Пример #1
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def plot_cup(img, bbox):
    img = FC.resize_image(img)
    lab_img = FC.apply_color_model(img)

    labels1, labels2 = FC.segment_image(img, lab_img)

    cup_labels = FC.select_cup_segment(labels2, bbox)
#     out3 = skic.label2rgb(cup_labels, img, kind='avg')
    out3 = cup_labels

    neg_segments = FC.select_negative_segments(cup_labels, labels2)
    out4 = skic.label2rgb(neg_segments, img, kind='avg')

    plt.figure()
    plt.imshow(img)
    plt.title("Image")
    plt.figure()
    plt.imshow(skic.label2rgb(labels1, img, kind='avg'))
    plt.title("Plain SLIC result")
    plt.figure()
    plt.imshow(skic.label2rgb(labels2, img, kind='avg'))
    plt.title("Merged segments")
    plt.figure()
    plt.imshow(out3)
    if np.max(out3.ravel()) == 1:
        plt.title("cup only")
    else:
        plt.title("rejected cup")

    plt.figure()
    plt.imshow(out4)
    plt.title("Negative segments")
Пример #2
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def logo_iterate(labels, image, fns=d + 'logo-%03i.png'):
    height, width = labels.shape
    background = (labels == 0)
    foreground = ~background
    counter = it.count()

    # part one: just foreground/background
    colorcombos = it.permutations(colors, 2)
    lab2 = np.zeros(labels.shape, np.uint8)
    lab2[foreground] = 1
    for cs in colorcombos:
        img = color.label2rgb(lab2, image, colors=cs)
        io.imsave(fns % next(counter), img)

    # part two: background split
    splits = np.arange(500, 1600, 100).astype(int)
    colorcombos = it.permutations(colors, 3)
    for s, cs in it.product(splits, colorcombos):
        im, lab = _split_img_horizontal(image, lab2, background, s)
        img = color.label2rgb(lab, im, colors=cs)
        io.imsave(fns % next(counter), img)

    # part three: foreground split
    colorcombos = it.permutations(colors, 3)
    for cs in colorcombos:
        img = color.label2rgb(labels, image, colors=cs)
        io.imsave(fns % next(counter), img)

    # part four: both split
    colorcombos = it.permutations(colors, 4)
    for s, cs in it.product(splits, colorcombos):
        im, lab = _split_img_horizontal(image, labels, background, s)
        img = color.label2rgb(lab, im, colors=cs)
        io.imsave(fns % next(counter), img)
Пример #3
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def normcut_segmentations(img):

    #labels1 = segmentation.slic(img, compactness=3, n_segments=50)
    labels1 = segmentation.slic(img,compactness=3,n_segments=20)
    out1 = color.label2rgb(labels1, img)#, kind='avg')
    #return labels1
    g = graph.rag_mean_color(img, labels1, mode='similarity')
    labels2 = graph.cut_normalized(labels1, g)
    out2 = color.label2rgb(labels2, img,image_alpha=0.2)#, kind='avg')
    return (labels1,labels2)
Пример #4
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def number_nucleus(image):

    elevation_map = sobel(image)
    markers = np.zeros_like(image)
    markers[image < 250] = 1
    markers[image > 2000] = 2

    segmentation = watershed(elevation_map, markers)
    label_img = label(segmentation)
    prop = regionprops(label_img)

    width, height = plt.rcParams['figure.figsize']
    plt.rcParams['image.cmap'] = 'gray'

    image_label_overlay = label2rgb(label_img, image=image)

    fig, (ax1, ax2) = plt.subplots(ncols=2, nrows=1, figsize=(15, 8))
    ax1.imshow(image_label_overlay)
    ax2.imshow(image, cmap=plt.cm.gray, interpolation='nearest')

    # create list of region with are < 1000
    image_labeled = [region for region in prop if region.area > 5000]


    return len(image_labeled)
Пример #5
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def show_all(fname,images,titles,numsegs=1):
    
    num_images = len(images)
    num_titles = len(titles)
    titles += ['']*(num_images-num_titles)
    
    fig, axes = plt.subplots(ncols=num_images, figsize=(9, 2.5))

    im = images[0]

    for i in range(numsegs):
        axes[i].imshow(images[i])
        axes[i].set_title(titles[i])
        print titles[i]

    for i in range(numsegs,num_images) :
        j=i #numsegs+i
        segimg = label2rgb(images[j], image=im, image_alpha=0.5)
        axes[j].imshow(segimg, interpolation='nearest')
        axes[j].set_title(titles[j])
        print titles[j]

    for ax in axes:
        ax.axis('off')
    fig.subplots_adjust(hspace=0.01, wspace=0.01, top=1, bottom=0, left=0, right=1)
    plt.show()
    #plt.savefig(fname+"_seg.jpg")
    print fname+"_seg.jpg"
    return True
Пример #6
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	def build_region(self):
		start_time = time.time();
		labels = segmentation.slic(self.q_frame,self.num_superpixels, self.compactness,convert2lab=True,multichannel=True)
		_num_superpixels = np.max(labels) + 1;
		self.s_frame = color.label2rgb(labels,self.q_frame, kind='avg')
		self.freq = np.array([np.sum(labels==label) for label in range(_num_superpixels)])
		self.mean = np.array([region['centroid'] for region in regionprops(labels+1)],dtype=np.int16);	
		
		self.color_data = np.array([np.sum(self.q_frame[np.where(labels==label)],0) for label in range(_num_superpixels)])		
		_inv_freq = 1/(self.freq+0.0000001);  self.color_data = self.color_data*_inv_freq[:,None]
		gray_frame = cv2.cvtColor(self.q_frame,cv2.COLOR_RGB2GRAY)
		def texture_prop(label,patch_size = 5):
			_mean_min = self.mean[label]-patch_size;
			_mean_max = self.mean[label]+patch_size;
			glcm = greycomatrix(gray_frame[_mean_min[0]:_mean_max[0],_mean_min[1]:_mean_max[1]],
						[3], [0], 256, symmetric=True, normed=True)
			_dis = greycoprops(glcm, 'dissimilarity')[0, 0];
			_cor = greycoprops(glcm, 'correlation')[0, 0];
			return (_dis,_cor);
		self.texture_data = np.array([texture_prop(label) for label in range(_num_superpixels)])
		self.data = np.hstack((self.color_data,self.texture_data))
		
		cv2.imwrite('outs.png',self.s_frame);				
		print "Build region (preprocess) : ",time.time()-start_time
		return (labels,_num_superpixels);
Пример #7
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def roofRegion(edge):
    """Estimate region based on edges of roofRegion
    """
    # apply threshold
    thresh = threshold_otsu(image)
    bw = closing(image > thresh, square(3))

    # remove artifacts connected to image border
    cleared = bw.copy()
    clear_border(cleared)

    # label image regions
    label_image = label(cleared)
    borders = np.logical_xor(bw, cleared)
    label_image[borders] = -1
    image_label_overlay = label2rgb(label_image, image=image)

    fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(6, 6))
    ax.imshow(image_label_overlay)

    for region in regionprops(label_image):

        # skip small images
        if region.area < 100:
            continue

        # draw rectangle around segmented coins
        minr, minc, maxr, maxc = region.bbox
        rect = mpatches.Rectangle((minc, minr), maxc - minc, maxr - minr,
                                  fill=False, edgecolor='red', linewidth=2)
        ax.add_patch(rect)

    plt.show()
Пример #8
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	def __build_region__(self,q_frame):
		start_time = time.time();
		regions = segmentation.slic(q_frame,self.props.num_superpixels, self.props.compactness,
				convert2lab=self.props.useLAB,multichannel=True)
		num_regions = len(np.unique(regions));
		s_frame = color.label2rgb(regions,q_frame, kind='avg')
		mean = np.array([region['centroid'] for region in regionprops(regions+1)])
		freq = np.array([np.sum(regions==region) for region in range(num_regions)])
		region_props = (mean,freq);
		
		if self.props.useColor:
			color_data = self.__extract_color__(q_frame,regions,region_props);		
		if self.props.useTexture:
			texture_data = self.__extract_texture__(q_frame,regions,region_props);
			
		if self.props.useTexture and self.props.useColor:
			data = np.hstack((color_data,texture_data))
		elif self.props.useTexture:
			data = texture_data
		else :
			data = color_data
				
		if self.props.doProfile:
			cv2.imwrite(self.PROFILE_PATH+self.method+'_s.png',s_frame);					
			print "Build region (preprocess) : ",time.time()-start_time
	
		return (num_regions,regions,region_props,data);
def get_cells(image):
    '''
    Get cellls from the polygon.
    '''
    new_image=np.ones([3,image.shape[0],image.shape[1]],dtype=float)
    # apply threshold
    thresh = threshold_otsu(image)
    bw=image

    # remove artifacts connected to image border
    cleared = bw.copy()
    clear_border(cleared)

    # label image regions
    label_image = label(cleared)
    #skimage.measure.label
    #find_contours
    borders = np.logical_xor(bw, cleared)
    label_image[borders] = -1
    image_label_overlay = label2rgb(label_image, image=image)

