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Added optional mask output for LivePortraitCropper #105

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Added optional mask output for LivePortraitCropper #105

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27 changes: 20 additions & 7 deletions nodes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -39,7 +39,7 @@
StitchingRetargetingNetwork,
)
from .liveportrait.utils.camera import get_rotation_matrix
from .liveportrait.utils.crop import _transform_img_kornia
from .liveportrait.utils.crop import _transform_img_kornia, _transform_pts


class InferenceConfig:
Expand Down Expand Up @@ -616,20 +616,22 @@ def INPUT_TYPES(s):
],
),
"rotate": ("BOOLEAN", {"default": True}),
"output_masks": ("BOOLEAN", {"default": False}),
},
}

RETURN_TYPES = ("IMAGE", "CROPINFO",)
RETURN_NAMES = ("cropped_image", "crop_info",)
RETURN_TYPES = ("IMAGE", "CROPINFO", "MASK")
RETURN_NAMES = ("cropped_image", "crop_info", "mask")
FUNCTION = "process"
CATEGORY = "LivePortrait"

def process(self, pipeline, cropper, source_image, dsize, scale, vx_ratio, vy_ratio, face_index, face_index_order, rotate):
def process(self, pipeline, cropper, source_image, dsize, scale, vx_ratio, vy_ratio, face_index, face_index_order, rotate, output_masks):
source_image_np = (source_image.contiguous() * 255).byte().numpy()

# Initialize lists
crop_info_list = []
cropped_images_list = []
masks_list = []
source_info = []
source_rot_list = []
f_s_list = []
Expand All @@ -640,7 +642,8 @@ def process(self, pipeline, cropper, source_image, dsize, scale, vx_ratio, vy_ra
for i in tqdm(range(len(source_image_np)), desc='Detecting, cropping, and processing..', total=len(source_image_np)):
# Cropping operation
crop_info, cropped_image_256 = cropper.crop_single_image(source_image_np[i], dsize, scale, vy_ratio, vx_ratio, face_index, face_index_order, rotate)

if output_masks:
mask = np.zeros(source_image_np[i].shape[:2],dtype=float)
# Processing source images
if crop_info:
crop_info_list.append(crop_info)
Expand All @@ -661,6 +664,11 @@ def process(self, pipeline, cropper, source_image, dsize, scale, vx_ratio, vy_ra
f_s = pipeline.live_portrait_wrapper.extract_feature_3d(I_s)
f_s_list.append(f_s)

if output_masks:
pts = np.array([[0,0],[0,dsize],[dsize,dsize],[dsize,0]],dtype=float)
pts = (_transform_pts(pts,crop_info["M_c2o"])+0.5).astype(np.int32)
cv2.fillPoly(mask, pts=[pts], color=1.0)

del I_s

else:
Expand All @@ -671,14 +679,19 @@ def process(self, pipeline, cropper, source_image, dsize, scale, vx_ratio, vy_ra
x_s_list.append(None)
source_info.append(None)
source_rot_list.append(None)


if output_masks:
masks_list.append(torch.from_numpy(mask).unsqueeze(0))

# Update progress bar
pbar.update(1)

cropped_tensors_out = (
torch.stack([torch.from_numpy(np_array) for np_array in cropped_images_list])
/ 255
)
if output_masks:
masks_list = torch.cat(masks_list,axis=0).float()

crop_info_dict = {
'crop_info_list': crop_info_list,
Expand All @@ -688,7 +701,7 @@ def process(self, pipeline, cropper, source_image, dsize, scale, vx_ratio, vy_ra
'source_info': source_info
}

return (cropped_tensors_out, crop_info_dict)
return (cropped_tensors_out, crop_info_dict, masks_list)

class LivePortraitRetargeting:
@classmethod
Expand Down