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67 lines
2.3 KiB
Python
67 lines
2.3 KiB
Python
# Adapted from https://github.com/huggingface/controlnet_aux
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import pathlib
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import cv2
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import huggingface_hub
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import numpy as np
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import torch
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from PIL import Image
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from invokeai.backend.image_util.mlsd.models.mbv2_mlsd_large import MobileV2_MLSD_Large
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from invokeai.backend.image_util.mlsd.utils import pred_lines
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from invokeai.backend.image_util.util import np_to_pil, pil_to_np, resize_to_multiple
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class MLSDDetector:
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"""Simple wrapper around a MLSD model for detecting edges as line segments in an image."""
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hf_repo_id = "lllyasviel/ControlNet"
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hf_filename = "annotator/ckpts/mlsd_large_512_fp32.pth"
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@classmethod
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def get_model_url(cls) -> str:
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"""Get the URL to download the model from the Hugging Face Hub."""
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return huggingface_hub.hf_hub_url(cls.hf_repo_id, cls.hf_filename)
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@classmethod
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def load_model(cls, model_path: pathlib.Path) -> MobileV2_MLSD_Large:
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"""Load the model from a file."""
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model = MobileV2_MLSD_Large()
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model.load_state_dict(torch.load(model_path), strict=True)
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model.eval()
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return model
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def __init__(self, model: MobileV2_MLSD_Large) -> None:
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self.model = model
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def to(self, device: torch.device):
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self.model.to(device)
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return self
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def run(self, image: Image.Image, score_threshold: float = 0.1, distance_threshold: float = 20.0) -> Image.Image:
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"""Processes an image and returns the detected edges."""
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np_img = pil_to_np(image)
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height, width, _channels = np_img.shape
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# This model requires the input image to have a resolution that is a multiple of 64
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np_img = resize_to_multiple(np_img, 64)
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img_output = np.zeros_like(np_img)
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with torch.no_grad():
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lines = pred_lines(np_img, self.model, [np_img.shape[0], np_img.shape[1]], score_threshold, distance_threshold)
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for line in lines:
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x_start, y_start, x_end, y_end = [int(val) for val in line]
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cv2.line(img_output, (x_start, y_start), (x_end, y_end), [255, 255, 255], 1)
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detected_map = img_output[:, :, 0]
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# Back to the original size
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output_image = cv2.resize(detected_map, (width, height), interpolation=cv2.INTER_LINEAR)
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return np_to_pil(output_image)
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