    #extract the regions and get a polygon per region
    polygons=[]
    for i,region in enumerate(regionprops(label_image)):
        # skip small images
        if region.area < 100:
            continue
        #polygons.append(matplotlib.path.Path(region.coords))
        print (region.coords.shape)
        a=np.zeros(region.coords.shape)
        a[:,0]=region.coords[:,1]
        a[:,1]=region.coords[:,0]
        polygons.append(a)   
    return polygons
 def _apply(self, img_msg, label_msg):
     bridge = cv_bridge.CvBridge()
     img = bridge.imgmsg_to_cv2(img_msg)
     label_img = bridge.imgmsg_to_cv2(label_msg)
     # publish only valid label region
     applied = img.copy()
     applied[label_img == 0] = 0
     applied_msg = bridge.cv2_to_imgmsg(applied, encoding=img_msg.encoding)
     applied_msg.header = img_msg.header
     self.pub_img.publish(applied_msg)
     # publish visualized label
     if img_msg.encoding in {'16UC1', '32SC1'}:
         # do dynamic scaling to make it look nicely
         min_value, max_value = img.min(), img.max()
         img = (img - min_value) / (max_value - min_value) * 255
         img = gray2rgb(img)
     label_viz_img = label2rgb(label_img, img, bg_label=0)
     label_viz_img = mark_boundaries(label_viz_img, label_img, (1, 0, 0))
     label_viz_img = (label_viz_img * 255).astype(np.uint8)
     label_viz_msg = bridge.cv2_to_imgmsg(label_viz_img, encoding='rgb8')
     label_viz_msg.header = img_msg.header
     self.pub_label_viz.publish(label_viz_msg)
     # publish mask
     if self._publish_mask:
         bg_mask = (label_img == 0)
         fg_mask = ~bg_mask
         bg_mask = (bg_mask * 255).astype(np.uint8)
         fg_mask = (fg_mask * 255).astype(np.uint8)
         fg_mask_msg = bridge.cv2_to_imgmsg(fg_mask, encoding='mono8')
         fg_mask_msg.header = img_msg.header
         bg_mask_msg = bridge.cv2_to_imgmsg(bg_mask, encoding='mono8')
         bg_mask_msg.header = img_msg.header
         self.pub_fg_mask.publish(fg_mask_msg)
         self.pub_bg_mask.publish(bg_mask_msg)
Пример #11
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def detectOpticDisc(image):
    labels = segmentation.slic(image, n_segments = 70)
    out = color.label2rgb(labels, image, kind='avg')
    gray = cv2.cvtColor(out, cv2.COLOR_RGB2GRAY)
    minimum = np.max(gray)
    image[gray==minimum] = 255
    return image
    def plot_preprocessed_image(self):
        """
        plots pre-processed image. The plotted image is the same as obtained at the end
        of the get_text_candidates method.
        """
        image = restoration.denoise_tv_chambolle(self.image, weight=0.1)
        thresh = threshold_otsu(image)
        bw = closing(image > thresh, square(2))
        cleared = bw.copy()

        label_image = measure.label(cleared)
        borders = np.logical_xor(bw, cleared)

        label_image[borders] = -1
        image_label_overlay = label2rgb(label_image, image=image)

        fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(12, 12))
        ax.imshow(image_label_overlay)

        for region in regionprops(label_image):
            if region.area < 10:
                continue

            minr, minc, maxr, maxc = region.bbox
            rect = mpatches.Rectangle((minc, minr), maxc - minc, maxr - minr,
                                      fill=False, edgecolor='red', linewidth=2)
            ax.add_patch(rect)

        plt.show()
Пример #13
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 def SuperPixel(self, Image):
     segments = slic(Image, n_segments=20, sigma=5)
     # show the output of SLIC
     segments = segments + 1
     # So that no labelled region is 0 and ignored by regionprops
     label_rgb = color.label2rgb(segments, Image, kind='avg')
     return label_rgb
Пример #14
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def detectOpticDisc(image):
    kernel = octagon(10, 10)
    thresh = threshold_otsu(image[:,:,1])
    binary = image > thresh
    print binary.dtype
    luminance = convertToHLS(image)[:,:,2]
    t = threshold_otsu(luminance)
    t = erosion(luminance, kernel)
    
    
    labels = segmentation.slic(image[:,:,1], n_segments = 3)
    out = color.label2rgb(labels, image[:,:,1], kind='avg')
    skio.imshow(out)
    
    x, y = computeCentroid(t)
    print x, y
    rows, cols, _ = image.shape
    p1 = closing(image[:,:,1],kernel)
    p2 = opening(p1, kernel)
    p3 = reconstruction(p2, p1, 'dilation')
    p3 = p3.astype(np.uint8)
    #g = dilation(p3, kernel)-erosion(p3, kernel)
    #g = rank.gradient(p3, disk(5))
    g = cv2.morphologyEx(p3, cv2.MORPH_GRADIENT, kernel)
    #markers = rank.gradient(p3, disk(5)) < 10
    markers = drawCircle(rows, cols, x, y, 85)
    #markers = ndimage.label(markers)[0]
    #skio.imshow(markers)
    g = g.astype(np.uint8)
    #g = cv2.cvtColor(g, cv2.COLOR_GRAY2RGB)
    w = watershed(g, markers)
    print np.max(w), np.min(w)
    w = w.astype(np.uint8)
    #skio.imshow(w)
    return w
Пример #15
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 def view_dataset(self):
     for datum in self.val:
         rgb, label = self.load_datum(datum, train=False)
         label_viz = label2rgb(label, rgb, bg_label=-1)
         label_viz[label == 0] = 0
         plt.imshow(label_viz)
         plt.show()
Пример #16
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def get_cells(image):
    '''
    Get cellls from the polygon.
    '''
    # apply threshold    
    thresh = threshold_otsu(image)
    binary = image > thresh
    bw=binary
    plt.imshow(bw)

    # Remove connected to image border
    cleared = bw.copy()
    clear_border(cleared)

    # label image regions
    label_image = skimage.measure.label(cleared)
    #find_contours
    borders = np.logical_xor(bw, cleared)
    label_image[borders] = -1
    image_label_overlay = label2rgb(label_image, image=image)

    #extract the regions and get a polygon per region
    polygons=[]
    for i,region in enumerate(regionprops(label_image)):
        # skip small images
        if region.area < 100:
            continue
        a=np.zeros([len(region.coords),2])
        #a=np.zeros(
        plt.imshow(bw)
        for i in range(len(region.coords)):
            a[i,:]=[region.coords[i][0],region.coords[i][1]]
        polygons.append(a)
    return polygons     
Пример #17
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def getRegions():
    """Geocode address and retreive image centered
    around lat/long"""
    address = request.args.get('address')
    results = Geocoder.geocode(address)
    lat, lng = results[0].coordinates
    zip_code = results[0].postal_code

    map_url = 'https://maps.googleapis.com/maps/api/staticmap?center={0},{1}&size=640x640&zoom=19&sensor=false&maptype=roadmap&&style=visibility:simplified|gamma:0.1'
    request_url = map_url.format(lat, lng)
    req = urllib.urlopen(request_url)
    img = io.imread(req.geturl(),flatten=True)
    labels, numobjects = ndimage.label(img)
    image = filter.canny(img, sigma=3)
    thresh = threshold_otsu(image)
    bw = closing(image > thresh, square(3))

    # remove artifacts connected to image border
    cleared = bw.copy()
    clear_border(cleared)

    # label image regions
    label_image = label(cleared)
    borders = np.logical_xor(bw, cleared)
    label_image[borders] = -1
    image_label_overlay = label2rgb(label_image, image=image)

    fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(6, 6))
    ax.imshow(image_label_overlay)
    def _callback(self, img_msg, mask_msg):
        bridge = cv_bridge.CvBridge()
        bgr_img = bridge.imgmsg_to_cv2(img_msg, desired_encoding='bgr8')
        mask_img = bridge.imgmsg_to_cv2(mask_msg, desired_encoding='mono8')
        if mask_img.size < 1:
            logwarn_throttle(10, 'Too small sized image')
            return
        logwarn_throttle(10, '[FCNMaskForLabelNames] >> Start Processing <<')
        if mask_img.ndim == 3 and mask_img.shape[2] == 1:
            mask_img = mask_img.reshape(mask_img.shape[:2])
        if mask_img.shape != bgr_img.shape[:2]:
            jsk_logwarn('Size of mask and color image is different.'
                        'Resizing.. mask {0} to {1}'
                        .format(mask_img.shape, bgr_img.shape[:2]))
            mask_img = resize(mask_img, bgr_img.shape[:2],
                              preserve_range=True).astype(np.uint8)

        blob = bgr_img - self.mean_bgr
        blob = blob.transpose((2, 0, 1))

        x_data = np.array([blob], dtype=np.float32)
        if self.gpu != -1:
            x_data = cuda.to_gpu(x_data, device=self.gpu)
        x = Variable(x_data, volatile=True)
        self.model(x)
        pred_datum = cuda.to_cpu(self.model.score.data[0])

        candidate_labels = [self.target_names.index(name)
                            for name in self.tote_contents]
        label_pred_in_candidates = pred_datum[candidate_labels].argmax(axis=0)
        label_pred = np.zeros_like(label_pred_in_candidates)
        for idx, label_val in enumerate(candidate_labels):
            label_pred[label_pred_in_candidates == idx] = label_val
        label_pred[mask_img == 0] = 0  # set bg_label

        label_viz = label2rgb(label_pred, bgr_img, bg_label=0)
        label_viz = (label_viz * 255).astype(np.uint8)
        debug_msg = bridge.cv2_to_imgmsg(label_viz, encoding='rgb8')
        debug_msg.header = img_msg.header
        self.pub_debug.publish(debug_msg)

