Esempio n. 1
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def get_focus_points(im, debug=False):
    edges = image.dilate(image.auto_edges(im))
    #d = 1
    #sobel = image.sobel(im, xorder=d, yorder=d)
    sobel = image.laplace(im)
    hsv = image.rgb2hsv(im)

    focused = image.And(sobel, edges)
    if im.nChannels == 3:
        hue, saturation, value = image.split(hsv)
        saturation = image.dilate(saturation)
        focused = image.And(focused, saturation)
    focused = image.threshold(image.dilate(focused), threshold=32)

    if debug:
        image.show(edges, "1. Edges")
        image.show(sobel, "2. Sobel")
        if im.nChannels == 3:
            image.show(saturation, "3. Saturation")
    return focused
Esempio n. 2
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def readfile():
    """
    read the config file and execute the command
    :return: nothing
    """
    file = open("conf.txt", 'r')
    for line in file:

        isActive = line.split("=")

        isActive[1] = isActive[1]

        if line.find("input_dir=") >= 0 and isActive[1] != "\n":
            if line.find("= ") >= 0:
                command = line.split("= ")
            else:
                command = line.split("=")

            i.path = command[1].rstrip("\n")

        if line.find("output_dir=") >= 0 and isActive[1] != "\n":
            print(isActive[1] + "s")
            if line.find("= ") >= 0:
                command = line.split("= ")
            else:
                command = line.split("=")
            i.folder = command[1]

        if line.find("filters=") >= 0 and isActive[1] != "":

            i.dico = i.open_image()
            if line.find("= ") >= 0:
                command = line.split("= ")
            else:
                command = line.split("=")
            filtre = command[1].split("|")

            for c in filtre:

                if c == "grayscale":

                    i.dico = i.nb()

                if c.find("blur") >= 0:
                    sep = c.split(":")
                    i.dico = i.blur(int(sep[1]))
                if c.find("dilate") >= 0:
                    sep = c.split(":")

                    i.dico = i.dilate(int(sep[1]))
                if c.find("FilterZeTeam") >= 0:
                    i.dico = i.FilterZeTeam()

        i.save()
Esempio n. 3
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def get_focus_points(im,debug=False):
    edges = image.dilate(image.auto_edges(im))
    #d = 1
    #sobel = image.sobel(im, xorder=d, yorder=d)
    sobel = image.laplace(im)
    hsv = image.rgb2hsv(im)

    focused = image.And(sobel, edges)
    if im.nChannels == 3:
        hue, saturation, value = image.split(hsv)
        saturation = image.dilate(saturation)
        focused = image.And(focused, saturation)
    focused = image.threshold(image.dilate(focused), threshold=32)
    
    if debug:
        image.show(edges, "1. Edges")
        image.show(sobel, "2. Sobel")
        if im.nChannels == 3:
            image.show(saturation, "3. Saturation")
    return focused
Esempio n. 4
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def measure(im, debug=False):
    gray = image.rgb2gray(im)
    size = cv.GetSize(im)
    total = float(size[0] * size[1])
    edges = image.auto_edges(im)

    hue, sat, val = tuple(
        map(image.equalize_hist, image.split(image.rgb2hsv(im))))
    l, u, v = tuple(map(image.equalize_hist, image.split(image.rgb2luv(im))))

    values = []
    if debug:
        image.show(l, "L")
        image.show(val, "Value")
    sat = image.threshold(val, 255 - 32)  #image.And(val, sat)
    if debug:
        image.show(sat, "Thresh")
    #cv.And(val, l, val)
    cv.Sub(l, sat, l)
    cv.Set(l, 0, image.dilate(edges, iterations=3))
    if debug:
        image.show(l, "L - Value")
    val = l
    g = Grid(cv.GetSize(val))
    images = g.split_into(val, 16)
    arr = image.cv2array(val)
    avgmean, avgstd = arr.mean(), arr.std()
    for i in images:
        a = image.cv2array(i)
        mean, std = abs(a.mean() - avgmean), max(a.std(), 0)
        values.append((mean + std))

    if debug:
        print values
        print "AVG", avgmean, avgstd
        image.show(val, "Result")

    return val, (avgmean, avgstd, len([v for v in values if v > avgstd * 2]))
Esempio n. 5
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def measure(im, debug=False):

    gray = image.rgb2gray(im)
    size = cv.GetSize(im)
    total = float(size[0] * size[1])
    edges = image.auto_edges(im)

