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background-replacement / segmenter.py

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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]