        output_mask = np.ones(mask_img.shape, dtype=np.uint8)
        output_mask *= 255
        for label_val, label_name in enumerate(self.target_names):
            if label_name in self.label_names:
                assert label_name == 'kleenex_paper_towels'
                assert label_val == 21
                label_mask = ((label_pred == label_val) * 255).astype(np.uint8)
                contours, hierachy = cv2.findContours(
                    label_mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
                cv2.drawContours(output_mask, contours, -1, 255, -1)
                # output_mask[label_pred == label_val] = False
        # output_mask = output_mask.astype(np.uint8)
        # output_mask[output_mask == 1] = 255
        output_mask[mask_img == 0] = 0
        output_mask_msg = bridge.cv2_to_imgmsg(output_mask, encoding='mono8')
        output_mask_msg.header = img_msg.header
        self.pub.publish(output_mask_msg)
        logwarn_throttle(10, '[FCNMaskForLabelNames] >> Finshed processing <<')
Пример #19
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 def validate(self):
     """Validate training with data."""
     log_templ = ('{i_iter}: type={type}, loss={loss}, acc={acc}, '
                  'acc_cls={acc_cls}, iu={iu}, fwavacc={fwavacc}')
     type = 'val'
     self.model.train = False
     N_data = len(self.dataset.val)
     result = defaultdict(list)
     desc = '{0}: validating'.format(self.i_iter)
     for indice in tqdm.tqdm(xrange(N_data), ncols=80, desc=desc):
         loss, acc, acc_cls, iu, fwavacc = self._iterate_once(
             type=type, indices=[indice])
         result['loss'].append(loss)
         result['acc'].append(acc)
         result['acc_cls'].append(acc_cls)
         result['iu'].append(iu)
         result['fwavacc'].append(fwavacc)
     # visualize predicted label
     blob = cuda.to_cpu(self.model.x.data)[0]
     label_true = cuda.to_cpu(self.model.t.data)[0]
     img = self.dataset.datum_to_img(blob)
     label_true_viz = label2rgb(label_true, img, bg_label=0)
     label_true_viz[label_true == 0] = 0
     label_true_viz = (label_true_viz * 255).astype(np.uint8)
     label = cuda.to_cpu(self.model.score.data)[0].argmax(axis=0)
     label_viz = label2rgb(label, img, bg_label=0)
     label_viz[label == 0] = 0
     label_viz = (label_viz * 255).astype(np.uint8)
     hline = np.zeros((5, img.shape[1], 3), dtype=np.uint8)
     hline.fill(255)
     imsave(
         osp.join(self.log_dir, 'visualize_{0}.jpg'.format(self.i_iter)),
         np.vstack([img, hline, label_true_viz, hline, label_viz, hline]))
     log = dict(
         i_iter=self.i_iter,
         type=type,
         loss=np.array(result['loss']).mean(),
         acc=np.array(result['acc']).mean(),
         acc_cls=np.array(result['acc_cls']).mean(),
         iu=np.array(result['iu']).mean(),
         fwavacc=np.array(result['fwavacc']).mean(),
     )
     print(log_templ.format(**log))
     self.logfile.write(
         '{i_iter},{type},{loss},{acc},{acc_cls},{iu},{fwavacc}\n'
         .format(**log))
 def forward(self, bottom, top):
     # bottom[0]: images N*3*W*H
     # bottom[1]: prediction N*1*W*H
     n = bottom[0].data.shape[0]
     for i in range(n):
         labels = segmentation.slic( bottom[0].data[i].transpose((1,2,0)), 
                 compactness=self.compactness, n_segments=self.n_segs)
         top[0].data[i, ...] = color.label2rgb(labels, bottom[1].data[i].transpose((1,2,0)), kind='avg').transpose((2,0,1)) #.reshape(top[0].data[i].shape)
Пример #21
0
def fig_label_segments(fig, image, segments, label):
    labels, _ = ndimage.label(segments)
    image_label_overlay=label2rgb(labels, image=image)
    image_label_overlay=mark_boundaries(image, segments)
    fig.set_title(label)
    fig.axis('off')
    fig.imshow(image_label_overlay)
    print ("%s number of segments: %d" % (label, len(np.unique(segments))))
Пример #22
0
def agglomerativeClusteringFeatures(image):
    connectivity = grid_to_graph(*image[:,:,2].shape)
    X = np.reshape(image[:,:,2], (-1,1))
    ward = AgglomerativeClustering(n_clusters=150,
        linkage = 'ward', connectivity = connectivity).fit(X)
    labels = np.reshape(ward.labels_, image[:,:,2].shape)
    averageIntensity = color.label2rgb(labels, image[:,:,2], kind = 'avg')
    #areas = getAreas(labels) 
    return averageIntensity
Пример #23
0
 def build_region(self):
     start_time = time.time()
     labels = segmentation.slic(
         self.q_cur_frame, self.num_superpixels, self.compactness, convert2lab=True, multichannel=True
     )
     self.s_frame = color.label2rgb(labels, self.q_cur_frame, kind="avg")
     cv2.imwrite("outs.png", self.s_frame)
     print "Slic time : ", time.time() - start_time
     return labels
Пример #24
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def segment_image(image, n_segments=400, compactness=30, sigma=5, verbose=True):
    if verbose:
        print("segmenting image")
        print("n_segments=%d, compactness=%d, sigma=%d" % (n_segments, compactness, sigma))

    image = image.astype("float64")
    labels = slic(image, compactness=compactness, n_segments=n_segments, sigma=sigma)
    segmented = color.label2rgb(labels, image, kind="avg")
    return segmented.astype("uint8")
Пример #25
0
def spectral_cluster(filename, compactness_val=30, n=6):
    img = misc.imread(filename)
    labels1 = segmentation.slic(img, compactness=compactness_val, n_segments=n)
    out1 = color.label2rgb(labels1, img, kind='overlay', colors=['red','green','blue','cyan','magenta','yellow'])

    fig, ax = plt.subplots()
    ax.imshow(out1, interpolation='nearest')
    ax.set_title("Compactness: {} | Segments: {}".format(compactness_val, n))
    plt.show()
Пример #26
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def createImage(thres):
    # do simple filter based on color value
    filtered_image = np.zeros_like(image) # set up all-zero image
    filtered_image[image > thres] = 1 # filtered values set to 1

    # label features and convert to rgb image
    labeled_particles, num_features = ndi.label(filtered_image)
    image_label_overlay = label2rgb(labeled_particles, image=image, bg_label=0)
    return image_label_overlay
Пример #27
0
def getArea(address):
    """Geocode address and retreive image centered
    around lat/long"""
    address = address
    results = Geocoder.geocode(address)
    lat, lng = results[0].coordinates
    zip_code = results[0].postal_code

    map_url = 'https://maps.googleapis.com/maps/api/staticmap?center={0},{1}&size=640x640&zoom=19&sensor=false&maptype=roadmap&&style=visibility:simplified|gamma:0.1'
    request_url = map_url.format(lat, lng)
    req = urllib.urlopen(request_url)
    img = io.imread(req.geturl(),flatten=True)
    labels, numobjects = ndimage.label(img)
    image = filter.canny(img, sigma=3)
    thresh = threshold_otsu(image)
    bw = closing(image > thresh, square(3))

    # remove artifacts connected to image border
    cleared = bw.copy()
    clear_border(cleared)

    # label image regions
    label_image = label(cleared)
    borders = np.logical_xor(bw, cleared)
    label_image[borders] = -1
    image_label_overlay = label2rgb(label_image, image=image)

    fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(6, 6))
    ax.imshow(image_label_overlay)
    dist = []
    rp = regionprops(label_image)
    rp = [x for x in rp if 100 < x.area <= 900]

    for region in rp:

        # skip small images
        #if region.area < 100:
        #    continue
        dist.append(sqrt( ( 320-region.centroid[0] )**2 + ( 320-region.centroid[1] )**2 ))
        # draw rectangle around segmented coins
        #minr, minc, maxr, maxc = region.bbox
        #rect = mpatches.Rectangle((minc, minr), maxc - minc, maxr - minr,
        #                      fill=False, edgecolor='red', linewidth=2)
        #ax.add_patch(rect)

    roof_index = dist.index(min(dist))
    minr, minc, maxr, maxc = rp[roof_index].bbox
    rect = mpatches.Rectangle((minc, minr), maxc - minc, maxr - minr,
                          fill=False, edgecolor='red', linewidth=2)
    ax.add_patch(rect)

    img = StringIO()
    fig.savefig(img)
    img.seek(0)
    session['roof_area'] = rp[roof_index].area
    roof_area = (rp[roof_index].area)*12
    return(roof_area)
def overlay_cells_on_image(image, boundries, cells, offset=(0,0)):
    mask = numpy.zeros(image.shape[:2])
    boundries -= numpy.asarray(tuple(offset) * 2)
    label = 1
    for cell in filter_cells_by_boundry(cells, boundries):
        math.modify_with_bounding_box(cell.bbox, mask, cell.mask.astype(numpy.uint16)*label)
        label += 1

    return label2rgb(mask, image=image, bg_label=0)
Пример #29
0
	def parse_image(self):
		self.m_print("Parsing panel image",0)
		thresh = threshold_otsu(self.i_file)
		bw = closing(self.i_file > thresh, square(3))
		# remove artifacts connected to image border
		cleared = bw.copy()
		clear_border(cleared)
		# label image regions
		label_image = label(cleared)
		borders = np.logical_xor(bw, cleared)
		label_image[borders] = -1
		return label2rgb(label_image, image=self.i_file),label_image
	def build_region(self):
		start_time = time.time();
		labels = segmentation.slic(self.q_cur_frame,self.num_superpixels, self.compactness,convert2lab=True,multichannel=True)
		_num_superpixels = np.max(labels) + 1;
		self.s_frame = color.label2rgb(labels,self.q_cur_frame, kind='avg')
		self.mean = np.array([region['centroid'] for region in regionprops(labels+1)])
		self.color_data = np.array([np.sum(self.q_cur_frame[np.where(labels==label)],0) for label in range(_num_superpixels)])
		self.freq = np.array([np.sum(labels==label) for label in range(_num_superpixels)])
		_inv_freq = 1/(self.freq+0.0000001); self.color_data = self.color_data*_inv_freq[:,None]
		cv2.imwrite('outs.png',self.s_frame);				
		print "Build region (preprocess) : ",time.time()-start_time
		return (labels,_num_superpixels);
Пример #31
0
def image_pre_process(image):
    # Apply otsu filter to find the thresholds
    print('starting with multi otsu filter')
    thresholds = threshold_multiotsu(image, classes=THO_N + 1)
    print('thresholds are', thresholds)
    regions = np.digitize(image, bins=thresholds)
    otsu_regions_img = img_as_ubyte(regions)

