app.py

import gradio as gr
import gradio as gr
from pathlib import Path
from PIL import Image
import numpy as np
import torch
from torch.autograd import Variable
from torchvision import transforms
import torch.nn.functional as F
import matplotlib.pyplot as plt
import warnings
from zipfile import ZipFile

warnings.filterwarnings("ignore")


# project imports
from data_loader_cache import normalize, im_reader, im_preprocess 
from models import *

#Helpers
device = 'cuda' if torch.cuda.is_available() else 'cpu'

    
class GOSNormalize(object):
    '''
    Normalize the Image using torch.transforms
    '''
    def __init__(self, mean=[0.485,0.456,0.406], std=[0.229,0.224,0.225]):
        self.mean = mean
        self.std = std

    def __call__(self,image):
        image = normalize(image,self.mean,self.std)
        return image


transform =  transforms.Compose([GOSNormalize([0.5,0.5,0.5],[1.0,1.0,1.0])])

def load_image(im_path, hypar):
    im = im_reader(im_path)
    im, im_shp = im_preprocess(im, hypar["cache_size"])
    im = torch.divide(im,255.0)
    shape = torch.from_numpy(np.array(im_shp))
    return transform(im).unsqueeze(0), shape.unsqueeze(0) # make a batch of image, shape


def build_model(hypar,device):
    net = hypar["model"]#GOSNETINC(3,1)

    # convert to half precision
    if(hypar["model_digit"]=="half"):
        net.half()
        for layer in net.modules():
            if isinstance(layer, nn.BatchNorm2d):
                layer.float()

    net.to(device)

    if(hypar["restore_model"]!=""):
        net.load_state_dict(torch.load(hypar["model_path"]+"/"+hypar["restore_model"], map_location=device))
        net.to(device)
    net.eval()  
    return net

    
def predict(net,  inputs_val, shapes_val, hypar, device):
    '''
    Given an Image, predict the mask
    '''
    net.eval()

    if(hypar["model_digit"]=="full"):
        inputs_val = inputs_val.type(torch.FloatTensor)
    else:
        inputs_val = inputs_val.type(torch.HalfTensor)

  
    inputs_val_v = Variable(inputs_val, requires_grad=False).to(device) # wrap inputs in Variable
   
    ds_val = net(inputs_val_v)[0] # list of 6 results

    pred_val = ds_val[0][0,:,:,:] # B x 1 x H x W    # we want the first one which is the most accurate prediction

    ## recover the prediction spatial size to the orignal image size
    pred_val = torch.squeeze(F.upsample(torch.unsqueeze(pred_val,0),(shapes_val[0][0],shapes_val[0][1]),mode='bilinear'))

    ma = torch.max(pred_val)
    mi = torch.min(pred_val)
    pred_val = (pred_val-mi)/(ma-mi) # max = 1

    if device == 'cuda': torch.cuda.empty_cache()
    return (pred_val.detach().cpu().numpy()*255).astype(np.uint8) # it is the mask we need
    
# Set Parameters
hypar = {} # paramters for inferencing


hypar["model_path"] ="./saved_models" ## load trained weights from this path
hypar["restore_model"] = "isnet.pth" ## name of the to-be-loaded weights
hypar["interm_sup"] = False ## indicate if activate intermediate feature supervision

##  choose floating point accuracy --
hypar["model_digit"] = "full" ## indicates "half" or "full" accuracy of float number
hypar["seed"] = 0

hypar["cache_size"] = [1024, 1024] ## cached input spatial resolution, can be configured into different size

## data augmentation parameters ---
hypar["input_size"] = [1024, 1024] ## mdoel input spatial size, usually use the same value hypar["cache_size"], which means we don't further resize the images
hypar["crop_size"] = [1024, 1024] ## random crop size from the input, it is usually set as smaller than hypar["cache_size"], e.g., [920,920] for data augmentation

hypar["model"] = ISNetDIS()

 # Build Model
net = build_model(hypar, device)


def inference(image_path):
  
  image_tensor, orig_size = load_image(image_path, hypar) 
  mask = predict(net, image_tensor, orig_size, hypar, device)
  
  pil_mask = Image.fromarray(mask).convert('L')
  im_rgb = Image.open(image_path).convert("RGB")
  
  im_rgba = im_rgb.copy()
  im_rgba.putalpha(pil_mask)
  file_name = Path(image_path).stem+"_nobg.png"
  file_path = Path(Path(image_path).parent,file_name)
  im_rgba.save(file_path)
  return str(file_path.resolve())

def bw(image_files):
    print(image_files)
    output = []
    for idx, file in enumerate(image_files):
        print(file.name)
        img = Image.open(file.name)
        img = img.convert("L")
        output.append(img)
    print(output)
    return output

def bw_single(image_file):
    img = Image.open(image_file)
    img = img.convert("L")
    return img

def batch(image_files):
    output = []
    for idx, file in enumerate(image_files):
        file = inference(file.name)
        output.append(file)
    
    with ZipFile("tmp.zip", "w") as zipObj:
        for idx, file in enumerate(output):
            zipObj.write(file, file.split("/")[-1])
    return output,"tmp.zip"

with gr.Blocks() as iface:
    gr.Markdown("# Remove Background")
    with gr.Tab("Single Image"):
        with gr.Row():
            with gr.Column():
                image = gr.Image(type='filepath')
            with gr.Column():
                image_output = gr.Image(interactive=False)
        with gr.Row():
            with gr.Column():
                single_removebg = gr.Button("Remove Bg")
            with gr.Column():
                single_clear = gr.Button("Clear")

    
    with gr.Tab("Batch"):
        with gr.Row():
            with gr.Column():
                images = gr.File(file_count="multiple", file_types=["image"])
            with gr.Column():
                gallery = gr.Gallery()
                file_list = gr.Files(interactive=False)

        with gr.Row():
            with gr.Column():
                batch_removebg = gr.Button("Batch Process")
            with gr.Column():
                batch_clear = gr.Button("Clear")
    #Events
    single_removebg.click(inference, inputs=image, outputs=image_output)
    batch_removebg.click(batch, inputs=images, outputs=[gallery,file_list])
    single_clear.click(lambda: None, None, image, queue=False)
    batch_clear.click(lambda: None, None, images, queue=False)

iface.launch()