def __init__(self, dir_list, state=None):
self.dir_list = dir_list
self.input_A_path = get_dir(dir_list, 't')
self.input_B_path = get_dir(dir_list, 'r')
self.model_path = get_dir(dir_list, 'm')
self.output_path = dir_list[3]
self.state = state
def __init__(self, dir_list, state=None):
self.input_A_path = get_dir(dir_list, 't')
self.input_B_path = get_dir(dir_list, 'r')
self.model_path = get_dir(dir_list, 'm')
self.state = state
self.GPU_check()
keras.backend.clear_session()
self.process()
def __init__(self, dir_list, state):
try:
os.mkdir('ImageCache')
except:
import shutil
shutil.rmtree('ImageCache')
os.mkdir('ImageCache')
self.exit_flag = False
self.state = state
if not self.state.empty():
if self.state.get() == 'stop_pre':
self.exit_flag = True
for temp in ['t', 'r']:
if self.exit_flag:
break
if temp == 't':
print('-----------------开始提取目标视频帧------------------')
else:
print('-----------------开始提取素材视频帧------------------')
self.input_video_name = get_dir(dir_list, temp)
if temp == 't':
self.v2f(dir_list[0])
elif temp == 'r':
self.v2f(dir_list[1])
def dir_check(self, dir):
Ready_flag = True
if dir == 'm':
if not os.path.exists(utils.get_dir(
self.path_list(), 'e_t')) or not os.path.exists(
utils.get_dir(self.path_list(), 'e_r')):
Ready_flag = False
print('未能成功读取面部识别文件，请进行预处理')
elif dir == 'c':
if not os.path.exists(utils.get_dir(
self.path_list(), 't')) or not os.path.exists(
utils.get_dir(self.path_list(), 'r')):
Ready_flag = False
print('未能成功读取预处理文件，请进行预处理')
if not os.path.exists(utils.get_dir(self.path_list(), 'm')):
Ready_flag = False
print('未能成功读取训练模型文件，请进行训练')
return Ready_flag
def convert_process(self, state):
con = convert.ConvertImage(self.path_list(), state)
con.process()
if state.empty():
merge.MergeImage(self, state)
elif not state.empty():
if state.get() == 'stop_con':
path = utils.get_dir(self, 't') + '_convert'
time.sleep(1)
if os.path.exists(path):
import shutil
shutil.rmtree(path)
def __init__(self, dir_list, state=None, fps=30):
self.state = state
self.output_path = dir_list[3]
self.img_path = get_dir(dir_list, 't')
self.raw_path = get_dir(dir_list, 'r')
self.exit_flag = False
self.fps = fps
try:
if self.state is not None:
if not self.state.empty():
if self.state.get() == 'stop_con':
self.exit_flag = True
print('取消合成，正在清理缓存文件。')
return
self.process()
finally:
if not self.exit_flag:
print('合成完成')
elif self.exit_flag:
path = self.img_path + '_convert'
if os.path.exists(path):
import shutil
shutil.rmtree(path)
def process(self, state):
self.exit_flag = False
self.state = state
if not self.state.empty():
if self.state.get() == 'stop_pre':
self.exit_flag = True
extractor = Extract_Align.Extract()
for temp in ['t','r']:
if self.exit_flag:
break
if temp == 't':
print('---------------开始提取目标视频面部区域---------------')
else:
print('---------------开始提取素材视频面部区域---------------')
output_image_filename = get_dir(self.dir_list, temp)
filenames = os.listdir(output_image_filename)
Path_Manager.make_path(output_image_filename+'\\Extracted_image')
index = 0
for filename in filenames:
if self.exit_flag:
break
if not self.state.empty():
if self.state.get() == 'stop_pre':
self.exit_flag = True
if filename == 'Extracted_image':
continue
index += 1
image = cv2.imread(output_image_filename + '\\' + filename)
for idx, face in self.get_faces(image):
if index %50 == 49:
print('面部提取已完成%i帧'%(index + 1))
resized_image = extractor.extract(image, face, 256)
cv2.imwrite(output_image_filename + '\\Extracted_image\\' + 'Extracted_image_' + str(index) + '.jpg',
resized_image)
queue_clear(self.state)
print('-------------------面部区域提取结束------------------')