    # Binary threshold methods
    # https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_thresholding/py_thresholding.html
    ret, img_binary = cv2.threshold(image, thresholds[0], 255,
                                    cv2.THRESH_BINARY)

    # Dilation and erosion
    # https://www.youtube.com/watch?v=WQK_oOWW5Zo
    kernel = np.ones((3, 3), np.uint8)
    erosion = cv2.erode(img_binary, kernel, iterations=1)
    dilation = cv2.dilate(img_binary, kernel, iterations=1)
    opening = cv2.morphologyEx(img_binary, cv2.MORPH_OPEN, kernel)

    # Label image
    # https://www.youtube.com/watch?v=u3nG5_EjfM0&list=PLZsOBAyNTZwbIjGnolFydAN33gyyGP7lT&index=119
    #label_image = measure.label(otsu_regions_img, connectivity=img_binary.ndim)
    label_image = measure.label(opening, connectivity=img_binary.ndim)
    image_label_overlay = label2rgb(label_image, image=otsu_regions_img)

    # Get different regions from the labeled image
    # https://scikit-image.org/docs/dev/auto_examples/segmentation/plot_label.html
    i = 0
    regions = []
    bbx = []
    for region in measure.regionprops(label_image):
        i += 1
        print('i is', i)
        print('region is', region.image)
        regions.append(region.image)
        bbx.append(region.bbox)
        figure = plt.figure()
        plt.imshow(region.image)
        plt.title('region ' + np.str(i))
        plt.savefig(
            'G:\\My Drive\\Project\\IntraOral Scanner Registration\\Results\\Segmentation test\\region '
            + np.str(i))
        plt.close()
    print('shape of regions is', np.shape(regions))
    for bbx_ind in bbx:
        #figure = plt.figure()
        #plt.imshow(regions[-3])
        #plt.title('label image check')
        print('region bounding box is', bbx_ind)

    # Edge detection
    # https://www.youtube.com/watch?v=Oy4duAOGdWQ&list=PLZsOBAyNTZwbIjGnolFydAN33gyyGP7lT&index=105
    robert_image = roberts(img_binary)
    sobel_image = sobel(img_binary)
    scharr_image = scharr(img_binary)
    prewitt_image = prewitt(img_binary)
    farid_image = farid(img_binary)
    print('detected edge is', sobel_image)

    return otsu_regions_img, image_label_overlay, sobel_image
Пример #32
0
 def run(self, ips, snap, img, para=None):
     lab = segmentation.felzenszwalb(snap, para['scale'], para['sigma'],
                                     para['min_size'])
     return color.label2rgb(lab, snap, kind='avg')
Пример #33
0
 def run(self, ips, snap, img, para=None):
     lab = segmentation.quickshift(snap, para['ratio'], para['kernel_size'],
                                   para['max_dist'], para['sigma'])
     return color.label2rgb(lab, snap, kind='avg')
Пример #34
0
def labelsConnection(data):
    labels = measure.label(data,
                           connectivity=2)  #connectivity表示连接的模式,1代表4连通,2代表8连通
    dst = color.label2rgb(labels)  #根据不同的标记显示不同的颜色
    #print('regions number:',labels.max()+1)  #显示连通区域块数(从0开始标记)
    return dst
So you will reduce this image from 265×191=50,615 pixels down to 400 regions.

Young woman
Already preloaded as face_image.
The show_image() function has been preloaded for you as well.

Instructions
100 XP

Import the slic() function from the segmentation module.
Import the label2rgb() function from the color module.
Obtain the segmentation with 400 regions using slic().
Put segments on top of original image to compare with label2rgb().
'''
SOLUTION

# Import the slic function from segmentation module
from skimage.segmentation import slic

# Import the label2rgb function from color module
from skimage.color import label2rgb

# Obtain the segmentation with 400 regions
segments = slic(face_image, n_segments=400)

# Put segments on top of original image to compare
segmented_image = label2rgb(segments, face_image, kind='avg')

# Show the segmented image
show_image(segmented_image, "Segmented image, 400 superpixels")
Пример #36
0
# -*- coding: utf-8 -*-
"""
Created on Mon Sep  2 22:18:57 2019

@author: dtket
"""

import imageio as iio
from skimage import filters
from skimage.color import rgb2gray  # only needed for incorrectly saved images
from skimage.measure import regionprops
import matplotlib.pyplot as plt
from skimage.color import label2rgb

image = rgb2gray(iio.imread('cube.png'))
threshold_value = filters.threshold_otsu(image)
labeled_foreground = (image > threshold_value).astype(int)
properties = regionprops(labeled_foreground, image)
center_of_mass = properties[0].centroid
weighted_center_of_mass = properties[0].weighted_centroid

colorized = label2rgb(labeled_foreground, image, colors=['black'], alpha=0.1)
fig, ax = plt.subplots()
ax.imshow(colorized)
# Note the inverted coordinates because plt uses (x, y) while NumPy uses (row, column)
ax.scatter(center_of_mass[1], center_of_mass[0], s=160, c='C0', marker='+')
plt.show()
Пример #37
0
from skimage import data, segmentation, color
from skimage.future import graph
from matplotlib import pyplot as plt


img = data.coffee()

labels1 = segmentation.slic(img, compactness=30, n_segments=400)
out1 = color.label2rgb(labels1, img, kind='avg')

g = graph.rag_mean_color(img, labels1, mode='similarity')
labels2 = graph.cut_normalized(labels1, g)
out2 = color.label2rgb(labels2, img, kind='avg')

plt.imshow(out1	)
plt.show()
Пример #38
0
icount = len(image_set)

start_time = datetime.datetime.now()

#开始去掉一些噪音
for ii, image_name in enumerate(image_set):

    #   image_name='003.png'
    image = cv2.imread(os.path.join(image_predict_dir, image_name))

    image2 = image[:, :, 0]  # 只取一个分量即可
    image2[image2 > 0] = 1  # 原始数据的掩模,非0的有很多种数,而模型的输入要求是二值的

    # 采用skimage中的measure,寻找每一个连通区域
    labeled_img, num = measure.label(image2, background=0, return_num=True)
    dst = color.label2rgb(labeled_img)

    classes = np.unique(labeled_img)
    classes = classes[1:]  # 不要背景0这一类
    for c in classes:
        inds = np.where(labeled_img == c)
        inds = np.array(inds).T

        # 忽略太少的点组成的连通区域
        if len(inds[:, 1]) < 1500:
            image2[labeled_img == c] = 0
        else:
            trans_pca = PCA(n_components=2).fit(inds)  # 对该连通区域所有点的坐标集合,进行PCA变换
            pcas = trans_pca.components_  # PCA的两个主成分,即为点的坐标集合的主要方向和次要方向
            # 最主要的方向,计算方向角度theta,准备旋转theta角变成水平方向
            pca1 = pcas[0]
Пример #39
0
ax = axes.ravel()

ax[0].imshow(camera, cmap=cm.gray)
ax[0].set_title('Input image')

ax[1].imshow(edges, cmap=cm.gray)
ax[1].set_title('Canny edges')
ax[2].imshow(edges * 0)
for line in lines:
    p0, p1 = line
    ax[2].plot((p0[0], p1[0]), (p0[1], p1[1]))
ax[2].set_xlim((0, camera2.shape[1]))
ax[2].set_ylim((camera2.shape[0], 0))
ax[2].set_title('Probabilistic Hough')
'''
camera = label2rgb(camera2, image=camera1)
fig, ax = plt.subplots(figsize=(10, 6))
ax.imshow(camera, cmap='gray', interpolation='nearest')
#ax.imshow(camera , cmap='gray', interpolation='nearest')
count = 0
regions = regionprops(camera2)
for region in regions:
    # take regions with large enough areas
    if region.area >= 650:
        # draw rectangle around segmented coins
        minr, minc, maxr, maxc = region.bbox
        slice_hei = int((maxr - minr) * 0.1)
        #print(minr, minc, maxr, maxc)
        croped = camera1[minr - slice_hei:maxr + slice_hei, minc:maxc]
        binary_crop = cam_clear[minr - slice_hei:maxr + slice_hei, minc:maxc]
        #print("Here",camera.shape)
Пример #40
0
    # Binary threshold methods
    # https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_thresholding/py_thresholding.html
    ret, img_binary = cv2.threshold(img_segment, thresholds[0], 255,
                                    cv2.THRESH_BINARY)

