61 lines
1.8 KiB
Python
61 lines
1.8 KiB
Python
import torch
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import numpy as np
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from PIL import Image
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from .utils import generate
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['Background', 'Head', 'Torso', 'Upper Arms', 'Lower Arms', 'Upper Legs', 'Lower Legs']
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class HumanParserPascalCustomNode:
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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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"image" : ("IMAGE", {}),
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"background": ("BOOLEAN", {"default": False}),
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"head": ("BOOLEAN", {"default": False}),
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"torso": ("BOOLEAN", {"default": False}),
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"upper_arms": ("BOOLEAN", {"default": False}),
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"lower_arms": ("BOOLEAN", {"default": False}),
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"upper_legs": ("BOOLEAN", {"default": False}),
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"lower_legs": ("BOOLEAN", {"default": False}),
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},
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}
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RETURN_TYPES = ("MASK", "IMAGE")
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RETURN_NAMES = ("mask", "map")
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FUNCTION = "run"
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CATEGORY = "CozyMantis"
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def run(self, image, background, head, torso, upper_arms, lower_arms, upper_legs, lower_legs):
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if torch.cuda.is_available():
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device = 'cuda'
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else:
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device = 'cpu'
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output_img = generate(image[0], 'pascal', device)
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mask_components = []
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if background:
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mask_components.append(0)
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if head:
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mask_components.append(1)
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if torso:
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mask_components.append(2)
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if upper_arms:
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mask_components.append(3)
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if lower_arms:
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mask_components.append(4)
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if upper_legs:
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mask_components.append(5)
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if lower_legs:
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mask_components.append(6)
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mask = np.isin(output_img, mask_components).astype(np.uint8)
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mask_image = Image.fromarray(mask * 255)
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mask_image = mask_image.convert("RGB")
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mask_image = torch.from_numpy(np.array(mask_image).astype(np.float32) / 255.0).unsqueeze(0)
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output_img = output_img.convert('RGB')
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output_img = torch.from_numpy(np.array(output_img).astype(np.float32) / 255.0).unsqueeze(0)
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return (mask_image[:, :, :, 0], output_img,)
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