我们从Python开源项目中，提取了以下15个代码示例，用于说明如何使用skimage.filters.sobel()。
def segmentation(_image, typeOfFruit): #_denoisedImage = rank.median(_image, disk(2)); #_elevationMap = sobel(_denoisedImage); #print(_image); _elevationMap = sobel(_image); #print(_image); _marker = np.zeros_like(_image); if (typeOfFruit == 'Counting'): _marker[_image < 1998] = 1; _marker[_image > 61541] = 2; elif (typeOfFruit == 'Temperature'): #print("Printing Image"); #print(_image < 10); _marker[_image < 30] = 1; #print(_marker); _marker[_image > 150] = 2; #_marker = rank.gradient(_denoisedImage, disk(5)) < 10; #_marker = ndi.label(_marker)[0]; #_elevationMap = rank.gradient(_denoisedImage, disk(2)); _segmentation = watershed(_elevationMap, _marker); #print(_segmentation); return _segmentation;
def edges_each(image): return filters.sobel(image)
def edges_hsv(image): return filters.sobel(image)
def edges_hsv_uint(image): with expected_warnings(['precision loss']): return img_as_uint(filters.sobel(image))
def test_gray_scale_image(): # We don't need to test both `hsv_value` and `each_channel` since # `adapt_rgb` is handling gray-scale inputs. assert_allclose(edges_each(GRAY_IMAGE), filters.sobel(GRAY_IMAGE))
def test_each_channel(): filtered = edges_each(COLOR_IMAGE) for i, channel in enumerate(np.rollaxis(filtered, axis=-1)): expected = img_as_float(filters.sobel(COLOR_IMAGE[:, :, i])) assert_allclose(channel, expected)
def test_hsv_value_with_non_float_output(): # Since `rgb2hsv` returns a float image and the result of the filtered # result is inserted into the HSV image, we want to make sure there isn't # a dtype mismatch. filtered = edges_hsv_uint(COLOR_IMAGE) filtered_value = color.rgb2hsv(filtered)[:, :, 2] value = color.rgb2hsv(COLOR_IMAGE)[:, :, 2] # Reduce tolerance because dtype conversion. assert_allclose(filtered_value, filters.sobel(value), rtol=1e-5, atol=1e-5)
def edgeDetector(_imageFile): #_edge = feature.canny((_imageFile/255.), sigma = 3); _edge = filters.sobel(_imageFile); return _edge
def seam_carve(img): """ Seam carve image :param img: PIL image object :return: PIL image object """ # Convert to skimage image img_to_convert = img.copy() img_to_convert = pil_to_skimage(img_to_convert) # Energy Map, used to determine which pixels will be removed eimg = filters.sobel(color.rgb2gray(img_to_convert)) # (height, width) img_dimensions = img_to_convert.shape # Squish width if width >= height, squish height if height > width # Number of pixels to keep along the outer edges (5% of largest dimension) # Number of seams to be removed, (1 to 10% of largest dimension) if img_dimensions[1] >= img_dimensions[0]: mode = "horizontal" border = round(img_dimensions[1] * 0.05) num_seams = random.randint(1, round(0.1*img_dimensions[1])) else: mode = "vertical" border = round(img_dimensions[0] * 0.05) num_seams = random.randint(1, round(0.1*img_dimensions[0])) try: img_to_convert = transform.seam_carve(img_to_convert, eimg, mode, num_seams, border) except Exception as e: print("Unable to seam_carve: " + str(e)) # Convert back to PIL image img_to_convert = skimage_to_pil(img_to_convert) return img_to_convert
def run(self, ips, snap, img, para = None): edge = sobel(snap) img[:] = 0 img[snap>para['thr2']] = 2 img[snap<para['thr1']] = 1 ips.lut = self.buflut mark = watershed(edge, img, line=True) if para['type'] == 'line': img[mark==0] = ips.range[1] elif para['type'] == 'up area': img[mark!=1] = ips.range[1] elif para['type'] == 'down area': img[mark!=2] = ips.range[1]
def compute_filters(data): filter_data = np.zeros((1, data.shape[1], data.shape[2]), dtype=np.float32) filter_data[0] = sobel(np.clip(data[0] / 600.0, 0, 1)) + sobel(np.clip(data[1] / 600.0, 0, 1)) + sobel(np.clip(data[2] / 600.0, 0, 1)) return filter_data
def main(): """Load image, apply sobel (to get x/y gradients), plot the results.""" img = data.camera() sobel_y = np.array([ [-1, -2, -1], [0, 0, 0], [1, 2, 1] ]) sobel_x = np.rot90(sobel_y) # rotates counter-clockwise # apply x/y sobel filter to get x/y gradients img_sx = signal.correlate(img, sobel_x, mode="same") img_sy = signal.correlate(img, sobel_y, mode="same") # combine x/y gradients to gradient magnitude # scikit-image's implementation divides by sqrt(2), not sure why img_s = np.sqrt(img_sx**2 + img_sy**2) / np.sqrt(2) # create binarized image threshold = np.average(img_s) img_s_bin = np.zeros(img_s.shape) img_s_bin[img_s > threshold] = 1 # generate ground truth (scikit-image method) ground_truth = skifilters.sobel(data.camera()) # plot util.plot_images_grayscale( [img, img_sx, img_sy, img_s, img_s_bin, ground_truth], ["Image", "Sobel (x)", "Sobel (y)", "Sobel (magnitude)", "Sobel (magnitude, binarized)", "Sobel (Ground Truth)"] )
def gridsize(original, segdst=SEGMENT_DISTANCE): global im # read the image from disk im = original.copy() add_image('Original') # edge detection im = sobel(im) # blurring im = filters.gaussian(im, sigma=5) # thresholding: convert to binary rage loc = threshold_local(im, 31) im = im > loc if (DEBUG): add_image('Threshold') # detect straight lines longer than 150px segs = probabilistic_hough_line( im, threshold=30, line_length=250, line_gap=7) #segs = [seg for seg in segs if vertical(seg)] # draw the segments im[:] = 0 # set image to black for seg in segs: ((x1, y1), (x2, y2)) = seg rr,cc = draw.line(y1,x1,y2,x2) im[rr, cc] = 1 if (DEBUG): add_image('Hough Lines') hh, vv = process_segments(segs) # draw the segments im[:] = 0 # set image to black num = 0 for yy in hh: for yyy in yy: (_,y),_ = yyy rr,cc = draw.line(y,0,y,999) im[rr, cc] = 1 num += 1 for xx in vv: for xxx in xx: (x,_),_ = xxx rr,cc = draw.line(0,x,999,x) im[rr, cc] = 1 num += 1 if (DEBUG): add_image('Filtered Segs') # finally save the result displ() return len(vv)-1, len(hh)-1
