# -*- coding: utf-8 -*-
"""DIS.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1MI9utM7GJbz0w_zw1GJNU-ay15SzZHIN
# Clone official repo
"""
# Commented out IPython magic to ensure Python compatibility.
! git clone https://github.com/xuebinqin/DIS
# %cd ./DIS/IS-Net
!pip install gdown
!mkdir ./saved_models
"""# Imports"""
import numpy as np
from PIL import Image
import torch
from torch.autograd import Variable
from torchvision import transforms
import torch.nn.functional as F
import gdown
import os
import requests
import matplotlib.pyplot as plt
from io import BytesIO
# project imports
from data_loader_cache import normalize, im_reader, im_preprocess
from models import *
"""# Helpers"""
drive_link = "https://drive.google.com/uc?id=1XHIzgTzY5BQHw140EDIgwIb53K659ENH"
# Specify the local path and filename
local_path = "/content/DIS/IS-Net/saved_models/isnet.pth"
# Download the file
gdown.download(drive_link, local_path, quiet=False)
device = 'cuda' if torch.cuda.is_available() else 'cpu'
# Download official weights
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):
if im_path.startswith("http"):
im_path = BytesIO(requests.get(im_path).content)
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)
"""# Predict Mask"""
gsheetid = "1n9kk7IHyBzkw5e08wpjjt-Ry5aE_thqGrJ97rMeN-K4"
sheet_name = "sarvm"
gsheet_url = "https://docs.google.com/spreadsheets/d/{}/gviz/tq?tqx=out:csv&sheet={}".format(gsheetid, sheet_name)
gsheet_url
import pandas as pd
df = pd.read_csv(gsheet_url)
image_path = df.iloc[-1]['Image']
drive_link = image_path
# Specify the local path and filename
local_path = "/content/DIS/IS-Net/saved_models/input2.jpg"
# Download the file
gdown.download(drive_link, local_path, quiet=False)
from google.colab.patches import cv2_imshow
from PIL import Image
image_path = "/content/DIS/IS-Net/saved_models/input2.jpg"
# image_bytes = BytesIO(requests.get(image_path).content)
# print(image_bytes)
image_tensor, orig_size = load_image(image_path, hypar)
mask = predict(net,image_tensor,orig_size, hypar, device)
image = Image.open(image_path)
f, ax = plt.subplots(1,2, figsize = (35,20))
# ax[0].imshow(np.array(Image.open(image_bytes))) # Original image
# cv2_imshow(image_path)
ax[0].imshow(mask, cmap = 'gray') # retouched image
# ax[0].set_title("Original Image")
ax[0].set_title("Mask")
plt.show()
import cv2
image = cv2.imread(image_path)
h, w , _ = image.shape
# print(h)
# print(w)
# print(_)
# print(image)
h, w , _ = image.shape
# print(h)
# print(w)
# print(_)
# new_image = np.zeros_like(image)
# new_image[mask] = image[mask]
new_image = cv2.bitwise_and(image, image, mask=mask)
transparent_bg = np.zeros((new_image.shape[0],new_image.shape[1], new_image.shape[2]+1) , dtype=np.uint8)
# Apply the mask to the transparent background
transparent_bg[:, :, :3] = new_image
# Set the alpha channel using the mask
transparent_bg[:, :, 3] = mask
# Save the new image with a transparent background
output_path = "/content/output.png"
cv2.imwrite(output_path, transparent_bg)
# Save the new image
# output_path = "/content/output.jpg"
# cv2.imwrite(output_path, new_image)