    _, sat, val = image.split(image.rgb2hsv(im))
    edges = image.auto_edges(im)
    l, u, v = tuple(map(image.equalize_hist, image.split(image.rgb2luv(im))))
    u, v = tuple(map(image.gaussian, (u, v)))
    if debug:
        image.show(l, "1. L")
        image.show(u, "1. U")
        image.show(v, "1. V")
    la, ua, va, uva = tuple(map(image.cv2array, (l, u, v, image.And(l, u, v))))
    test = image.new_from(gray)
    test2 = image.new_from(gray)
    cv.Xor(u, v, test)
    #cv.AbsDiff(u,v, test2)
    if debug:
        #cv.Threshold(test, test, 32, 255, cv.CV_THRESH_BINARY)
        image.show(test, "2. U Xor V")
        #image.show(test2, "TEST 2")
        #test = image.dilate(test)
    cv.Set(test, 0, image.dilate(edges))
    #cv.Set(test, 0, image.invert(image.threshold(sat, threshold=8)))
    uv_score = cv.CountNonZero(test) / total
    if debug:
        image.show(
            test, "3. U Xor V - dilate(Edges) - invert(threshold(Saturation))")

    arr = image.cv2array(test)
    avg_mean, avg_std = arr.mean(), arr.std()

    score = uv_score, avg_std

    return test, score
Esempio n. 6
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def measure(im, debug=False):       
    gray = image.rgb2gray(im)
    size = cv.GetSize(im)
    total = float(size[0] * size[1])
    edges = image.auto_edges(im)

    hue, sat, val = tuple(map(image.equalize_hist, image.split(image.rgb2hsv(im)) ))
    l,u,v = tuple(map(image.equalize_hist, image.split(image.rgb2luv(im))))
    
    values = []
    if debug:
        image.show(l, "L")
        image.show(val, "Value")
    sat = image.threshold(val,255-32)#image.And(val, sat)
    if debug:
        image.show(sat, "Thresh")
    #cv.And(val, l, val)
    cv.Sub(l, sat, l)
    cv.Set(l, 0, image.dilate(edges, iterations=3))
    if debug:
        image.show(l, "L - Value")
    val = l
    g = Grid(cv.GetSize(val))
    images = g.split_into(val, 16)
    arr = image.cv2array(val)
    avgmean, avgstd = arr.mean(), arr.std()
    for i in images:
        a = image.cv2array(i)
        mean, std = abs(a.mean() - avgmean), max(a.std(), 0)
        values.append((mean+std))
    
    if debug:
        print values
        print "AVG", avgmean, avgstd
        image.show(val, "Result")
        
    return val, (avgmean, avgstd, len([v for v in values if v > avgstd*2]))
Esempio n. 7
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def measure(im, debug=False):
    
    gray = image.rgb2gray(im)
    size = cv.GetSize(im)
    total = float(size[0] * size[1])
    edges = image.auto_edges(im)

    _, sat, val = image.split(image.rgb2hsv(im))
    edges = image.auto_edges(im)
    l,u,v = tuple(map(image.equalize_hist, image.split(image.rgb2luv(im))))
    u,v = tuple(map(image.gaussian, (u,v)))
    if debug:
        image.show(l, "1. L")
        image.show(u, "1. U")
        image.show(v, "1. V")
    la,ua,va,uva = tuple(map(image.cv2array, (l,u,v, image.And(l,u,v))))
    test = image.new_from(gray)
    test2 = image.new_from(gray)
    cv.Xor(u,v,test)
    #cv.AbsDiff(u,v, test2)
    if debug:
        #cv.Threshold(test, test, 32, 255, cv.CV_THRESH_BINARY)
        image.show(test, "2. U Xor V")
        #image.show(test2, "TEST 2")
        #test = image.dilate(test)
    cv.Set(test, 0, image.dilate(edges))
    #cv.Set(test, 0, image.invert(image.threshold(sat, threshold=8)))
    uv_score = cv.CountNonZero(test) / total
    if debug:
        image.show(test, "3. U Xor V - dilate(Edges) - invert(threshold(Saturation))")
    
    arr = image.cv2array(test)
    avg_mean, avg_std = arr.mean(), arr.std()
    
    score = uv_score, avg_std
    
    return test, score
def detect_skin(im, debug=False):
    hsv = image.rgb2hsv(im)
    
    if debug:
        image.show(hsv, 'hsv')
    h,s,v = image.split(hsv)
    
