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| #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") | |