mirror of
https://git.datalinker.icu/kijai/ComfyUI-KJNodes.git
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Merge f69b96f7f0d735c9c0fedbd427c78117e38d6e76 into 3fcd22f2fe2be69c3229f192362b91888277cbcb
This commit is contained in:
siraxe
GitHub
commit
aa9346c052
@ -150,6 +150,7 @@ NODE_CONFIG = {
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"SplineEditor": {"class": SplineEditor, "name": "Spline Editor"},
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"CreateShapeImageOnPath": {"class": CreateShapeImageOnPath, "name": "Create Shape Image On Path"},
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"CreateShapeMaskOnPath": {"class": CreateShapeMaskOnPath, "name": "Create Shape Mask On Path"},
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"CreateShapeJointOnPath": {"class": CreateShapeJointOnPath, "name": "Create Shape Joint On Path"},
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"CreateTextOnPath": {"class": CreateTextOnPath, "name": "Create Text On Path"},
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"CreateGradientFromCoords": {"class": CreateGradientFromCoords, "name": "Create Gradient From Coords"},
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"CutAndDragOnPath": {"class": CutAndDragOnPath, "name": "Cut And Drag On Path"},
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@ -163,6 +164,7 @@ NODE_CONFIG = {
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"PlotCoordinates": {"class": PlotCoordinates, "name": "Plot Coordinates"},
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"InterpolateCoords": {"class": InterpolateCoords, "name": "Interpolate Coords"},
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"PointsEditor": {"class": PointsEditor, "name": "Points Editor"},
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"DriverOffsetCoordinates": {"class": DriverOffsetCoordinates, "name": "Driver Offset Coordinates"},
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#experimental
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"SoundReactive": {"class": SoundReactive, "name": "Sound Reactive"},
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"StableZero123_BatchSchedule": {"class": StableZero123_BatchSchedule, "name": "Stable Zero123 Batch Schedule"},
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@ -3,6 +3,7 @@ from torchvision import transforms
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import json
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from PIL import Image, ImageDraw, ImageFont, ImageColor, ImageFilter, ImageChops
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import numpy as np
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import math
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from ..utility.utility import pil2tensor, tensor2pil
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import folder_paths
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import io
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@ -1633,4 +1634,565 @@ Cuts the masked area from the image, and drags it along the path. If inpaint is
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out_images = torch.cat(images_list, dim=0).cpu().float()
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out_masks = torch.cat(masks_list, dim=0)
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return (out_images, out_masks)
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return (out_images, out_masks)
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class CreateShapeJointOnPath:
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("image",)
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FUNCTION = "create"
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CATEGORY = "KJNodes/image/generate"
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DESCRIPTION = """
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The width is controlled by shape_width, and the length is the distance between the first and second points.
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If pivot_coordinates are provided:
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- relative=True: The pivot movement offsets the entire shape from its path-defined position.
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- relative=False: The pivot replaces the starting point of the shape for positioning.
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"""
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"coordinates": ("STRING", {"multiline": True, "default": '[{"x":100,"y":100},{"x":400,"y":400}]'}),
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"frame_width": ("INT", {"default": 512, "min": 8, "max": 4096, "step": 8}),
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"frame_height": ("INT", {"default": 512, "min": 8, "max": 4096, "step": 8}),
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"total_frames": ("INT", {"default": 10, "min": 1, "max": 10000, "step": 1}),
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"scaling_enabled": ("BOOLEAN", {"default": True}),
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"shape_width": ("INT", {"default": 20, "min": 1, "max": 4096, "step": 1}),
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"shape_width_end": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 0}),
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"bg_color": ("STRING", {"default": "black"}),
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"fill_color": ("STRING", {"default": "white"}),
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"easing_function": (["linear", "ease_in", "ease_out", "ease_in_out"], {"default": "linear"}),
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"blur_radius": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step": 0.1}),
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"intensity": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}),
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"trailing": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 2.0, "step": 0.01}), # Changed default/max from original node
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"bounce_between": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
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},
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"optional": { # Make pivot_coordinates optional
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"pivot_coordinates": ("STRING", {"multiline": True}),
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"relative": ("BOOLEAN", {"default": True}), # Added relative input
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}
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}
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def create(self, coordinates, frame_width, frame_height, shape_width, shape_width_end, fill_color, bg_color, scaling_enabled, total_frames, easing_function, blur_radius, intensity, trailing, bounce_between, pivot_coordinates=None, relative=True): # Added relative param
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# --- Standardize coordinates input ---
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if isinstance(coordinates, str):
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# Try parsing as a list of lists first, if it looks like it
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try:
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potential_list = json.loads(coordinates.replace("'", '"'))
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if isinstance(potential_list, list) and all(isinstance(item, list) for item in potential_list):
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# It's likely a string representation of a list of paths
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# Re-dump each inner list to treat them as separate coord strings
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coord_strings = [json.dumps(path) for path in potential_list]
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print(f"Interpreted single string input as {len(coord_strings)} paths.")