    # Dilation and erosion
    # https://www.youtube.com/watch?v=WQK_oOWW5Zo
    kernel = np.ones((3, 3), np.uint8)
    erosion = cv2.erode(img_binary, kernel, iterations=1)
    dilation = cv2.dilate(img_binary, kernel, iterations=1)

    # Label image
    # https://www.youtube.com/watch?v=u3nG5_EjfM0&list=PLZsOBAyNTZwbIjGnolFydAN33gyyGP7lT&index=119
    label_image = measure.label(output, connectivity=img_binary.ndim)
    image_label_overlay = label2rgb(label_image, image=output)

    # Edge detection
    # https://www.youtube.com/watch?v=Oy4duAOGdWQ&list=PLZsOBAyNTZwbIjGnolFydAN33gyyGP7lT&index=105
    robert_image = roberts(img_binary)
    sobel_image = sobel(img_binary)
    scharr_image = scharr(img_binary)
    prewitt_image = prewitt(img_binary)
    farid_image = farid(img_binary)

    #img_mutso = multiOtsu(3, img)
    plt.figure()
    plt.imshow(img_gray, cmap='gray')
    plt.title("Grayscale original image")

    plt.figure()
Пример #41
0
def augment_and_show(aug,
                     image,
                     mask=None,
                     bboxes=[],
                     categories=[],
                     category_id_to_name=[],
                     filename=None,
                     font_scale_orig=0.35,
                     font_scale_aug=0.35,
                     show_title=True,
                     **kwargs):

    augmented = aug(image=image,
                    mask=mask,
                    bboxes=bboxes,
                    category_id=categories)

    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    image_aug = cv2.cvtColor(augmented['image'], cv2.COLOR_BGR2RGB)

    for bbox in bboxes:
        visualize_bbox(image, bbox, **kwargs)

    for bbox in augmented['bboxes']:
        visualize_bbox(image_aug, bbox, **kwargs)

    if show_title:
        for bbox, cat_id in zip(bboxes, categories):
            visualize_titles(image,
                             bbox,
                             category_id_to_name[cat_id],
                             font_scale=font_scale_orig,
                             **kwargs)
        for bbox, cat_id in zip(augmented['bboxes'], augmented['category_id']):
            visualize_titles(image_aug,
                             bbox,
                             category_id_to_name[cat_id],
                             font_scale=font_scale_aug,
                             **kwargs)

    if mask is None:
        f, ax = plt.subplots(1, 2, figsize=(16, 8))

        ax[0].imshow(image)
        ax[0].set_title('Original image')

        ax[1].imshow(image_aug)
        ax[1].set_title('Augmented image')
    else:
        f, ax = plt.subplots(2, 2, figsize=(16, 16))

        if len(mask.shape) != 3:
            mask = label2rgb(mask, bg_label=0)
            mask_aug = label2rgb(augmented['mask'], bg_label=0)
        else:
            mask = cv2.cvtColor(mask, cv2.COLOR_BGR2RGB)
            mask_aug = cv2.cvtColor(augmented['mask'], cv2.COLOR_BGR2RGB)

        ax[0, 0].imshow(image)
        ax[0, 0].set_title('Original image')

        ax[0, 1].imshow(image_aug)
        ax[0, 1].set_title('Augmented image')

        ax[1, 0].imshow(mask, interpolation='nearest')
        ax[1, 0].set_title('Original mask')

        ax[1, 1].imshow(mask_aug, interpolation='nearest')
        ax[1, 1].set_title('Augmented mask')

    f.tight_layout()
    if filename is not None:
        f.savefig(filename)

    return augmented['image'], augmented['mask'], augmented['bboxes']
Пример #42
0
def image_segmentation(in_file_name, out_file_name, show_image):
    #example_ni1 = os.path.join(data_path, in_file_name)
    n1_img = nib.load(in_file_name)
    img_data = n1_img.get_data()
    print(img_data.shape)
    #save_example_ni1 = os.path.join(data_path, out_file_name)

    slice = np.zeros((176, 176, 208))
    segm = np.zeros((176, 176, 208))
    for i in range(175):
        slice[i] = img_data[:, :, i, 0]
        slice[i] = exposure.rescale_intensity(slice[i], out_range=(0, 256))
        img = color.gray2rgb(slice[i])
        if (img.min() >= 0):
            labels1 = segmentation.slic(img,
                                        compactness=30,
                                        n_segments=200,
                                        multichannel=False)
            out1 = color.label2rgb(labels1, img, kind='avg')
            #g = graph.rag_mean_color(img, labels1, mode='similarity')
            #labels2 = graph.cut_normalized(labels1, g)
            #out2 = color.label2rgb(labels2, img, kind='avg')
            segm[i] = color.rgb2gray(out1)
            #segm[i] = out1

    if (show_image):
        show_slices([slice[100], slice[110], slice[120]])
        plt.suptitle("slices")

    for i in range(175):
        img_data[:, :, i, 0] = segm[i]

    if (show_image):
        # display results
        fig, (ax1, ax2, ax3) = plt.subplots(nrows=1,
                                            ncols=3,
                                            figsize=(8, 3),
                                            sharex=True,
                                            sharey=True)

        ax1.imshow(img_data[:, :, 100, 0])
        ax1.axis('off')
        ax1.set_title('image 100', fontsize=20)

        ax2.imshow(img_data[:, :, 110, 0])
        ax2.axis('off')
        ax2.set_title('image 110', fontsize=20)

        ax3.imshow(img_data[:, :, 120, 0])
        ax3.axis('off')
        ax3.set_title('image 120', fontsize=20)

        fig.subplots_adjust(wspace=0.02,
                            hspace=0.02,
                            top=0.9,
                            bottom=0.02,
                            left=0.02,
                            right=0.98)

        plt.show()

    save_img = nib.Nifti1Image(img_data, np.eye(4))
    nib.save(save_img, save_example_ni1)
Пример #43
0
import numpy as np
from skimage import data, util, filters, color
from skimage.segmentation import watershed
import matplotlib.pyplot as plt

coins = data.coins()
edges = filters.sobel(coins)

grid = util.regular_grid(coins.shape, n_points=468)

seeds = np.zeros(coins.shape, dtype=int)
seeds[grid] = np.arange(seeds[grid].size).reshape(seeds[grid].shape) + 1

w0 = watershed(edges, seeds)
w1 = watershed(edges, seeds, compactness=0.01)

fig, (ax0, ax1) = plt.subplots(1, 2)

ax0.imshow(color.label2rgb(w0, coins, bg_label=-1))
ax0.set_title('Classical watershed')

ax1.imshow(color.label2rgb(w1, coins, bg_label=-1))
ax1.set_title('Compact watershed')

plt.show()
Пример #44
0
    def extract(self, data):
        path = data.basePath + "\\" + data.name
        print(path)
        img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
        # print(img)
        treshold = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)[1]

        dots = treshold > treshold.mean()
        dots_labels = measure.label(dots, background=1)

        image_label_overlay = label2rgb(dots_labels, image=treshold)

        max_area = 0
        total_area = 0
        count_connected_group = 0
        average = 0.0

        for region in regionprops(dots_labels):
            if (region.area > 10):
                total_area += region.area
                count_connected_group += 1

            if (region.area >= 250):
                if (region.area > max_area):
                    max_area = region.area

        average = (total_area / count_connected_group)

        a4_constant = ((average / 84.0) * 250.0) + 100

        b = morphology.remove_small_objects(dots_labels, a4_constant)

        if os.path.isdir(data.basePath + '\\outputs'):
            pass
        else:
            os.mkdir(data.basePath + '\\outputs')

        plt.imsave(data.basePath + '\\outputs\\pre_version.png', b)

        img = cv2.imread(data.basePath + '\\outputs\\pre_version.png',
                         cv2.IMREAD_GRAYSCALE)
        img = cv2.threshold(img, 0, 255,
                            cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]

        cv2.imwrite(data.basePath + "\\outputs\\output.png", img)

        print('Signature Extraction Success!')

        image = cv2.imread(data.basePath + '\\outputs\\output.png')
        result = image.copy()

        image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
        lower = np.array([0, 0, 0])
        upper = np.array([255, 255, 200])
        mask = cv2.inRange(image, lower, upper)

        kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
        close = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel, iterations=1)

        contours = cv2.findContours(close, cv2.RETR_EXTERNAL,
                                    cv2.CHAIN_APPROX_SIMPLE)

        if len(contours) == 2:
            contours = contours[0]
        else:
            contours = contours[1]

        boxes = []
        for c in contours:
            (x, y, w, h) = cv2.boundingRect(c)
            boxes.append([x, y, x + w, y + h])

        boxes = np.asarray(boxes)
        left = np.min(boxes[:, 0])
        top = np.min(boxes[:, 1])
        right = np.max(boxes[:, 2])
        bottom = np.max(boxes[:, 3])

        result[close == 0] = (255, 255, 255)
        ROI = result[top:bottom, left:right].copy()

        path = data.basePath + '\\result_' + data.name
        cv2.imwrite(path, ROI)

        # print('Signature Capture Success')
        return path
Пример #45
0
# Voila!
#
# This solution works reasonably well. Of course there is still a lot of room for improvement.
#
# For cells that get oversegmented we should implement a function that glues those oversegmented cells together rather than drop them.
#
# But that I will leave for you to create!
#
# If you liked the solution and would like to see how it works as a part of the end-to-end pipeline please go to:
#
# https://github.com/neptune-ml/data-science-bowl-2018
#
# To stay up to date with new features and fully open sourced solution (with predictions) read our data science bowl journal thread https://www.kaggle.com/c/data-science-bowl-2018/discussion/47590
#
# Cheers and good luck!

