app.py

import gradio as gr
from PIL import Image, ImageFilter
import numpy as np
import torch
from torch.autograd import Variable
from torchvision import transforms
import torch.nn.functional as F
import gdown
import os

os.system("git clone https://github.com/xuebinqin/DIS")
os.system("mv DIS/IS-Net/* .")

# 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'

# Download official weights
if not os.path.exists("saved_models"):
    os.mkdir("saved_models")
    MODEL_PATH_URL = "https://drive.google.com/uc?id=1KyMpRjewZdyYfxHPYcd-ZbanIXtin0Sn"
    gdown.download(MODEL_PATH_URL, "saved_models/isnet.pth", use_cookies=False)
    
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 infer_mask(image: Image):
  image_path = image
  
  image_tensor, orig_size = load_image(image_path, hypar) 
  mask = predict(net, image_tensor, orig_size, hypar, device)
  
  return Image.fromarray(mask).convert("L")

def blur(image_set: list, blur_amount: int):
    blurred_image = image_set[0].filter(ImageFilter.GaussianBlur(blur_amount))

    return Image.composite(image_set[0], blurred_image, image_set[1])


with gr.Blocks() as interface:
    default_im = Image.open("newman.jpg").convert("RGB")
    default_mask = Image.open("newman_mask.jpg").convert("RGB")
    examples_list = [os.path.join(os.path.dirname(__file__), "newman.jpg"),
                os.path.join(os.path.dirname(__file__), "abbey.jpg"),
                os.path.join(os.path.dirname(__file__), "julia.jpg")
                ]

    current_images = gr.State([default_im, default_mask])
    mask_toggle = gr.State(False)

    gr.Markdown(
        """
        ### Intelligent Photo Blur Using Dichotomous Image Segmentation

        This app leverages the machine learning engine built by Xuebin Qin ([https://github.com/xuebinqin/DIS](https://github.com/xuebinqin/DIS)) to mask the prominent subject within a photograph.
        The mask is used to keep the subject in clear focus while an adjustable slider is available to interactively blur the background.
        To use, upload a photo and press the run button. You can adjust the level of blur through the slider and view the mask using the "Show Generated Mask" button.
        """
    )
    with gr.Row():
        with gr.Column():
            input_image = gr.Image(value=default_im, type='filepath')
            run_button = gr.Button()
            gr.Examples(inputs=input_image, examples=examples_list)
        with gr.Column():
            output_image = gr.Image()
            blur_slider = gr.Slider(0, 16, 5, step=1, label="Blur Amount")
            mask_button = gr.Button(value="Show Generated Mask")
            mask_image = gr.Image(value=default_mask, visible=False)

        def run(image: Image, current_images: gr.State):
            im_rgb = Image.open(image).convert("RGB")
            mask = infer_mask(image)

            return (
                blur([im_rgb, mask], 5),
                mask,
                [im_rgb, mask]
            )
        
        def reset_slider():
            return gr.update(value=5)

        def show_mask(mask_toggle: gr.State):
            if mask_toggle == True:
                return gr.update(visible=False)
            else:
                return gr.update(visible=True)

        def toggle_mask(mask_toggle: gr.State):
            if mask_toggle == True:
                return False
            else:
                return True

        run_button.click(run, [input_image, current_images], [output_image, mask_image, current_images])
        run_button.click(reset_slider, outputs=blur_slider)
        blur_slider.change(blur, [current_images, blur_slider], output_image, show_progress=False)
        mask_button.click(show_mask, inputs=mask_toggle, outputs=mask_image)
        mask_button.click(toggle_mask, inputs=mask_toggle, outputs=mask_toggle)

interface.launch()