def get_ROI_position(img, net, opt, keepsize=True):
mask = run_segment(img, net, size=360, gpu_id=opt.gpu_id)
mask = impro.mask_threshold(mask, opt.mask_extend, opt.mask_threshold)
if keepsize:
mask = impro.resize_like(mask, img)
x, y, halfsize, area = impro.boundingSquare(mask, 1)
return mask, x, y, halfsize, area
def get_mosaic_position(img_origin, net_mosaic_pos, opt):
mask = run_unet_rectim(img_origin, net_mosaic_pos, use_gpu=opt.use_gpu)
mask = impro.mask_threshold(mask, 10, 128)
x, y, size, area = impro.boundingSquare(mask, Ex_mul=1.5)
rat = min(img_origin.shape[:2]) / 128.0
x, y, size = int(rat * x), int(rat * y), int(rat * size)
return x, y, size
def get_mosaic_position(img_origin,net_mosaic_pos,opt,threshold = 128 ):
mask = run_unet(img_origin,net_mosaic_pos,size=224,use_gpu = opt.use_gpu)
mask = impro.mask_threshold(mask,30,threshold)
if not opt.all_mosaic_area:
mask = impro.find_mostlikely_ROI(mask)
x,y,size,area = impro.boundingSquare(mask,Ex_mul=opt.ex_mult)
rat = min(img_origin.shape[:2])/224.0
x,y,size = int(rat*x),int(rat*y),int(rat*size)
return x,y,size,mask
def get_mosaic_position(img_origin, net_mosaic_pos, opt, threshold=128):
mask = run_unet_rectim(img_origin, net_mosaic_pos, use_gpu=opt.use_gpu)
#mask_1 = mask.copy()
mask = impro.mask_threshold(mask, 30, threshold)
if not opt.no_large_area:
mask = impro.find_best_ROI(mask)
x, y, size, area = impro.boundingSquare(mask, Ex_mul=opt.ex_mult)
rat = min(img_origin.shape[:2]) / 224.0
x, y, size = int(rat * x), int(rat * y), int(rat * size)
return x, y, size, mask
def get_mosaic_position(img_origin, net_mosaic_pos, opt):
h, w = img_origin.shape[:2]
mask = run_segment(img_origin, net_mosaic_pos, size=360, gpu_id=opt.gpu_id)
# mask_1 = mask.copy()
mask = impro.mask_threshold(mask,
ex_mun=int(min(h, w) / 20),
threshold=opt.mask_threshold)
if not opt.all_mosaic_area:
mask = impro.find_mostlikely_ROI(mask)
x, y, size, area = impro.boundingSquare(mask, Ex_mul=opt.ex_mult)
#Location fix
rat = min(h, w) / 360.0
x, y, size = int(rat * x), int(rat * y), int(rat * size)
x, y = np.clip(x, 0, w), np.clip(y, 0, h)
size = np.clip(size, 0, min(w - x, h - y))
# print(x,y,size)
return x, y, size, mask
def get_ROI_position(img, net, opt):
mask = run_unet_rectim(img, net, use_gpu=opt.use_gpu)
mask = impro.mask_threshold(mask, opt.mask_extend, opt.mask_threshold)
x, y, halfsize, area = impro.boundingSquare(mask, 1)
return mask, x, y, area
opt.temp_dir + '/video2image/%05d.' + opt.tempimage_type,
fps=1,
start_time=util.second2stamp(cut_point * opt.interval),
last_time=util.second2stamp(opt.time))
imagepaths = util.Traversal(opt.temp_dir + '/video2image')
imagepaths = sorted(imagepaths)
cnt = 0
for i in range(opt.time):
img = impro.imread(imagepaths[i])
mask = runmodel.get_ROI_position(img,
net,
opt,
keepsize=True)[0]
if not opt.all_mosaic_area:
mask = impro.find_mostlikely_ROI(mask)
x, y, size, area = impro.boundingSquare(mask, Ex_mul=1)
if area > opt.minmaskarea and size > opt.minsize and impro.Q_lapulase(
img) > opt.quality:
cnt += 1
if cnt == opt.time:
# print(second)
timestamps.append(
util.second2stamp(cut_point * opt.interval))
util.writelog(os.path.join(opt.savedir, 'opt.txt'),
videopath + '\n' + str(timestamps))
#print(timestamps)
#generate datasets
print('Generate datasets...')