# # LB Test set predictions

# In[102]:

test_masks = joblib.load('../input/test-predictions/test_masks.pkl')

# In[103]:

from skimage.color import label2rgb

for mask in test_masks[:5]:
    plt.imshow(label2rgb(mask))
    plt.show()
Пример #46
0
#LOCAL SOURCE image_rgb = io.imread('examples/leds_off.jpg')
image = rgb2gray(image_rgb)

# apply threshold
bw = closing(image > detect_th, square(3))

# remove artifacts connected to image border
cleared = clear_border(bw)

# label image regions
label_image = label(cleared)

# to make the background transparent, pass the value of `bg_label`,
# and leave `bg_color` as `None` and `kind` as `overlay`
image_label_overlay = label2rgb(label_image, image=image, bg_label=0)

fig, ax = plt.subplots(figsize=(10, 6))
ax.imshow(image_label_overlay)

leds = {}  #led objects

for region in regionprops(label_image):
    # take regions with large enough areas

    if region.area > 1000:
        # draw rectangle around segmented item
        minr, minc, maxr, maxc = region.bbox

        cropped = image_rgb[minr:maxr, minc:maxc]
        skimage.io.imsave("temp/" + str(region.label) + ".jpg", cropped)
Пример #47
0
def cConexas2(image):

    label_image = label(image)
    image_label_overlay = label2rgb(image, image=image)

    fig, ax = plt.subplots(figsize=(10, 6))

    imageComponentesConexas = np.zeros(image.shape)

    ax.imshow(image_label_overlay)
    i = 0
    regions = []
    for region in regionprops(label_image):
        # take regions with large enough areas
        regions.append(region)
        # draw rectangle around segmented coins
        minr, minc, maxr, maxc = region.bbox
        if (i == 0):
            rect = mpatches.Rectangle((minc, minr),
                                      maxc - minc,
                                      maxr - minr,
                                      fill=False,
                                      edgecolor='red',
                                      linewidth=2)
        elif (i == 1):
            rect = mpatches.Rectangle((minc, minr),
                                      maxc - minc,
                                      maxr - minr,
                                      fill=False,
                                      edgecolor='green',
                                      linewidth=2)
        elif (i == 2):
            rect = mpatches.Rectangle((minc, minr),
                                      maxc - minc,
                                      maxr - minr,
                                      fill=False,
                                      edgecolor='blue',
                                      linewidth=2)
        elif (i == 3):
            rect = mpatches.Rectangle((minc, minr),
                                      maxc - minc,
                                      maxr - minr,
                                      fill=False,
                                      edgecolor='yellow',
                                      linewidth=2)
        else:
            rect = mpatches.Rectangle((minc, minr),
                                      maxc - minc,
                                      maxr - minr,
                                      fill=False,
                                      edgecolor='purple',
                                      linewidth=2)

        i = i + 1
        ax.add_patch(rect)

        imageComponentesConexas = newImage(imageComponentesConexas,
                                           region.coords)

    ax.set_axis_off()
    plt.tight_layout()
    plt.imshow(imageComponentesConexas, cmap='gray')
    plt.title("CC Finales")
    return imageComponentesConexas, regions
Пример #48
0
    def MERGEIAMGES(self, displaygui, CH1, RGB_Channels):

        if displaygui.Ch1CheckBox.isChecked() == True:
            ch1_rgb = np.stack((CH1, ) * 3, axis=-1)
        else:
            ch1_rgb = np.zeros(RGB_Channels.shape, dtype=np.uint8)
        All_Channels = cv2.addWeighted(ch1_rgb, 1, RGB_Channels, 1, 0)
        height, width, ch = np.shape(All_Channels)
        totalBytes = All_Channels.nbytes
        #print(self.AnalysisGui.NucleiChannel.currentIndex().dtype)
        if displaygui.NuclMaskCheckBox.isChecked() == True:

            self.input_image = self.IMAGE_TO_BE_MASKED(displaygui)
            bound, filled_res = ImageAnalyzer.neuceli_segmenter(
                self.input_image)
            #cv2.imwrite('mask_saved.jpg',bound)
            if displaygui.NucPreviewMethod.currentText() == "Boundary":

                All_Channels[bound != 0] = [255, 0, 0]

            if displaygui.NucPreviewMethod.currentText() == "Area":

                labeled_array, num_features = label(filled_res)
                rgblabel = label2rgb(labeled_array, alpha=0.1, bg_label=0)
                rgblabel = cv2.normalize(rgblabel,
                                         None,
                                         0,
                                         255,
                                         cv2.NORM_MINMAX,
                                         dtype=cv2.CV_8U)
                image_input_stack = np.stack((self.input_image, ) * 3, axis=-1)
                All_Channels = cv2.addWeighted(rgblabel, 0.2, ch1_rgb, 1, 0)
                ##############
        if displaygui.SpotsCheckBox.isChecked() == True:

            ch1_spots_log, ch2_spots_log, ch3_spots_log, ch4_spots_log = self.IMAGE_FOR_SPOT_DETECTION(
                displaygui)

            if displaygui.SpotPreviewMethod.currentText() == "Dots":

                if ch1_spots_log != []:

                    All_Channels[ch1_spots_log != 0] = [255, 255, 255]

                if ch2_spots_log != []:

                    All_Channels[ch2_spots_log != 0] = [255, 0, 0]

                if ch3_spots_log != []:

                    All_Channels[ch3_spots_log != 0] = [0, 255, 0]

                if ch4_spots_log != []:

                    All_Channels[ch4_spots_log != 0] = [0, 0, 255]

            if displaygui.NucPreviewMethod.currentText() == "Cross":

                pass

        self.SHOWIMAGE(displaygui, All_Channels, width, height, totalBytes)
Пример #49
0
    def _callback(self, img_msg, mask_msg):
        bridge = cv_bridge.CvBridge()
        bgr_img = bridge.imgmsg_to_cv2(img_msg, desired_encoding='bgr8')
        mask_img = bridge.imgmsg_to_cv2(mask_msg, desired_encoding='mono8')
        if mask_img.size < 1:
            logwarn_throttle(10, 'Too small sized image')
            return
        logwarn_throttle(10, '[FCNMaskForLabelNames] >> Start Processing <<')
        if mask_img.ndim == 3 and mask_img.shape[2] == 1:
            mask_img = mask_img.reshape(mask_img.shape[:2])
        if mask_img.shape != bgr_img.shape[:2]:
            jsk_logwarn('Size of mask and color image is different.'
                        'Resizing.. mask {0} to {1}'.format(
                            mask_img.shape, bgr_img.shape[:2]))
            mask_img = resize(mask_img, bgr_img.shape[:2],
                              preserve_range=True).astype(np.uint8)

        blob = bgr_img - self.mean_bgr
        blob = blob.transpose((2, 0, 1))

        x_data = np.array([blob], dtype=np.float32)
        if self.gpu != -1:
            x_data = cuda.to_gpu(x_data, device=self.gpu)
        x = Variable(x_data, volatile=True)
        self.model(x)
        pred_datum = cuda.to_cpu(self.model.score.data[0])

        candidate_labels = [
            self.target_names.index(name) for name in self.tote_contents
        ]
        label_pred_in_candidates = pred_datum[candidate_labels].argmax(axis=0)
        label_pred = np.zeros_like(label_pred_in_candidates)
        for idx, label_val in enumerate(candidate_labels):
            label_pred[label_pred_in_candidates == idx] = label_val
        label_pred[mask_img == 0] = 0  # set bg_label

        label_viz = label2rgb(label_pred, bgr_img, bg_label=0)
        label_viz = (label_viz * 255).astype(np.uint8)
        debug_msg = bridge.cv2_to_imgmsg(label_viz, encoding='rgb8')
        debug_msg.header = img_msg.header
        self.pub_debug.publish(debug_msg)

        output_mask = np.ones(mask_img.shape, dtype=np.uint8)
        output_mask *= 255
        for label_val, label_name in enumerate(self.target_names):
            if label_name in self.label_names:
                assert label_name == 'kleenex_paper_towels'
                assert label_val == 21
                label_mask = ((label_pred == label_val) * 255).astype(np.uint8)
                contours, hierachy = cv2.findContours(label_mask,
                                                      cv2.RETR_TREE,
                                                      cv2.CHAIN_APPROX_SIMPLE)
                cv2.drawContours(output_mask, contours, -1, 255, -1)
                # output_mask[label_pred == label_val] = False
        # output_mask = output_mask.astype(np.uint8)
        # output_mask[output_mask == 1] = 255
        output_mask[mask_img == 0] = 0
        output_mask_msg = bridge.cv2_to_imgmsg(output_mask, encoding='mono8')
        output_mask_msg.header = img_msg.header
        self.pub.publish(output_mask_msg)
        logwarn_throttle(10, '[FCNMaskForLabelNames] >> Finshed processing <<')
Пример #50
0
def show(*obj, file_name=None, overlay=False, pred=False,
         show_bbox=True, figsize=(10,10), cmap='binary_r', **kwargs):
    "Show image, mask, and weight (optional)"
    if len(obj)==3:
        img,msk,weight = obj
    elif len(obj)==2:
        img,msk = obj
        weight = None
    elif len(obj)==1:
        img = obj[0]
        msk, weight = None, None

    else:
        raise ValueError(f'Function not defined for {len(obj)} arguments.')

