mirror of https://github.com/vladmandic/automatic
79 lines
2.7 KiB
Python
79 lines
2.7 KiB
Python
import os
|
|
import warnings
|
|
|
|
import cv2
|
|
import numpy as np
|
|
import torch
|
|
from huggingface_hub import hf_hub_download
|
|
from PIL import Image
|
|
|
|
from modules.control.util import HWC3, resize_image
|
|
from .models.mbv2_mlsd_large import MobileV2_MLSD_Large
|
|
from .utils import pred_lines
|
|
|
|
|
|
class MLSDdetector:
|
|
def __init__(self, model):
|
|
self.model = model
|
|
|
|
@classmethod
|
|
def from_pretrained(cls, pretrained_model_or_path, filename=None, cache_dir=None):
|
|
if pretrained_model_or_path == "lllyasviel/ControlNet":
|
|
filename = filename or "annotator/ckpts/mlsd_large_512_fp32.pth"
|
|
else:
|
|
filename = filename or "mlsd_large_512_fp32.pth"
|
|
|
|
if os.path.isdir(pretrained_model_or_path):
|
|
model_path = os.path.join(pretrained_model_or_path, filename)
|
|
else:
|
|
model_path = hf_hub_download(pretrained_model_or_path, filename, cache_dir=cache_dir)
|
|
|
|
model = MobileV2_MLSD_Large()
|
|
model.load_state_dict(torch.load(model_path), strict=True)
|
|
model.eval()
|
|
|
|
return cls(model)
|
|
|
|
def to(self, device):
|
|
self.model.to(device)
|
|
return self
|
|
|
|
def __call__(self, input_image, thr_v=0.1, thr_d=0.1, detect_resolution=512, image_resolution=512, output_type="pil", **kwargs):
|
|
if "return_pil" in kwargs:
|
|
warnings.warn("return_pil is deprecated. Use output_type instead.", DeprecationWarning)
|
|
output_type = "pil" if kwargs["return_pil"] else "np"
|
|
if type(output_type) is bool:
|
|
warnings.warn("Passing `True` or `False` to `output_type` is deprecated and will raise an error in future versions")
|
|
if output_type:
|
|
output_type = "pil"
|
|
|
|
if not isinstance(input_image, np.ndarray):
|
|
input_image = np.array(input_image, dtype=np.uint8)
|
|
|
|
input_image = HWC3(input_image)
|
|
input_image = resize_image(input_image, detect_resolution)
|
|
|
|
assert input_image.ndim == 3
|
|
img = input_image
|
|
img_output = np.zeros_like(img)
|
|
try:
|
|
lines = pred_lines(img, self.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:
|
|
pass
|
|
|
|
detected_map = img_output[:, :, 0]
|
|
detected_map = HWC3(detected_map)
|
|
|
|
img = resize_image(input_image, image_resolution)
|
|
H, W, C = img.shape
|
|
|
|
detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)
|
|
|
|
if output_type == "pil":
|
|
detected_map = Image.fromarray(detected_map)
|
|
|
|
return detected_map
|