Python segmentation.clear_border函数代码示例

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 setROILED(self, im):
		im1 = im.copy()

		box = self.pupil_BOX

		x,y,w,h = box + [-20,-30, 40, 60]
		self.led_OFFSET = np.array([x,y])
		im2 = im[y:y+h, x:x+w]

		center_point = [e / 2 for e in im2.shape]

		for ledthreshold in xrange(210, 150, -2):

			ret, bw_im = cv2.threshold(im2, ledthreshold, 255, cv2.THRESH_BINARY)
			cleared = bw_im.copy()
			clear_border(cleared)
			label_img = label(cleared)

			props = regionprops(label_img)

			if len(props) < 2:
				continue
			elif len(props) >= 2:
				dist = [euclidean(center_point, e.centroid) for e in props]
				minDist_idx = [dist.index(e) for e in heapq.nsmallest(2, dist)]
				props[0], props[1] = props[minDist_idx[0]], props[minDist_idx[1]]

			if props[0].area > 27 and props[1].area > 27 and props[0].area < 60 and props[1].area < 60:
				return bw_im, props[0].centroid, props[1].centroid

		return None, None, None

    def del_small(self,img,min_size):
        '''
            args :      -> 画像,1ch
            dst  :      -> 端に繋がってるのと小さいやつ除去した画像
            param:      -> 
        '''

        bounding_box = np.zeros_like(img)

        # 画像の端に繋がってるやつ削除
        cleared = img.copy()
        skseg.clear_border(cleared)

        # ラベリング
        labels, num = sk.label(cleared, return_num = True) # numが一個多い？
        
        # bounding boxの描画
        # for region in sk.regionprops(labels):
            
            # minr, minc, maxr, maxc = region.bbox

            # if region.area < min_size:
                # cleared[minr:maxr,minc:maxc][region.convex_image == True] =0 
                # num = num - 1
                
            # else:    
                # cv2.rectangle(bounding_box,(minc,minr),(maxc,maxr),255)
        bounding_box = 0

        return cleared, num, bounding_box

 def applyMorphologicalCleaning(self, image):
 	"""
 	Applies a variety of morphological operations to improve the detection
 	of worms in the image.
 	Takes 0.030 s on MUSSORGSKY for a typical frame region
 	Takes 0.030 s in MATLAB too
 	"""
     # start with worm == 1
     image = image.copy()
     segmentation.clear_border(image)  # remove objects at edge (worm == 1)
     # fix defects in the thresholding by closing with a worm-width disk
     # worm == 1
     wormSE = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,
                                        (self.wormDiskRadius+1,
                                        	self.wormDiskRadius+1))
     imcl = cv2.morphologyEx(np.uint8(image), cv2.MORPH_CLOSE, wormSE)
     imcl = np.equal(imcl, 1)
     # fix defects by filling holes
     imholes = ndimage.binary_fill_holes(imcl)
     imcl = np.logical_or(imholes, imcl)
     # fix barely touching regions
     # majority with worm pixels == 1 (median filter same?)
     imcl = nf.median_filter(imcl, footprint=[[1, 1, 1],
                                              [1, 0, 1],
                                              [1, 1, 1]])
     # diag with worm pixels == 0
     imcl = np.logical_not(bwdiagfill(np.logical_not(imcl)))
     # open with worm pixels == 1
     openSE = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 1))
     imcl = cv2.morphologyEx(np.uint8(imcl), cv2.MORPH_OPEN, openSE)
     return np.equal(imcl, 1)

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()

def detect_edges(image_array):
    """ Detect edges in a given image
    Takes a numpy.array representing an image,
    apply filters and edge detection and return a numpy.array

    Parameters
    ----------
    image_array : ndarray (2D)
        Image data to be processed. Detect edges on this 2D array representing the image

    Returns
    -------
    edges : ndarray (2D)
        Edges of an image.
    """
    #Transform image into grayscale
    img = rgb2gray(image_array)
    #Remove some noise from the image
    img = denoise_tv_chambolle(img, weight=0.55)
    #Apply canny
    edges = filter.canny(img, sigma=3.2)
    #Clear the borders
    clear_border(edges, 15)
    #Dilate edges to make them more visible and connected
    edges = binary_dilation(edges, selem=diamond(3))
    return edges

def test_clear_border_3d():
    image = np.array([
        [[0, 0, 0, 0],
         [0, 0, 0, 0],
         [0, 0, 0, 0],
         [1, 0, 0, 0]],
        [[0, 0, 0, 0],
         [0, 1, 1, 0],
         [0, 0, 1, 0],
         [0, 0, 0, 0]],
        [[0, 0, 0, 0],
         [0, 0, 0, 0],
         [0, 0, 0, 0],
         [0, 0, 0, 0]],
        ])
    # test default case
    result = clear_border(image.copy())
    ref = image.copy()
    ref[0, 3, 0] = 0
    assert_array_equal(result, ref)