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elif isinstance(potential_list, list) and all(isinstance(item, dict) for item in potential_list):
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# It's a single path represented as a string
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coord_strings = [coordinates]
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else:
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# Fallback: treat as single path string if format is unexpected
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print("Warning: Unexpected format in single coordinate string. Treating as one path.")
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coord_strings = [coordinates]
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except Exception as e:
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print(f"Warning: Could not parse single coordinate string as JSON list. Treating as one path string. Error: {e}")
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coord_strings = [coordinates] # Treat as a single path string if parsing fails
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elif isinstance(coordinates, list) and all(isinstance(item, str) for item in coordinates):
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coord_strings = coordinates # Already a list of strings
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else:
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print(f"Error: Invalid coordinates input type: {type(coordinates)}. Expected string or list of strings.")
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img = Image.new('RGB', (frame_width, frame_height), color=bg_color)
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return (pil2tensor(img),)
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all_paths_control_points = []
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all_paths_original_p0 = []
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all_paths_initial_p1 = []
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valid_paths_found = False
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for i, coord_string in enumerate(coord_strings):
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try:
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coords = json.loads(coord_string.replace("'", '"'))
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if not isinstance(coords, list) or len(coords) < 2:
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print(f"Warning: Path {i+1} has < 2 points or invalid format. Skipping.")
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all_paths_control_points.append(None) # Placeholder for skipped path
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all_paths_original_p0.append(None)
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all_paths_initial_p1.append(None)
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continue
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points = [(c['x'], c['y']) for c in coords]
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control_points = [np.array(p) for p in points]
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all_paths_control_points.append(control_points)
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all_paths_original_p0.append(control_points[0])
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all_paths_initial_p1.append(control_points[1])
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valid_paths_found = True
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except Exception as e:
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print(f"Error parsing coordinates for path {i+1}: {e}. Skipping path.")
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all_paths_control_points.append(None) # Placeholder for skipped path
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all_paths_original_p0.append(None)
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all_paths_initial_p1.append(None)
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continue
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if not valid_paths_found:
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print("Error: No valid coordinate paths found.")
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img = Image.new('RGB', (frame_width, frame_height), color=bg_color)
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return (pil2tensor(img),)
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output_images = []
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previous_frame_tensor = None
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# --- Parse and Adjust Pivot Coordinates ---
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# (Applies the *same* pivot motion to *all* paths if provided)
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pivot_points_adjusted = None
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use_dynamic_pivot = False
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static_pivot_point = None # Used if pivot_coordinates is None or invalid
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if pivot_coordinates and pivot_coordinates.strip() and pivot_coordinates.strip() != '[]':
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try:
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pivot_coords_raw = json.loads(pivot_coordinates.replace("'", '"'))
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if isinstance(pivot_coords_raw, list) and len(pivot_coords_raw) > 0:
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pivot_points_raw = [np.array((c['x'], c['y'])) for c in pivot_coords_raw]
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current_len = len(pivot_points_raw)
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if current_len < total_frames:
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last_point = pivot_points_raw[-1]
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padding = [last_point] * (total_frames - current_len)
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pivot_points_adjusted = pivot_points_raw + padding
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elif current_len > total_frames:
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pivot_points_adjusted = pivot_points_raw[:total_frames]
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else:
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pivot_points_adjusted = pivot_points_raw
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if pivot_points_adjusted:
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use_dynamic_pivot = True
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# print(f"Using dynamic pivot points. Adjusted count: {len(pivot_points_adjusted)}")
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except Exception as e:
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print(f"Warning: Error parsing pivot_coordinates: {e}. Using static p0 for each path.")
