import gradio as gr
import cv2
import gradio as gr
import os
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
import tempfile
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("# Removedor de Fundo💚")
gr.HTML("Usa DIS para remover o plano de fundo")
with gr.Tab("Uma Imagem"):
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("Remover Fundo")
with gr.Column():
single_clear = gr.Button("Limpar")
with gr.Tab("Lote de Imagens"):
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("Processar Lote")
with gr.Column():
batch_clear = gr.Button("Limpar")
with gr.Tab("Sobre"):
with gr.Row():
gr.HTML("Usa Estamos testando esse código usando o DIS para remover o plano de fundo. Envie sua imagem para que o nosso removedor faça sua parte.")
#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()