    # test buffer
    result = clear_border(image.copy(), 1)
    assert_array_equal(result, np.zeros(result.shape))

    # test background value
    result = clear_border(image.copy(), buffer_size=1, bgval=2)
    assert_array_equal(result, 2 * np.ones_like(image))

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 segmentToRegions(image, num_of_ones, bw):
    #apply threshold
    struct = buildStruct(30, num_of_ones)
    if num_of_ones == 0:
        img_close = bw
    else:
        img_close = opening(bw, struct)
    # remove artifacts connected to image border
    cleared = img_close.copy()
    clear_border(cleared)
    # label image regions
    label_image = label(cleared)
    borders = np.logical_xor(img_close, cleared)
    label_image[borders] = -1
    #image_label_overlay = label2rgb(label_image, image=image)
    regions = regionprops(label_image)
    """
    fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(6, 6))
    ax.imshow(image_label_overlay)
    for region in regions:
        # skip small images
        if region.area < 10:
            continue
        # draw rectangle around segmented coins
        minr, minc, maxr, maxc = region.bbox

        rect = mpatches.Rectangle((minc, minr), maxc - minc, maxr - minr,
                                  fill=False, edgecolor='blue', linewidth=2)
        ax.add_patch(rect)
    """
    return regions

    def __call__(self, image, window_size=10, threshold=0, fill_holes=True,
                 outline_smoothing=2, remove_borderobjects=True, size_min=1,
                 *args, **kw):

        thresh = threshold_adaptive(image, block_size=window_size,
                                    offset=-1*threshold)

        if outline_smoothing >= 1:
            thresh = outlineSmoothing(thresh, outline_smoothing)

        thresh = remove_small_objects(thresh, size_min)

        seeds = ndi.label(clear_border(~thresh))[0]
        thresh = ndi.binary_fill_holes(thresh)
        smask = seeds.astype(bool)

        # object don't touch border after outline smoothing
        if remove_borderobjects:
            thresh = clear_border(thresh)

        img = np.zeros(thresh.shape)
        img[~smask] = 1
        edt = ndi.morphology.distance_transform_edt(img)
        edt -= ndi.morphology.distance_transform_edt(seeds)

        labels = watershed(edt, seeds)
        labels[smask] = 0
        labels[~thresh] = 0

        return labels

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 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 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 adaptivethresh_and_hough(image):
    '''
    image = square NDVI image of field
    
    Applies adaptive thresholding and cleaning on NDVI image to prepare for hough line transformation
    ''' 

    # apply adaptive opencv threshold
    th3 = cv2.adaptiveThreshold(image,255,cv2.ADAPTIVE_THRESH_MEAN_C,\
                cv2.THRESH_BINARY,95,2)

    # Remove noise bij opening
    kernel = np.ones((3,3),np.uint8)
    opening = cv2.morphologyEx(th3, cv2.MORPH_OPEN, kernel)

    # close image using scikit
    #bw = closing(th3 > opening, square(14))

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

    # label image regions
    crop_regions = label(cleared)

    # Remove noise by area
    region_size = np.bincount(crop_regions.ravel())
    region_mask = region_size > 200
    region_mask[0] = 0
    regions_cleaned = region_mask[crop_regions]

    # Convert image to CV image
    cv_image = img_as_ubyte(regions_cleaned)

    # Apply hough line transformation
    lines = cv2.HoughLinesP(cv_image,rho=1,theta=np.pi/180,threshold=200,lines=np.array([]),
                        minLineLength=100,maxLineGap=5) # TO DO: MAKE SURE ONLY 180 RANGE IS RETURNED and minlinelength automatic adjust
                        
    if not lines.any():
        print "Error: No Hough lines detected! Try to increase cropping area" # <- line not working, as lines is more than 1!
        sys.exit()
        
    else:
        for line in lines:
            x1,y1,x2,y2 = line[0]
            cv2.line(cv_image,(x1,y1),(x2,y2),(50,255,10),2)
     
    # Extract only the coordinates from NP array
    coordinates = lines[0:,0,]
    np.save('coordinates.npy', coordinates)
    np.savetxt('coordinates.txt', coordinates)
    cv2.imwrite('detected_lines.png', cv_image)
    
    return coordinates

	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