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use_dynamic_pivot = False
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# else: use_dynamic_pivot remains False
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# --- Pre-calculate fixed length and direction for paths if needed ---
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all_paths_fixed_length = []
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all_paths_fixed_v_normalized = []
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for i in range(len(all_paths_control_points)):
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if all_paths_control_points[i] is None:
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all_paths_fixed_length.append(0)
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all_paths_fixed_v_normalized.append(None)
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continue
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p0_orig = all_paths_original_p0[i]
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p1_init = all_paths_initial_p1[i]
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fixed_v = p1_init - p0_orig
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fixed_len = np.linalg.norm(fixed_v)
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fixed_v_norm = None
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if fixed_len > 0 and not scaling_enabled:
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fixed_v_norm = fixed_v / fixed_len
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elif fixed_len == 0 and not scaling_enabled:
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print(f"Warning: Path {i+1} initial control points p0 and p1 are identical. Fixed length is 0.")
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all_paths_fixed_length.append(fixed_len)
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all_paths_fixed_v_normalized.append(fixed_v_norm)
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try:
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fill_rgb = ImageColor.getrgb(fill_color)
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except ValueError:
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print(f"Warning: Invalid fill_color '{fill_color}'. Defaulting to white.")
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fill_rgb = (255, 255, 255)
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# --- Easing function definitions ---
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def ease_in(t): return t * t
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def ease_out(t): return 1 - (1 - t) * (1 - t)
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def ease_in_out(t): return 2 * t * t if t < 0.5 else 1 - pow(-2 * t + 2, 2) / 2
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easing_map = {"linear": lambda t: t, "ease_in": ease_in, "ease_out": ease_out, "ease_in_out": ease_in_out}
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apply_easing = easing_map.get(easing_function, lambda t: t)
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# --- Loop through frames ---
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for frame_index in range(total_frames):
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img_frame = Image.new('RGB', (frame_width, frame_height), color=bg_color)
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draw_frame = ImageDraw.Draw(img_frame)
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# --- Loop through paths for the current frame ---
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for path_idx, control_points in enumerate(all_paths_control_points):
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if control_points is None: # Skip invalid/skipped paths
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continue
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p0_original = all_paths_original_p0[path_idx]
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p1_initial = all_paths_initial_p1[path_idx]
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fixed_length = all_paths_fixed_length[path_idx]
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fixed_v_normalized = all_paths_fixed_v_normalized[path_idx]
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# --- Determine current pivot for this frame ---
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# If dynamic pivot is used, all paths use the same pivot point for this frame.
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# Otherwise, each path uses its own original p0 as the static pivot.
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current_pivot = p0_original # Default to path's own p0 if no dynamic pivot
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if use_dynamic_pivot and pivot_points_adjusted:
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current_pivot = pivot_points_adjusted[frame_index]
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# --- Determine target point for this path and frame (relative to its p0_original) ---
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target_point_relative_to_p0 = None
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num_control_points = len(control_points)
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if frame_index == 0:
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target_point_relative_to_p0 = p1_initial - p0_original
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elif num_control_points >= 3:
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# --- Interpolate to find target point ---
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num_animation_segments = num_control_points - 2 # Segments p1->p2, p2->p3, ...
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num_animation_frames = total_frames - 1 # Frames 1 to total_frames-1
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if num_animation_segments > 0 and num_animation_frames > 0:
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# Find which segment and t value corresponds to frame_index
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target_frame_in_animation = frame_index # (since frame_index starts at 0, frame 1 is target 1)
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cumulative_frames = 0
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segment_found = False
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for k in range(num_animation_segments):
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base_frames_per_segment = num_animation_frames // num_animation_segments
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remainder_frames = num_animation_frames % num_animation_segments
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num_steps_this_segment = base_frames_per_segment + (1 if k < remainder_frames else 0)
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if num_steps_this_segment == 0: continue
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if target_frame_in_animation <= cumulative_frames + num_steps_this_segment:
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# Target frame falls within this segment (k)
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p_segment_start = control_points[k + 1]
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p_segment_end = control_points[k + 2]
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frame_within_segment = target_frame_in_animation - cumulative_frames
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t = frame_within_segment / num_steps_this_segment # Linear t [slightly > 0 to 1.0]
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eased_t = 0.0
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if bounce_between > 0.0 and num_steps_this_segment > 1: # Need at least 2 steps to bounce
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target_t_near = 1.0 - bounce_between * 0.5
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target_t_near = max(0.01, target_t_near) # Avoid division by zero or extreme scaling
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# Scale t based on reaching target_t_near
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current_t_scaled_for_ease = min(1.0, t / target_t_near) if target_t_near > 0 else 1.0
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eased_t_part1 = ease_out(current_t_scaled_for_ease)
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# Calculate bounce phase if t is past target_t_near
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bounce_phase_t = max(0.0, (t - target_t_near) / (1.0 - target_t_near)) if (1.0 - target_t_near) > 1e-6 else 0.0
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# Apply bounce curve (cosine based)
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# Change overshoot factor to directly use bounce_between for amplitude
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bounce_curve = 0.5 * (1 - np.cos(bounce_phase_t * np.pi))
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# Blend between eased approach and bounce motion
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# Target amplitude of bounce relative to segment length
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target_overshoot_displacement = (p_segment_end - p_segment_start) * bounce_between
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# Calculate position based on eased part and add bounce displacement
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position_at_eased_t = p_segment_start + (p_segment_end - p_segment_start) * eased_t_part1
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# Direction of bounce is typically along the segment direction
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segment_vector = p_segment_end - p_segment_start
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segment_direction = segment_vector / np.linalg.norm(segment_vector) if np.linalg.norm(segment_vector) > 0 else np.array([0,0])
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# Apply bounce displacement along the segment direction
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bounce_displacement_vector = segment_direction * bounce_curve * np.linalg.norm(target_overshoot_displacement) * bounce_phase_t
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p_interpolated = position_at_eased_t + bounce_displacement_vector
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else: # No bounce or not enough steps for bounce
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eased_t = apply_easing(t)
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p_interpolated = p_segment_start + (p_segment_end - p_segment_start) * eased_t
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target_point_relative_to_p0 = p_interpolated - p0_original
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segment_found = True
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break # Found the segment for this frame
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cumulative_frames += num_steps_this_segment
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if not segment_found:
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# Should not happen if logic is correct, but fallback to last point relative to p0
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target_point_relative_to_p0 = control_points[-1] - p0_original
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print(f"Warning: Segment not found for frame {frame_index}, path {path_idx}. Using last point.")
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else:
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# Not enough segments/frames for animation beyond frame 0
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target_point_relative_to_p0 = p1_initial - p0_original # Stay at initial target
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else:
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# Only 2 control points, target stays at p1 relative to p0
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target_point_relative_to_p0 = p1_initial - p0_original
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if target_point_relative_to_p0 is None:
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print(f"Warning: Could not determine target point for frame {frame_index}, path {path_idx}. Skipping draw.")
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continue
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# --- Apply Relative vs Absolute Pivot Logic ---
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draw_start_point = None
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draw_end_point = None
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length_for_draw = 0
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normalized_v_for_draw = None
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if relative:
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# 1. Calculate the shape's geometry based *only* on its own path, originating at p0_original
