def measure(im, debug=False):
    im2 = image.max_size(im, (800, 600))
    
    b,g,r = image.split(im2)
    #cv.EqualizeHist(r,r)
    ##cv.EqualizeHist(g,g)
    ##cv.EqualizeHist(b,b)
    im2 = image.merge(b,g,r)
    
    eyes = 0
    #objs = []
    #for cascade in eye_cascades:
    #    print cascade
    #    cascade = cv.Load(cascade)
    #    objs = filter_overlap(detect(im, cascade))
    #    draw_objects(im, objs, color=cv.RGB(0,255,0))
    #    eyes += len(objs)
    #faces = 0
    if debug:
        im3 = cv.CloneImage(im2)
    faces = []
    for cascade in face_cascades:#(face_cascades[0],face_cascades[-1]):
        cascade = cv.Load(cascade)
        detected_faces = detect(im2, cascade)
        faces += detected_faces
        if debug:
            for i,rect in enumerate(faces):
                rect.draw(im3, color=cv.RGB(255,16*i,16*i))
    if debug:
        image.show(im3, "Faces + Repeats")
    faces = filter_overlap(faces)
    #print (objs[1], objs[6])
    #draw_objects(im2, map(tuple, faces.keys()))
    for rect, count in faces.iteritems():
        rect.draw(im2, color=cv.RGB(255,0,0))
    #print (objs[3],objs[13])
    #draw_objects(im, filter_overlap((objs[3],objs[13])))
    
    #objs = []
    #for cascade in body_cascades:
    #    print cascade
    #    cascade = cv.Load(cascade)
    #    objs += detect(im, cascade)
    #draw_objects(im, filter_overlap(objs), color=cv.RGB(0,0,255))

    #objs = []
    #for cascade in mouth_cascades:
    #    print cascade
    #    cascade = cv.Load(cascade)
    #    objs += detect(im, cascade)
    #draw_objects(im,  filter_overlap(objs), color=cv.RGB(255,0,255))
    
    score = 0
    for face_rect, count in faces.iteritems():
        score += count * 0.25 + 0.15
    print faces

    if debug:
        image.show(im2, "Faces")
    return (im2, faces), score
 def __init__(self, im, title=None):
     assert im.nChannels == 3
     self.title = title
     ims = image.split(im)
     if self.title is None:
         self.title = "Histogram-" + str(id(im))+ "-"
     self.histograms = (
         HistogramWindow(ims[0], self.title+"red", color=cv.Scalar(255,0,0)),
         HistogramWindow(ims[1], self.title+"green", color=cv.Scalar(0,255,0)),
         HistogramWindow(ims[2], self.title+"blue", color=cv.Scalar(0,0,255)),
     )
Example #3
0
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
Example #4
0
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]))
def measure(im, debug=False):
    gray = image.rgb2gray(im)
    _, s, v = image.split(image.rgb2hsv(im))
    h = GrayscaleHist(bins=64).use_image(v)
    s = GrayscaleHist(bins=64).use_image(s)
    scores = [score_hist(h)]

    if debug:
        image.show(v, "1. Value")
        image.show(h.to_img(), "2. Value Histogram")
        image.show(s.to_img(), "2. Saturation Histogram")
        print score_hist(s)

    return (None, scores[0])  # h.to_img(),
Example #6
0
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]))
Example #7
0
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
Example #8
0
 def __init__(self, im, title=None):
     assert im.nChannels == 3
     self.title = title
     ims = image.split(im)
     if self.title is None:
         self.title = "Histogram-" + str(id(im)) + "-"
     self.histograms = (
         HistogramWindow(ims[0],
                         self.title + "red",
                         color=cv.Scalar(255, 0, 0)),
         HistogramWindow(ims[1],
                         self.title + "green",
                         color=cv.Scalar(0, 255, 0)),
         HistogramWindow(ims[2],
                         self.title + "blue",
                         color=cv.Scalar(0, 0, 255)),
     )
def measure(im, debug=False):
    gray = image.rgb2gray(im)
    _, s, v = image.split(image.rgb2hsv(im))
    h = GrayscaleHist(bins=64).use_image(v)
    s = GrayscaleHist(bins=64).use_image(s)
    scores = [score_hist(h)]

