = video_init(opt,path) positions = get_mosaic_positions(opt,netM,imagepaths,savemask=True) # clean mosaic img_pool = np.zeros((height,width,3*N), dtype='uint8') for i,imagepath in enumerate(imagepaths,0): x,y,size = positions[i][0],positions[i][1],positions[i][2] # image read stream mask = cv2.imread(os.path.join('./tmp/mosaic_mask',imagepath),0) if i==0 : for j in range(0,N): img_pool[:,:,j*3:(j+1)*3] = impro.imread(os.path.join('./tmp/video2image',imagepaths[np.clip(i+j-12,0,len(imagepaths)-1)])) else: img_pool[:,:,0:(N-1)*3] = img_pool[:,:,3:N*3] img_pool[:,:,(N-1)*3:] = impro.imread(os.path.join('./tmp/video2image',imagepaths[np.clip(i+12,0,len(imagepaths)-1)])) img_origin = img_pool[:,:,int((N-1)/2)*3:(int((N-1)/2)+1)*3] if size==0: # can not find mosaic, cv2.imwrite(os.path.join('./tmp/replace_mosaic',imagepath),img_origin) else: mosaic_input = np.zeros((INPUT_SIZE,INPUT_SIZE,3*N+1), dtype='uint8') mosaic_input[:,:,0:N*3] = impro.resize(img_pool[y-size:y+size,x-size:x+size,:], INPUT_SIZE) mask_input = impro.resize(mask,np.min(img_origin.shape[:2]))[y-size:y+size,x-size:x+size] mosaic_input[:,:,-1] = impro.resize(mask_input, INPUT_SIZE) mosaic_input = data.im2tensor(mosaic_input,bgr2rgb=False,use_gpu=opt.use_gpu,use_transform = False,is0_1 = False) unmosaic_pred = netG(mosaic_input) img_fake = data.tensor2im(unmosaic_pred,rgb2bgr = False ,is0_1 = False) img_result = impro.replace_mosaic(img_origin,img_fake,mask,x,y,size,opt.no_feather) cv2.imwrite(os.path.join('./tmp/replace_mosaic',imagepath),img_result) print('\r','Clean Mosaic:'+str(i+1)+'/'+str(len(imagepaths)),util.get_bar(100*i/len(imagepaths),num=35),end='') print() ffmpeg.image2video( fps, './tmp/replace_mosaic/output_%05d.'+opt.tempimage_type, './tmp/voice_tmp.mp3', os.path.join(opt.result_dir,os.path.splitext(os.path.basename(path))[0]+'_clean.mp4'))
def run_pix2pix(img, net, size=128, use_gpu=True): img = impro.resize(img, size) img = data.im2tensor(img, use_gpu=use_gpu) img_fake = net(img) img_fake = data.tensor2im(img_fake) return img_fake
#test if os.path.isdir('./test'): netG.eval() test_names = os.listdir('./test') test_names.sort() result = np.zeros((opt.finesize*2,opt.finesize*len(test_names),3), dtype='uint8') for cnt,test_name in enumerate(test_names,0): img_names = os.listdir(os.path.join('./test',test_name,'image')) img_names.sort() inputdata = np.zeros((opt.finesize,opt.finesize,3*N+1), dtype='uint8') for i in range(0,N): img = impro.imread(os.path.join('./test',test_name,'image',img_names[i])) img = impro.resize(img,opt.finesize) inputdata[:,:,i*3:(i+1)*3] = img mask = impro.imread(os.path.join('./test',test_name,'mask.png'),'gray') mask = impro.resize(mask,opt.finesize) mask = impro.mask_threshold(mask,15,128) inputdata[:,:,-1] = mask result[0:opt.finesize,opt.finesize*cnt:opt.finesize*(cnt+1),:] = inputdata[:,:,int((N-1)/2)*3:(int((N-1)/2)+1)*3] inputdata = data.im2tensor(inputdata,bgr2rgb=False,use_gpu=opt.use_gpu,use_transform = False,is0_1 = False) pred = netG(inputdata) pred = data.tensor2im(pred,rgb2bgr = False, is0_1 = False) result[opt.finesize:opt.finesize*2,opt.finesize*cnt:opt.finesize*(cnt+1),:] = pred cv2.imwrite(os.path.join(dir_checkpoint,str(iter+1)+'_test.jpg'), result) netG.train()
