File size: 4,571 Bytes
d493482 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 |
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
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
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/* .")
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 == "cpu":
torch.cpu.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)
def remove_background(image):
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
inputs = gr.inputs.Image()
outputs = gr.outputs.Image(type="pil")
interface = gr.Interface(
fn=remove_background,
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)
|