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RIFE-CUDA: Parallel image writing

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N00MKRAD
2021-01-22 16:48:08 +01:00
parent 2526063d1f
commit ea8b8eb713
1 changed files with 23 additions and 15 deletions
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38
Pkgs/rife-cuda/inference_video.py
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@@ -39,6 +39,7 @@ parser.add_argument('--input', dest='input', type=str, default=None)
parser.add_argument('--output', required=False, default='frames-interpolated')
parser.add_argument('--model', required=False, default='models')
parser.add_argument('--imgformat', default="png")
parser.add_argument('--wthreads', dest='wthreads', type=int, default=4)
parser.add_argument('--UHD', dest='UHD', action='store_true', help='support 4k video')
parser.add_argument('--exp', dest='exp', type=int, default=1)
args = parser.parse_args()
@@ -55,6 +56,8 @@ name = os.path.basename(path)
interp_output_path = (args.output).join(path.rsplit(name, 1))
print("\ninterp_output_path: " + interp_output_path)
cnt = 1
videogen = []
for f in os.listdir(args.input):
if 'png' in f:
@@ -68,19 +71,21 @@ vid_out = None
if not os.path.exists(interp_output_path):
os.mkdir(interp_output_path)
def clear_write_buffer(user_args, write_buffer):
cnt = 1
def clear_write_buffer(user_args, write_buffer, thread_id):
while True:
item = write_buffer.get()
if item is None:
break
print('=> {:0>8d}.{}'.format(cnt, args.imgformat))
cv2.imwrite('{}/{:0>8d}.{}'.format(interp_output_path, cnt, args.imgformat), item[:, :, ::-1])
cnt += 1
frameNum = item[0]
img = item[1]
print('[T{}] => {:0>8d}.{}'.format(thread_id, frameNum, args.imgformat))
cv2.imwrite('{}/{:0>8d}.{}'.format(interp_output_path, frameNum, args.imgformat), img[:, :, ::-1], [cv2.IMWRITE_PNG_COMPRESSION, 2])
def build_read_buffer(user_args, read_buffer, videogen):
for frame in videogen:
if not user_args.input is None:
#print("Loading input frame " + str(frame))
frame = cv2.imread(os.path.join(user_args.input, frame))[:, :, ::-1].copy()
read_buffer.put(frame)
read_buffer.put(None)
@@ -102,12 +107,13 @@ else:
ph = ((h - 1) // 32 + 1) * 32
pw = ((w - 1) // 32 + 1) * 32
padding = (0, pw - w, 0, ph - h)
skip_frame = 1
write_buffer = Queue(maxsize=200)
read_buffer = Queue(maxsize=200)
write_buffer = Queue(maxsize=160)
read_buffer = Queue(maxsize=160)
_thread.start_new_thread(build_read_buffer, (args, read_buffer, videogen))
_thread.start_new_thread(clear_write_buffer, (args, write_buffer))
for x in range(args.wthreads):
_thread.start_new_thread(clear_write_buffer, (args, write_buffer, x))
I1 = torch.from_numpy(np.transpose(lastframe, (2,0,1))).to(device, non_blocking=True).unsqueeze(0).float() / 255.
I1 = F.pad(I1, padding)
@@ -121,18 +127,20 @@ while True:
I1 = F.pad(I1, padding)
output = make_inference(I0, I1, args.exp)
write_buffer.put(lastframe)
write_buffer.put([cnt, lastframe])
cnt += 1
for mid in output:
mid = (((mid[0] * 255.).byte().cpu().numpy().transpose(1, 2, 0)))
write_buffer.put(mid[:h, :w])
# print(f"Adding #{cnt} to buffer.")
write_buffer.put([cnt, mid[:h, :w]])
cnt += 1
lastframe = frame
write_buffer.put(lastframe)
write_buffer.put([cnt, lastframe])
import time
while(not write_buffer.empty()):
time.sleep(0.1)
if not vid_out is None:
vid_out.release()
time.sleep(0.2)
time.sleep(0.5)