    if cv.CountNonZero(h) == cv.CountNonZero(s) == 0:
        white = image.new_from(im)
        cv.Set(white, 255)
        return white
    
    if debug:
        image.show(h, "Hue")
        image.show(s,"sat1")
    
    h_rng = 0, 46
    s_rng = 48, 178
    
    h = image.threshold(image.gaussian(h, 5), threshold=h_rng[1], type=cv.CV_THRESH_TOZERO_INV)
    h = image.threshold(h, threshold=h_rng[0], type=cv.CV_THRESH_TOZERO)
    h = image.threshold(h, threshold=1)
    
    s = image.threshold(image.gaussian(s, 5), threshold=s_rng[1], type=cv.CV_THRESH_TOZERO_INV)
    s = image.threshold(s, threshold=s_rng[0], type=cv.CV_THRESH_TOZERO)
    if debug:
        image.show(s,"sat2")
    s = image.threshold(s, threshold=1)
    
    v = image.dilate(image.erode(image.And(s, h)))
    
    
    #im = image.hsv2rgb(image.merge(h,s,v))
    if debug:
        image.show(v, "Human")
    return image.threshold(v, threshold=1)
Esempio n. 9
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def measure(im, debug=False):
    gray = image.rgb2gray(im)
    size = cv.GetSize(im)
    total = float(size[0] * size[1])
    l = image.sub(gray, image.gaussian(gray, 5))
    l2 = image.sub(gray, image.gaussian(gray, 9))
    edges = image.dilate(image.auto_edges(im, percentage=0.2))
    if debug:
        image.show(image.threshold(l, threshold=1),
                   "Before Edge Removal (kernel=5)")
        image.show(image.threshold(l2, threshold=1),
                   "Before Edge Removal (kernel=9)")
    cv.Set(l, 0, image.threshold(edges, threshold=1))
    cv.Set(l2, 0, image.threshold(edges, threshold=1))

    l = image.threshold(l, threshold=1)
    l2 = image.threshold(l2, threshold=1)

    if debug:
        image.show(image.threshold(edges, threshold=1), "Edges")
        image.show(l, "After Edge Removal (kernel=5)")
        image.show(l2, "After Edge Removal (kernel=9)")

    noise2 = image.new_from(gray)
    cv.EqualizeHist(gray, noise2)
    cv.AbsDiff(noise2, gray, noise2)
    cv.Set(noise2, 0,
           image.threshold(image.sobel(im, xorder=2, yorder=2), threshold=4))
    diff = image.cv2array(noise2)
    if debug:
        image.show(noise2, "DIFF")
        print "M", diff.mean(), "S", diff.std()
    diff_stat = (diff.mean(), diff.std())
    percent_noise = cv.CountNonZero(noise2) / total
    if debug:
        image.show(noise2, "NOISE2")

    # magical, I don't understand how this works
    _, sat, _ = image.split(image.rgb2hsv(im))
    edges = image.auto_edges(im)
    l, u, v = tuple(map(image.equalize_hist, image.split(image.rgb2luv(im))))
    u, v = tuple(map(image.gaussian, (u, v)))
    if debug:
        image.show(l, "1. L")
        image.show(u, "1. U")
        image.show(v, "1. V")
    la, ua, va, uva = tuple(map(image.cv2array, (l, u, v, image.And(l, u, v))))
    test = image.new_from(gray)
    test2 = image.new_from(gray)
    cv.Xor(u, v, test)
    if debug:
        image.show(test, "2. U Xor V")
    cv.Set(test, 0, image.dilate(edges))
    #cv.Set(test, 0, image.invert(image.threshold(sat, threshold=8)))
    uv_score = cv.CountNonZero(test) / total
    if debug:
        image.show(
            test, "3. U Xor V - dilate(Edges) - invert(threshold(Saturation))")

    g = Grid(size)
    images = map(image.cv2array, g.split_into(test, 6))
    arr = image.cv2array(test)
    avg_mean, avg_std = arr.mean(), arr.std()

