mirror of
https://github.com/invoke-ai/InvokeAI.git
synced 2026-02-01 16:45:13 -05:00
67 lines
2.3 KiB
Python
67 lines
2.3 KiB
Python
# Adapted from https://github.com/huggingface/controlnet_aux
|
|
|
|
import pathlib
|
|
|
|
import cv2
|
|
import huggingface_hub
|
|
import numpy as np
|
|
import torch
|
|
from PIL import Image
|
|
|
|
from invokeai.backend.image_util.mlsd.models.mbv2_mlsd_large import MobileV2_MLSD_Large
|
|
from invokeai.backend.image_util.mlsd.utils import pred_lines
|
|
from invokeai.backend.image_util.util import np_to_pil, pil_to_np, resize_to_multiple
|
|
|
|
|
|
class MLSDDetector:
|
|
"""Simple wrapper around a MLSD model for detecting edges as line segments in an image."""
|
|
|
|
hf_repo_id = "lllyasviel/ControlNet"
|
|
hf_filename = "annotator/ckpts/mlsd_large_512_fp32.pth"
|
|
|
|
@classmethod
|
|
def get_model_url(cls) -> str:
|
|
"""Get the URL to download the model from the Hugging Face Hub."""
|
|
|
|
return huggingface_hub.hf_hub_url(cls.hf_repo_id, cls.hf_filename)
|
|
|
|
@classmethod
|
|
def load_model(cls, model_path: pathlib.Path) -> MobileV2_MLSD_Large:
|
|
"""Load the model from a file."""
|
|
|
|
model = MobileV2_MLSD_Large()
|
|
model.load_state_dict(torch.load(model_path), strict=True)
|
|
model.eval()
|
|
return model
|
|
|
|
def __init__(self, model: MobileV2_MLSD_Large) -> None:
|
|
self.model = model
|
|
|
|
def to(self, device: torch.device):
|
|
self.model.to(device)
|
|
return self
|
|
|
|
def run(self, image: Image.Image, score_threshold: float = 0.1, distance_threshold: float = 20.0) -> Image.Image:
|
|
"""Processes an image and returns the detected edges."""
|
|
|
|
np_img = pil_to_np(image)
|
|
|
|
height, width, _channels = np_img.shape
|
|
|
|
# This model requires the input image to have a resolution that is a multiple of 64
|
|
np_img = resize_to_multiple(np_img, 64)
|
|
img_output = np.zeros_like(np_img)
|
|
|
|
with torch.no_grad():
|
|
lines = pred_lines(np_img, self.model, [np_img.shape[0], np_img.shape[1]], score_threshold, distance_threshold)
|
|
for line in lines:
|
|
x_start, y_start, x_end, y_end = [int(val) for val in line]
|
|
cv2.line(img_output, (x_start, y_start), (x_end, y_end), [255, 255, 255], 1)
|
|
|
|
detected_map = img_output[:, :, 0]
|
|
|
|
# Back to the original size
|
|
output_image = cv2.resize(detected_map, (width, height), interpolation=cv2.INTER_LINEAR)
|
|
|
|
return np_to_pil(output_image)
|