def adaptive_none(self, grid_number):
		# First divide the image into number of grid.
		# Total, grid_number^2 number of grids.
		# 7, 5 are not working
		x, y = 0, 0
		img = deepcopy(self.img)
		while(True):
			# Find left upper corner only.
			if x + grid_number >= self.col and y + grid_number >= self.row:
				# at last grid.
				subimg = self.img[y: , x: ]
				opt = threshold_otsu(subimg)
				img[y:, x:] = self.img[y:, x:] > opt
				break
			elif x + grid_number >= self.col and y + grid_number < self.row:
				# at the right edge.
				subimg = self.img[y : y + grid_number, x :]
				opt = threshold_otsu(subimg)
				img[y : y + grid_number, x :] = self.img[y : y + grid_number, x :] > opt
				y = y+grid_number
				x = 0
			elif x + grid_number < self.col and y + grid_number >= self.row:
				subimg = self.img[y : , x : x + grid_number]
				opt = threshold_otsu(subimg)
				img[y : , x : x + grid_number] = self.img[y : , x : x + grid_number] > opt
				# print self.img[y : , x : x + width]
				x = x + grid_number
			else:
				subimg = self.img[y : y + grid_number, x : x + grid_number]
				opt = threshold_otsu(subimg)
				img[y : y + grid_number, x : x + grid_number] = self.img[y : y + grid_number, x : x + grid_number] > opt
				# print self.img[y : y + height, x : x + width]
				x = x+grid_number

		return img

def main():
    parser = OptionParser()
    (options, args) = parser.parse_args()
    # reading input files 
    path = args[0] 

    # ref is the image that is used as a reference to subtract the background.
    roi = [125,125,1050,1250]
    roi_minx = roi[0]
    roi_miny = roi[1]
    roi_maxx = roi[2]
    roi_maxy = roi[3]
    
    template = io.imread(args[1])  
    template = template[:,:,0] 
    #ref = ref[roi_minx:roi_maxx, roi_miny:roi_maxy] 
    
    # otsu threshold the ref
    thresh = filter.threshold_otsu(template)
    template = template < thresh
    #plt.imshow(ref)
    #plt.show()    
    
    ref = io.imread(args[2])
    ref = ref[:,:,0]
    ref = ref[roi_minx:roi_maxx, roi_miny:roi_maxy] 
    thresh = filter.threshold_otsu(ref)
    ref= ref < thresh
    #plt.imshow(ref)
    #plt.show()
    
    output = []    
    
    for subdir, dirs, files in os.walk(path):
        for file in files:
            fn = os.path.join(subdir, file) 
            img = io.imread(fn)
            img = img[:,:,0]  
            img = img[roi_minx:roi_maxx, roi_miny:roi_maxy] 
            # do otsu thresholding
            thresh = filter.threshold_otsu(img)
            img = img < thresh
            #img = abs(img-ref) 
            #plt.imshow(img)
            #plt.show()
            centers = find_centers(img,template)
            # get the image number from the file 
            
            n = int(re.search(r'\d+', file).group())
            output.append((n, centers))
            
    output = sorted(output, key=lambda x:x[0])

    # now post process the output
    post_process(output,roi)

def generate_template(digit_dir_path):
	template = zeros((42, 42))

	for filename in iglob(digit_dir_path + '/*.bmp'):
		img = imread(filename)
		img = resize(img, [44, 44])
		img = img[1:43,1:43]
		img = (img > (threshold_otsu(img, nbins=256)))
		template += img

	template = (template < (threshold_otsu(template, nbins=256)-5))
	return template

def robertCross(A):
	robert_minus=np.array([[1,0],[0,-1]])
	robert_plus=np.array([[0,1],[-1,0]])
	result1=convolution(A,robert_minus);
	result1=np.abs(result1)
	thresh1=np.floor(threshold_otsu(result1))
	result1=np.logical_or(result1<0,result1>thresh1)
	result2=convolution(A,robert_plus);
	result2=np.abs(result2)
	thresh2=np.floor(threshold_otsu(result2))
	result2=np.logical_or(result2<0,result2>thresh2)
	result=np.logical_or(result1,result2);
	return result

def scikit_otsu(img):
	rows,cols,depth=img.shape
	from skimage.filter import threshold_otsu
	print "threshold using scikit" , np.floor(threshold_otsu(img))
	threshold=np.floor(threshold_otsu(img))
	imgO=np.zeros((rows,cols),int)
	for i in range(cols):
		for j in range(rows):
			if img[i,j,0]>=threshold: 
				imgO[i,j]=255
			elif img[i,j,0]<threshold:
				imgO[i,j]=0
	return imgO

def percent_vert_horiz_lines(FilledBlobImg, props_area):
    v = filter.vsobel(FilledBlobImg)
    v_floor = filter.threshold_otsu(v)
    ind_v = np.where(v > v_floor)
    h = filter.hsobel(FilledBlobImg)
    h_floor = filter.threshold_otsu(h)
    ind_h = np.where(h > h_floor)

    vert_and_horiz = np.zeros(v.shape).astype("bool")
    vert_and_horiz[ind_v] = True
    vert_and_horiz[ind_h] = True
    ind = np.where(vert_and_horiz)[0]
    return float(ind.size) / props_area

def ifcb_segment(img):
    Y = img_as_float(img)
    # step 1. local variance
    Yv = rescale_intensity(generic_filter(Y, np.var, footprint=disk(3)))
    # step 2. threshold local variance, aggressively
    Ye = Yv > (threshold_otsu(Yv) / 2.)
    # step 3. dark areas
    Yt = Y < threshold_otsu(Y)
    thin_blob = Ye | Yt
    # step 4. morphological reconstruction
    seed = np.copy(thin_blob)
    seed[1:-1,1:-1] = 1
    four=np.disk(1).astype(np.bool)
    return reconstruction(seed,thin_blob,method='erosion',selem=four)

 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()       

    def one_file_features(self, im, demo=False):

        """
        Zde je kontruován vektor příznaků pro klasfikátor
        """
        fd = np.array([])

        import skimage.transform
        import skimage

        # Zmena velikosti obrazku
        image = skimage.transform.resize(im, [50, 50])

        #%% Vyriznuti objektu kolem stredu obrazu
        image = image[int(image.shape[0]/2)-20:int(image.shape[0]/2)+20, int(image.shape[1]/2)-20:int(image.shape[1]/2)+20]
        fd = np.append(fd, image.reshape(-1))

        # Vyuziti Otsuova filtru
        from skimage import filter

        threshold = filter.threshold_otsu(image)
        image =image < threshold

        fd = np.append(fd, image.reshape(-1))

        #%% Změna velikosti


        #fd.append(hsvft[:])
       # if self.colorFeatures:
       #     fd = np.append(fd, self.colorFeatures)
           # pass

        return fd

    def __transform(self):
        self.__img_gray = io.imread(self.__img_path, True)

        self.__otsu = filter.threshold_otsu(self.__img_gray) #Aplicar otsu para binarizar a imagem
        self.__img_gray = self.__img_gray < self.__otsu

        # Find contours at a constant value of 0.5
        self.__contours = measure.find_contours(self.__img_gray, 0.5)

        self.__arclen = 0.0
        for n, contour in enumerate(self.__contours):
            arclenTemp=0.0
            for indice, valor in enumerate(contour):
               if indice > 0:
                    d1 = math.fabs(round(valor[0]) - round(contour[indice-1,0]))
                    d2 = math.fabs(round(valor[1]) - round(contour[indice-1,1]))
                    if d1+d2>1.0:
                        arclenTemp+=math.sqrt(2)
                    elif d1+d2 == 1:
                        arclenTemp+=1

            if arclenTemp > self.__arclen:
                self.__arclen = arclenTemp
                self.__bestn = n
        #self.__bestn = 0
        print self.__contours[0]

def scikit_example_plot_label():
    image = data.coins()[50:-50, 50:-50]
    
    # 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
    
    fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(6, 6))
    ax.imshow(label_image, cmap='jet')
    
    for region in regionprops(label_image, ['Area', 'BoundingBox']):
    
        # skip small images
        if region['Area'] < 100:
            continue
    
        # draw rectangle around segmented coins
        minr, minc, maxr, maxc = region['BoundingBox']
        rect = mpatches.Rectangle((minc, minr), maxc - minc, maxr - minr,
                                  fill=False, edgecolor='red', linewidth=2)
        ax.add_patch(rect)
    
    plt.show()

def label_stack(z_stack, parameters):
    '''
    Apply an automated threshold and a watershed algorithm to
    label cells in each planes of the stack

    Parameters:
    -----------
    z_stack: 3d array of shape (nz, nx, ny)

    Returns:
    --------
    labeled_stack: 3d array of the same shape as z_stack,
    with each detected region labeled
    '''
    segment_method = parameters['segment_method']
    correction = parameters['correction']
    labeled_stack = np.zeros(z_stack.shape, dtype=np.uint8)
    max_int_proj = z_stack.max(axis=0)
    thresh = None

    if segment_method == 'otsu':
        thresh = threshold_otsu(max_int_proj) * correction
    elif segment_method == 'naive':
        thresh = max_int_proj.max() * correction
    else:
        err_string = ('Segmentation method {}'
                      'is not implemented'.format(segment_method))
        raise NotImplementedError(err_string)
    while thresh > z_stack.max():
        log.warning('''Reducing threshold''')
        thresh *= 0.9
    for n, frame in enumerate(z_stack):
        labeled_stack[n] = label_from_thresh(frame, thresh, parameters)
    return labeled_stack

def threshold_image(image, threshold=0):
	"""
	This function takes out any values in an image's RGB matrix that are
	below the threshold value.

