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import os
import cv2
import numpy as np
import torch
from einops import rearrange
from huggingface_hub import hf_hub_download
from PIL import Image
from modules import devices
from modules.shared import opts
from modules.control.util import HWC3, nms, resize_image, safe_step
from .pidi_model import pidinet
class PidiNetDetector:
def __init__(self, model):
self.model = model
@classmethod
def from_pretrained(cls, pretrained_model_or_path, filename=None, cache_dir=None):
filename = filename or "table5_pidinet.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 = pidinet()
model.load_state_dict({k.replace('module.', ''): v for k, v in torch.load(model_path)['state_dict'].items()})
model.eval()
return cls(model)
def to(self, device):
self.model.to(device)
return self
def __call__(self, input_image, detect_resolution=512, image_resolution=512, safe=False, output_type="pil", scribble=False, apply_filter=False, **kwargs):
self.model.to(devices.device)
device = next(iter(self.model.parameters())).device
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
input_image = input_image[:, :, ::-1].copy()
image_pidi = torch.from_numpy(input_image).float().to(device)
image_pidi = image_pidi / 255.0
image_pidi = rearrange(image_pidi, 'h w c -> 1 c h w')
edge = self.model(image_pidi)[-1]
edge = edge.cpu().numpy()
if apply_filter:
edge = edge > 0.5
if safe:
edge = safe_step(edge)
edge = (edge * 255.0).clip(0, 255).astype(np.uint8)
detected_map = edge[0, 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 scribble:
detected_map = nms(detected_map, 127, 3.0)
detected_map = cv2.GaussianBlur(detected_map, (0, 0), 3.0)
detected_map[detected_map > 4] = 255
detected_map[detected_map < 255] = 0
if opts.control_move_processor:
self.model.to('cpu')
if output_type == "pil":
detected_map = Image.fromarray(detected_map)
return detected_map
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