def run_segment(img, net, size=360, gpu_id='-1'): img = impro.resize(img, size) img = data.im2tensor(img, gpu_id=gpu_id, bgr2rgb=False, is0_1=True) mask = net(img) mask = data.tensor2im(mask, gray=True, is0_1=True) return mask
def cleanmosaic_video_fusion(opt, netG, netM): path = opt.media_path N, T, S = 2, 5, 3 LEFT_FRAME = (N * S) POOL_NUM = LEFT_FRAME * 2 + 1 INPUT_SIZE = 256 FRAME_POS = np.linspace(0, (T - 1) * S, T, dtype=np.int64) img_pool = [] previous_frame = None init_flag = True fps, imagepaths, height, width = video_init(opt, path) positions = get_mosaic_positions(opt, netM, imagepaths, savemask=True) t1 = time.time() if not opt.no_preview: cv2.namedWindow('clean', cv2.WINDOW_NORMAL) # clean mosaic print('Step:3/4 -- Clean Mosaic:') length = len(imagepaths) for i, imagepath in enumerate(imagepaths, 0): x, y, size = positions[i][0], positions[i][1], positions[i][2] input_stream = [] # image read stream if i == 0: # init for j in range(POOL_NUM): img_pool.append( impro.imread( os.path.join( opt.temp_dir + '/video2image', imagepaths[np.clip(i + j - LEFT_FRAME, 0, len(imagepaths) - 1)]))) else: # load next frame img_pool.pop(0) img_pool.append( impro.imread( os.path.join( opt.temp_dir + '/video2image', imagepaths[np.clip(i + LEFT_FRAME, 0, len(imagepaths) - 1)]))) img_origin = img_pool[LEFT_FRAME] img_result = img_origin.copy() if size > 50: try: #Avoid unknown errors for pos in FRAME_POS: input_stream.append( impro.resize( img_pool[pos][y - size:y + size, x - size:x + size], INPUT_SIZE)[:, :, ::-1]) if init_flag: init_flag = False previous_frame = input_stream[N] previous_frame = data.im2tensor(previous_frame, bgr2rgb=True, gpu_id=opt.gpu_id) input_stream = np.array(input_stream).reshape( 1, T, INPUT_SIZE, INPUT_SIZE, 3).transpose((0, 4, 1, 2, 3)) input_stream = data.to_tensor(data.normalize(input_stream), gpu_id=opt.gpu_id) with torch.no_grad(): unmosaic_pred = netG(input_stream, previous_frame) img_fake = data.tensor2im(unmosaic_pred, rgb2bgr=True) previous_frame = unmosaic_pred # previous_frame = data.tensor2im(unmosaic_pred,rgb2bgr = True) mask = cv2.imread( os.path.join(opt.temp_dir + '/mosaic_mask', imagepath), 0) img_result = impro.replace_mosaic(img_origin, img_fake, mask, x, y, size, opt.no_feather) except Exception as e: init_flag = True print('Error:', e) else: init_flag = True cv2.imwrite(os.path.join(opt.temp_dir + '/replace_mosaic', imagepath), img_result) os.remove(os.path.join(opt.temp_dir + '/video2image', imagepath)) #preview result and print if not opt.no_preview: cv2.imshow('clean', img_result) cv2.waitKey(1) & 0xFF t2 = time.time() print('\r', str(i + 1) + '/' + str(length), util.get_bar(100 * i / length, num=35), util.counttime(t1, t2, i + 1, len(imagepaths)), end='') print() if not opt.no_preview: cv2.destroyAllWindows() print('Step:4/4 -- Convert images to video') ffmpeg.image2video( fps, opt.temp_dir + '/replace_mosaic/output_%06d.' + opt.tempimage_type, opt.temp_dir + '/voice_tmp.mp3', os.path.join( opt.result_dir, os.path.splitext(os.path.basename(path))[0] + '_clean.mp4'))