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# (p0_calc, pn_calc, length_calc, normalized_v_calc)
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p0_calc = p0_original
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target_calc = p0_calc + target_point_relative_to_p0
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v_dir_calc = target_calc - p0_calc
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dir_length_calc = np.linalg.norm(v_dir_calc)
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pn_calc = p0_calc # Default end point is start
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length_calc = 0
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normalized_v_calc = None
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if scaling_enabled:
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if dir_length_calc > 0:
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pn_calc = target_calc
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length_calc = dir_length_calc
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normalized_v_calc = v_dir_calc / length_calc
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else: # Fixed Length
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if fixed_length > 0:
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length_calc = fixed_length
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if dir_length_calc > 0:
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normalized_v_calc = v_dir_calc / dir_length_calc
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pn_calc = p0_calc + normalized_v_calc * length_calc
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elif fixed_v_normalized is not None:
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normalized_v_calc = fixed_v_normalized
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pn_calc = p0_calc + normalized_v_calc * length_calc
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# 2. Determine the initial offset (once per path, could be cached outside frame loop if performance needed)
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initial_pivot_point = p0_original # Default if no dynamic pivot used for frame 0
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if use_dynamic_pivot and pivot_points_adjusted:
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initial_pivot_point = pivot_points_adjusted[0]
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initial_offset_vector = p0_original - initial_pivot_point
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# 3. Apply the *initial* offset to the *current* pivot point to get the draw start point
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frame_pivot_point = current_pivot # Already determined for this frame
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draw_start_point = frame_pivot_point + initial_offset_vector
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# 4. Calculate the draw end point by applying the shape's calculated vector to the draw start point
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shape_vector = pn_calc - p0_calc
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draw_end_point = draw_start_point + shape_vector
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# 5. Set draw parameters
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length_for_draw = length_calc
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normalized_v_for_draw = normalized_v_calc
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else: # Absolute pivot positioning (previous logic)
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# Calculate offset target based on current pivot
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offset_target = current_pivot + target_point_relative_to_p0
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# Determine vector, length, end point (pn) based on current_pivot, offset_target, scaling
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v_dir = offset_target - current_pivot
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dir_length = np.linalg.norm(v_dir)
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pn = current_pivot # Default end point is the pivot itself
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length = 0
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normalized_v = None
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||||
if scaling_enabled:
|
||||
if dir_length > 0:
|
||||
pn = offset_target
|
||||
length = dir_length
|
||||
normalized_v = v_dir / length
|
||||
else: # Fixed Length
|
||||
if fixed_length > 0:
|
||||
length = fixed_length
|
||||
if dir_length > 0:
|
||||
normalized_v = v_dir / dir_length
|
||||
pn = current_pivot + normalized_v * length
|
||||
elif fixed_v_normalized is not None:
|
||||
normalized_v = fixed_v_normalized
|
||||
pn = current_pivot + normalized_v * length
|
||||
|
||||
# Set draw parameters
|
||||
draw_start_point = current_pivot
|
||||
draw_end_point = pn
|
||||
length_for_draw = length
|
||||
normalized_v_for_draw = normalized_v
|
||||
|
||||
# --- Draw the polygon for this path using calculated/offset points ---
|
||||
if length_for_draw > 0 and normalized_v_for_draw is not None and draw_start_point is not None and draw_end_point is not None:
|
||||
perp_v = np.array([-normalized_v_for_draw[1], normalized_v_for_draw[0]])
|
||||
|
||||
# Calculate half-widths for start and end based on inputs
|
||||
half_w_start = perp_v * (shape_width / 2.0)
|
||||
|
||||
# Use shape_width_end if > 0, otherwise use shape_width
|
||||
end_width = shape_width_end if shape_width_end > 0 else shape_width
|
||||
half_w_end = perp_v * (end_width / 2.0)
|
||||
|
||||
# Use draw_start_point and draw_end_point for corners with respective widths
|
||||
c1 = tuple((draw_start_point - half_w_start).astype(int))
|
||||
c2 = tuple((draw_start_point + half_w_start).astype(int))
|
||||
c3 = tuple((draw_end_point + half_w_end).astype(int)) # Use end width at the end point
|
||||
c4 = tuple((draw_end_point - half_w_end).astype(int)) # Use end width at the end point
|
||||
|
||||
draw_frame.polygon([c1, c2, c3, c4], fill=fill_rgb)
|
||||
|
||||
# --- Post-processing for the completed frame ---
|
||||
if blur_radius > 0.0:
|
||||
img_frame = img_frame.filter(ImageFilter.GaussianBlur(blur_radius))
|
||||
|
||||
current_frame_tensor = pil2tensor(img_frame)
|
||||
|
||||
if trailing > 0.0 and previous_frame_tensor is not None:
|
||||
current_frame_tensor = current_frame_tensor + trailing * previous_frame_tensor
|
||||
# Normalize after adding trailing to prevent exceeding 1.0 (or clamp)
|
||||
max_val = torch.max(current_frame_tensor)
|
||||
if max_val > 1.0:
|
||||
current_frame_tensor = current_frame_tensor / max_val # Normalize
|
||||
# Alternative: Clamping
|
||||
# current_frame_tensor = torch.clamp(current_frame_tensor, 0.0, 1.0)
|
||||
|
||||
previous_frame_tensor = current_frame_tensor.clone() # Store state before intensity multiplication
|
||||
|
||||
current_frame_tensor = current_frame_tensor * intensity
|
||||
# Optional: Clamp again after intensity if intensity > 1.0
|
||||
# current_frame_tensor = torch.clamp(current_frame_tensor, 0.0) # Clamp min at 0?
|
||||
|
||||
output_images.append(current_frame_tensor)
|
||||
|
||||
# --- Final Output ---
|
||||
if not output_images:
|
||||
print("Warning: No frames generated. Returning a single blank image.")
