def measure(im, blur, noise=None, debug=False):
focus_points = blur[0]
#is_noisy = noise[2]
size = cv.GetSize(im)
npixels = size[0] * size[1]
#if focused_regions is None:
# focused_regions = image.new_from(im)
# cv.Set(focused_regions, 0)
# groups = form_groups(focus_points,
# estimated_size=min(max(int(len(npixels) / 1000), 2), 15))
# #groups = form_groups(points, threshold=max(cv.GetSize(im))/16)
# #print 'groups', len(groups)
# draw_groups(groups, focused_regions)
im2 = cv.CloneImage(im)
g = Grid(cv.GetSize(im2))
if debug:
image.show(g.draw(im2), "Image with Grid + ROI")
roi = image.new_from(im, nChannels=1)
cv.Set(roi, 0)
#g.draw_lines(roi, thickness=int(max(min((size[0] + size[1]) * 1/100.0, 255), 1)))
g.draw_regions(roi)
area = cv.Sum(roi)[0]
(_, face_rects), face_score = faces.measure(im)
face_block = image.new_from(im, nChannels=1)
cv.Set(face_block, 0)
for r in face_rects:
r.draw(face_block, color=cv.RGB(255,255,255), thickness=cv.CV_FILLED)
if debug:
face_roi = cv.CloneImage(im)
cv.Set(face_roi, 0, image.invert(roi))
cv.Set(face_roi, 0, image.invert(image.threshold(face_block, threshold=1)))
image.show(face_block, "Faces in Binary")
image.show(g.draw(face_roi), "Face + ROI")
return (im, (
measure_focused_roi(im, roi, area, focus_points, debug),
#measure_color_roi(im, roi, area, focus_points, debug),
measure_contrast(im, debug),
measure_saturation(im, debug),
faces.measure(im, debug)[1],
))
def main(progname, *args):
parser = OptionParser()
parser.add_option("-f", "--file", dest="filename", default=None,
help="analyze a given FILE ending in .jpg or .jpeg", metavar="FILE")
parser.add_option("-i", "--imageset", dest="imageset", default=None,
help="Runs on a predefined set of algorithms (li,chow,china,custom)")
parser.add_option("-d", "--debug", dest="debug", action="store_true", default=False,
help="Enable visual debugging.")
parser.add_option("-t", "--type", dest="type", default="all",
help="Specifies the type of feature to debug. Defaults to all.")
(options, args) = parser.parse_args(list(args))
if options.imageset:
if options.imageset == 'li':
process(li_dir)
return 0
elif options.imageset == 'chow':
process(chow_dir)
return 0
elif options.imageset == 'china':
process(china_dir)
return 0
elif options.imageset == 'custom':
process()
return 0
if not options.filename:
print "Please specify a file (--file) or image set (--imageset)."
return 1
if not options.debug:
process([load_image(options.filename)])
return 0
if options.filename.startswith('data/'):
options.filename = options.filename[len('data/'):]
tdata = load_image(options.filename)
kind = options.type.lower()
size = None #(320,240,'crop') # (0.5, 0.5, 'resize-p')
if size is None:
im = tdata.load()
elif size[-1] == 'crop':
im = image.random_cropped_region(tdata.load(), size[:2])
elif size[-1] == 'resize':
im = tdata.load(size[:2])
elif size[-1] == 'resize-p':
im = image.resize(tdata.load(), by_percent=size[:2])
else:
raise TypeError, "Invalid image sizing type."