	Inputs:
	- image: a matrix describing an image with only one channel represented.
	- threshold: a value, between 0 and 1, for which if an image matrix's
				 value is below, will be set to 0, and if above, will be 
				 set to 1.

				 If the threshold is set to 0, then an Otsu thresholding will
				 be returned.

	Outputs:
	- thresholded_image: a matrix representation of the thresholded image.
						 this is essentially a black and white image.
	- thresh: the threshold value

	To screen: the black-and-white image representation.
	- 
	"""
	if threshold == 0:
		thresh = threshold_otsu(image)

	if threshold != 0:
		thresh = threshold

	thresholded_image = closing(image > thresh, square(3), out=None)
	imshow(thresholded_image)

	return thresholded_image, thresh

def variance_otsu(image, labels):
    """
    Read pixel intensities from 'image' for each class given in 'labels' (a corresponding cluster
    label array), and use Otsu's binarisation to threshold the image.

    :param image: Pre-processed input image
    :param labels: Array with same shape as input image, containing cluster labels
    """
    img = image.ravel()
    shadow_seg = img.copy()

    hist, _ = np.histogram(labels)
    n_clusters = hist.shape[0]
    for i in range(0, n_clusters):
        # set up mask of pixel indices matching cluster
        mask = np.nonzero((labels.ravel() == i) == True)[0]
        if len(mask) > 0:
            if img[mask].var() > 0.005:
                thresh = threshold_otsu(img[mask])
                shadow_seg[mask] = shadow_seg[mask] < thresh
            else:
                shadow_seg[mask] = 0

    shadow_seg = shadow_seg.reshape(*image.shape)
    return shadow_seg

def watershed_3d(sphere):
    """
    Markers should be int8
    Image should be uint8
    """
   
    sphere = median_filter(sphere, 3)
    thresh = threshold_otsu(sphere)
    sphere = (sphere >= thresh) * 1
    sphere = sobel(sphere)
    
    size = (sphere.shape[0], sphere.shape[1], sphere.shape[2])
    
    marker = np.zeros(size, dtype=np.int16)
    pl.imshow(sphere[:,:,50])
    pl.show()
    # mark everything outside as background
    marker[5, :, :] = -1
    marker[size[0] - 5, :, :] = -1
    marker[:, :, 5] = -1
    marker[:, :, size[2] - 5] = -1
    marker[:, 5, :] = -1
    marker[:, size[1] - 5, :] = -1
    marker[:,0,0] = -1
    # mark everything inside as a sphere
    marker[size[0] / 2., size[1] / 2., size[2] / 2.] = 5

    result = measurements.watershed_ift(sphere.astype(dtype=np.uint16), marker)
    pl.imshow(result[:,:,50])
    pl.show()
    
    return result

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 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     

def threshold(data):
	thresh1	= threshold_otsu(data)
	thresh2 = thresh1*2
	print "ndi thresh", thresh1, thresh2
	data[data<thresh2] 	= 0
	data[data>=thresh2]	= 255
	return data

def intensity_object_features(im, adaptive_t_radius=51, sample_size=None):
    """Segment objects based on intensity threshold and compute properties.

    Parameters
    ----------
    im : 2D np.ndarray of float or uint8.
        The input image.
    adaptive_t_radius : int, optional
        The radius to calculate background with adaptive threshold.
    sample_size : int, optional
        Sample this many objects randomly, rather than measuring all
        objects.

    Returns
    -------
    f : 1D np.ndarray of float
        The feature vector.
    names : list of string
        The list of feature names.
    """
    tim1 = im > imfilter.threshold_otsu(im)
    f1, names1 = object_features(tim1, im, sample_size=sample_size)
    names1 = ['otsu-threshold-' + name for name in names1]
    tim2 = imfilter.threshold_adaptive(im, adaptive_t_radius)
    f2, names2 = object_features(tim2, im, sample_size=sample_size)
    names2 = ['adaptive-threshold-' + name for name in names2]
    f = np.concatenate([f1, f2])
    return f, names1 + names2

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