|
||||
img = Image.new('RGB', (frame_width, frame_height), color=bg_color)
|
||||
return (pil2tensor(img),)
|
||||
|
||||
batch_output = torch.cat(output_images, dim=0)
|
||||
return (batch_output,)
|
||||
|
||||
class DriverOffsetCoordinates:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"driver_coords": ("STRING", {"multiline": False, "forceInput": True}),
|
||||
"driven_coords": ("STRING", {"multiline": False, "forceInput": True}),
|
||||
"smooth_out": ("FLOAT", {"default": 0.85, "min": 0.0, "max": 1.0, "step": 0.01}),
|
||||
"delay": ("INT", {"default": 0, "min": 0, "max": 100, "step": 1}),
|
||||
"rotate": ("INT", {"default": 0, "min": 0, "max": 360, "step": 1}),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("STRING",)
|
||||
RETURN_NAMES = ("output_coords",)
|
||||
FUNCTION = "execute"
|
||||
CATEGORY = "KJNodes/coords"
|
||||
DESCRIPTION = """Applies rotated, smoothed, and delayed offsets from driver coordinates to driven coordinates."""
|
||||
|
||||
def execute(self, driver_coords, driven_coords, smooth_out, delay, rotate):
|
||||
try:
|
||||
# Use replace("'", '"') for potentially malformed JSON strings
|
||||
D_orig = json.loads(driver_coords.replace("'", '"'))
|
||||
Dn = json.loads(driven_coords.replace("'", '"'))
|
||||
except json.JSONDecodeError as e:
|
||||
print(f"DriverOffsetCoordinates Error: Invalid JSON input - {e}")
|
||||
return (driven_coords,)
|
||||
except Exception as e:
|
||||
print(f"DriverOffsetCoordinates Error: Could not parse coordinates - {e}")
|
||||
return (driven_coords,)
|
||||
|
||||
len_dn = len(Dn)
|
||||
if len_dn == 0:
|
||||
return (driven_coords,)
|
||||
|
||||
len_d_orig = len(D_orig)
|
||||
if len_d_orig == 0:
|
||||
print("DriverOffsetCoordinates Warning: Driver coordinates are empty. Returning driven coordinates unchanged.")
|
||||
return (driven_coords,)
|
||||
|
||||
# --- Rotation Step --- (Operate on a copy)
|
||||
D = [coord.copy() for coord in D_orig] # Work on a copy for rotation
|
||||
len_d = len(D)
|
||||
|
||||
if rotate != 0 and len_d >= 2:
|
||||
try:
|
||||
pivot_x = float(D[0]['x'])
|
||||
pivot_y = float(D[0]['y'])
|
||||
angle_rad = math.radians(rotate)
|
||||
cos_a = math.cos(angle_rad)
|
||||
sin_a = math.sin(angle_rad)
|
||||
|
||||
for j in range(1, len_d):
|
||||
px = float(D[j]['x'])
|
||||
py = float(D[j]['y'])
|
||||
|
||||
rel_x = px - pivot_x
|
||||
rel_y = py - pivot_y
|
||||
|
||||
new_rel_x = rel_x * cos_a - rel_y * sin_a
|
||||
new_rel_y = rel_x * sin_a + rel_y * cos_a
|
||||
|
||||
D[j]['x'] = new_rel_x + pivot_x
|
||||
D[j]['y'] = new_rel_y + pivot_y
|
||||
except KeyError as e:
|
||||
print(f"DriverOffsetCoordinates Error: Missing 'x' or 'y' key during rotation - {e}")
|
||||
return (driven_coords,) # Abort if keys are missing
|
||||
except ValueError as e:
|
||||
print(f"DriverOffsetCoordinates Error: Cannot convert coordinate to float during rotation - {e}")
|
||||
return (driven_coords,) # Abort if conversion fails
|
||||
|
||||
# --- Padding Step --- (Use potentially rotated D)
|
||||
if len_d < len_dn:
|
||||
print(f"DriverOffsetCoordinates Info: Driver coords shorter ({len_d}) than driven ({len_dn}). Padding driver with last coordinate.")
|
||||
if len_d > 0:
|
||||
last_driver_coord = D[-1]
|
||||
D.extend([last_driver_coord.copy() for _ in range(len_dn - len_d)])
|
||||
else:
|
||||
# This case should be impossible now due to earlier check
|
||||
print("DriverOffsetCoordinates Warning: Driver coords empty after rotation (should not happen), padding with {'x':0, 'y':0}.")
