def generate_video(
model,
video_frames,
merge=False,
axis=1,
verbose=True,
horizontal_flip=False,
relative=False,
model_input_size=(256, 256),
crop_bbox=[],
):
output = []
stream_img_size = None
video_frames = (tqdm(video_frames, total=len(video_frames))
if verbose else video_frames)
for frame in video_frames:
if crop_bbox:
x1, y1, x2, y2 = crop_bbox
frame = frame[y1:y2, x1:x2]
if horizontal_flip:
frame = cv2.flip(frame, 1)
if stream_img_size is None:
stream_img_size = frame.shape[1], frame.shape[0]
# input_frame, lrudwh = crop(
# frame, p=frame_proportion, offset_x=frame_offset_x, offset_y=frame_offset_y,
# )
input_frame = cv2.resize(frame, model_input_size)
out = model.predict(input_frame, relative=relative)
out = pad_img(out, stream_img_size)
out = cv2.resize(out, stream_img_size)
if merge:
out = np.concatenate([frame, out], axis=axis)
output.append(out)
print(out.shape)
return output
1)[0]
cv2.putText(preview_frame, 'rocking',
(IMG_SIZE - textw - 10, 20), 0,
0.5 * IMG_SIZE / 256, (255, 255, 255), 1)
out = loop_frames[loop_frame_idx]
loop_frame_idx += rock_frame_delta
if loop_frame_idx >= len(loop_frames) - 1:
rock_frame_delta = -1
elif loop_frame_idx <= 0:
rock_frame_delta = 1
cv2.imshow('cam', preview_frame[..., ::-1])
if out is not None:
if not opt.no_pad:
out = pad_img(out, stream_img_size)
if output_flip:
out = cv2.flip(out, 1)
if enable_vcam:
out = resize(out, stream_img_size)
stream.schedule_frame(out)
cv2.imshow('avatarify', out[..., ::-1])
fps_hist.append(tt.toc(total=True))
if len(fps_hist) == 10:
fps = 10 / (sum(fps_hist) / 1000)
fps_hist = []
except KeyboardInterrupt:
def generate_video(
model,
video_frames,
merge=False,
axis=1,
verbose=True,
horizontal_flip=False,
relative=False,
model_input_size=(256, 256),
crop_bbox=[],
watermark="app/watermark.png",
debug=False,
):
output = []
stream_img_size = None
video_frames = (tqdm(video_frames, total=len(video_frames))
if verbose else video_frames)
# load watermark image
final_watermark = None
if watermark is not None:
watermark = io.read_image(watermark)
for frame in video_frames:
if crop_bbox:
x1, y1, x2, y2 = crop_bbox
frame = frame[y1:y2, x1:x2]
if horizontal_flip:
frame = cv2.flip(frame, 1)
if stream_img_size is None:
stream_img_size = frame.shape[1], frame.shape[0]
input_frame = cv2.resize(frame, model_input_size)
out = model.predict(input_frame, relative=relative)
out = pad_img(out, stream_img_size)
out = cv2.resize(out, stream_img_size)
if merge:
out = np.concatenate([frame, out], axis=axis)
if watermark is not None:
# Calculate just the first time
if final_watermark is None:
out_h, out_w = out.shape[:2]
wm_h, wm_w = watermark.shape[:2]
final_h = out_h * 0.2
final_watermark = cv2.resize(watermark,
fx=final_h / wm_h,
fy=final_h / wm_h,
dsize=None)
x = out_w - int(final_h / wm_h * wm_w)
y = out_h - int(final_h)
# put watermark in center
if merge:
x = out_w // 2 - int(final_h / wm_h * wm_w) // 2
# Add watermark
out = io.overlay(out, final_watermark, x, y)
if debug:
cv2.imshow("Otuput", out)
cv2.imshow("Model input", input_frame)
cv2.waitKey(1)
h, w = out.shape[:2]
resize_even = False
# make it even
if h % 2 != 0:
h -= 1
resize_even = True
if w % 2 != 0:
w -= 1
resize_even = True
if resize_even:
out = cv2.resize(
out,
(w, h),
)
output.append(out)
print(out.shape)
return output