def cleanmosaic_video_fusion(opt): net = loadmodel.pix2pix(opt) net_mosaic_pos = loadmodel.unet_clean(opt) path = opt.media_path N = 25 util.clean_tempfiles() fps = ffmpeg.get_video_infos(path)[0] ffmpeg.video2voice(path, './tmp/voice_tmp.mp3') ffmpeg.video2image(path, './tmp/video2image/output_%05d.' + opt.tempimage_type) positions = [] imagepaths = os.listdir('./tmp/video2image') imagepaths.sort() # get position for imagepath in imagepaths: img_origin = impro.imread(os.path.join('./tmp/video2image', imagepath)) # x,y,size = runmodel.get_mosaic_position(img_origin,net_mosaic_pos,opt)[:3] x, y, size, mask = runmodel.get_mosaic_position( img_origin, net_mosaic_pos, opt) cv2.imwrite(os.path.join('./tmp/mosaic_mask', imagepath), mask) positions.append([x, y, size]) print('Find mosaic location:', imagepath) print('Optimize mosaic locations...') positions = np.array(positions) for i in range(3): positions[:, i] = filt.medfilt(positions[:, i], opt.medfilt_num) # clean mosaic print('Clean mosaic...') for i, imagepath in enumerate(imagepaths, 0): print('Clean mosaic:', imagepath) x, y, size = positions[i][0], positions[i][1], positions[i][2] img_origin = impro.imread(os.path.join('./tmp/video2image', imagepath)) mask = cv2.imread(os.path.join('./tmp/mosaic_mask', imagepath), 0) if size == 0: cv2.imwrite(os.path.join('./tmp/replace_mosaic', imagepath), img_origin) else: mosaic_input = np.zeros((256, 256, 3 * N + 1), dtype='uint8') for j in range(0, N): img = impro.imread( os.path.join( './tmp/video2image', imagepaths[np.clip(i + j - 12, 0, len(imagepaths) - 1)])) img = img[y - size:y + size, x - size:x + size] img = impro.resize(img, 256) mosaic_input[:, :, j * 3:(j + 1) * 3] = img mask = impro.resize(mask, np.min(img_origin.shape[:2])) mask = mask[y - size:y + size, x - size:x + size] mask = impro.resize(mask, 256) mosaic_input[:, :, -1] = mask mosaic_input = data.im2tensor(mosaic_input, bgr2rgb=False, use_gpu=opt.use_gpu, use_transform=False) unmosaic_pred = net(mosaic_input) unmosaic_pred = (unmosaic_pred.cpu().detach().numpy() * 255)[0] img_fake = unmosaic_pred.transpose((1, 2, 0)) img_result = impro.replace_mosaic(img_origin, img_fake, x, y, size, opt.no_feather) cv2.imwrite(os.path.join('./tmp/replace_mosaic', imagepath), img_result) ffmpeg.image2video( fps, './tmp/replace_mosaic/output_%05d.' + opt.tempimage_type, './tmp/voice_tmp.mp3', os.path.join( opt.result_dir, os.path.splitext(os.path.basename(path))[0] + '_clean.mp4'))
loss_L2.item(),loss_VGG.item(),psnr,ssmi) ) t_strat = time.time() ''' --------------------------Test-------------------------- ''' if train_iter % opt.showresult_freq == 0 and os.path.isdir(opt.dataset_test): show_imgs = [] videos = os.listdir(opt.dataset_test) sorted(videos) for video in videos: frames = os.listdir(os.path.join(opt.dataset_test,video,'image')) sorted(frames) for step in range(5): mosaic_stream = [] for i in range(opt.T): _mosaic = impro.imread(os.path.join(opt.dataset_test,video,'image',frames[i*opt.S+step]),loadsize=opt.finesize,rgb=True) mosaic_stream.append(_mosaic) if step == 0: previous = impro.imread(os.path.join(opt.dataset_test,video,'image',frames[opt.N*opt.S-1]),loadsize=opt.finesize,rgb=True) previous = data.im2tensor(previous,bgr2rgb = False, gpu_id = opt.gpu_id, is0_1 = False) mosaic_stream = (np.array(mosaic_stream).astype(np.float32)/255.0-0.5)/0.5 mosaic_stream = mosaic_stream.reshape(1,opt.T,opt.finesize,opt.finesize,3).transpose((0,4,1,2,3)) mosaic_stream = data.to_tensor(mosaic_stream, opt.gpu_id) with torch.no_grad(): out = netG(mosaic_stream,previous) previous = out show_imgs+= [data.tensor2im(mosaic_stream[:,:,opt.N],rgb2bgr = False),data.tensor2im(out,rgb2bgr = False)] show_img = impro.splice(show_imgs, (len(videos),2)) TBGlobalWriter.add_image('test', show_img,train_iter,dataformats='HWC')
def cleanmosaic_video_fusion(opt, netG, netM): path = opt.media_path N = 25 if 'HD' in os.path.basename(opt.model_path): INPUT_SIZE = 256 else: INPUT_SIZE = 128 fps, imagepaths, height, width = video_init(opt, path) positions = get_mosaic_positions(opt, netM, imagepaths, savemask=True) t1 = time.time() if not opt.no_preview: cv2.namedWindow('clean', cv2.WINDOW_NORMAL) # clean mosaic print('Clean Mosaic:') length = len(imagepaths) img_pool = np.zeros((height, width, 3 * N), dtype='uint8') mosaic_input = np.zeros((INPUT_SIZE, INPUT_SIZE, 3 * N + 1), dtype='uint8') for i, imagepath in enumerate(imagepaths, 0): x, y, size = positions[i][0], positions[i][1], positions[i][2] # image read stream mask = cv2.imread( os.path.join(opt.temp_dir + '/mosaic_mask', imagepath), 0) if i == 0: for j in range(0, N): img_pool[:, :, j * 3:(j + 1) * 3] = impro.imread( os.path.join( opt.temp_dir + '/video2image', imagepaths[np.clip(i + j - 12, 0, len(imagepaths) - 1)])) else: img_pool[:, :, 0:(N - 1) * 3] = img_pool[:, :, 3:N * 3] img_pool[:, :, (N - 1) * 3:] = impro.imread( os.path.join( opt.temp_dir + '/video2image', imagepaths[np.clip(i + 12, 0, len(imagepaths) - 1)])) img_origin = img_pool[:, :, int((N - 1) / 2) * 3:(int((N - 1) / 2) + 1) * 3] img_result = img_origin.copy() if size > 100: try: #Avoid unknown errors #reshape to network input shape mosaic_input[:, :, 0:N * 3] = impro.resize( img_pool[y - size:y + size, x - size:x + size, :], INPUT_SIZE) mask_input = impro.resize(mask, np.min( img_origin.shape[:2]))[y - size:y + size, x - size:x + size] mosaic_input[:, :, -1] = impro.resize(mask_input, INPUT_SIZE) mosaic_input_tensor = data.im2tensor(mosaic_input, bgr2rgb=False, use_gpu=opt.use_gpu, use_transform=False, is0_1=False) unmosaic_pred = netG(mosaic_input_tensor) img_fake = data.tensor2im(unmosaic_pred, rgb2bgr=False, is0_1=False) img_result = impro.replace_mosaic(img_origin, img_fake, mask, x, y, size, opt.no_feather) except Exception as e: print('Warning:', e) cv2.imwrite(os.path.join(opt.temp_dir + '/replace_mosaic', imagepath), img_result) #preview result and print if not opt.no_preview: cv2.imshow('clean', img_result) cv2.waitKey(1) & 0xFF t2 = time.time() print('\r', str(i + 1) + '/' + str(length), util.get_bar(100 * i / length, num=35), util.counttime(t1, t2, i + 1, len(imagepaths)), end='') print() if not opt.no_preview: cv2.destroyAllWindows() ffmpeg.image2video( fps, opt.temp_dir + '/replace_mosaic/output_%06d.' + opt.tempimage_type, opt.temp_dir + '/voice_tmp.mp3', os.path.join( opt.result_dir, os.path.splitext(os.path.basename(path))[0] + '_clean.mp4'))