|
||||
D.extend([{'x':0.0, 'y':0.0}] * len_dn)
|
||||
len_d = len(D)
|
||||
|
||||
# --- Smoothing Step --- (Use potentially rotated and padded D)
|
||||
SmoothD = [None] * len_d
|
||||
if len_d > 0:
|
||||
try:
|
||||
# Ensure coords are floats for calculation
|
||||
SmoothD[0] = {'x': float(D[0]['x']), 'y': float(D[0]['y'])}
|
||||
alpha = 1.0 - smooth_out
|
||||
for j in range(1, len_d):
|
||||
prev_smooth_x = SmoothD[j-1]['x']
|
||||
prev_smooth_y = SmoothD[j-1]['y']
|
||||
# Ensure current coords are floats
|
||||
current_x = float(D[j]['x'])
|
||||
current_y = float(D[j]['y'])
|
||||
smooth_x = alpha * current_x + (1.0 - alpha) * prev_smooth_x
|
||||
smooth_y = alpha * current_y + (1.0 - alpha) * prev_smooth_y
|
||||
SmoothD[j] = {'x': smooth_x, 'y': smooth_y}
|
||||
except KeyError as e:
|
||||
print(f"DriverOffsetCoordinates Error: Missing 'x' or 'y' key during smoothing - {e}")
|
||||
return (driven_coords,)
|
||||
except ValueError as e:
|
||||
print(f"DriverOffsetCoordinates Error: Cannot convert coordinate to float during smoothing - {e}")
|
||||
return (driven_coords,)
|
||||
else:
|
||||
SmoothD = []
|
||||
|
||||
# --- Offset Application Step ---
|
||||
OutputCoords = [None] * len_dn
|
||||
RefOffsetX = SmoothD[0]['x'] if len(SmoothD) > 0 else 0.0
|
||||
RefOffsetY = SmoothD[0]['y'] if len(SmoothD) > 0 else 0.0
|
||||
|
||||
for i in range(len_dn):
|
||||
try:
|
||||
# Ensure driven coords are floats
|
||||
current_driven_x = float(Dn[i]['x'])
|
||||
current_driven_y = float(Dn[i]['y'])
|
||||
|
||||
if i < delay:
|
||||
# Keep original driven coord (as float)
|
||||
OutputCoords[i] = {'x': current_driven_x, 'y': current_driven_y}
|
||||
else:
|
||||
# Apply offset after delay period
|
||||
driver_idx = i - delay
|
||||
if 0 <= driver_idx < len(SmoothD):
|
||||
driver_smooth_x = SmoothD[driver_idx]['x']
|
||||
driver_smooth_y = SmoothD[driver_idx]['y']
|
||||
elif len(SmoothD) > 0:
|
||||
print(f"DriverOffsetCoordinates Warning: driver_idx {driver_idx} out of bounds for SmoothD (len {len(SmoothD)}). Using last.")
|
||||
driver_smooth_x = SmoothD[-1]['x']
|
||||
driver_smooth_y = SmoothD[-1]['y']
|
||||
else:
|
||||
driver_smooth_x = 0.0
|
||||
driver_smooth_y = 0.0
|
||||
|
||||
offset_x = driver_smooth_x - RefOffsetX
|
||||
offset_y = driver_smooth_y - RefOffsetY
|
||||
|
||||
output_x = current_driven_x + offset_x
|
||||
output_y = current_driven_y + offset_y
|
||||
|
||||
OutputCoords[i] = {'x': output_x, 'y': output_y}
|
||||
except KeyError as e:
|
||||
print(f"DriverOffsetCoordinates Error: Missing 'x' or 'y' key during offset application - {e}")
|
||||
# Return partially processed coords or original driven? Return original for safety.
|
||||
return (driven_coords,)
|
||||
except ValueError as e:
|
||||
print(f"DriverOffsetCoordinates Error: Cannot convert coordinate to float during offset application - {e}")
|
||||
return (driven_coords,)
|
||||
|
||||
# Format output as JSON string
|
||||
output_json = json.dumps(OutputCoords, separators=(',', ':'))
|
||||
|
||||
return (output_json,)
|
||||
Loading…
x
Reference in New Issue
Block a user