Python morphology.disk函数代码示例 |
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本文整理汇总了Python中skimage.morphology.disk函数的典型用法代码示例。如果您正苦于以下问题：Python disk函数的具体用法？Python disk怎么用？Python disk使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。 在下文中一共展示了disk函数的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的Python代码示例。 示例1: label_image def label_image(image): ROI = np.zeros((470,400,3), dtype=np.uint8) for c in range(3): for i in range(50,520): for j in range(240,640): ROI[i-50,j-240,c] = image[i,j,c] gray_ROI = cv2.cvtColor(ROI,cv2.COLOR_BGR2GRAY) ROI_flou = cv2.medianBlur((ROI).astype('uint8'),3) Laser = Detecte_laser.Detect_laser(ROI_flou) open_laser = cv2.morphologyEx(Laser, cv2.MORPH_DILATE, disk(3)) skel = skeletonize(open_laser > 0) tranche = Detecte_laser.tranche(skel,90,30) ret, thresh = cv2.threshold(gray_ROI*tranche.astype('uint8'),0,1,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU) thresh01 = thresh 2: for region in regionprops(label_image): if region.area < areas[-2]: for coordinates in region.coords: label_image[coordinates[0], coordinates[1]] = 0 binary = label_image > 0 # Step 5: Erosion operation with a disk of radius 2. This operation is seperate the lung nodules attached to the blood vessels. selem = disk(2) binary = binary_erosion(binary, selem) # Step 6: Closure operation with a disk of radius 10. This operation is to keep nodules attached to the lung wall. selem = disk(10) # CHANGE BACK TO 10 binary = binary_closing(binary, selem) # Step 7: Fill in the small holes inside the binary mask of lungs. edges = roberts(binary) binary = ndi.binary_fill_holes(edges) # Step 8: Superimpose the binary mask on the input image. get_high_vals = binary == 0 im[get_high_vals] = -2000 return im, binary开发者ID:ericsolo，项目名称:python，代码行数:29，代码来源:helpers.py 示例4: returnProcessedImage def returnProcessedImage(que,folder,img_flist): X = [] for fname in img_flist: cur_img = imread(folder+'/'+fname , as_grey=True) cur_img = 1 - cur_img ######## randomly add samples # random add contrast r_for_eq = random() cur_img = equalize_adapthist(cur_img,ntiles_x=8,ntiles_y=8,clip_limit=(r_for_eq+0.5)/3) #random morphological operation r_for_mf_1 = random() if 0.05 < r_for_mf_1 < 0.25: # small vessel selem1 = disk(0.5+r_for_mf_1) cur_img = dilation(cur_img,selem1) cur_img = erosion(cur_img,selem1) elif 0.25 < r_for_mf_1 < 0.5: # large vessel selem2 = disk(2.5+r_for_mf_1*3) cur_img = dilation(cur_img,selem2) cur_img = erosion(cur_img,selem2) elif 0.5 < r_for_mf_1 < 0.75: # exudate selem1 = disk(9.21) selem2 = disk(7.21) dilated1 = dilation(cur_img, selem1) dilated2 = dilation(cur_img, selem2) cur_img = np.subtract(dilated1, dilated2) cur_img = img_as_float(cur_img) X.append([cur_img.tolist()]) # X = np.array(X , dtype = theano.config.floatX) que.put(X) return X开发者ID:stegben，项目名称:Competitions，代码行数:35，代码来源:train_whole_g_channel_3_class.py 示例5: find_grains def find_grains(input_file, output_dir=None): """Return tuple of segmentaitons (grains, difficult_regions).""" name = fpath2name(input_file) name = "grains-" + name + ".png" if output_dir: name = os.path.join(output_dir, name) image = Image.from_file(input_file) intensity = mean_intensity_projection(image) image = remove_scalebar(intensity, np.mean(intensity)) image = threshold_abs(image, 75) image = invert(image) image = fill_holes(image, min_size=500) image = erode_binary(image, selem=disk(4)) image = remove_small_objects(image, min_size=500) image = dilate_binary(image, selem=disk(4)) dist = distance(image) seeds = local_maxima(dist) seeds = dilate_binary(seeds) # Merge spurious double peaks. seeds = connected_components(seeds, background=0) segmentation = watershed_with_seeds(dist, seeds=seeds, mask=image) # Need a copy to avoid over-writing original. initial_segmentation = np.copy(segmentation) # Remove