Delete inference_utils.py

inference_utils.py

@@ -1,44 +0,0 @@
-import cv2
-import torch
-import numpy as np
-from PIL import Image
-from torchvision import transforms
-from models.deepcrack_model import DeepCrackModel
-from cv2_utils import getContours
-
-def create_model(opt, cp_path='pretrained_net_G.pth'):
-    model = DeepCrackModel(opt)
-    checkpoint = torch.load(cp_path, map_location="cpu")
-
-    if hasattr(model.netG, "module"):
-        model.netG.module.load_state_dict(checkpoint, strict=False)
-    else:
-        model.netG.load_state_dict(checkpoint, strict=False)
-
-    model.eval()
-    return model
-
-
-def preprocess(img: Image.Image):
-    transform = transforms.Compose([
-        transforms.Resize((256, 256)),
-        transforms.ToTensor(),
-        transforms.Normalize((0.5,), (0.5,))
-    ])
-    return transform(img).unsqueeze(0)
-
-
-def inference(model, img: Image.Image):
-    tensor = preprocess(img)
-
-    model.set_input({"image": tensor, "label": torch.zeros_like(tensor), "A_paths": ""})
-    model.test()
-
-    visuals = model.get_current_visuals()
-    fused = visuals["fused"].detach().cpu().numpy()[0]
-    confidence = float(fused.max())
-
-    fused_img = (fused * 255).astype("uint8")
-    contour_img = getContours(fused_img, np.array(img), img.size[1], img.size[0], "px", confidence)
-
-    return contour_img, confidence