def applyFilter(f):
global image, workingImage, app
if(workingImage == None):
app.loadFile()
width, height = workingImage["size"][0], workingImage["size"][1]
pixels = workingImage["pixels"]
if(f == "g"):
pixels = filters.grayscale(pixels)
elif(f == "t"):
minT, maxT = app.minThreshold.get(), app.maxThreshold.get()
pixels = filters.grayscale(pixels, lmin=minT, lmax=maxT)
elif(f == "b"):
pixels = filters.blur(pixels, width, height)
elif(f == "n"):
pixels = filters.negative(pixels)
elif(f == "s"):
pixels = filters.sepia(pixels)
elif(f == "no"):
level, intensity = app.noiseLevel.get(), app.noiseIntensity.get()
pixels = filters.noise(pixels, level, intensity)
elif(f == "rn"):
aggressiveness = app.noiseAggressiveness.get()
pixels = filters.removeNoise(pixels, width, height, aggressiveness)
elif(f == "d"):
pixels = filters.difference(pixels, width, height)
elif(f == "c"):
pixels = filters.applyMask(pixels, width)
elif(f == "bd"):
pixels = filters.borderDetection(pixels, width)
elif(f == "od"):
coordinates = app.mouseCoords
color = (0,0,255)
pixels, dmp1, dmp2 = filters.objectDetection(pixels, width, height, coordinates, color)
else:
pass
saveImageChanges(pixels)
app.updateCanvas()
print "Done!"
return
def applyFilter(f):
global image, lastImage, app
if(f == "o"):
i = image
else:
i = "tmp.png"
a, width, height, pixels = imageToPixels(lastImage)
if(f == "g"):
pixels = filters.grayscale(pixels)
elif(f == "t"):
minT, maxT = app.minThreshold.get(), app.maxThreshold.get()
pixels = filters.grayscale(pixels, lmin=minT, lmax=maxT)
elif(f == "b"):
pixels = filters.blur(pixels, width, height)
elif(f == "n"):
pixels = filters.negative(pixels)
elif(f == "s"):
pixels = filters.sepia(pixels)
elif(f == "no"):
level, intensity = app.noiseLevel.get(), app.noiseIntensity.get()
pixels = filters.noise(pixels, level, intensity)
elif(f == "rn"):
aggressiveness = app.noiseAggressiveness.get()
pixels = filters.removeNoise(pixels, width, height, aggressiveness)
elif(f == "d"):
pixels = filters.difference(pixels, width, height)
elif(f == "c"):
pixels = filters.applyMask(pixels, width)
elif(f == "bd"):
pixels = filters.borderDetection(pixels, width)
elif(f == "od"):
coordinates = app.mouseCoords
color = (0,0,255)
pixels, dmp1, dmp2 = filters.objectDetection(pixels, width, height, coordinates, color)
else:
pass
saveImage((width, height), pixels, i)
lastImage = i
app.updateCanvas(i)
print "Done!"
return
def extractOutline(settings, image_name, image, run3dmorph):
'''
Runs image filters (see Hsiang et al. 2017, Figure 4) to extract 2D outline
of object.
'''
if not run3dmorph:
print ' '.join(["INFO: Extracting outline for", image_name, '\n'])
size = image.shape
# Morphological opening (erosion followed by dilation)
image_open = filters.morphOpen(image, settings['disk_size_opening'])
# RGB filter
image_rgb = filters.rgbFilter(image_open)
# Contrast adjustment
image_contrast = filters.contrastAdjust(image_rgb,
settings['contrast_adjustment'])
# Grayscale conversion
image_gray = filters.rgb2gray(image_contrast)
# Black & White conversion
image_bw = filters.convertBW(image_gray, image_name,
settings['threshold_adjustment'])
# Created hole-filled version and -unfilled version (the latter is for
# run3dmorph)
image_filled = filters.fillHoles(image_bw)
if run3dmorph:
image_unfilled = image_bw
# Remove objects that touch border
image_border = filters.clearBorder(image_filled, size)
if run3dmorph:
image_border_unfilled = filters.clearBorder(image_unfilled, size)
# Remove background noise
image_clean = filters.removeNoise(image_border)
if run3dmorph:
image_clean_unfilled = filters.removeNoise(image_border_unfilled)
# Smooth image
if not run3dmorph:
image_smoothed = filters.smoothImage(image_clean,
settings['disk_size_smoothing'])
# Edge detection (smoothed)
edge_smoothed = cv2.Canny(image_smoothed, 100, 200) * 1
# Edge detection (original)
edge_unsmoothed = cv2.Canny(image_clean, 100, 200) * 1
# Save output files
if not run3dmorph:
# Save intermediates if requested by user
if settings['save_intermediates']:
save.saveIntermediates(settings, image_rgb, image_contrast,
image_gray, image_bw, image_filled,
image_border, image_clean, edge_unsmoothed,
edge_smoothed, image_name)
# Save final image of extracted outline overlaid on original image
save.saveFinalOverlay(settings, image, edge_unsmoothed, image_name)
return edge_unsmoothed, edge_smoothed, image_clean, image_smoothed, image_border
else:
return edge_unsmoothed, image_clean, image_clean_unfilled
def extractOutline(settings, image_name, image, run3dmorph):
'''
Runs image filters (see Hsiang et al. 2017, Figure 4) to extract 2D outline
of object.