for timestamp in timestamps:
savecnt = '%05d' % result_cnt
util.clean_tempfiles()
ffmpeg.video2image(videopath,
'./tmp/video2image/%05d.' + opt.tempimage_type,
fps=1,
start_time=util.second2stamp(cut_point *
opt.interval),
last_time=util.second2stamp(opt.time))
imagepaths = util.Traversal('./tmp/video2image')
cnt = 0
for i in range(opt.time):
img = impro.imread(imagepaths[i])
mask = runmodel.get_ROI_position(img, net, opt,
keepsize=True)[0]
if not opt.all_mosaic_area:
mask = impro.find_mostlikely_ROI(mask)
x, y, size, area = impro.boundingSquare(mask, Ex_mul=1)
if area > opt.minmaskarea and size > opt.minsize and impro.Q_lapulase(
img) > opt.quality:
cnt += 1
if cnt == opt.time:
# print(second)
timestamps.append(util.second2stamp(cut_point * opt.interval))
util.writelog(os.path.join(opt.savedir, 'opt.txt'),
videopath + '\n' + str(timestamps))
#print(timestamps)
# util.clean_tempfiles()
# fps,endtime,height,width = ffmpeg.get_video_infos(videopath)
# # print(fps,endtime,height,width)
# ffmpeg.continuous_screenshot(videopath, './tmp/video2image', 1)
util.makedirs(mask_path)
mask_names = os.listdir(mask_dir)
img_names = os.listdir(img_dir)
mask_names.sort()
img_names.sort()
print('Find images:', len(img_names))
cnt = 0
for fold in range(FOLD_NUM):
for img_name, mask_name in zip(img_names, mask_names):
try:
img = impro.imread(os.path.join(img_dir, img_name))
mask = impro.imread(os.path.join(mask_dir, mask_name), 'gray')
mask = impro.resize_like(mask, img)
x, y, size, area = impro.boundingSquare(mask, 1.5)
if area > 100:
if Bounding:
img = impro.resize(
img[y - size:y + size, x - size:x + size], OUT_SIZE)
mask = impro.resize(
mask[y - size:y + size, x - size:x + size], OUT_SIZE)
img_mosaic = mosaic.addmosaic_random(img, mask)
if HD:
cv2.imwrite(
os.path.join(train_A_path, '%05d' % cnt + '.jpg'),
img_mosaic)
cv2.imwrite(
os.path.join(train_B_path, '%05d' % cnt + '.jpg'), img)
else:
torch.cuda.empty_cache()
if not opt.all_mosaic_area:
mask_net = impro.find_mostlikely_ROI(mask_net)
mask = mask_net
if opt.mod == ['drawn', 'irregular']:
mask = cv2.bitwise_and(mask_irr, mask_drawn)
if opt.mod == ['network', 'irregular']:
mask = cv2.bitwise_and(mask_irr, mask_net)
#checkandsave
# t=threading.Thread(target=checksaveimage,args=(opt,img,mask,))
# t.start()
saveflag = True
if opt.mod == ['drawn', 'irregular']:
x, y, size, area = impro.boundingSquare(
mask_drawn, random.uniform(1.2, 1.6))
elif opt.mod == ['network', 'irregular']:
x, y, size, area = impro.boundingSquare(
mask_net, random.uniform(1.2, 1.6))
else:
x, y, size, area = impro.boundingSquare(
mask, random.uniform(1.2, 1.6))
if area < 1000:
saveflag = False
else:
if opt.square:
if size < opt.minsize:
saveflag = False
else:
img = impro.resize(img[y - size:y + size,
opt,
keepsize=True)[0]
if not opt.all_mosaic_area:
mask_net = impro.find_mostlikely_ROI(mask_net)
mask = mask_net
if opt.mod == ['drawn', 'irregular']:
mask = cv2.bitwise_and(mask_irr, mask_drawn)
if opt.mod == ['network', 'irregular']:
mask = cv2.bitwise_and(mask_irr, mask_net)
#checkandsave
# t=threading.Thread(target=checksaveimage,args=(opt,img,mask,))
# t.start()
saveflag = True
x, y, size, area = impro.boundingSquare(
mask, random.uniform(1.4, 1.6))
if area < 1000:
saveflag = False
else:
if opt.square:
if size < opt.minsize:
saveflag = False
else:
img = impro.resize(img[y - size:y + size,
x - size:x + size],
opt.outsize,
interpolation=cv2.INTER_CUBIC)
mask = impro.resize(mask[y - size:y + size,
x - size:x + size],
opt.outsize,
interpolation=cv2.INTER_CUBIC)