    # Image preprocessing
    img = np.array(img)
    # Swap axis to channels last
    if img.shape[0]<20: img=np.moveaxis(img,0,-1)
    # One channel images
    if img.ndim == 3 and img.shape[-1] == 1:
        img=img[...,0]

    # Mask preprocessing
    if msk is not None:
        msk = np.array(msk)
        # Remove background class from masks
        if msk.shape[0]==2: msk=msk[1,...]
        # Create bbox

        pad = (np.array(img.shape[:2])-np.array(msk.shape))//2
        bbox = Rectangle((pad[0]-1,pad[1]-1),img.shape[0]-2*pad[0]+1,img.shape[0]-2*pad[0]+1,
                 edgecolor='r',linewidth=1,facecolor='none')

        # Padding mask and weights
        msk = np.pad(msk, pad, 'constant', constant_values=(0))

        if cmap is None:
            cmap = 'binary_r' if msk.max()==1 else cmap

    # Weights preprocessing
    if weight is not None:
        weight = np.array(weight)
        weight = np.pad(weight, pad, 'constant', constant_values=(0))

    ncol=1 if msk is None else 2
    ncol=ncol if weight is None else ncol+1
    fig, ax = plt.subplots(1,ncol,figsize=figsize)
    img_ax = ax[0] if ncol>1 else ax

    # Plot img
    img_ax.imshow(img, cmap=cmap)
    if file_name is not None:
        img_ax.set_title('Image {}'.format(file_name))
    else:
        img_ax.set_title('Image')
    img_ax.set_axis_off()

    # Plot img and mask
    if msk is not None:
        if overlay:
            label_image = label(msk)
            img_l2o = label2rgb(label_image, image=img, bg_label=0, alpha=.8, image_alpha=1)
            ax[1].set_title('Image + Mask (#ROIs: {})'.format(label_image.max()))
            ax[1].imshow(img_l2o)
        else:
            ax[1].imshow(msk, cmap=cmap)
            ax[1].set_title('Mask')
        if show_bbox: ax[1].add_patch(copy(bbox))

        ax[1].set_axis_off()

    # Plot weights
    if weight is not None:
        max_w = weight.max()
        vmax_w = max(1, max_w)
        ax[2].imshow(weight, vmax=vmax_w, cmap=cmap)
        if pred:
            ax[2].set_title('Prediction')
        else:
            ax[2].set_title('Weights (max value: {:.{p}f})'.format(max_w, p=1))
        if show_bbox: ax[2].add_patch(copy(bbox))
        ax[2].set_axis_off()

    #ax.set_axis_off()
    plt.tight_layout()
    plt.show()
Пример #51
0
    axes.imshow(image, cmap="gray")
    axes.set_title('Gray image')
    print(image.shape)

    global_thresh = threshold_otsu(image)
    binary_global = image < global_thresh

    fig, axes = plt.subplots(nrows=1, figsize=(40, 40))
    axes.imshow(binary_global, cmap="gray")
    axes.set_title('Binary image')

    label_image, n = measure.label(binary_global,
                                   neighbors=8,
                                   background=0,
                                   return_num=True)
    image_label_overlay = label2rgb(label_image, image=binary_global)

    fig, axes = plt.subplots(nrows=1, figsize=(40, 40))
    axes.imshow(image_label_overlay)
    axes.set_title('Conected components')

    area_thresh = 10000  #@param {type:"slider", min:0, max:1000000, step:1}
    seg_prop = 0.011  #@param {type:"slider", min:0.001, max:100.0, step:0.01}

    selected_components = np.zeros(label_image.shape, dtype=int)
    selected_components_slic = np.zeros(label_image.shape, dtype=int)

    properties = regionprops(label_image, intensity_image=binary_global)

    for region in properties:
        if region['convex_area'] > area_thresh:
Пример #52
0
def overlay_labels(image, lbp, labels):
    mask = np.logical_or.reduce([lbp == each for each in labels])
    return label2rgb(mask, image=image, bg_label=0, alpha=0.5)
Пример #53
0
from skimage import io
from skimage import data, segmentation, color
from skimage.io import imread
from skimage import data
from skimage.future import graph
img = io.imread("../pants.jpg")
img_segments = segmentation.slic(img, compactness=30, n_segments=200)
out1 = color.label2rgb(img_segments, img, kind='avg')
segment_graph = graph.rag_mean_color(img, img_segments, mode='similarity')
img_cuts = graph.cut_normalized(img_segments, segment_graph)
normalized_cut_segments = color.label2rgb(img_cuts, img, kind='avg')
io.imshow(normalized_cut_segments)
io.show()
Пример #54
0
 def preview(self, ips, para):
     lab = segmentation.quickshift(ips.snap, para['ratio'],
                                   para['kernel_size'], para['max_dist'],
                                   para['sigma'])
     ips.img[:] = color.label2rgb(lab, ips.snap, kind='avg')
Пример #55
0
 def preview(self, ips, para):
     lab = segmentation.felzenszwalb(ips.snap, para['scale'], para['sigma'],
                                     para['min_size'])
     ips.img[:] = color.label2rgb(lab, ips.snap, kind='avg')
Пример #56
0
 def run(self, ips, snap, img, para=None):
     lab = segmentation.slic(snap, para['n_segments'], para['compactness'],
                             para['max_iter'], para['sigma'])
     return color.label2rgb(lab, snap, kind='avg')
Пример #57
0
from minisom import MiniSom
import numpy as np
import argparse

parser = argparse.ArgumentParser()
parser.add_argument('input_image', type=str, help='input image path')
parser.add_argument('num_superpixel', type=int, help='number of segments')
parser.add_argument('compactness', type=int, help='compactness param of SLIC')
args = parser.parse_args()

#img = data.coffee()
img = io.imread(args.input_image)
labels = segmentation.slic(img,
                           n_segments=args.num_superpixel,
                           compactness=args.compactness)
out1 = color.label2rgb(labels, img, kind='avg')

io.imshow(out1)
io.show()

pixels = np.reshape(out1, (out1.shape[0] * out1.shape[1], 3)) / 255

print('training...')
som = MiniSom(2,
              1,
              3,
              sigma=1.,
              learning_rate=0.2,
              neighborhood_function='bubble')
som.random_weights_init(pixels)
starting_weights = som.get_weights().copy()  # saving the starting weights
Пример #58
0
import numpy as np
import matplotlib.pyplot as plt

from skimage import io, data, util, filters, color
from skimage.morphology import watershed

kitten = color.rgb2gray(io.imread("../images/kitten.jpeg"))

kitten_edge = filters.sobel(kitten)  # use edge detection algo before watershed

grid = util.regular_grid(
    kitten.shape, n_points=300)  # find 300 points evenly spaced in the image

# The seed matrix is the same shape as the original image, and it contains integers in the range [1, size of image]

seeds = np.zeros(kitten.shape, dtype=int)
seeds[grid] = np.arange(seeds[grid].size).reshape(seeds[grid].shape) + 1

w0 = watershed(kitten_edge, seeds)

water_classic = color.label2rgb(w0, kitten, alpha=0.4, kind="overlay")

plt.figure(figsize=(8, 8))
plt.imshow(water_classic)
def prepareDataset(basedir='WeizmannSingleScale/horses/training/images/',
                   labeldir='WeizmannSingleScale/horses/training/masks/'):

    global pixelClasses
    dataSetX = []
    dataSetX_layer2 = []
    dataSetY = []
    datasetGroundTruth = []

    for (dirpath, dirnames, filenames) in walk(basedir):
        n = 0
        for imageFilename in filenames:

            #if n>=1:
            #    break
            print imageFilename

            n = n + 1

            # Read RGB and label image
            image = img_as_float(skimageIO.imread(basedir + imageFilename))
            bgrImage = cv2.imread(basedir + imageFilename, cv2.IMREAD_COLOR)
            #bgrImage = cv2.fastNlMeansDenoisingColored(bgrImage)
            #bgrImage = exposure.adjust_sigmoid(bgrImage)
            labelImage = img_as_float(
                skimageIO.imread(labeldir +
                                 imageFilename.replace('image', 'mask')))
            if len(image.shape) == 2:
                image = color.gray2rgb(image)

            # Resize
            #image = resize(image,(120,120), preserve_range=True )
            #bgrImage = resize(bgrImage,(120,120), preserve_range=True)
            #labelImage = resize(labelImage,(120,120), preserve_range=True)

            # Scan label image for additional classes
            if len(labelImage.shape) == 2:
                labelImageRGB = color.gray2rgb(labelImage)
            else:
                labelImageRGB = labelImage
            #for i in range(0,labelImageRGB.shape[0]) :
            #    for j in range(0,labelImageRGB.shape[1]) :
            #        if len(pixelClasses) == 0 :
            #            pixelClasses.append(labelImageRGB[i][j])
            #        else :
            #            isAlreadyAPixelClass = False
            #            for pixelClass in pixelClasses:
            #                if numpy.array_equal(pixelClass, labelImageRGB[i][j]) :
            #                    isAlreadyAPixelClass = True
            #                    break
            #            if not isAlreadyAPixelClass :
            #                pixelClasses.append(labelImageRGB[i][j])
            #print pixelClasses

