init

app.py

@@ -6,15 +6,58 @@ import sys
 sys.path.insert(0, 'U-2-Net')
 
 from skimage import io, transform
+import torch
+import torchvision
+from torch.autograd import Variable
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.utils.data import Dataset, DataLoader
+from torchvision import transforms#, utils
+# import torch.optim as optim
 
 import numpy as np
 from PIL import Image
+import glob
 
+from data_loader import RescaleT
+from data_loader import ToTensor
+from data_loader import ToTensorLab
+from data_loader import SalObjDataset
 
+from model import U2NET # full size version 173.6 MB
+from model import U2NETP # small version u2net 4.7 MB
 
 from modnet import ModNet
 import huggingface_hub
 
+# normalize the predicted SOD probability map
+def normPRED(d):
+    ma = torch.max(d)
+    mi = torch.min(d)
+
+    dn = (d-mi)/(ma-mi)
+
+    return dn
+def save_output(image_name,pred,d_dir):
+    predict = pred
+    predict = predict.squeeze()
+    predict_np = predict.cpu().data.numpy()
+
+    im = Image.fromarray(predict_np*255).convert('RGB')
+    img_name = image_name.split(os.sep)[-1]
+    image = io.imread(image_name)
+    imo = im.resize((image.shape[1],image.shape[0]),resample=Image.BILINEAR)
+
+    pb_np = np.array(imo)
+
+    aaa = img_name.split(".")
+    bbb = aaa[0:-1]
+    imidx = bbb[0]
+    for i in range(1,len(bbb)):
+        imidx = imidx + "." + bbb[i]
+
+    imo.save(d_dir+'/'+imidx+'.png')
+    return d_dir+'/'+imidx+'.png'
 
 
 
@@ -23,15 +66,76 @@ modnet_path = huggingface_hub.hf_hub_download('hylee/apdrawing_model',
                                                       force_filename='modnet.onnx')
 modnet = ModNet(modnet_path)
 
- 
+ # --------- 1. get image path and name ---------
+model_name='u2net_portrait'#u2netp
+
+
+image_dir = 'portrait_im'
+prediction_dir = 'portrait_results'
+if(not os.path.exists(prediction_dir)):
+    os.mkdir(prediction_dir)
+
+model_dir = os.path.join(os.path.abspath(os.path.dirname(__file__)), 'U-2-Net/saved_models/u2net_portrait/u2net_portrait.pth')
+
+
+# --------- 3. model define ---------
+
+print("...load U2NET---173.6 MB")
+net = U2NET(3,1)
+
+net.load_state_dict(torch.load(model_dir, map_location='cpu'))
+# if torch.cuda.is_available():
+#     net.cuda()
+net.eval()
+
+
 def process(im):
-    image = modnet.segment(im.name)
-    im_path = os.path.abspath(os.path.basename(im.name))
-    Image.fromarray(np.uint8(image)).save(im_path)
+    #image = modnet.segment(im.name)
+    #im_path = os.path.abspath(os.path.basename(im.name))
+    #Image.fromarray(np.uint8(image)).save(im_path)
+
+    img_name_list = [im.name]
+    print("Number of images: ", len(img_name_list))
+    # --------- 2. dataloader ---------
+    # 1. dataloader
+    test_salobj_dataset = SalObjDataset(img_name_list=img_name_list,
+                                        lbl_name_list=[],
+                                        transform=transforms.Compose([RescaleT(512),
+                                                                      ToTensorLab(flag=0)])
+                                        )
+    test_salobj_dataloader = DataLoader(test_salobj_dataset,
+                                        batch_size=1,
+                                        shuffle=False,
+                                        num_workers=1)
+
+    results = []
+    # --------- 4. inference for each image ---------
+    for i_test, data_test in enumerate(test_salobj_dataloader):
+
+        print("inferencing:", img_name_list[i_test].split(os.sep)[-1])
+
+        inputs_test = data_test['image']
+        inputs_test = inputs_test.type(torch.FloatTensor)
+
+        # if torch.cuda.is_available():
+        #     inputs_test = Variable(inputs_test.cuda())
+        # else:
+        inputs_test = Variable(inputs_test)
+
+        d1, d2, d3, d4, d5, d6, d7 = net(inputs_test)
+
+        # normalization
+        pred = 1.0 - d1[:, 0, :, :]
+        pred = normPRED(pred)
+
+        # save results to test_results folder
+        results.append(save_output(img_name_list[i_test], pred, prediction_dir))
+
+        del d1, d2, d3, d4, d5, d6, d7
 
-    
+    print(results)
 
-    return Image.open(im_path)
+    return Image.open(results[0])
         
 title = "U-2-Net"
 description = "Gradio demo for U-2-Net, https://github.com/xuebinqin/U-2-Net"