mirror of https://github.com/kritiksoman/GIMP-ML
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66 lines
2.0 KiB
Python
66 lines
2.0 KiB
Python
import os
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baseLoc = os.path.dirname(os.path.realpath(__file__))+'/'
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from gimpfu import *
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import sys
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sys.path.extend([baseLoc+'gimpenv/lib/python2.7',baseLoc+'gimpenv/lib/python2.7/site-packages',baseLoc+'gimpenv/lib/python2.7/site-packages/setuptools',baseLoc+'DeblurGANv2'])
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import cv2
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from predictorClass import Predictor
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import numpy as np
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import torch
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def channelData(layer):#convert gimp image to numpy
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region=layer.get_pixel_rgn(0, 0, layer.width,layer.height)
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pixChars=region[:,:] # Take whole layer
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bpp=region.bpp
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# return np.frombuffer(pixChars,dtype=np.uint8).reshape(len(pixChars)/bpp,bpp)
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return np.frombuffer(pixChars,dtype=np.uint8).reshape(layer.height,layer.width,bpp)
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def createResultLayer(image,name,result):
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rlBytes=np.uint8(result).tobytes();
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rl=gimp.Layer(image,name,image.width,image.height,0,100,NORMAL_MODE)
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region=rl.get_pixel_rgn(0, 0, rl.width,rl.height,True)
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region[:,:]=rlBytes
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image.add_layer(rl,0)
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gimp.displays_flush()
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def getdeblur(img,flag):
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predictor = Predictor(weights_path=baseLoc+'weights/deblur/'+'best_fpn.h5',cf=flag)
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if img.shape[2] == 4: # get rid of alpha channel
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img = img[:,:,0:3]
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pred = predictor(img, None,cf=flag)
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return pred
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def deblur(img, layer,flag):
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if torch.cuda.is_available() and not flag:
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gimp.progress_init("(Using GPU) Deblurring " + layer.name + "...")
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else:
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gimp.progress_init("(Using CPU) Deblurring " + layer.name + "...")
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imgmat = channelData(layer)
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pred = getdeblur(imgmat,flag)
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createResultLayer(img,'deblur_'+layer.name,pred)
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register(
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"deblur",
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"deblur",
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"Running deblurring.",
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"Kritik Soman",
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"Your",
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"2020",
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"deblur...",
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"*", # Alternately use RGB, RGB*, GRAY*, INDEXED etc.
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[ (PF_IMAGE, "image", "Input image", None),
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(PF_DRAWABLE, "drawable", "Input drawable", None),
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# (PF_LAYER, "drawinglayer", "Original Image", None),
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(PF_BOOL, "fcpu", "Force CPU", False)
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],
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[],
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deblur, menu="<Image>/Layer/GIML-ML")
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main()
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