mirror of
https://github.com/vladmandic/sdnext.git
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68 lines
2.7 KiB
Python
68 lines
2.7 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 einops import rearrange
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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, nms, resize_image, safe_step
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from .pidi_model import pidinet
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class PidiNetDetector:
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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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filename = filename or "table5_pidinet.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 = pidinet()
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model.load_state_dict({k.replace('module.', ''): v for k, v in torch.load(model_path)['state_dict'].items()})
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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, detect_resolution=512, image_resolution=512, safe=False, output_type="pil", scribble=False, apply_filter=False, **kwargs):
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self.model.to(devices.device)
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device = next(iter(self.model.parameters())).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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input_image = input_image[:, :, ::-1].copy()
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image_pidi = torch.from_numpy(input_image).float().to(device)
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image_pidi = image_pidi / 255.0
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image_pidi = rearrange(image_pidi, 'h w c -> 1 c h w')
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edge = self.model(image_pidi)[-1]
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edge = edge.cpu().numpy()
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if apply_filter:
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edge = edge > 0.5
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if safe:
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edge = safe_step(edge)
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edge = (edge * 255.0).clip(0, 255).astype(np.uint8)
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detected_map = edge[0, 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 scribble:
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detected_map = nms(detected_map, 127, 3.0)
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detected_map = cv2.GaussianBlur(detected_map, (0, 0), 3.0)
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detected_map[detected_map > 4] = 255
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detected_map[detected_map < 255] = 0
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if opts.control_move_processor:
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self.model.to('cpu')
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if output_type == "pil":
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detected_map = Image.fromarray(detected_map)
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return detected_map
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