Create app.py

app.py

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+import tqdm
+#import fastCNN
+import numpy as np
+
+
+import gradio as gr
+import os
+#os.system("sudo apt-get install nvIDia-cuda-toolkit")
+#os.system("/usr/local/bin/python -m pip install --upgrade pip")
+#os.system("pip install argparse")
+import pydensecrf.densecrf as dcrf
+from PIL import Image
+import torch
+import torch.nn.functional as F
+from torchvision import transforms
+import numpy as np
+import collections
+import cv2
+
+
+#import argparse
+device='cpu'
+
+def test(gpu_id, net, img_list, group_size, img_size):
+    print('test')
+    #device=device
+    hl,wl=[_.shape[0] for _ in img_list],[_.shape[1] for _ in img_list]
+    img_transform = transforms.Compose([transforms.Resize((img_size, img_size)), transforms.ToTensor(),
+                    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
+    img_transform_gray = transforms.Compose([transforms.Resize((img_size, img_size)), transforms.ToTensor(),
+                                             transforms.Normalize(mean=[0.449], std=[0.226])])
+    with torch.no_grad():
+        
+        group_img=torch.rand(5,3,224,224)
+        for i in range(5):
+          group_img[i]=img_transform(Image.fromarray(img_list[i]))
+        _,pred_mask=net(group_img*1)
+        pred_mask=(pred_mask.detach().squeeze()*255)#.numpy().astype(np.uint8)
+        #pred_mask=[F.interpolate(pred_mask[i].reshape(1,1,pred_mask[i].shape[-2],pred_mask[i].shape[-1]),size=(size,size),mode='bilinear').squeeze().numpy().astype(np.uint8) for i in range(5)]
+        img_resize=[((group_img[i]-group_img[i].min())/(group_img[i].max()-group_img[i].min())*255).permute(1,2,0).contiguous().numpy().astype(np.uint8) 
+                    for i in range(5)]
+        pred_mask=[crf_refine(img_resize[i],pred_mask[i].numpy().astype(np.uint8)) for i in range(5)]
+        #for i in range(5):
+        #    print(img_list[i].shape,pred_mask[i].shape)
+        #pred_mask=[crf_refine(img_list[i],pred_mask[i]) for i in range(5)]
+        print(pred_mask[0].shape)
+        white=(torch.ones(2,pred_mask[0].shape[1],3)*255).long()
+        result = [torch.cat([torch.from_numpy(img_resize[i]),white,torch.from_numpy(pred_mask[i]).unsqueeze(2).repeat(1,1,3)],dim=0).numpy() for i in range(5)]
+        #w, h = 224,224#Image.open(image_list[i][j]).size
+        #result = result.resize((w, h), Image.BILINEAR)
+        #result.convert('L').save('0.png')
+        print('done')
+        return result
+        
+img_lst=[(torch.rand(352,352,3)*255).numpy().astype(np.uint8) for i in range(5)]
+outputpath1='img2.png'
+outputpath2='img2.png'
+outputpath3='img2.png'
+def sepia(opt,img1):
+  
+  #img_list=[img1,img2,img3,img4,img5]
+  #h_list,w_list=[_.shape[0] for _ in img_list],[_.shape[1] for _ in img_list]
+  #print(type(img1))
+  #print(img1.shape)
+  #result_list=test(device,net,img_list,5,224)
+  #result_list=[result_list[i].resize((w_list[i], h_list[i]), Image.BILINEAR) for i in range(5)]
+  #img1,img2,img3,img4,img5=result_list#test('cpu',net,img_list,5,224)
+  #white=(torch.ones(img1.shape[0],2,3)*255).numpy().astype(np.uint8)
+  
+  output=cv2.imread('bike'+opt.replace(':','_')+'.png')
+  output=cv2.resize(output,(output.shape[1]*256//output.shape[0],256))
+  return output[:,:,::-1]#np.concatenate([img1,white,img2,white,img3,white,img4,white,img5],axis=1)
+with gr.Blocks() as demo:    
+    gr.Markdown("image cropping")
+    #with gr.Tab("Component test"):
+    with gr.Row():
+            with gr.Column(): 
+                #slider1 = gr.Slider(2, 20, value=2, label="Count", info="Choose betwen 2 and 20") 
+
+                #drop1 = gr.Dropdown(["cat", "dog", "bird"], label="Animal", info="Will add more animals later!")
+
+                #checklist1 = gr.CheckboxGroup(["4:3", "3:4", "16:9"], label="Shape", info="The shape of cropped image") 
+                radio2 = gr.Radio(["9:16", "3:4","1:1","4:3", "16:9"], value="3:4",label="Shape", info="The shape of cropped image")
+                #radio1 = gr.Radio(["park", "zoo", "road"], label="Location", info="Where did they go?")
+                src_img1 = gr.Image()
+                bottom1 = gr.Button(label="cropping component")
+
+            out1 = gr.Image()#gr.Textbox()
+
+    bottom1.click(sepia, inputs=[radio2,src_img1], outputs=out1)
+#gr.Image(shape=(224, 2))
+#demo = gr.Interface(sepia, inputs=["image","image","image","image","image"], outputs=["image","image","image","image","image"])#gr.Interface(sepia, gr.Image(shape=(200, 200)), "image")
+#demo = gr.Interface(sepia, inputs=["image"], outputs=["image"])
+demo.launch(debug=True,share=True)