    if debug:
        image.show(v, "1. Value")
        image.show(h.to_img(), "2. Value Histogram")
        image.show(s.to_img(), "2. Saturation Histogram")
        print score_hist(s)

    return (
        None,  #h.to_img(),
        scores[0],
    )
Example #10
0
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
Example #11
0
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
Example #12
0
def predict(clf, boundaries, img=None, align_template=None):
    if isinstance(img, basestring):
        raw_img = io.imread(img)
    elif isinstance(img, np.ndarray):
        raw_img = img
    else:
        raise TypeError()

    if align_template is None:
        aligned_img = raw_img
    else:
        aligned_img = align_fft(raw_img, align_template)

    cropped_img = crop(aligned_img, boundaries)
    binarized_img = preprocess(cropped_img)
    chars = split(binarized_img)

    # this gets an array of ints
    predictions = clf.predict(np.array([c.reshape(-1) for c in chars]))
    # make into a string
    prediction = ''.join([str(p) for p in predictions])
    return prediction, binarized_img, chars
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)
Example #14
0
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
Example #15
0
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
Example #16
0
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
Example #17
0
def measure_saturation(im, debug=False):
    _, sat, _ = image.split(image.rgb2hsv(im))
    arr = image.cv2array(sat)
    return arr.mean(), arr.std()
Example #18
0
cv.Set(u, 0)
cv.Set(v, 0)

size = cv.GetSize(l)
print size

for x in range(256):
    for y in range(size[1]):
        cv.Set2D(u, y, x, x)
        cv.Set2D(v, 255 - x, min(y, 255), x)

image.show(u, "U")
image.show(v, "V")

rgb = image.luv2rgb(image.merge(l, u, v))
r, g, b = image.split(rgb)
#xor = image.threshold(image.Xor(u,v), 0, cv.CV_THRESH_BINARY)
xor = image.Xor(u, v)
cv.Threshold(xor, xor, 16, 255, cv.CV_THRESH_TOZERO)
image.show(rgb, "RGB")
image.show(xor, "Xor")

#cv.Sub(rgb, image.gray2rgb(image.invert(xor)), rgb)
_, sat, _ = image.split(image.rgb2hsv(rgb))
image.show(sat, 'Saturation')
#cv.Set(xor, 0, image.invert(image.threshold(sat, threshold=4)))

cv.Sub(rgb, image.invert(image.gray2rgb(xor)), rgb)

image.show(rgb, "Rgb - Xor")
arr = image.cv2array(xor)
Example #19
0
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
Example #20
0
cv.Set(u, 0)
cv.Set(v, 0)

size = cv.GetSize(l)
print size

for x in range(256):
    for y in range(size[1]):
        cv.Set2D(u, y, x, x)
        cv.Set2D(v, 255-x, min(y, 255), x)
        
image.show(u, "U")
image.show(v, "V")

rgb = image.luv2rgb(image.merge(l,u,v))
r,g,b = image.split(rgb)
#xor = image.threshold(image.Xor(u,v), 0, cv.CV_THRESH_BINARY)
xor = image.Xor(u,v)
cv.Threshold(xor, xor, 16, 255, cv.CV_THRESH_TOZERO)
image.show(rgb, "RGB")
image.show(xor, "Xor")

#cv.Sub(rgb, image.gray2rgb(image.invert(xor)), rgb)
_, sat, _ = image.split(image.rgb2hsv(rgb))
image.show(sat, 'Saturation')
#cv.Set(xor, 0, image.invert(image.threshold(sat, threshold=4)))

cv.Sub(rgb, image.invert(image.gray2rgb(xor)), rgb)

image.show(rgb, "Rgb - Xor")
arr = image.cv2array(xor)