Spaces:
Configuration error
Configuration error
File size: 2,322 Bytes
0034848 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 |
import os
import warnings
import cv2
import numpy as np
import torch
from einops import rearrange
from PIL import Image
from custom_controlnet_aux.util import HWC3, nms, resize_image_with_pad, safe_step,common_input_validate, custom_hf_download, HF_MODEL_NAME
from .model import pidinet
class PidiNetDetector:
def __init__(self, netNetwork):
self.netNetwork = netNetwork
self.device = "cpu"
@classmethod
def from_pretrained(cls, pretrained_model_or_path=HF_MODEL_NAME, filename="table5_pidinet.pth"):
model_path = custom_hf_download(pretrained_model_or_path, filename)
netNetwork = pidinet()
netNetwork.load_state_dict({k.replace('module.', ''): v for k, v in torch.load(model_path)['state_dict'].items()})
netNetwork.eval()
return cls(netNetwork)
def to(self, device):
self.netNetwork.to(device)
self.device = device
return self
def __call__(self, input_image, detect_resolution=512, safe=False, output_type="pil", scribble=False, apply_filter=False, upscale_method="INTER_CUBIC", **kwargs):
input_image, output_type = common_input_validate(input_image, output_type, **kwargs)
detected_map, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method)
detected_map = detected_map[:, :, ::-1].copy()
with torch.no_grad():
image_pidi = torch.from_numpy(detected_map).float().to(self.device)
image_pidi = image_pidi / 255.0
image_pidi = rearrange(image_pidi, 'h w c -> 1 c h w')
edge = self.netNetwork(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]
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
detected_map = HWC3(remove_pad(detected_map))
if output_type == "pil":
detected_map = Image.fromarray(detected_map)
return detected_map
|