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#start
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import cv2
import uuid
import os
from model.u2net import U2NET
from torch.autograd import Variable
from skimage import io, transform
from PIL import Image
import shutil
# Get The Current Directory
currentDir = os.path.dirname(__file__)
# Functions:
# Save Results
def save_output(image_name, output_name, pred, d_dir, type):
predict = pred
predict = predict.squeeze()
predict_np = predict.cpu().data.numpy()
im = Image.fromarray(predict_np*255).convert('RGB')
image = io.imread(image_name)
imo = im.resize((image.shape[1], image.shape[0]))
pb_np = np.array(imo)
if type == 'image':
# Make and apply mask
mask = pb_np[:, :, 0]
mask = np.expand_dims(mask, axis=2)
imo = np.concatenate((image, mask), axis=2)
imo = Image.fromarray(imo, 'RGBA')
imo.save(d_dir+output_name)
# Remove Background From Image (Generate Mask, and Final Results)
def removeBg(imagePath):
if not os.path.exists('static'):
os.mkdir('static')
inputs_dir = os.path.join(currentDir, 'static/inputs/')
results_dir = os.path.join(currentDir, 'static/results/')
masks_dir = os.path.join(currentDir, 'static/masks/')
dirs_list = [inputs_dir, results_dir, masks_dir]
for temp_dir in dirs_list:
if not os.path.exists(temp_dir):
os.mkdir(temp_dir)
else:
shutil.rmtree(temp_dir)
os.mkdir(temp_dir)
# convert string of image data to uint8
with open(imagePath, "rb") as image:
f = image.read()
img = bytearray(f)
nparr = np.frombuffer(img, np.uint8)
if len(nparr) == 0:
return '---Empty image---'
# decode image
try:
img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
except:
# build a response dict to send back to client
return "---Empty image---"
# save image to inputs
unique_filename = str(uuid.uuid4())
cv2.imwrite(inputs_dir+unique_filename+'.jpg', img)
# processing
image = transform.resize(img, (320, 320), mode='constant')
tmpImg = np.zeros((image.shape[0], image.shape[1], 3))
tmpImg[:, :, 0] = (image[:, :, 0]-0.485)/0.229
tmpImg[:, :, 1] = (image[:, :, 1]-0.456)/0.224
tmpImg[:, :, 2] = (image[:, :, 2]-0.406)/0.225
tmpImg = tmpImg.transpose((2, 0, 1))
tmpImg = np.expand_dims(tmpImg, 0)
image = torch.from_numpy(tmpImg)
image = image.type(torch.FloatTensor)
image = Variable(image)
print("---Loading Model---")
model_name = 'u2net'
model_dir = os.path.join(currentDir, 'saved_models',
model_name, model_name + '.pth')
net = U2NET(3, 1)
if torch.cuda.is_available():
net.load_state_dict(torch.load(model_dir))
net.cuda()
else:
net.load_state_dict(torch.load(model_dir, map_location='cpu'))
# ------- Load Trained Model --------
print("---Removing Background...")
d1, d2, d3, d4, d5, d6, d7 = net(image)
pred = d1[:, 0, :, :]
ma = torch.max(pred)
mi = torch.min(pred)
dn = (pred-mi)/(ma-mi)
pred = dn
save_output(inputs_dir+unique_filename+'.jpg', unique_filename +
'.png', pred, results_dir, 'image')
save_output(inputs_dir+unique_filename+'.jpg', unique_filename +
'.png', pred, masks_dir, 'mask')
return "---Success---"
# ------- Load Trained Model --------
def filter_background(imgPath):
print("---Loading Model---")
model_name = 'u2net'
model_dir = os.path.join(currentDir, 'saved_models',
model_name, model_name + '.pth')
net = U2NET(3, 1)
if torch.cuda.is_available():
net.load_state_dict(torch.load(model_dir))
net.cuda()
else:
net.load_state_dict(torch.load(model_dir, map_location='cpu'))
# ------- Load Trained Model --------
print("---Removing Background...")
# ------- Call The removeBg Function --------
# imgPath = "1.jpg" # Change this to your image path
print(removeBg(imgPath))
#end |