    #ms = [(a.mean(), a.std()) for a in images]
    #min_mean = min_std = 255
    #max_mean = max_std = 0
    #for m,s in ms:
    #    min_mean = min(min_mean, m)
    #    min_std = min(min_std, s)
    #    max_mean = max(max_mean, m)
    #    max_std = max(max_std, s)
    #if debug:
    #    print min_mean, min_std
    #    print avg_mean, avg_std
    #    print max_mean, max_std
    #
    #score = uv_score, min_mean, avg_mean, avg_std, max_mean
    uv_score = uv_score, avg_std

    score = cv.CountNonZero(l) / total,  cv.CountNonZero(l2) / total, \
        diff_stat[0], diff_stat[1], uv_score

    return l, score
Esempio n. 10
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def measure(im, debug=False):
    size = cv.GetSize(im)
    npixels = size[0] * size[1]
    #print 'np', npixels

    focused = get_focus_points(im, debug)
    points = convert_to_points(focused)

    if debug:
        print "\t" + str(
            len(points)), '/', npixels, '=', len(points) / float(npixels)
        print "\tlen(points) =", len(points)
        image.show(focused, "4. Focused Points")

    saturation_score = 0
    if not image.is_grayscale(im):
        edges = image.auto_edges(im)
        _, saturation, _ = image.split(image.rgb2hsv(im))
        if debug:
            image.show(saturation, "5. Saturation")
        #saturation = image.laplace(image.gaussian(saturation, 3))
        saturation = image.invert(saturation)
        mask = image.invert(image.threshold(im, threshold=16))
        if debug:
            image.show(saturation, "5.3. Laplace of Saturation")
        cv.Set(saturation, 0, mask)
        cv.Set(saturation, 0, focused)
        if debug:
            image.show(mask,
                       "5.6. Mask(focused AND invert(threshold(im, 16)))")
            image.show(saturation, "6. Set(<5.3>, 0, <5.6>)")

        saturation_score = cv.Sum(saturation)[0] / float(npixels * 255)
        print "\tSaturation Score:", saturation_score

    # light exposure
    h, s, v = image.split(image.rgb2hsv(im))
    if debug:
        image.show(h, "7. Hue")
        image.show(s, "7. Saturation")
        image.show(v, "7. Value")
    diff = cv.CloneImage(v)
    cv.Set(diff, 0, image.threshold(s, threshold=16))
    diff = image.dilate(diff, iterations=10)
    if debug:
        thres_s = image.threshold(s, threshold=16)
        image.show(thres_s, "8.3. Mask(threshold(<7.Saturation>, 16))")
        image.show(diff, "8.6. Dilate(Set(<7.Value>, 0, <8.3>), 10)")

    cdiff = cv.CountNonZero(diff)
    if cdiff > 0 and cdiff / float(npixels) > 0.01:
        test = cv.CloneImage(v)
        cv.Set(test, 0, image.invert(diff))
        s = cv.Sum(test)[0] / float(cdiff * 255)
        if debug:
            print '\tLight Exposure Score:', s
    else:
        s = 0

    if image.is_grayscale(im):
        return focused, (1, 1, 1, saturation_score, s)

    # we want to short circuit ASAP to avoid doing KMeans 50% of the image's pixels
    if len(points) > npixels / 2:
        return focused, (1, 1, 1, saturation_score, s)

    # we're so blurry we don't have any points!
    if len(points) < 1:
        return focused, (0, 0, 0, saturation_score, s)

    if debug:
        im2 = cv.CloneImage(im)
    focused_regions = image.new_from(im)
    cv.Set(focused_regions, 0)

    r = lambda x: random.randrange(1, x)
    groups = form_groups(points,
                         estimated_size=min(max(int(len(points) / 1000), 2),
                                            15))
    #groups = form_groups(points, threshold=max(cv.GetSize(im))/16)
    #print 'groups', len(groups)
    hulls = draw_groups(groups, focused_regions)
    focused_regions = image.threshold(focused_regions,
                                      threshold=32,
                                      type=cv.CV_THRESH_TOZERO)
    min_area = npixels * 0.0005
    densities = [h.points_per_pixel() for h in hulls if h.area() >= min_area]

    if debug:
        #image.show(focused, "Focused Points")
        image.show(focused_regions, "9. Focused Regions from <4>")
        cv.Sub(
            im2,
            image.gray2rgb(
                image.invert(image.threshold(focused_regions, threshold=1))),
            im2)
        image.show(im2, "10. threshold(<9>)")

    focused_regions = image.rgb2gray(focused_regions)

    densities = array(densities)
    c = cv.CountNonZero(focused_regions)
    c /= float(npixels)

    score = (c, densities.mean(), densities.std(), saturation_score, s)

    return focused, score
Esempio n. 11
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def commande():
    """
    check all the commande on cli
    :return: nothing
    """
    nbargs = 1

    taille = 0
    if args[1] == "-h":
        print("usage: imagefilter")
        print("--h,----help")
        print("-i,--input-dir <directory>")
        print("-o,--output-dir <directory>")
        print(
            "--filters \"filters1|filters2|filters3\",--add filters on images")
        print("--------------filters:-----------")
        print(
            "greyscale,--convert all the image of the input directory to black and white"
        )
        print("blur,--convert all the image of the input directory to blur")
        print(
            "dilate,--convert all the image of the input directory to dilate")
        sys.exit()

    if args[1] == "-i" or args[1] == "-o":

        if args[1] == "-i":

            i.path = args[2]
            nbargs = 3
        elif args[1] == "-o":
            i.folder = args[2]
            nbargs = 3
        if args[2] != None and args[3] == "-o":
            i.folder = args[4]
            nbargs = 5
    if args[nbargs] == "--filters":
        sep = args[nbargs + 1].split("|")
        taille = len(sep)

    i.dico = i.open_image()

    for c in range(taille):
        arg = {}
        arg[0] = " "
        argument = sep[c]
        print(argument)
        if argument.find(":") >= 0:
            arg = argument.split(":")
            print(arg)
        if argument == "FilterZeTeam":
            i.dico == i.FilterZeTeam()
        if argument == "grayscale":

            i.dico = i.nb()

        elif arg[0] == "blur":

            i.dico = i.blur(int(arg[1]))

        elif arg[0] == "dilate":

            i.dico = i.dilate(int(arg[1]))

    i.save()
Esempio n. 12
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def measure(im, debug=False):
    size = cv.GetSize(im)
    npixels = size[0] * size[1]
    #print 'np', npixels
    
    
    focused = get_focus_points(im, debug)
    points = convert_to_points(focused)
    
    if debug:
        print "\t"+str(len(points)), '/', npixels, '=', len(points) / float(npixels)
        print "\tlen(points) =", len(points)
        image.show(focused, "4. Focused Points")

    saturation_score = 0
    if not image.is_grayscale(im):
        edges = image.auto_edges(im)
        _, saturation, _ = image.split(image.rgb2hsv(im))
        if debug:
            image.show(saturation, "5. Saturation")
        #saturation = image.laplace(image.gaussian(saturation, 3))
        saturation = image.invert(saturation)
        mask = image.invert(image.threshold(im, threshold=16))
        if debug:
            image.show(saturation, "5.3. Laplace of Saturation")
        cv.Set(saturation, 0, mask)
        cv.Set(saturation, 0, focused)
        if debug:
            image.show(mask, "5.6. Mask(focused AND invert(threshold(im, 16)))")
            image.show(saturation, "6. Set(<5.3>, 0, <5.6>)")

        saturation_score = cv.Sum(saturation)[0] / float(npixels * 255)
        print "\tSaturation Score:", saturation_score
        
    # light exposure
    h,s,v = image.split(image.rgb2hsv(im))
    if debug:
        image.show(h, "7. Hue")
        image.show(s, "7. Saturation")
        image.show(v, "7. Value")
    diff = cv.CloneImage(v)
    cv.Set(diff, 0, image.threshold(s, threshold=16))
    diff = image.dilate(diff, iterations=10)
    if debug:
        thres_s = image.threshold(s, threshold=16)
        image.show(thres_s, "8.3. Mask(threshold(<7.Saturation>, 16))")
        image.show(diff, "8.6. Dilate(Set(<7.Value>, 0, <8.3>), 10)")

    cdiff = cv.CountNonZero(diff)
    if cdiff > 0 and cdiff / float(npixels) > 0.01:
        test = cv.CloneImage(v)
        cv.Set(test, 0, image.invert(diff))
        s = cv.Sum(test)[0] / float(cdiff * 255)
        if debug:
            print '\tLight Exposure Score:', s
    else:
        s = 0
        
    if image.is_grayscale(im):
        return focused, (1, 1, 1, saturation_score, s)
    
    # we want to short circuit ASAP to avoid doing KMeans 50% of the image's pixels
    if len(points) > npixels/2:
        return focused, (1, 1, 1, saturation_score, s)

    # we're so blurry we don't have any points!
    if len(points) < 1:
        return focused, (0, 0, 0, saturation_score, s)
    
    if debug:
        im2 = cv.CloneImage(im)
    focused_regions = image.new_from(im)
    cv.Set(focused_regions, 0)
    
    r = lambda x: random.randrange(1, x)
    groups = form_groups(points,
        estimated_size=min(max(int(len(points) / 1000), 2), 15))
    #groups = form_groups(points, threshold=max(cv.GetSize(im))/16)
    #print 'groups', len(groups)
    hulls = draw_groups(groups, focused_regions)
    focused_regions = image.threshold(focused_regions, threshold=32, type=cv.CV_THRESH_TOZERO)
    min_area = npixels * 0.0005
    densities = [h.points_per_pixel() for h in hulls if h.area() >= min_area]
    
    if debug:    
        #image.show(focused, "Focused Points")
        image.show(focused_regions, "9. Focused Regions from <4>")
        cv.Sub(im2, image.gray2rgb(image.invert(image.threshold(focused_regions, threshold=1))), im2)
        image.show(im2, "10. threshold(<9>)")
    
    
    focused_regions = image.rgb2gray(focused_regions)
    
    densities = array(densities)
    c = cv.CountNonZero(focused_regions)
    c /= float(npixels)
    
    score = (c, densities.mean(), densities.std(), saturation_score, s)
    
    return focused, score
Esempio n. 13
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def measure(im, debug=False):
    gray = image.rgb2gray(im)
    size = cv.GetSize(im)
    total = float(size[0] * size[1])
    l = image.sub(gray, image.gaussian(gray, 5))
    l2 = image.sub(gray, image.gaussian(gray, 9))
    edges = image.dilate(image.auto_edges(im, percentage=0.2))
    if debug:
        image.show(image.threshold(l, threshold=1), "Before Edge Removal (kernel=5)")
        image.show(image.threshold(l2, threshold=1), "Before Edge Removal (kernel=9)")
    cv.Set(l, 0, image.threshold(edges, threshold=1))
    cv.Set(l2, 0, image.threshold(edges, threshold=1))
    
    l = image.threshold(l, threshold=1)
    l2 = image.threshold(l2, threshold=1)
    
    
    
    if debug:
        image.show(image.threshold(edges, threshold=1), "Edges")
        image.show(l, "After Edge Removal (kernel=5)")
        image.show(l2, "After Edge Removal (kernel=9)")
        
    noise2 = image.new_from(gray)
    cv.EqualizeHist(gray, noise2)
    cv.AbsDiff(noise2, gray, noise2)
    cv.Set(noise2, 0, image.threshold(image.sobel(im, xorder=2, yorder=2), threshold=4))
    diff = image.cv2array(noise2)
    if debug:
        image.show(noise2, "DIFF")
        print "M", diff.mean(), "S", diff.std()
    diff_stat = (diff.mean(), diff.std())
    percent_noise = cv.CountNonZero(noise2) / total
    if debug:
        image.show(noise2, "NOISE2")
        


    # magical, I don't understand how this works
    _, sat, _ = image.split(image.rgb2hsv(im))
    edges = image.auto_edges(im)
    l,u,v = tuple(map(image.equalize_hist, image.split(image.rgb2luv(im))))
    u,v = tuple(map(image.gaussian, (u,v)))
    if debug:
        image.show(l, "1. L")
        image.show(u, "1. U")
        image.show(v, "1. V")
    la,ua,va,uva = tuple(map(image.cv2array, (l,u,v, image.And(l,u,v))))
    test = image.new_from(gray)
    test2 = image.new_from(gray)
    cv.Xor(u,v,test)
    if debug:
        image.show(test, "2. U Xor V")
    cv.Set(test, 0, image.dilate(edges))
    #cv.Set(test, 0, image.invert(image.threshold(sat, threshold=8)))
    uv_score = cv.CountNonZero(test) / total
    if debug:
        image.show(test, "3. U Xor V - dilate(Edges) - invert(threshold(Saturation))")

    g = Grid(size)
    images = map(image.cv2array, g.split_into(test, 6))
    arr = image.cv2array(test)
    avg_mean, avg_std = arr.mean(), arr.std()


    #ms = [(a.mean(), a.std()) for a in images]
    #min_mean = min_std = 255
    #max_mean = max_std = 0
    #for m,s in ms:
    #    min_mean = min(min_mean, m)
    #    min_std = min(min_std, s)
    #    max_mean = max(max_mean, m)
    #    max_std = max(max_std, s)
    #if debug:
    #    print min_mean, min_std
    #    print avg_mean, avg_std
    #    print max_mean, max_std
    #
    #score = uv_score, min_mean, avg_mean, avg_std, max_mean
    uv_score = uv_score, avg_std

    score = cv.CountNonZero(l) / total,  cv.CountNonZero(l2) / total, \
        diff_stat[0], diff_stat[1], uv_score
    
    return l, score