def _process(self, videofile):
'''
videofile:要处理的视频文件
这里对类型进行检测
把 目标类型,时间点,坐标,以及附属关系
输出到相应的 目录一个json文件
'''
print('开始任务1:%s' % videofile)
stream = None
writer = None
final = self._defaultReturn(getId(videofile))
try:
stream, videoinfo = readVideo(videofile)
outputfile = os.path.join(self.outputpath,
os.path.basename(videofile))
writer = videoWriter(outputfile,
scale=(videoinfo['width'],
videoinfo['height']),
fps=30 // self.skip)
final['job2_video'] = outputfile
frames = videoinfo['frames']
queue, ts = [], []
for t in tqdm(range(0, frames, self.skip)):
stream.set(1, t)
retval, frame = stream.read()
if not retval: break
queue.append(frame)
ts.append(t)
if len(queue) == self.batchsize:
self._handle(ts, queue, final, writer)
queue = []
ts = []
if len(queue) > 0:
self._handle(ts, queue, final, writer)
print('任务1:%s完成' % videofile)
except Exception as e:
final['status'] = 'fail'
final['error'] = 'At job1,' + str(e)
if self.config['debug']:
raise e
finally:
if stream is not None:
stream.release()
if writer is not None:
writer.release()
self._after_precessing(final, videofile)
def _process(self, filename):
'''
videofile:要处理的视频文件
这里对类型进行检测
把 目标类型,时间点,坐标,以及附属关系
输出到相应的 目录一个json文件
'''
id = getId(filename)
print('开始任务2:%s' % filename)
stream = None
final = self._defaultReturn(filename)
try:
if final['status'] == 'success':
videofile = final['job2_video']
stream, videoinfo = readVideo(videofile)
frames = videoinfo['frames']
for t in tqdm(range(frames)):
retval, frame = stream.read()
if not retval: break
frame = frame[:, :, ::-1]
faceboxes, landmarks = self.det.detect_faces(
Image.fromarray(frame),
self.config['mtcnn']['min_face_size'],
self.config['mtcnn']['thresholds'])
objs_at_t = final['track']['objs'][t]
self._insertFace(objs_at_t, faceboxes, landmarks)
self._after_precessing(final, videofile, filename)
print('任务2:%s完成' % videofile)
except Exception as e:
final['status'] = 'fail'
final['error'] = 'At job2,' + str(e)
if self.config['debug']:
raise e
finally:
if stream is not None:
stream.release()
def _process(self, filename):
print('开始任务3:%s' % filename)
stream = None
final = self._defaultReturn(filename)
queue = []
try:
if final['status'] == 'success':
videofile = final['job3_video']
stream, videoinfo = readVideo(videofile)
frames = videoinfo['frames']
for t in tqdm(range(frames)):
retval, frame = stream.read()
if not retval: break
objs_at_t = final['track']['objs'][t]
for obj in objs_at_t:
if 'face_box' in obj:
queue.append(self._get_input(frame, obj))
if len(queue) == self.batchsize:
self._handle(queue)
del queue
queue = []
if len(queue) > 0:
self._handle(queue)
self._after_precessing(final, videofile, filename)
print('任务3:%s完成' % videofile)
except Exception as e:
final['status'] = 'fail'
final['error'] = 'At job3,' + str(e)
if self.config['debug']:
raise e
finally:
if stream is not None:
stream.release()
print('任务3:%s完成' % filename)
for i in range(cnt):
lines = fs.readline().strip().split(' ')
x1, y1 = int(lines[0]), int(lines[1])
x2, y2 = x1 + int(lines[2]), y1 + int(lines[3])
plot_one_box([x1, y1, x2, y2], I, None)
return I
if __name__ == '__main__':
imgpath = '/home/zxk/AI/faceswap-GAN/videoplayback.mp4'
# imgpath = '/home/zxk/AI/faceswap-GAN/videoplayback1.webm'
out = '/home/zxk/AI/faceswap-GAN/1.avi'
cap, info = readVideo(imgpath)
writer = videoWriter(out,
videoformat='XVID',
scale=(info['width'], info['height']),
fps=30)
import tqdm
start = 2600
# start=5400
end = 4440
for i in tqdm.tqdm(range(start, start + 25 * 30)):
cap.set(1, i)
_, frame = cap.read()
# print(frame.shape)
# print(frame)
writer.write(frame)
stopThreads()
def stopThreads():
global STOP_CLASSIFIER_THREAD
STOP_CLASSIFIER_THREAD = True
def main(video, classifier):
global STOP_CLASSIFIER_THREAD
STOP_CLASSIFIER_THREAD = st.checkbox("Stop recognition",
value=False,
key=None)
# STOP_CLASSIFIER_THREAD = False
try:
classifierThread = threading.Thread(target=classifyWithYOLO,
args=(classifier, ))
classifierThread.start()
runVideo(video)
except Exception as e:
# just in case to stop threads
print(traceback.format_exc())
stopThreads()
if __name__ == "__main__":
video = readVideo('../videos/droga.mp4')
main(video, None)
if __name__ == '__main__':
device = torch.device('cpu')
# detector = CCPD_YOLO_Detector(device=device)
mtcnn = MTCNN(
image_size=160,
margin=0,
min_face_size=20,
thresholds=[0.6, 0.7, 0.7],
factor=0.709,
prewhiten=True,
select_largest=True, #True,boxes按照面积的大小降序排列
keep_all=True,
device=device)
cap, videoinfo = readVideo('data/laofoye_high.avi')
print(videoinfo)
cap_writer = videoWriter('hello.avi',
scale=(videoinfo['width'], videoinfo['height']),
fps=3)
cnt = 0
rate = 10
buf = []
pos = 0
while True:
retval, frame = cap.read()
if not retval: break
pos += 1
if pos % rate == 0: