#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='cuda:0') 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='cuda:0') 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.cuda()) 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.cuda()) 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")