app.py

import numpy as np

import gradio as gr
import os
os.system("pip3 install torch")
os.system("pip3 install collections")
os.system("pip3 install torchvision")
os.system("pip3 install einops")
#os.system("pip3 install argparse")

from PIL import Image
import torch
from torchvision import transforms
from model_video import build_model
import numpy as np
import collections
#import argparse

net = build_model('cpu').to('cpu')
    #net=torch.nn.DataParallel(net)
model_path = 'image_best.pth'
print(model_path)
weight=torch.load(model_path,map_location=torch.device('cpu'))
#print(type(weight))
new_dict=collections.OrderedDict()
for k in weight.keys():
  new_dict[k[len('module.'):]]=weight[k]
net.load_state_dict(new_dict)
net.eval()
net = net.to('cpu')
def test(gpu_id, net, img_list, group_size, img_size):
    print('test')
    device='cpu'
    
    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)
        print(pred_mask.shape)
        result = [Image.fromarray((pred_mask[i].detach().squeeze().unsqueeze(2).repeat(1,1,3) * 255).numpy().astype(np.uint8)) 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
def sepia(img1,img2,img3,img4,img5):
  print('sepia')
  '''ans=[]
  print(len(input_imgs))
  for input_img in input_imgs:
    sepia_filter = np.array(
        [[0.393, 0.769, 0.189], [0.349, 0.686, 0.168], [0.272, 0.534, 0.131]]
    )
    sepia_img = input_img.dot(sepia_filter.T)
    sepia_img /= sepia_img.max()
    ans.append(input_img)'''
  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('cpu',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)
  return img1,img2,img3,img4,img5

#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.launch(debug=True)