mirror of
https://github.com/kritiksoman/GIMP-ML
synced 2024-10-31 09:20:18 +00:00
110 lines
3.3 KiB
Python
Executable File
110 lines
3.3 KiB
Python
Executable File
import os
|
|
baseLoc = os.path.dirname(os.path.realpath(__file__))+'/'
|
|
|
|
|
|
from gimpfu import *
|
|
import sys
|
|
|
|
sys.path.extend([baseLoc+'gimpenv/lib/python2.7',baseLoc+'gimpenv/lib/python2.7/site-packages',baseLoc+'gimpenv/lib/python2.7/site-packages/setuptools',baseLoc+'pytorch-deep-image-matting'])
|
|
|
|
|
|
import torch
|
|
from argparse import Namespace
|
|
import net
|
|
import cv2
|
|
import os
|
|
import numpy as np
|
|
from deploy import inference_img_whole
|
|
|
|
|
|
|
|
def channelData(layer):#convert gimp image to numpy
|
|
region=layer.get_pixel_rgn(0, 0, layer.width,layer.height)
|
|
pixChars=region[:,:] # Take whole layer
|
|
bpp=region.bpp
|
|
# return np.frombuffer(pixChars,dtype=np.uint8).reshape(len(pixChars)/bpp,bpp)
|
|
return np.frombuffer(pixChars,dtype=np.uint8).reshape(layer.height,layer.width,bpp)
|
|
|
|
|
|
def createResultLayer(image,name,result):
|
|
rlBytes=np.uint8(result).tobytes();
|
|
rl=gimp.Layer(image,name,image.width,image.height,1,100,NORMAL_MODE)#image.active_layer.type or RGB_IMAGE
|
|
region=rl.get_pixel_rgn(0, 0, rl.width,rl.height,True)
|
|
region[:,:]=rlBytes
|
|
image.add_layer(rl,0)
|
|
gimp.displays_flush()
|
|
|
|
def getnewalpha(image,mask):
|
|
if image.shape[2] == 4: # get rid of alpha channel
|
|
image = image[:,:,0:3]
|
|
if mask.shape[2] == 4: # get rid of alpha channel
|
|
mask = mask[:,:,0:3]
|
|
|
|
image = cv2.cvtColor(image,cv2.COLOR_RGB2BGR)
|
|
trimap = mask[:, :, 0]
|
|
|
|
cudaFlag = False
|
|
if torch.cuda.is_available():
|
|
cudaFlag = True
|
|
|
|
args = Namespace(crop_or_resize='whole', cuda=cudaFlag, max_size=1600, resume=baseLoc+'weights/deepmatting/stage1_sad_57.1.pth', stage=1)
|
|
model = net.VGG16(args)
|
|
|
|
if cudaFlag:
|
|
ckpt = torch.load(args.resume)
|
|
else:
|
|
ckpt = torch.load(args.resume,map_location=torch.device("cpu"))
|
|
model.load_state_dict(ckpt['state_dict'], strict=True)
|
|
if cudaFlag:
|
|
model = model.cuda()
|
|
|
|
# ckpt = torch.load(args.resume)
|
|
# model.load_state_dict(ckpt['state_dict'], strict=True)
|
|
# model = model.cuda()
|
|
|
|
torch.cuda.empty_cache()
|
|
with torch.no_grad():
|
|
pred_mattes = inference_img_whole(args, model, image, trimap)
|
|
pred_mattes = (pred_mattes * 255).astype(np.uint8)
|
|
pred_mattes[trimap == 255] = 255
|
|
pred_mattes[trimap == 0] = 0
|
|
# pred_mattes = np.repeat(pred_mattes[:, :, np.newaxis], 3, axis=2)
|
|
|
|
image = cv2.cvtColor(image,cv2.COLOR_BGR2RGB)
|
|
pred_mattes = np.dstack((image,pred_mattes))
|
|
return pred_mattes
|
|
|
|
|
|
def deepmatting(imggimp, curlayer,layeri,layerm) :
|
|
if torch.cuda.is_available():
|
|
gimp.progress_init("(Using GPU) Running deep-matting for " + layeri.name + "...")
|
|
else:
|
|
gimp.progress_init("(Using CPU) Running deep-matting for " + layeri.name + "...")
|
|
|
|
img = channelData(layeri)
|
|
mask = channelData(layerm)
|
|
|
|
cpy=getnewalpha(img,mask)
|
|
createResultLayer(imggimp,'new_output',cpy)
|
|
|
|
|
|
|
|
register(
|
|
"deep-matting",
|
|
"deep-matting",
|
|
"Running image matting.",
|
|
"Kritik Soman",
|
|
"Your",
|
|
"2020",
|
|
"deepmatting...",
|
|
"*", # Alternately use RGB, RGB*, GRAY*, INDEXED etc.
|
|
[ (PF_IMAGE, "image", "Input image", None),
|
|
(PF_DRAWABLE, "drawable", "Input drawable", None),
|
|
(PF_LAYER, "drawinglayer", "Original Image:", None),
|
|
(PF_LAYER, "drawinglayer", "Trimap Mask:", None)
|
|
],
|
|
[],
|
|
deepmatting, menu="<Image>/Layer/GIML-ML")
|
|
|
|
main()
|