            # Derive superpixels and get their average RGB component
            segments = slic(image, n_segments=500, sigma=1.0)
            rgb_segments = img_as_ubyte(mark_boundaries(image, segments))
            label_segments = img_as_ubyte(
                mark_boundaries(labelImageRGB, segments))
            avg_rgb = color.label2rgb(segments, image, kind='avg')
            avg_label = color.label2rgb(segments, labelImageRGB, kind='avg')
            #avg_cie_rgb = color.rgb2lab(avg_rgb)
            #avg_cie_label = color.rgb2lab(avg_label)

            # Create graph of superpixels and compute their centers
            vertices, edges = make_graph(segments)
            gridx, gridy = numpy.mgrid[:segments.shape[0], :segments.shape[1]]
            centers = dict()
            for v in vertices:
                centers[v] = [
                    gridy[segments == v].mean(), gridx[segments == v].mean()
                ]
            #print vertices
            #print edges
            #print centers

            # Build training instances
            n_features = []
            edge_features = []
            n_labels = []

            # Compute image centers
            centerX = labelImageRGB.shape[1] / 2.0
            centerY = labelImageRGB.shape[0] / 2.0

            for v in vertices:
                # unary features layer 1 - average rgb of superpixel, histogram of patch surrounding center and CNN features
                avg_rgb2 = avg_rgb[int(centers[v][1])][int(centers[v][0])]
                hist, hogFeatures = getHistogramFeatures(bgrImage,
                                                         int(centers[v][1]),
                                                         int(centers[v][0]),
                                                         forUnaryFeature=True)
                #relativeX = (centers[v][1] - centerX) / centerX
                #relativeY = (centers[v][0] - centerY) / centerY
                node_feature = numpy.concatenate([avg_rgb2, hist, hogFeatures])
                n_features.append(node_feature)

                # label
                minEuclideanDistance = numpy.inf  # simulate infinity
                pixelClass = -1
                for i in range(0, len(pixelClasses)):
                    # set the label of the superpixel to the pixelClass with minimum euclidean distance
                    dist = numpy.linalg.norm(
                        avg_label[int(centers[v][1])][int(centers[v][0])] -
                        pixelClasses[i])
                    if dist < minEuclideanDistance:
                        pixelClass = i
                        minEuclideanDistance = dist
                n_labels.append(pixelClass)

            histogramCache = {}
            for e in edges:
                # pairwise feature layer 1 - histogram distance, avg RGB euclidean distance of adjacent superpixels , texture similarity
                dist = numpy.linalg.norm(
                    avg_rgb[int(centers[e[0]][1])][int(centers[e[0]][0])] -
                    avg_rgb[int(centers[e[1]][1])][int(centers[e[1]][0])])

                if e[0] not in histogramCache:
                    hist1, lbphist1 = getHistogramFeatures(
                        bgrImage, int(centers[e[0]][1]), int(centers[e[0]][0]))
                    histogramCache[e[0]] = {'hist': hist1, 'lbphist': lbphist1}
                else:
                    hist1 = histogramCache[e[0]]['hist']
                    lbphist1 = histogramCache[e[0]]['lbphist']
                if e[1] not in histogramCache:
                    hist2, lbphist2 = getHistogramFeatures(
                        bgrImage, int(centers[e[1]][1]), int(centers[e[1]][0]))
                    histogramCache[e[1]] = {'hist': hist2, 'lbphist': lbphist2}
                else:
                    hist2 = histogramCache[e[1]]['hist']
                    lbphist2 = histogramCache[e[1]]['lbphist']

                histogramDist = cv2.compareHist(hist1, hist2,
                                                3)  # Bhattacharyya distance
                textureSimilarity = kullback_leibler_divergence(
                    lbphist1, lbphist2)  # KL divergence

                pairwise_feature = numpy.array(
                    [dist, histogramDist, textureSimilarity])
                edge_features.append(pairwise_feature)

            # Add to dataset
            dataSetX.append((numpy.array(n_features), numpy.array(edges),
                             numpy.array(edge_features)))
            dataSetY.append(numpy.array(n_labels))

    return dataSetX, dataSetY
Пример #60
0
def test_rrssd_box_coder_synthetic():
    label_image = np.zeros((768, 768), dtype=np.uint8)

    cv2.fillConvexPoly(
        label_image,
        np.expand_dims(cv2.boxPoints(((100, 100), (100, 20), 0)),
                       1).astype(int), (1, 1, 1))
    cv2.fillConvexPoly(
        label_image,
        np.expand_dims(cv2.boxPoints(((200, 100), (100, 20), 45)),
                       1).astype(int), (2, 2, 2))
    cv2.fillConvexPoly(
        label_image,
        np.expand_dims(cv2.boxPoints(((100, 200), (100, 20), 90)),
                       1).astype(int), (3, 3, 3))
    cv2.fillConvexPoly(
        label_image,
        np.expand_dims(cv2.boxPoints(((200, 200), (100, 20), 135)),
                       1).astype(int), (4, 4, 4))

    cv2.fillConvexPoly(
        label_image,
        np.expand_dims(cv2.boxPoints(((100 + 200, 100), (20, 100), 0)),
                       1).astype(int), (5, 5, 5))
    cv2.fillConvexPoly(
        label_image,
        np.expand_dims(cv2.boxPoints(((200 + 200, 100), (20, 100), 45)),
                       1).astype(int), (6, 6, 6))
    cv2.fillConvexPoly(
        label_image,
        np.expand_dims(cv2.boxPoints(((100 + 200, 200), (20, 100), 90)),
                       1).astype(int), (7, 7, 7))
    cv2.fillConvexPoly(
        label_image,
        np.expand_dims(cv2.boxPoints(((200 + 200, 200), (20, 100), 135)),
                       1).astype(int), (8, 8, 8))

    cv2.fillConvexPoly(
        label_image,
        np.expand_dims(cv2.boxPoints(((100, 105 + 200), (100, 20), 45. / 2)),
                       1).astype(int), (9, 9, 9))
    cv2.fillConvexPoly(
        label_image,
        np.expand_dims(cv2.boxPoints(((200, 100 + 200), (16, 4), 49)),
                       1).astype(int), (10, 10, 10))
    cv2.fillConvexPoly(
        label_image,
        np.expand_dims(cv2.boxPoints(((200, 100 + 210), (100, 20), 49)),
                       1).astype(int), (11, 11, 11))
    cv2.fillConvexPoly(
        label_image,
        np.expand_dims(cv2.boxPoints(((200, 200 + 200), (100, 20), 165)),
                       1).astype(int), (12, 12, 12))

    cv2.fillConvexPoly(
        label_image,
        np.expand_dims(cv2.boxPoints(((300, 300), (10, 6), 49)),
                       1).astype(int), (13, 13, 13))
    cv2.fillConvexPoly(
        label_image,
        np.expand_dims(cv2.boxPoints(((300 + 50, 300), (200, 40), 90)),
                       1).astype(int), (14, 14, 14))
    cv2.fillConvexPoly(
        label_image,
        np.expand_dims(cv2.boxPoints(((300 + 70, 300), (100, 20), 90)),
                       1).astype(int), (15, 15, 15))

    cv2.fillConvexPoly(
        label_image,
        np.expand_dims(cv2.boxPoints(((500, 500), (2, 3), 9)), 1).astype(int),
        (16, 16, 16))
    cv2.fillConvexPoly(
        label_image,
        np.expand_dims(cv2.boxPoints(((510, 500), (2, 3), 19)), 1).astype(int),
        (17, 17, 17))
    cv2.fillConvexPoly(
        label_image,
        np.expand_dims(cv2.boxPoints(((540, 500), (4, 6), 29)), 1).astype(int),
        (18, 18, 18))
    cv2.fillConvexPoly(
        label_image,
        np.expand_dims(cv2.boxPoints(((560, 500), (2, 3), 39)), 1).astype(int),
        (19, 19, 19))

    image = (label2rgb(label_image, bg_label=0) * 255).astype(np.uint8)

    # Test what happens if we rotate
    # image = np.rot90(image).copy()
    # label_image = np.rot90(label_image).copy()

    rbboxes = instance_mask_to_rbboxes(label_image)
    print(rbboxes)

    labels = np.zeros(len(rbboxes), dtype=np.intp)

    box_coder = RRNBoxCoder(768, 768)

    loc_targets, cls_targets, anchors = box_coder.encode([], [],
                                                         return_anchors=True)

    loc_targets, cls_targets, anchors = box_coder.encode(rbboxes,
                                                         labels,
                                                         return_anchors=True)
    print(loc_targets.shape, cls_targets.shape, anchors.shape)
    print('Object anchors', (cls_targets > 0).sum())
    print('Background anchors', (cls_targets == 0).sum())
    print('Ignore anchors', (cls_targets == -1).sum())

    # cls_targets = np.expand_dims(cls_targets)
    # print(cls_targets_one_hot.shape)

    dec_boxes, dec_scores = box_coder.decode(loc_targets, cls_targets)
    print(dec_boxes)
    print('Total anchors', len(anchors))
    for bbox in dec_boxes:
        visualize_rbbox(image, bbox, (255, 0, 255), thickness=3)

    for bbox in rbboxes:
        visualize_rbbox(image, bbox, (0, 255, 0), thickness=1)

    for bbox in anchors:
        visualize_rbbox(image, bbox, (255, 255, 255), thickness=1)

    cv2.imshow('overlays', image)
    cv2.waitKey(-1)