image.show(im, "Image")
#l,u,v = image.split(image.rgb2luv(im))
##cv.Set(l, 128)
##cv.EqualizeHist(l, l)
##cv.EqualizeHist(u, u)
##image.show(image.luv2rgb(image.merge(l,u,v)), "test")
#s = cv.GetSize(im)
#t = image.absDiff(u,v)
#image.show(t, "test")
#print "Test Score:", cv.CountNonZero(t) / float(s[0] * s[1])
##image.show(image.threshold(image.And(u,v), threshold=1), "LUV")
# noise
if kind in ('all','noise'):
noise_img, score = noise.measure(im, debug=True)
#image.show(noise_img, "Noise Result")
print 'Noise Score:', score, noise.boolean(score)
# contrast
if kind in ('all','contrast'):
contrast_img, score = contrast.measure(im, debug=True)
#image.show(contrast_img, "Contrast Result")
print 'Contrast Score:', score, contrast.boolean(score)
# blur
if kind in ('all','blur','composition'):
focused, score = blur.measure(im, debug=kind in ('all','blur'))
#image.show(focused, "Blur Result")
print 'Blur Score:', score, blur.boolean(score)
# composition
if kind in ('all','composition'):
composition_img, score = composition.measure(im,
(focused,score, blur.boolean(score)), debug=True)
print 'Composition Score:', score, composition.boolean(score)
if kind in ('faces',):
result, score = faces.measure(im,debug=True)
print "Face Score:", score, faces.boolean(faces)
#win = CornerTweaker(im)
#win.show()
#_, sat, _ = image.split(image.rgb2hsv(im))
#arr = image.cv2array(sat)
#print arr.mean(), arr.std()
# faces
#im, score = faces.measure(im, debug=True)
#print score, faces.boolean(score)
# composition
#noise_img, score = noise.measure(im, debug=False)
##n = (noise_img, score, noise.boolean(score))
#hulls, score = blur.measure(im, debug=False)
#b = (hulls, score, blur.boolean(score))
#cimg, score = composition.measure(im, b, debug=True)
#print score, composition.boolean(score)
# BLUR
#from time import time
#start = time()
##im2 = image.threshold(image.laplace(im), threshold=75, type=cv.CV_THRESH_TOZERO)
#hulls, score = blur.measure(im, debug=True)
##blur_img, score = blur.measure(im, debug=True)
#end = time()
#print "Time:", (end - start), "seconds"
#image.show(im, "image")
##image.show(noise_img, "Noise Image")
#print score, blur.boolean(score)
#CONTRAST
#_, score = contrast.measure(im, debug=True)
#image.show(im, "Image")
#print score, contrast.boolean(score)
"""
#BLUR
#im2 = image.threshold(image.laplace(im), threshold=75, type=cv.CV_THRESH_TOZERO)
im3, score = blur.measure(im, debug=True)
image.show(im, "image")
image.show(im3, "Focus Mask")
print score, blur.boolean(score)
#plt.show()
"""
#NOISE
#noise_img, score = noise.measure(im, debug=True)
#image.show(noise_img, "Noise")
#print score, noise.boolean(score)
"""
#hwin = ColorHistograms(im)
#hwin.show()
hwin = HistogramWindow(image.rgb2gray(im))
hwin.show()
print cv.GetSize(im), cv.GetSize(im2)
print 'blur', papers.blurry_histogram(im)
#print papers.blurry_histogram(im2)
wind = DerivativeTweaker(im, title="image derivative")
wind.show()
win = EdgeThresholdTweaker(im, title="image edges")
win.show(50)#edge_threshold(im))
#win2 = EdgeThresholdTweaker(im2, title="image resized edges")
#win2.show(edge_threshold(im2))
"""
cv.WaitKey()
cv.DestroyAllWindows()
return 0
def main(progname, *args):
parser = OptionParser()
parser.add_option("-f",
"--file",
dest="filename",
default=None,
help="analyze a given FILE ending in .jpg or .jpeg",
metavar="FILE")
parser.add_option(
"-i",
"--imageset",
dest="imageset",
default=None,
help="Runs on a predefined set of algorithms (li,chow,china,custom)")
parser.add_option("-d",
"--debug",
dest="debug",
action="store_true",
default=False,
help="Enable visual debugging.")
parser.add_option(
"-t",
"--type",
dest="type",
default="all",
help="Specifies the type of feature to debug. Defaults to all.")
