mirror of https://github.com/kritiksoman/GIMP-ML
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66 lines
2.1 KiB
Python
66 lines
2.1 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 + 'MiDaS'])
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from run import run_depth
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from monodepth_net import MonoDepthNet
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import MiDaS_utils as MiDaS_utils
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import numpy as np
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import cv2
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def getMonoDepth(input_image):
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image = input_image / 255.0
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out = run_depth(image, baseLoc+'MiDaS/model.pt', MonoDepthNet, MiDaS_utils, target_w=640)
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out = np.repeat(out[:, :, np.newaxis], 3, axis=2)
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d1,d2 = input_image.shape[:2]
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out = cv2.resize(out,(d2,d1))
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# cv2.imwrite("/Users/kritiksoman/PycharmProjects/new/out.png", out)
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return out
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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, image.active_layer.type, 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 MonoDepth(img, layer):
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gimp.progress_init("Generating disparity map for " + layer.name + "...")
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imgmat = channelData(layer)
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cpy = getMonoDepth(imgmat)
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createResultLayer(img, 'new_output', cpy)
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register(
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"MonoDepth",
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"MonoDepth",
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"Generate monocular disparity map based on deep learning.",
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"Kritik Soman",
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"Your",
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"2020",
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"MonoDepth...",
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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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],
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[],
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MonoDepth, menu="<Image>/Layer/GIML-ML")
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main() |