spurious blobs. segmentation = remove_large_segments(segmentation, max_size=3000) segmentation = remove_small_segments(segmentation, min_size=500) props = skimage.measure.regionprops(segmentation) segmentation = remove_non_round(segmentation, props, 0.6) difficult = initial_segmentation - segmentation return segmentation, difficult开发者ID:JIC-Image-Analysis，项目名称:pollen_tubes，代码行数:35，代码来源:annotate.py 示例6: compute def compute(self, src): image = img_as_ubyte(src) # denoise image denoised = denoise_tv_chambolle(image, weight=0.05) denoised_equalize= exposure.equalize_hist(denoised) # find continuous region (low gradient) --> markers markers = rank.gradient(denoised_equalize, disk(5)) < 10 markers = ndi.label(markers)[0] # local gradient gradient = rank.gradient(denoised, disk(2)) # labels labels = watershed(gradient, markers) # display results fig, axes = plt.subplots(2,3) axes[0, 0].imshow(image)#, cmap=plt.cm.spectral, interpolation='nearest') axes[0, 1].imshow(denoised, cmap=plt.cm.spectral, interpolation='nearest') axes[0, 2].imshow(markers, cmap=plt.cm.spectral, interpolation='nearest') axes[1, 0].imshow(gradient, cmap=plt.cm.spectral, interpolation='nearest') axes[1, 1].imshow(labels, cmap=plt.cm.spectral, interpolation='nearest', alpha=.7) plt.show()开发者ID:roboticslab-uc3m，项目名称:textiles，代码行数:25，代码来源:GarmentAnalysis.py 示例7: find_grains def find_grains(input_file, output_dir=None): """Return tuple of segmentaitons (grains, difficult_regions).""" name = fpath2name(input_file) name = "grains-" + name + ".png" if output_dir: name = os.path.join(output_dir, name) image = Image.from_file(input_file) intensity = mean_intensity_projection(image) # Median filter seems more robust than Otsu. # image = threshold_otsu(intensity) image = threshold_median(intensity, scale=0.8) image = invert(image) image = erode_binary(image, selem=disk(2)) image = dilate_binary(image, selem=disk(2)) image = remove_small_objects(image, min_size=200) image = fill_holes(image, min_size=50) dist = distance(image) seeds = local_maxima(dist) seeds = dilate_binary(seeds) # Merge spurious double peaks. seeds = connected_components(seeds, background=0) segmentation = watershed_with_seeds(dist, seeds=seeds, mask=image) # Remove spurious blobs. segmentation = remove_large_segments(segmentation, max_size=3000) segmentation = remove_small_segments(segmentation, min_size=100) return segmentation开发者ID:JIC-Image-Analysis，项目名称:pollen_tubes，代码行数:32，代码来源:nikonE800_annotate.py 示例8: extract_region_opening def extract_region_opening(img, is_demo=False): """ Extracts fingerprint region of image via mophological opening """ after_median = skimage.filter.rank.median(img, skmorph.disk(9)) after_erode = skmorph.erosion(after_median, skmorph.disk(11)) after_dil = skmorph.dilation(after_erode, skmorph.disk(5)) _, t_dil_img = cv2.threshold(after_dil, 240, 40, cv2.THRESH_BINARY) if is_demo: _, t_med_img = cv2.threshold(after_median, 240, 255, cv2.THRESH_BINARY) _, t_erd_img = cv2.threshold(after_erode, 240, 40, cv2.THRESH_BINARY) erd_gry = t_erd_img.astype(np.uint8) * 255 rgb_erd = np.dstack((erd_gry, img, img)) dil_gry = t_dil_img.astype(np.uint8) * 255 rgb_dil = np.dstack((dil_gry, img, img)) plt.subplot(2,2,1) plt.imshow(after_erode, cmap="gray", interpolation="nearest") plt.subplot(2,2,2) plt.imshow(rgb_erd, interpolation="nearest") plt.subplot(2,2,3) plt.imshow(after_dil, cmap="gray", interpolation="nearest") plt.subplot(2,2,4) plt.imshow(rgb_dil, interpolation="nearest") plt.show() return t_dil_img开发者ID:rahulsingh786，项目名称:overlapped-fingerprint-seperator，代码行数:32，代码来源:utils.py 示例9: split_object def split_object(self, labeled_image): """ split object when it's necessary """ labeled_image = labeled_image.astype(np.uint16) labeled_mask = np.zeros_like(labeled_image, dtype=np.uint16) labeled_mask[labeled_image != 0] = 1 #ift structuring element about center point. This only affects eccentric structuring elements (i.e. selem with even num=============================== labeled_image = skr.median(labeled_image, skm.disk(4)) labeled_mask = np.zeros_like(labeled_image, dtype=np.uint16) labeled_mask[labeled_image != 0] = 1 distance = scipym.distance_transform_edt(labeled_image).astype(np.uint16) #======================================================================= # binary = np.zeros(np.shape(labeled_image)) # binary[labeled_image > 0] = 1 #======================================================================= distance = skr.mean(distance, skm.disk(15)) l_max = skr.maximum(distance, skm.disk(5)) #l_max = skf.peak_local_max(distance, indices=False,labels=labeled_image, footprint=np.ones((3,3))) l_max = l_max - distance 2: for region in regionprops(label_image): if region.area < areas[-2]: for coordinates in region.coords: label_image[coordinates[0], coordinates[1]] = 0 binary = label_image > 0 selem = disk(2) binary = binary_erosion(binary, selem) selem = disk(10) binary = binary_closing(binary, selem) edges = roberts(binary) binary = ndi.binary_fill_holes(edges) get_high_vals = binary == 0 im[get_high_vals] = 0 binary = morphology.dilation(binary,np.ones([5,5])) return binary开发者ID:skconsulting，项目名称:ild，代码行数:28，代码来源:data_roifull1.py 示例11: watershed def watershed(image): hsv_image = color.rgb2hsv(image) low_res_image = rescale(hsv_image[:, :, 0], SCALE) local_mean = mean(low_res_image, disk(50)) local_minimum_flat = np.argmin(local_mean) local_minimum = np.multiply(np.unravel_index(local_minimum_flat, low_res_image.shape), round(1 / SCALE)) certain_bone_pixels = np.full_like(hsv_image[:, :, 0], False, bool) certain_bone_pixels[ local_minimum[0] - INITIAL_WINDOW_SIZE/2:local_minimum[0]+INITIAL_WINDOW_SIZE/2, local_minimum[1] - INITIAL_WINDOW_SIZE/2:local_minimum[1]+INITIAL_WINDOW_SIZE/2 ] = True certain_non_bone_pixels = np.full_like(hsv_image[:, :, 0], False, bool) certain_non_bone_pixels[0:BORDER_SIZE, :] = True certain_non_bone_pixels[-BORDER_SIZE:-1, :] = True certain_non_bone_pixels[:, 0:BORDER_SIZE] = True certain_non_bone_pixels[:, -BORDER_SIZE:-1] = True smoothed_hsv = median(hsv_image[:, :, 0], disk(50)) threshold = MU * np.median(smoothed_hsv[certain_bone_pixels]) possible_bones = np.zeros_like(hsv_image[:, :, 0]) possible_bones[smoothed_hsv < threshold] = 1 markers = np.zeros_like(possible_bones) markers[certain_bone_pixels] = 1 markers[certain_non_bone_pixels] = 2 labels = morphology.watershed(-possible_bones, markers) return labels开发者ID:selaux，项目名称:master-of-bones，代码行数:33，代码来源:segmentation.py 示例12: Image_ws_tranche def Image_ws_tranche(image): laser = Detect_laser(image) laser_tranche = tranche_image(laser,60) image_g = skimage.color.rgb2gray(image) image_g = image_g * laser_tranche image_med = rank2.median((image_g*255).astype('uint8'),disk(8)) image_clahe = exposure.equalize_adapthist(image_med, clip_limit=0.03) image_clahe_stretch = exposure.rescale_intensity(image_clahe, out_range=(0, 256)) image_grad = rank2.gradient(image_clahe_stretch,disk(3)) image_grad_mark = image_grad markers markers = rank.gradient(denoised, disk(5)) < 10 markers = nd.label(markers)[0] seg = watershed(gray,markers) return seg开发者ID:prateek-s，项目名称:cartmandetect，代码行数:8，代码来源:segmentation.py 示例15: outlineSmoothing def outlineSmoothing(image, radius=1): """Smooth outlines of foreground object using morphological operations.""" struct = median(disk(radius), disk(1)) label_image = binary_closing(image, struct) label_image = binary_opening(label_image, struct) return label_image.astype(image.dtype)开发者ID:rhoef，项目名称:afw，代码行数:8，代码来源:toolbox.py注：本文中的skimage.morphology.disk函数示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。 |
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