mirror of
https://github.com/vladmandic/sdnext.git
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63 lines
2.5 KiB
Python
63 lines
2.5 KiB
Python
import os
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import cv2
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import numpy as np
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import torch
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from huggingface_hub import hf_hub_download
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from PIL import Image
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from modules import devices
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from modules.shared import opts
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from modules.control.util import HWC3, resize_image
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from .models.mbv2_mlsd_large import MobileV2_MLSD_Large
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from .utils import pred_lines
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class MLSDdetector:
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def __init__(self, model):
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self.model = model
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@classmethod
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def from_pretrained(cls, pretrained_model_or_path, filename=None, cache_dir=None, local_files_only=False):
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if pretrained_model_or_path == "lllyasviel/ControlNet":
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filename = filename or "annotator/ckpts/mlsd_large_512_fp32.pth"
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else:
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filename = filename or "mlsd_large_512_fp32.pth"
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if os.path.isdir(pretrained_model_or_path):
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model_path = os.path.join(pretrained_model_or_path, filename)
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else:
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model_path = hf_hub_download(pretrained_model_or_path, filename, cache_dir=cache_dir, local_files_only=local_files_only)
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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 cls(model)
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def to(self, device):
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self.model.to(device)
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return self
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def __call__(self, input_image, thr_v=0.1, thr_d=0.1, detect_resolution=512, image_resolution=512, output_type="pil", **kwargs):
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self.model.to(devices.device)
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if not isinstance(input_image, np.ndarray):
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input_image = np.array(input_image, dtype=np.uint8)
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input_image = HWC3(input_image)
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input_image = resize_image(input_image, detect_resolution)
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assert input_image.ndim == 3
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img = input_image
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img_output = np.zeros_like(img)
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try:
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lines = pred_lines(img, self.model, [img.shape[0], img.shape[1]], thr_v, thr_d)
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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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except Exception:
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pass
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detected_map = img_output[:, :, 0]
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detected_map = HWC3(detected_map)
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img = resize_image(input_image, image_resolution)
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H, W, _C = img.shape
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detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)
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if output_type == "pil":
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detected_map = Image.fromarray(detected_map)
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if opts.control_move_processor:
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self.model.to('cpu')
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return detected_map
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