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# Based on https://github.com/xuebinqin/DIS/blob/main/Colab_Demo.ipynb
import os
from huggingface_hub import hf_hub_download
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
device = None
ISNetDIS = None
normalize = None
im_preprocess = None
hypar = None
net = None
def init():
global device, ISNetDIS, normalize, im_preprocess, hypar, net
print("Initializing segmenter...")
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 models
import data_loader_cache
device = 'cuda' if torch.cuda.is_available() else '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)
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_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
return transform(im).unsqueeze(0), shape.unsqueeze(0)
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
# 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 segment(image):
image_tensor, orig_size = load_image(image, hypar)
mask = predict(net, image_tensor, orig_size, hypar, device)
mask = Image.fromarray(mask).convert('L')
im_rgb = image.convert("RGB")
cropped = im_rgb.copy()
cropped.putalpha(mask)
return [cropped, mask]
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