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Fix the coordinate interpolation to properly work in 0.0-1.0 range

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Kijai 2024-05-07 10:11:18 +03:00
parent 24f27c5b82
commit d12a011943
1 changed files with 26 additions and 16 deletions
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42
nodes/curve_nodes.py
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@ -751,6 +751,13 @@ for example:
top_left_y = max(0, top_left_y)
bottom_right_x = min(width, bottom_right_x)
bottom_right_y = min(height, bottom_right_y)
# Ensure width and height are positive
adjusted_bbox_width = max(1, bottom_right_x - top_left_x)
adjusted_bbox_height = max(1, bottom_right_y - top_left_y)
# Update the coordinates with the new width and height
bottom_right_x = top_left_x + adjusted_bbox_width
bottom_right_y = top_left_y + adjusted_bbox_height
# Append the top left and bottom right coordinates to the list for the current ID
id_coordinates.append([top_left_x, top_left_y, bottom_right_x, bottom_right_y, width, height])
@ -830,48 +837,51 @@ Interpolates coordinates based on a curve.
}
def interpolate(self, coordinates, interpolation_curve):
# Parse the JSON string to get the list of coordinates
# Parse the JSON string to get the list of coordinates
coordinates = json.loads(coordinates.replace("'", '"'))
# Convert the list of dictionaries to a list of (x, y) tuples for easier processing
coordinates = [(coord['x'], coord['y']) for coord in coordinates]
# Calculate the total length of the original path
path_length = sum(np.linalg.norm(np.array(coordinates[i]) - np.array(coordinates[i-1])) for i in range(1, len(coordinates)))
# Normalize the interpolation curve
normalized_curve = [x / path_length for x in interpolation_curve]
path_length = sum(np.linalg.norm(np.array(coordinates[i]) - np.array(coordinates[i-1]))
for i in range(1, len(coordinates)))
# Initialize variables for interpolation
interpolated_coords = []
current_length = 0
current_index = 1
current_index = 0
# Iterate over the normalized curve
for target_length in normalized_curve:
target_length *= path_length # Convert back to the original scale
while current_length < target_length and current_index < len(coordinates):
segment_length = np.linalg.norm(np.array(coordinates[current_index]) - np.array(coordinates[current_index-1]))
for normalized_length in interpolation_curve:
target_length = normalized_length * path_length # Convert to the original scale
while current_index < len(coordinates) - 1:
segment_start, segment_end = np.array(coordinates[current_index]), np.array(coordinates[current_index + 1])
segment_length = np.linalg.norm(segment_end - segment_start)
if current_length + segment_length >= target_length:
break
current_length += segment_length
current_index += 1
# Interpolate between the last two points
if current_index == 1:
interpolated_coords.append(coordinates[0])
else:
p1, p2 = np.array(coordinates[current_index-2]), np.array(coordinates[current_index-1])
if current_index < len(coordinates) - 1:
p1, p2 = np.array(coordinates[current_index]), np.array(coordinates[current_index + 1])
segment_length = np.linalg.norm(p2 - p1)
if segment_length > 0:
t = (target_length - (current_length - segment_length)) / segment_length
t = (target_length - current_length) / segment_length
interpolated_point = p1 + t * (p2 - p1)
interpolated_coords.append(interpolated_point.tolist())
else:
interpolated_coords.append(p1.tolist())
else:
# If the target_length is at or beyond the end of the path, add the last coordinate
interpolated_coords.append(coordinates[-1])
# Convert back to string format if necessary
interpolated_coords_str = "[" + ", ".join([f"{{'x': {round(coord[0])}, 'y': {round(coord[1])}}}" for coord in interpolated_coords]) + "]"
print(interpolated_coords_str)
return (interpolated_coords_str, )
return (interpolated_coords_str,)
class DrawInstanceDiffusionTracking: