Update app.py

app.py

@@ -1,7 +1,178 @@
+#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
 
-def greet(name):
-    return "Hello " + name + "!!"
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+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()
+