From 976b5caf62353abd5cf726396c52673c5ab92002 Mon Sep 17 00:00:00 2001
From: doevent <61069711+doevent@users.noreply.github.com>
Date: Wed, 20 Jul 2022 21:30:08 +0300
Subject: [PATCH] Add file
---
README.md | 8 +--
app.py | 155 +++++++++++++++++++++++++++++++++++++++++++++++
requirements.txt | 8 +++
robot.png | Bin 0 -> 817993 bytes
ship.png | Bin 0 -> 834377 bytes
5 files changed, 167 insertions(+), 4 deletions(-)
create mode 100644 app.py
create mode 100644 requirements.txt
create mode 100644 robot.png
create mode 100644 ship.png
diff --git a/README.md b/README.md
index 61c1ec3..b20aa84 100644
--- a/README.md
+++ b/README.md
@@ -1,8 +1,8 @@
---
-title: Dis Background Removal
-emoji: 🦀
-colorFrom: gray
-colorTo: red
+title: DIS Background Removal
+emoji: 🔥 🌠 🏰
+colorFrom: yellow
+colorTo: blue
sdk: gradio
sdk_version: 3.1.0
app_file: app.py
diff --git a/app.py b/app.py
new file mode 100644
index 0000000..f87d211
--- /dev/null
+++ b/app.py
@@ -0,0 +1,155 @@
+import cv2
+import gradio as gr
+import os
+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 gdown
+import matplotlib.pyplot as plt
+import warnings
+warnings.filterwarnings("ignore")
+
+os.system("git clone https://github.com/xuebinqin/DIS")
+os.system("mv DIS/IS-Net/* .")
+
+# 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'
+
+# Download official weights
+if not os.path.exists("saved_models"):
+ os.mkdir("saved_models")
+ MODEL_PATH_URL = "https://drive.google.com/uc?id=1KyMpRjewZdyYfxHPYcd-ZbanIXtin0Sn"
+ gdown.download(MODEL_PATH_URL, "saved_models/isnet.pth", use_cookies=False)
+
+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: Image):
+ image_path = image
+
+ 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).convert("RGB")
+
+ im_rgba = im_rgb.copy()
+ im_rgba.putalpha(pil_mask)
+
+ return [im_rgba, pil_mask]
+
+
+title = "Highly Accurate Dichotomous Image Segmentation"
+description = "This is an unofficial demo for DIS, a model that can remove the background from a given image. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below.
GitHub: https://github.com/xuebinqin/DIS
[](https://twitter.com/DoEvent)
Telegram bot: https://t.me/restoration_photo_bot"
+article = "