50 lines
1.8 KiB
Python
50 lines
1.8 KiB
Python
import cv2
|
|
import numpy as np
|
|
import torch
|
|
import os
|
|
|
|
from einops import rearrange
|
|
from .models.mbv2_mlsd_tiny import MobileV2_MLSD_Tiny
|
|
from .models.mbv2_mlsd_large import MobileV2_MLSD_Large
|
|
from .utils import pred_lines
|
|
from modules import devices
|
|
from annotator.annotator_path import models_path
|
|
|
|
mlsdmodel = None
|
|
remote_model_path = "https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/mlsd_large_512_fp32.pth"
|
|
old_modeldir = os.path.dirname(os.path.realpath(__file__))
|
|
modeldir = os.path.join(models_path, "mlsd")
|
|
|
|
def unload_mlsd_model():
|
|
global mlsdmodel
|
|
if mlsdmodel is not None:
|
|
mlsdmodel = mlsdmodel.cpu()
|
|
|
|
def apply_mlsd(input_image, thr_v, thr_d):
|
|
global modelpath, mlsdmodel
|
|
if mlsdmodel is None:
|
|
modelpath = os.path.join(modeldir, "mlsd_large_512_fp32.pth")
|
|
old_modelpath = os.path.join(old_modeldir, "mlsd_large_512_fp32.pth")
|
|
if os.path.exists(old_modelpath):
|
|
modelpath = old_modelpath
|
|
elif not os.path.exists(modelpath):
|
|
from basicsr.utils.download_util import load_file_from_url
|
|
load_file_from_url(remote_model_path, model_dir=modeldir)
|
|
mlsdmodel = MobileV2_MLSD_Large()
|
|
mlsdmodel.load_state_dict(torch.load(modelpath), strict=True)
|
|
mlsdmodel = mlsdmodel.to(devices.get_device_for("controlnet")).eval()
|
|
|
|
model = mlsdmodel
|
|
assert input_image.ndim == 3
|
|
img = input_image
|
|
img_output = np.zeros_like(img)
|
|
try:
|
|
with torch.no_grad():
|
|
lines = pred_lines(img, model, [img.shape[0], img.shape[1]], thr_v, thr_d)
|
|
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)
|
|
except Exception as e:
|
|
pass
|
|
return img_output[:, :, 0]
|