'''
if not run3dmorph:
print ' '.join(["INFO: Extracting outline for", image_name])
size = image.shape
# Tag dictionary for saving intermediate image files if requested by user
tagDict = {
1: 'morphopen.jpg',
2: 'rgbfilter.jpg',
3: 'contrast.jpg',
4: 'grayscale.jpg',
5: 'bw.jpg',
6: 'fill.jpg',
7: 'border.jpg',
8: 'noise.jpg',
9: 'edge.jpg',
10: 'smooth.jpg'
}
try:
# Morphological opening (erosion followed by dilation)
image_open = filters.morphOpen(image, settings['disk_size_opening'])
if settings['save_intermediates']:
save.saveIntermediates(settings, image_open, image_name,
'_'.join(['1', tagDict[1]]))
# RGB filter
image_rgb = filters.rgbFilter(image_open)
if settings['save_intermediates']:
save.saveIntermediates(settings, image_rgb, image_name,
'_'.join(['2', tagDict[2]]))
# Contrast adjustment
image_contrast = filters.contrastAdjust(
image_rgb, settings['contrast_adjustment'])
if settings['save_intermediates']:
save.saveIntermediates(settings, image_contrast, image_name,
'_'.join(['3', tagDict[3]]))
# Grayscale conversion
image_gray = filters.rgb2gray(image_contrast)
if settings['save_intermediates']:
save.saveIntermediates(settings, image_gray, image_name,
'_'.join(['4', tagDict[4]]))
# Black & White conversion
image_bw = filters.convertBW(image_gray, image_name,
settings['threshold_adjustment'])
if settings['save_intermediates']:
save.saveIntermediates(settings, image_bw, image_name,
'_'.join(['5', tagDict[5]]))
# Created hole-filled version and -unfilled version (the latter is for
# run3dmorph)
image_filled = filters.fillHoles(image_bw)
if settings['save_intermediates']:
save.saveIntermediates(settings, image_filled, image_name,
'_'.join(['6', tagDict[6]]))
if run3dmorph:
image_unfilled = image_bw
# Remove objects that touch border
image_border = filters.clearBorder(image_filled, size)
if settings['save_intermediates']:
save.saveIntermediates(settings, image_border, image_name,
'_'.join(['7', tagDict[7]]))
if run3dmorph:
image_border_unfilled = filters.clearBorder(image_unfilled, size)
# Remove background noise
image_clean = filters.removeNoise(image_border)
if settings['save_intermediates']:
save.saveIntermediates(settings, image_clean, image_name,
'_'.join(['8', tagDict[8]]))
if run3dmorph:
image_clean_unfilled = filters.removeNoise(image_border_unfilled)
# Edge detection (original)
edge_unsmoothed = cv2.Canny(image_clean, 100, 200) * 1
if settings['save_intermediates']:
save.saveIntermediates(settings, edge_unsmoothed, image_name,
'_'.join(['9', tagDict[9]]))
# Smooth image
if not run3dmorph:
image_smoothed = filters.smoothImage(
image_clean, settings['disk_size_smoothing'])
# Edge detection (smoothed)
edge_smoothed = cv2.Canny(image_smoothed, 100, 200) * 1
if settings['save_intermediates']:
save.saveIntermediates(settings, edge_smoothed, image_name,
'_'.join(['10', tagDict[10]]))
print ''
except:
print ' '.join([
"ERROR: Outline extraction failed; check intermediates and/or input parameters\n"
])
# Save final image of extracted outline overlaid on original image
if not run3dmorph:
try:
save.saveFinalOverlay(settings, image, edge_unsmoothed, image_name)
except:
return None # Return nothing if outline extraction runs into trouble
return edge_unsmoothed, edge_smoothed, image_clean, image_smoothed, image_border # Return necessary output for 2dmorph
else:
try:
return edge_unsmoothed, image_clean, image_clean_unfilled # Return necessary output for 3dmorph
except:
return None # Unless something went wrong
def extractOutline(settings,image_name,image,run3dmorph):
'''
Runs image filters (see Hsiang et al. 2017, Figure 4) to extract 2D outline
of object.
