Fill
0bcd5c3c7c
added the ability to schedule some values in the FX nodes, as well as optimized some code to make them a little faster. Iv also added a % complete print in the console so you can see how far along the processing is =)
111 lines
4.9 KiB
Python
111 lines
4.9 KiB
Python
import torch
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import numpy as np
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from PIL import Image, ImageDraw
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import math
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import sys
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class FL_HexagonalPattern:
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def __init__(self):
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self.hexagon_size_index = 0
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self.shadow_offset_index = 0
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self.shadow_color_index = 0
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self.background_color_index = 0
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self.rotation_index = 0
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self.spacing_index = 0
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"images": ("IMAGE",),
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},
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"optional": {
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"hexagon_size": ("INT", {"default": 100, "min": 50, "max": 500, "step": 10}),
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"shadow_offset": ("INT", {"default": 5, "min": 0, "max": 20, "step": 1}),
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"shadow_color": ("STRING", {"default": "purple"}),
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"background_color": ("STRING", {"default": "black"}),
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"rotation": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 360.0, "step": 1.0}),
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"spacing": ("FLOAT", {"default": 1.0, "min": 0.5, "max": 2.0, "step": 0.1}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "hexagonal_pattern"
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CATEGORY = "🏵️Fill Nodes"
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def t2p(self, t):
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if t is not None:
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i = 255.0 * t.cpu().numpy().squeeze()
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p = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
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return p
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def create_hexagon_mask(self, size):
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mask = Image.new("L", (size, size), 0)
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draw = ImageDraw.Draw(mask)
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draw.regular_polygon((size // 2, size // 2, size // 2), 6, fill=255)
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return mask
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def process_input_value(self, value, index):
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if isinstance(value, list):
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if index >= len(value):
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print(f"Warning: Value list index out of range. Using the last value.")
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index = len(value) - 1
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current_value = value[index]
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index = (index + 1) % len(value)
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else:
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current_value = value
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if hasattr(current_value, 'values'):
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current_value = float(current_value.values[0])
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return current_value, index
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def hexagonal_pattern(self, images, hexagon_size=100, shadow_offset=5, shadow_color="black", shadow_opacity=0.5,
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background_color="white", rotation=0.0, spacing=1.0):
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out = []
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total_images = len(images)
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for i, img_tensor in enumerate(images, start=1):
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p = self.t2p(img_tensor)
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width, height = p.size
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current_hexagon_size, self.hexagon_size_index = self.process_input_value(hexagon_size, self.hexagon_size_index)
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current_shadow_offset, self.shadow_offset_index = self.process_input_value(shadow_offset, self.shadow_offset_index)
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current_shadow_color, self.shadow_color_index = self.process_input_value(shadow_color, self.shadow_color_index)
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current_background_color, self.background_color_index = self.process_input_value(background_color, self.background_color_index)
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current_rotation, self.rotation_index = self.process_input_value(rotation, self.rotation_index)
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current_spacing, self.spacing_index = self.process_input_value(spacing, self.spacing_index)
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hexagon_mask = self.create_hexagon_mask(current_hexagon_size)
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output_image = Image.new("RGBA", (width, height), current_background_color)
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for y in range(0, height, int(current_hexagon_size * current_spacing * math.sqrt(3) / 2)):
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for x in range(0, width, int(current_hexagon_size * current_spacing)):
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if y % (2 * int(current_hexagon_size * current_spacing * math.sqrt(3) / 2)) == int(current_hexagon_size * current_spacing * math.sqrt(3) / 2):
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x += int(current_hexagon_size * current_spacing) // 2
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cropped_hexagon = p.crop((x, y, x + current_hexagon_size, y + current_hexagon_size)).rotate(current_rotation, expand=True)
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shadow = Image.new("RGBA", cropped_hexagon.size, (0, 0, 0, 0))
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shadow_mask = hexagon_mask.copy().resize(cropped_hexagon.size)
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shadow.paste(current_shadow_color, (current_shadow_offset, current_shadow_offset), shadow_mask)
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shadow.putalpha(int(255 * shadow_opacity))
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output_image.paste(shadow, (x + current_shadow_offset, y + current_shadow_offset), shadow_mask)
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output_image.paste(cropped_hexagon, (x, y), shadow_mask)
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o = np.array(output_image.convert("RGB")).astype(np.float32) / 255.0
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o = torch.from_numpy(o).unsqueeze(0)
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out.append(o)
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# Print progress update
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progress = i / total_images * 100
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sys.stdout.write(f"\rProcessing images: {progress:.2f}%")
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sys.stdout.flush()
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# Print a new line after the progress update
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print()
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out = torch.cat(out, 0)
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return (out,)
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