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import gradio as gr
import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from huggingface_hub import hf_hub_download
from torch.autograd import Variable
from PIL import Image
from transformers import pipeline
captioner = pipeline(
"image-to-text",
model="Salesforce/blip-image-captioning-base",
prompt="The main subject of this picture is a"
)
def removeBackground(image):
image = Image.fromarray(image).convert("RGB")
caption = captioner(image, max_new_tokens=20)[0]["generated_text"].lower().replace("The main subject of this picture is a".lower(), "").strip()
if not os.path.exists("saved_models"):
os.mkdir("saved_models")
os.mkdir("git")
os.system("git clone https://github.com/xuebinqin/DIS git/xuebinqin/DIS")
hf_hub_download(
repo_id="NimaBoscarino/IS-Net_DIS-general-use",
filename="isnet-general-use.pth",
local_dir="saved_models",
)
os.system("rm -r git/xuebinqin/DIS/IS-Net/__pycache__")
os.system("mv git/xuebinqin/DIS/IS-Net/* .")
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
import data_loader_cache
import models
device = "cpu"
ISNetDIS = models.ISNetDIS
normalize = data_loader_cache.normalize
im_preprocess = data_loader_cache.im_preprocess
# Set Parameters
hypar = {} # paramters for inferencing
# load trained weights from this path
hypar["model_path"] = "./saved_models"
# name of the to-be-loaded weights
hypar["restore_model"] = "isnet-general-use.pth"
# indicate if activate intermediate feature supervision
hypar["interm_sup"] = False
# choose floating point accuracy --
# indicates "half" or "full" accuracy of float number
hypar["model_digit"] = "full"
hypar["seed"] = 0
# cached input spatial resolution, can be configured into different size
hypar["cache_size"] = [1024, 1024]
# data augmentation parameters ---
# mdoel input spatial size, usually use the same value hypar["cache_size"], which means we don't further resize the images
hypar["input_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["crop_size"] = [1024, 1024]
hypar["model"] = ISNetDIS()
# Build Model
net = build_model(hypar, device)
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
# B x 1 x H x W # we want the first one which is the most accurate prediction
pred_val = ds_val[0][0, :, :, :]
# 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()
# it is the mask we need
return (pred_val.detach().cpu().numpy() * 255).astype(np.uint8)
def load_image(im_pil, hypar):
im = np.array(im_pil)
im, im_shp = im_preprocess(im, hypar["cache_size"])
im = torch.divide(im, 255.0)
shape = torch.from_numpy(np.array(im_shp))
# make a batch of image, shape
aa = normalize(im, [0.5, 0.5, 0.5], [1.0, 1.0, 1.0])
return aa.unsqueeze(0), shape.unsqueeze(0)
image_tensor, orig_size = load_image(image, hypar)
mask = predict(net, image_tensor, orig_size, hypar, "cpu")
mask = Image.fromarray(mask).convert("L")
im_rgb = image.convert("RGB")
cropped = im_rgb.copy()
cropped.putalpha(mask)
return cropped, caption
inputs = gr.inputs.Image()
outputs = [gr.outputs.Image(type="pil"), "text"]
interface = gr.Interface(
fn=removeBackground,
inputs=inputs,
outputs=outputs,
title="Remove Background",
description="This App removes the background from an image",
examples=[
"examples/input/1.jpeg",
"examples/input/2.jpeg",
"examples/input/3.jpeg",
],
cache_examples=True,
)
interface.launch(enable_queue=True)
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