Delete demo.py

demo.py

@@ -1,178 +0,0 @@
-#origin
-
-from seg import U2NETP
-from GeoTr import GeoTr
-from IllTr import IllTr
-from inference_ill import rec_ill
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import skimage.io as io
-import numpy as np
-import cv2
-import glob
-import os
-from PIL import Image
-import argparse
-import warnings
-warnings.filterwarnings('ignore')
-
-
-
-
-
-import gradio as gr
-
-
-class GeoTr_Seg(nn.Module):
-    def __init__(self):
-        super(GeoTr_Seg, self).__init__()
-        self.msk = U2NETP(3, 1)
-        self.GeoTr = GeoTr(num_attn_layers=6)
-        
-    def forward(self, x):
-        msk, _1,_2,_3,_4,_5,_6 = self.msk(x)
-        msk = (msk > 0.5).float()
-        x = msk * x
-
-        bm = self.GeoTr(x)
-        bm = (2 * (bm / 286.8) - 1) * 0.99
-        
-        return bm
-        
-
-def reload_model(model, path=""):
-    if not bool(path):
-        return model
-    else:
-        model_dict = model.state_dict()
-        pretrained_dict = torch.load(path, map_location='cpu')
-        #print(len(pretrained_dict.keys()))
-        pretrained_dict = {k[7:]: v for k, v in pretrained_dict.items() if k[7:] in model_dict}
-        #print(len(pretrained_dict.keys()))
-        model_dict.update(pretrained_dict)
-        model.load_state_dict(model_dict)
-
-        return model
-        
-
-def reload_segmodel(model, path=""):
-    if not bool(path):
-        return model
-    else:
-        model_dict = model.state_dict()
-        pretrained_dict = torch.load(path, map_location='cpu')
-        #print(len(pretrained_dict.keys()))
-        pretrained_dict = {k[6:]: v for k, v in pretrained_dict.items() if k[6:] in model_dict}
-        #print(len(pretrained_dict.keys()))
-        model_dict.update(pretrained_dict)
-        model.load_state_dict(model_dict)
-
-        return model
-        
-
-def rec(opt):
-    # print(torch.__version__) # 1.5.1
-    img_list = os.listdir(opt.distorrted_path)  # distorted images list
-
-    if not os.path.exists(opt.gsave_path):  # create save path
-        os.mkdir(opt.gsave_path)
-    if not os.path.exists(opt.isave_path):  # create save path
-        os.mkdir(opt.isave_path)
-    
-    GeoTr_Seg_model = GeoTr_Seg()#.cuda()
-    # reload segmentation model
-    reload_segmodel(GeoTr_Seg_model.msk, opt.Seg_path)
-    # reload geometric unwarping model
-    reload_model(GeoTr_Seg_model.GeoTr, opt.GeoTr_path)
-    
-    IllTr_model = IllTr()#.cuda()
-    # reload illumination rectification model
-    reload_model(IllTr_model, opt.IllTr_path)
-    
-    # To eval mode
-    GeoTr_Seg_model.eval()
-    IllTr_model.eval()
-  
-    for img_path in img_list:
-        name = img_path.split('.')[-2]  # image name
-
-        img_path = opt.distorrted_path + img_path  # read image and to tensor
-        im_ori = np.array(Image.open(img_path))[:, :, :3] / 255. 
-        h, w, _ = im_ori.shape
-        im = cv2.resize(im_ori, (288, 288))
-        im = im.transpose(2, 0, 1)
-        im = torch.from_numpy(im).float().unsqueeze(0)
-        
-        with torch.no_grad():
-            # geometric unwarping
-            bm = GeoTr_Seg_model(im)
-            bm = bm.cpu()
-            bm0 = cv2.resize(bm[0, 0].numpy(), (w, h))  # x flow
-            bm1 = cv2.resize(bm[0, 1].numpy(), (w, h))  # y flow
-            bm0 = cv2.blur(bm0, (3, 3))
-            bm1 = cv2.blur(bm1, (3, 3))
-            lbl = torch.from_numpy(np.stack([bm0, bm1], axis=2)).unsqueeze(0)  # h * w * 2
-            
-            out = F.grid_sample(torch.from_numpy(im_ori).permute(2,0,1).unsqueeze(0).float(), lbl, align_corners=True)
-            img_geo = ((out[0]*255).permute(1, 2, 0).numpy())[:,:,::-1].astype(np.uint8)
-            cv2.imwrite(opt.gsave_path + name + '_geo' + '.png', img_geo)  # save
-            
-            # illumination rectification
-            if opt.ill_rec:
-                ill_savep = opt.isave_path + name + '_ill' + '.png'
-                rec_ill(IllTr_model, img_geo, saveRecPath=ill_savep)
-        
-        print('Done: ', img_path)
-        
-
-
-
-
-
-def process_image(input_image):
-    GeoTr_Seg_model = GeoTr_Seg()#.cuda()
-    reload_segmodel(GeoTr_Seg_model.msk, './model_pretrained/seg.pth')
-    reload_model(GeoTr_Seg_model.GeoTr, './model_pretrained/geotr.pth')
-
-    IllTr_model = IllTr()#.cuda()
-    reload_model(IllTr_model, './model_pretrained/illtr.pth')
-
-    GeoTr_Seg_model.eval()
-    IllTr_model.eval()
-
-    im_ori = np.array(input_image)[:, :, :3] / 255.
-    h, w, _ = im_ori.shape
-    im = cv2.resize(im_ori, (288, 288))
-    im = im.transpose(2, 0, 1)
-    im = torch.from_numpy(im).float().unsqueeze(0)
-
-    with torch.no_grad():
-        bm = GeoTr_Seg_model(im)
-        bm = bm.cpu()
-        bm0 = cv2.resize(bm[0, 0].numpy(), (w, h))
-        bm1 = cv2.resize(bm[0, 1].numpy(), (w, h))
-        bm0 = cv2.blur(bm0, (3, 3))
-        bm1 = cv2.blur(bm1, (3, 3))
-        lbl = torch.from_numpy(np.stack([bm0, bm1], axis=2)).unsqueeze(0)
-
-        out = F.grid_sample(torch.from_numpy(im_ori).permute(2, 0, 1).unsqueeze(0).float(), lbl, align_corners=True)
-        img_geo = ((out[0] * 255).permute(1, 2, 0).numpy()).astype(np.uint8)
-        
-        ill_rec=False
-
-        if ill_rec:
-            img_ill = rec_ill(IllTr_model, img_geo)
-            return Image.fromarray(img_ill)
-        else:
-            return Image.fromarray(img_geo)
-
-# Define Gradio interface
-input_image = gr.inputs.Image()
-output_image = gr.outputs.Image(type='pil')
-
-
-iface = gr.Interface(fn=process_image, inputs=input_image, outputs=output_image, title="Image Correction")
-iface.launch(server_port=1234, server_name="0.0.0.0")
-