'''
if not run3dmorph:
print ' '.join(["INFO: Extracting outline for",image_name])
size = image.shape
# Tag dictionary for saving intermediate image files if requested by user
tagDict = { 1:'morphopen.jpg',
2:'rgbfilter.jpg',
3:'contrast.jpg',
4:'grayscale.jpg',
5:'bw.jpg',
6:'fill.jpg',
7:'border.jpg',
8:'noise.jpg',
9:'edge.jpg',
10:'smooth.jpg'
}
try:
# Morphological opening (erosion followed by dilation)
image_open = filters.morphOpen(image,settings['disk_size_opening'])
if settings['save_intermediates']:
save.saveIntermediates(settings,image_open,image_name,'_'.join(['1',tagDict[1]]))
# RGB filter
image_rgb = filters.rgbFilter(image_open)
if settings['save_intermediates']:
save.saveIntermediates(settings,image_rgb,image_name,'_'.join(['2',tagDict[2]]))
# Contrast adjustment
image_contrast = filters.contrastAdjust(image_rgb,settings['contrast_adjustment'])
if settings['save_intermediates']:
save.saveIntermediates(settings,image_contrast,image_name,'_'.join(['3',tagDict[3]]))
# Grayscale conversion
image_gray = filters.rgb2gray(image_contrast)
if settings['save_intermediates']:
save.saveIntermediates(settings,image_gray,image_name,'_'.join(['4',tagDict[4]]))
# Black & White conversion
image_bw = filters.convertBW(image_gray,image_name,settings['threshold_adjustment'])
if settings['save_intermediates']:
save.saveIntermediates(settings,image_bw,image_name,'_'.join(['5',tagDict[5]]))
# Created hole-filled version and -unfilled version (the latter is for
# run3dmorph)
image_filled = filters.fillHoles(image_bw)
if settings['save_intermediates']:
save.saveIntermediates(settings,image_filled,image_name,'_'.join(['6',tagDict[6]]))
if run3dmorph:
image_unfilled = image_bw
# Remove objects that touch border
image_border = filters.clearBorder(image_filled,size)
if settings['save_intermediates']:
save.saveIntermediates(settings,image_border,image_name,'_'.join(['7',tagDict[7]]))
if run3dmorph:
image_border_unfilled = filters.clearBorder(image_unfilled,size)
# Remove background noise
image_clean = filters.removeNoise(image_border)
if settings['save_intermediates']:
save.saveIntermediates(settings,image_clean,image_name,'_'.join(['8',tagDict[8]]))
if run3dmorph:
image_clean_unfilled = filters.removeNoise(image_border_unfilled)
# Edge detection (original)
edge_unsmoothed = cv2.Canny(image_clean,100,200) * 1
if settings['save_intermediates']:
save.saveIntermediates(settings,edge_unsmoothed,image_name,'_'.join(['9',tagDict[9]]))
# Smooth image
if not run3dmorph:
image_smoothed = filters.smoothImage(image_clean,settings['disk_size_smoothing'])
# Edge detection (smoothed)
edge_smoothed = cv2.Canny(image_smoothed,100,200) * 1
if settings['save_intermediates']:
save.saveIntermediates(settings,edge_smoothed,image_name,'_'.join(['10',tagDict[10]]))
print ''
except:
print ' '.join(["ERROR: Outline extraction failed; check intermediates and/or input parameters\n"])
# Save final image of extracted outline overlaid on original image
if not run3dmorph:
try:
save.saveFinalOverlay(settings,image,edge_unsmoothed,image_name)
except:
return None # Return nothing if outline extraction runs into trouble
return edge_unsmoothed,edge_smoothed,image_clean,image_smoothed,image_border # Return necessary output for 2dmorph
else:
try:
return edge_unsmoothed,image_clean,image_clean_unfilled # Return necessary output for 3dmorph
except:
return None # Unless something went wrong