(options, args) = parser.parse_args(list(args))
if options.imageset:
if options.imageset == 'li':
process(li_dir)
return 0
elif options.imageset == 'chow':
process(chow_dir)
return 0
elif options.imageset == 'china':
process(china_dir)
return 0
elif options.imageset == 'custom':
process()
return 0
if not options.filename:
print "Please specify a file (--file) or image set (--imageset)."
return 1
if not options.debug:
process([load_image(options.filename)])
return 0
if options.filename.startswith('data/'):
options.filename = options.filename[len('data/'):]
tdata = load_image(options.filename)
kind = options.type.lower()
size = None #(320,240,'crop') # (0.5, 0.5, 'resize-p')
if size is None:
im = tdata.load()
elif size[-1] == 'crop':
im = image.random_cropped_region(tdata.load(), size[:2])
elif size[-1] == 'resize':
im = tdata.load(size[:2])
elif size[-1] == 'resize-p':
im = image.resize(tdata.load(), by_percent=size[:2])
else:
raise TypeError, "Invalid image sizing type."
image.show(im, "Image")
#l,u,v = image.split(image.rgb2luv(im))
##cv.Set(l, 128)
##cv.EqualizeHist(l, l)
##cv.EqualizeHist(u, u)
##image.show(image.luv2rgb(image.merge(l,u,v)), "test")
#s = cv.GetSize(im)
#t = image.absDiff(u,v)
#image.show(t, "test")
#print "Test Score:", cv.CountNonZero(t) / float(s[0] * s[1])
##image.show(image.threshold(image.And(u,v), threshold=1), "LUV")
# noise
if kind in ('all', 'noise'):
noise_img, score = noise.measure(im, debug=True)
#image.show(noise_img, "Noise Result")
print 'Noise Score:', score, noise.boolean(score)
# contrast
if kind in ('all', 'contrast'):
contrast_img, score = contrast.measure(im, debug=True)
#image.show(contrast_img, "Contrast Result")
print 'Contrast Score:', score, contrast.boolean(score)
# blur
if kind in ('all', 'blur', 'composition'):
focused, score = blur.measure(im, debug=kind in ('all', 'blur'))
#image.show(focused, "Blur Result")
print 'Blur Score:', score, blur.boolean(score)
# composition
if kind in ('all', 'composition'):
composition_img, score = composition.measure(
im, (focused, score, blur.boolean(score)), debug=True)
print 'Composition Score:', score, composition.boolean(score)
if kind in ('faces', ):
result, score = faces.measure(im, debug=True)
print "Face Score:", score, faces.boolean(faces)
#win = CornerTweaker(im)
#win.show()
#_, sat, _ = image.split(image.rgb2hsv(im))
#arr = image.cv2array(sat)
#print arr.mean(), arr.std()
# faces
#im, score = faces.measure(im, debug=True)
#print score, faces.boolean(score)
# composition
#noise_img, score = noise.measure(im, debug=False)
##n = (noise_img, score, noise.boolean(score))
#hulls, score = blur.measure(im, debug=False)
#b = (hulls, score, blur.boolean(score))
#cimg, score = composition.measure(im, b, debug=True)
#print score, composition.boolean(score)
# BLUR
#from time import time
#start = time()
##im2 = image.threshold(image.laplace(im), threshold=75, type=cv.CV_THRESH_TOZERO)
#hulls, score = blur.measure(im, debug=True)
##blur_img, score = blur.measure(im, debug=True)
#end = time()
#print "Time:", (end - start), "seconds"
#image.show(im, "image")
##image.show(noise_img, "Noise Image")
#print score, blur.boolean(score)
#CONTRAST
#_, score = contrast.measure(im, debug=True)
#image.show(im, "Image")
#print score, contrast.boolean(score)
"""
#BLUR
#im2 = image.threshold(image.laplace(im), threshold=75, type=cv.CV_THRESH_TOZERO)
im3, score = blur.measure(im, debug=True)
image.show(im, "image")
image.show(im3, "Focus Mask")
print score, blur.boolean(score)
#plt.show()
"""
#NOISE
#noise_img, score = noise.measure(im, debug=True)
#image.show(noise_img, "Noise")
#print score, noise.boolean(score)
"""
#hwin = ColorHistograms(im)
#hwin.show()
hwin = HistogramWindow(image.rgb2gray(im))
hwin.show()
print cv.GetSize(im), cv.GetSize(im2)
print 'blur', papers.blurry_histogram(im)
#print papers.blurry_histogram(im2)
wind = DerivativeTweaker(im, title="image derivative")
wind.show()
win = EdgeThresholdTweaker(im, title="image edges")
win.show(50)#edge_threshold(im))
#win2 = EdgeThresholdTweaker(im2, title="image resized edges")
#win2.show(edge_threshold(im2))
"""
cv.WaitKey()
cv.DestroyAllWindows()
return 0
