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113 lines
3.8 KiB
Python
113 lines
3.8 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',
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baseLoc + 'gimpenv/lib/python2.7/site-packages/setuptools', baseLoc + 'pytorch-deep-image-matting'])
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import torch
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from argparse import Namespace
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import net
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import cv2
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import os
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import numpy as np
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from deploy import inference_img_whole
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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, 1, 100,
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NORMAL_MODE) # image.active_layer.type or RGB_IMAGE
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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 getnewalpha(image, mask, cFlag):
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if image.shape[2] == 4: # get rid of alpha channel
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image = image[:, :, 0:3]
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if mask.shape[2] == 4: # get rid of alpha channel
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mask = mask[:, :, 0:3]
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image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
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trimap = mask[:, :, 0]
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cudaFlag = False
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if torch.cuda.is_available() and not cFlag:
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cudaFlag = True
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args = Namespace(crop_or_resize='whole', cuda=cudaFlag, max_size=1600,
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resume=baseLoc + 'weights/deepmatting/stage1_sad_57.1.pth', stage=1)
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model = net.VGG16(args)
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if cudaFlag:
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ckpt = torch.load(args.resume)
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else:
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ckpt = torch.load(args.resume, map_location=torch.device("cpu"))
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model.load_state_dict(ckpt['state_dict'], strict=True)
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if cudaFlag:
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model = model.cuda()
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# ckpt = torch.load(args.resume)
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# model.load_state_dict(ckpt['state_dict'], strict=True)
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# model = model.cuda()
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torch.cuda.empty_cache()
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with torch.no_grad():
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pred_mattes = inference_img_whole(args, model, image, trimap)
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pred_mattes = (pred_mattes * 255).astype(np.uint8)
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pred_mattes[trimap == 255] = 255
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pred_mattes[trimap == 0] = 0
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# pred_mattes = np.repeat(pred_mattes[:, :, np.newaxis], 3, axis=2)
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image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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pred_mattes = np.dstack((image, pred_mattes))
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return pred_mattes
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def deepmatting(imggimp, curlayer, layeri, layerm, cFlag):
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img = channelData(layeri)
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mask = channelData(layerm)
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if img.shape[0] != imggimp.height or img.shape[1] != imggimp.width or mask.shape[0] != imggimp.height or mask.shape[1] != imggimp.width:
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pdb.gimp_message(" Do (Layer -> Layer to Image Size) for both layers and try again.")
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else:
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if torch.cuda.is_available() and not cFlag:
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gimp.progress_init("(Using GPU) Running deep-matting for " + layeri.name + "...")
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else:
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gimp.progress_init("(Using CPU) Running deep-matting for " + layeri.name + "...")
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cpy = getnewalpha(img, mask, cFlag)
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createResultLayer(imggimp, 'new_output', cpy)
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register(
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"deep-matting",
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"deep-matting",
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"Running image matting.",
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"Kritik Soman",
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"Your",
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"2020",
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"deepmatting...",
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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_LAYER, "drawinglayer", "Trimap Mask:", None),
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(PF_BOOL, "fcpu", "Force CPU", False)
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],
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[],
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deepmatting, menu="<Image>/Layer/GIML-ML")
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main()
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