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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")
os.system("pip install opencv-python")
#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)
name='bike'+opt.replace(':','_')+'.png'
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","circle"], 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()
exp=gr.Examples(["bike.png","img2.png"],src_img1)
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) |