mirror of https://github.com/kritiksoman/GIMP-ML
You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
77 lines
2.6 KiB
Python
77 lines
2.6 KiB
Python
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 + 'MiDaS'])
|
|
|
|
from run import run_depth
|
|
from monodepth_net import MonoDepthNet
|
|
import MiDaS_utils as MiDaS_utils
|
|
import numpy as np
|
|
import cv2
|
|
import torch
|
|
|
|
def getMonoDepth(input_image,cFlag):
|
|
image = input_image / 255.0
|
|
out = run_depth(image, baseLoc+'weights/MiDaS/model.pt', MonoDepthNet, MiDaS_utils, target_w=640,f=cFlag)
|
|
out = np.repeat(out[:, :, np.newaxis], 3, axis=2)
|
|
d1,d2 = input_image.shape[:2]
|
|
out = cv2.resize(out,(d2,d1))
|
|
# cv2.imwrite("/Users/kritiksoman/PycharmProjects/new/out.png", out)
|
|
return out
|
|
|
|
|
|
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, 0, 100, NORMAL_MODE)
|
|
region = rl.get_pixel_rgn(0, 0, rl.width, rl.height, True)
|
|
region[:, :] = rlBytes
|
|
image.add_layer(rl, 0)
|
|
gimp.displays_flush()
|
|
|
|
|
|
def MonoDepth(img, layer,cFlag):
|
|
imgmat = channelData(layer)
|
|
if imgmat.shape[0] != img.height or imgmat.shape[1] != img.width:
|
|
pdb.gimp_message(" Do (Layer -> Layer to Image Size) first and try again.")
|
|
else:
|
|
if torch.cuda.is_available() and not cFlag:
|
|
gimp.progress_init("(Using GPU) Generating disparity map for " + layer.name + "...")
|
|
else:
|
|
gimp.progress_init("(Using CPU) Generating disparity map for " + layer.name + "...")
|
|
|
|
if imgmat.shape[2] == 4: # get rid of alpha channel
|
|
imgmat = imgmat[:,:,0:3]
|
|
cpy = getMonoDepth(imgmat,cFlag)
|
|
createResultLayer(img, 'new_output', cpy)
|
|
|
|
|
|
register(
|
|
"MonoDepth",
|
|
"MonoDepth",
|
|
"Generate monocular disparity map based on deep learning.",
|
|
"Kritik Soman",
|
|
"Your",
|
|
"2020",
|
|
"MonoDepth...",
|
|
"*", # Alternately use RGB, RGB*, GRAY*, INDEXED etc.
|
|
[(PF_IMAGE, "image", "Input image", None),
|
|
(PF_DRAWABLE, "drawable", "Input drawable", None),
|
|
(PF_BOOL, "fcpu", "Force CPU", False)
|
|
],
|
|
[],
|
|
MonoDepth, menu="<Image>/Layer/GIML-ML")
|
|
|
|
main() |