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SplineEditor: Return only first spline length when using multiple splines

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kijai 2025-12-09 23:49:26 +02:00
parent 987b9a28e4
commit 557edbae16
1 changed files with 18 additions and 13 deletions
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31
nodes/curve_nodes.py
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@ -253,7 +253,7 @@ output types:
# Handle nested list structure if present
all_normalized = []
all_normalized_y_values = []
# Check if we have a nested list structure
if isinstance(coordinates, list) and len(coordinates) > 0 and isinstance(coordinates[0], list):
# Process each list of coordinates in the nested structure
@ -261,12 +261,14 @@ output types:
else:
# If not nested, treat as a single list of coordinates
coordinate_sets = [coordinates]
first_spline = coordinate_sets[0] if coordinate_sets else []
# Process each set of coordinates
for coord_set in coordinate_sets:
normalized = []
normalized_y_values = []
for coord in coord_set:
coord['x'] = int(round(coord['x']))
coord['y'] = int(round(coord['y']))
@ -274,10 +276,10 @@ output types:
norm_y = (1.0 - (coord['y'] / mask_height) - 0.0) * (max_value - min_value) + min_value
normalized_y_values.append(norm_y)
normalized.append({'x':norm_x, 'y':norm_y})
all_normalized.extend(normalized)
all_normalized_y_values.extend(normalized_y_values)
# Use the combined normalized values for output
if float_output_type == 'list':
out_floats = all_normalized_y_values * repeat_output
@ -289,18 +291,21 @@ output types:
out_floats = pd.Series(all_normalized_y_values * repeat_output),
elif float_output_type == 'tensor':
out_floats = torch.tensor(all_normalized_y_values * repeat_output, dtype=torch.float32)
# Create a color map for grayscale intensities
color_map = lambda y: torch.full((mask_height, mask_width, 3), y, dtype=torch.float32)
# Create image tensors for each normalized y value
mask_tensors = [color_map(y) for y in all_normalized_y_values]
# Create a color map for grayscale intensities (from first spline only)
color_map = lambda y: torch.full((mask_height, mask_width, 3), y, dtype=torch.float32)
mask_tensors = [color_map(y) for y in normalized_y_values]
masks_out = torch.stack(mask_tensors)
masks_out = masks_out.repeat(repeat_output, 1, 1, 1)
masks_out = masks_out.mean(dim=-1)
single_spline_count = len(first_spline)
if bg_image is None:
return (masks_out, json.dumps(coordinates if len(coordinates) > 1 else coordinates[0]), out_floats, len(out_floats), json.dumps(all_normalized))
return (masks_out, json.dumps(coordinates if len(coordinates) > 1 else coordinates[0]), out_floats, single_spline_count, json.dumps(all_normalized))
else:
transform = transforms.ToPILImage()
image = transform(bg_image[0].permute(2, 0, 1))
@ -310,12 +315,12 @@ output types:
# Encode the image bytes to a Base64 string
img_bytes = buffered.getvalue()
img_base64 = base64.b64encode(img_bytes).decode('utf-8')
return {
"ui": {"bg_image": [img_base64]},
"result": (masks_out, json.dumps(coordinates if len(coordinates) > 1 else coordinates[0]), out_floats, len(out_floats), json.dumps(all_normalized))
"result": (masks_out, json.dumps(coordinates if len(coordinates) > 1 else coordinates[0]), out_floats, single_spline_count, json.dumps(all_normalized))
}
class CreateShapeMaskOnPath: