我们从Python开源项目中，提取了以下19个代码示例，用于说明如何使用skimage.color.gray2rgb()。
def my_label2rgboverlay(labels, colors, image, alpha=0.2): """ Generates image with segmentation labels on top Parameters ---------- labels: labels of one image (0, 1) colors: colormap image: image (0, 1, c), where c=3 (rgb) alpha: transparency """ image_float = gray2rgb(img_as_float(rgb2gray(image) if image.shape[2] == 3 else np.squeeze(image))) label_image = my_label2rgb(labels, colors) output = image_float * alpha + label_image * (1 - alpha) return output
def gray2rgb(image): """ Grayscale scale image to RGB image >>> image = np.eye(3, dtype='uint8') * 255 >>> gray2rgb(image) array([[[255, 255, 255], [ 0, 0, 0], [ 0, 0, 0]], <BLANKLINE> [[ 0, 0, 0], [255, 255, 255], [ 0, 0, 0]], <BLANKLINE> [[ 0, 0, 0], [ 0, 0, 0], [255, 255, 255]]], dtype=uint8) :param numpy array image: Numpy array with range [0,255] and dtype 'uint8'. :return: RGB image :rtype: numpy array with range [0,255] and dtype 'uint8' """ return skc.gray2rgb(image)
def vis_result(image, seg, gt, file_name='test.png'): indices = np.where(seg == 1) indices_gt = np.where(gt == 1) im_norm = image / image.max() rgb_image = color.gray2rgb(im_norm) multiplier = [0., 1., 1.] multiplier_gt = [1., 1., 0.] im_seg = rgb_image.copy() im_gt = rgb_image.copy() im_seg[indices[0], indices[1], :] *= multiplier im_gt[indices_gt[0], indices_gt[1], :] *= multiplier_gt fig = plt.figure() a = fig.add_subplot(1, 2, 1) plt.imshow(im_seg) a.set_title('Segmentation') a = fig.add_subplot(1, 2, 2) plt.imshow(im_gt) a.set_title('Ground truth') plt.savefig(file_name)
def get(self, uri): i = imread(uri) if len(i.shape) == 2: i = gray2rgb(i) else: i = i[:, :, :3] c = self._image_to_color.get(i) dbg = self._settings['debug'] if dbg is None: return c c, imgs = c b = splitext(basename(uri))[0] imsave(join(dbg, b + '-resized.jpg'), imgs['resized']) imsave(join(dbg, b + '-back.jpg'), img_as_float(imgs['back'])) imsave(join(dbg, b + '-skin.jpg'), img_as_float(imgs['skin'])) imsave(join(dbg, b + '-clusters.jpg'), imgs['clusters']) return c, { 'resized': join(dbg, b + '-resized.jpg'), 'back': join(dbg, b + '-back.jpg'), 'skin': join(dbg, b + '-skin.jpg'), 'clusters': join(dbg, b + '-clusters.jpg'), }
def load_img(path, grayscale=False, resize=None, order=1): # Load image img = io.imread(path) # Resize # print('Desired resize: ' + str(resize)) if resize is not None: img = skimage.transform.resize(img, resize, order=order, preserve_range=True) # print('Final resize: ' + str(img.shape)) # Color conversion if len(img.shape)==2 and not grayscale: img = gray2rgb(img) elif len(img.shape)>2 and img.shape[2]==3 and grayscale: img = rgb2gray(img) # Return image return img
def load_img(path, grayscale=False, resize=None, order=1): # Load image img = io.imread(path) # Resize # print('Desired resize: ' + str(resize)) if resize is not None: img = skimage.transform.resize(img, resize, order=order, preserve_range=True) # print('Final resize: ' + str(img.shape)) # Color conversion if len(img.shape) == 2 and not grayscale: img = gray2rgb(img) elif len(img.shape) > 2 and img.shape[2] == 3 and grayscale: img = rgb2gray(img) # Return image return img
def main(args): """ Entry point. """ # load the image img = imread(args.input) if img.ndim == 2: img = gray2rgb(img) elif img.shape[2] == 4: img = img[:, :, :3] upper_dim = max(img.shape[:2]) if upper_dim > args.max_dim: img = rescale(img, args.max_dim/float(upper_dim), order=3) # compute saliency start = timeit.default_timer() img_sal = compute_saliency(img) runtime = timeit.default_timer() - start print("Took {0} seconds.".format(runtime)) # save image (fname, ext) = os.path.splitext(args.input) out_path = fname + "_saliency" + ext imsave(out_path, img_sal)
def _normalise_image(image, *, image_cmap=None): image = img_as_float(image) if image.ndim == 2: if image_cmap is None: image = gray2rgb(image) else: image = plt.get_cmap(image_cmap)(image)[..., :3] return image
def vis_col_im(im, gt): indices_0 = np.where(gt == 0) # nothing indices_1 = np.where(gt == 1) # necrosis indices_2 = np.where(gt == 2) # edema indices_3 = np.where(gt == 3) # non-enhancing tumor indices_4 = np.where(gt == 4) # enhancing tumor im = np.asarray(im, dtype='float32') im = im*1./im.max() rgb_image = color.gray2rgb(im) m0 = [1., 1., 1.] m1 = [1., 0., 0.] m2 = [0.2, 1., 0.2] m3 = [1., 1., 0.2] m4 = [1., 0.6, 0.2] im = rgb_image.copy() im[indices_0[0], indices_0[1], :] *= m0 im[indices_1[0], indices_1[1], :] *= m1 im[indices_2[0], indices_2[1], :] *= m2 im[indices_3[0], indices_3[1], :] *= m3 im[indices_4[0], indices_4[1], :] *= m4 plt.imshow(im) plt.show() plt.close()
def col_im(im, gt): im = np.asarray(im, dtype='float32') im = im*1./im.max() rgb_image = color.gray2rgb(im) im = rgb_image.copy() if gt is None: return im indices_0 = np.where(gt == 0) # nothing indices_1 = np.where(gt == 1) # necrosis indices_2 = np.where(gt == 2) # edema indices_3 = np.where(gt == 3) # non-enhancing tumor indices_4 = np.where(gt == 4) # enhancing tumor m0 = [1., 1., 1.] m1 = [1., 0., 0.] # red: necrosis m2 = [0.2, 1., 0.2] # green: edema m3 = [1., 1., 0.2] # yellow: non-enhancing tumor m4 = [1., 0.6, 0.2] # orange: enhancing tumor im[indices_0[0], indices_0[1], :] *= m0 im[indices_1[0], indices_1[1], :] *= m1 im[indices_2[0], indices_2[1], :] *= m2 im[indices_3[0], indices_3[1], :] *= m3 im[indices_4[0], indices_4[1], :] *= m4 return im
def vis_result(image, seg, gt, title1='Segmentation', title2='Ground truth', savefile=None): indices = np.where(seg >= 0.5) indices_gt = np.where(gt >= 0.5) im_norm = image / image.max() rgb_image = color.gray2rgb(im_norm) multiplier = [0., 1., 1.] multiplier_gt = [1., 1., 0.] im_seg = rgb_image.copy() im_gt = rgb_image.copy() im_seg[indices[0], indices[1], :] *= multiplier im_gt[indices_gt[0], indices_gt[1], :] *= multiplier_gt fig = plt.figure() a = fig.add_subplot(1, 2, 1) plt.imshow(im_seg) a.set_title(title1) a = fig.add_subplot(1, 2, 2) plt.imshow(im_gt) a.set_title(title2) if savefile is None: plt.show() else: plt.savefig(savefile) plt.close()
def my_label2rgboverlay(labels, colors, image, bglabel=None, bg_color=(0., 0., 0.), alpha=0.2): image_float = gray2rgb(img_as_float(rgb2gray(image))) label_image = my_label2rgb(labels, colors, bglabel=bglabel, bg_color=bg_color) output = image_float * alpha + label_image * (1 - alpha) return output # Save 3 images (Image, mask and result)
def my_label2rgboverlay(labels, cmap, image, bglabel=None, bg_color=(0., 0., 0.), alpha=0.2): '''Superimpose a mask over an image Convert a label mask to RGB applying a color map and superimposing it over an image as a transparent overlay''' image_float = gray2rgb(img_as_float(rgb2gray(image))) label_image = my_label2rgb(labels, cmap, bglabel=bglabel, bg_color=bg_color) output = image_float * alpha + label_image * (1 - alpha) return output
def vis_col_im(im, seg, gt, file_name='test.png'): indices_0 = np.where(gt == 0) indices_1 = np.where(gt == 1) # green - metacarpal - necrosis indices_2 = np.where(gt == 2) # yellow - proximal - edema indices_3 = np.where(gt == 3) # orange - middle - enhancing tumor indices_4 = np.where(gt == 4) # red - distal - nonenhancing tumor indices_s0 = np.where(seg == 0) indices_s1 = np.where(seg == 1) indices_s2 = np.where(seg == 2) indices_s3 = np.where(seg == 3) indices_s4 = np.where(seg == 4) im = im * 1. / im.max() rgb_image = color.gray2rgb(im) m0 = [0.6, 0.6, 1.] m1 = [0.2, 1., 0.2] m2 = [1., 1., 0.2] m3 = [1., 0.6, 0.2] m4 = [1., 0., 0.] im_gt = rgb_image.copy() im_seg = rgb_image.copy() im_gt[indices_0[0], indices_0[1], :] *= m0 im_gt[indices_1[0], indices_1[1], :] *= m1 im_gt[indices_2[0], indices_2[1], :] *= m2 im_gt[indices_3[0], indices_3[1], :] *= m3 im_gt[indices_4[0], indices_4[1], :] *= m4 im_seg[indices_s0[0], indices_s0[1], :] *= m0 im_seg[indices_s1[0], indices_s1[1], :] *= m1 im_seg[indices_s2[0], indices_s2[1], :] *= m2 im_seg[indices_s3[0], indices_s3[1], :] *= m3 im_seg[indices_s4[0], indices_s4[1], :] *= m4 fig = plt.figure() a = fig.add_subplot(1, 2, 1) plt.imshow(im_seg) a.set_title('Segmentation') a = fig.add_subplot(1, 2, 2) plt.imshow(im_gt) a.set_title('Ground truth') plt.savefig(file_name) plt.close()
def vis_col_result(im, seg, gt, savefile=None): indices_0 = np.where(gt == 0) indices_1 = np.where(gt == 1) # metacarpal indices_2 = np.where(gt == 2) # proximal indices_3 = np.where(gt == 3) # middle (thumb: distal) indices_4 = np.where(gt == 4) # distal (thumb: none) indices_s0 = np.where(seg == 0) indices_s1 = np.where(seg == 1) indices_s2 = np.where(seg == 2) indices_s3 = np.where(seg == 3) indices_s4 = np.where(seg == 4) im = im * 1. / im.max() rgb_image = color.gray2rgb(im) m0 = [0.6, 0.6, 1.] m1 = [0.2, 1., 0.2] m2 = [1., 1., 0.2] m3 = [1., 0.6, 0.2] m4 = [1., 0., 0.] im_gt = rgb_image.copy() im_seg = rgb_image.copy() im_gt[indices_0[0], indices_0[1], :] *= m0 im_gt[indices_1[0], indices_1[1], :] *= m1 im_gt[indices_2[0], indices_2[1], :] *= m2 im_gt[indices_3[0], indices_3[1], :] *= m3 im_gt[indices_4[0], indices_4[1], :] *= m4 im_seg[indices_s0[0], indices_s0[1], :] *= m0 im_seg[indices_s1[0], indices_s1[1], :] *= m1 im_seg[indices_s2[0], indices_s2[1], :] *= m2 im_seg[indices_s3[0], indices_s3[1], :] *= m3 im_seg[indices_s4[0], indices_s4[1], :] *= m4 fig = plt.figure() a = fig.add_subplot(1, 2, 1) plt.imshow(im_seg) a.set_title('Segmentation') a = fig.add_subplot(1, 2, 2) plt.imshow(im_gt) a.set_title('Ground truth') if savefile is not None: plt.savefig(savefile) else: plt.show() plt.close()
def save_segmented_image(self, filepath_image, modality='t1c', show=False): ''' Creates an image of original brain with segmentation overlay and save it in ./predictions INPUT (1) str 'filepath_image': filepath to test image for segmentation, including file extension (2) str 'modality': imaging modality to use as background. defaults to t1c. options: (flair, t1, t1c, t2) (3) bool 'show': If true, shows output image. defaults to False. OUTPUT (1) if show is True, shows image of segmentation results (2) if show is false, returns segmented image. ''' modes = {'flair': 0, 't1': 1, 't1c': 2, 't2': 3} segmentation = self.predict_image(filepath_image, show=False) print 'segmentation = ' + str(segmentation) img_mask = np.pad(segmentation, (16, 16), mode='edge') ones = np.argwhere(img_mask == 1) twos = np.argwhere(img_mask == 2) threes = np.argwhere(img_mask == 3) fours = np.argwhere(img_mask == 4) test_im = io.imread(filepath_image) test_back = test_im.reshape(5, 216, 160)[modes[modality]] # overlay = mark_boundaries(test_back, img_mask) gray_img = img_as_float(test_back) # adjust gamma of image image = adjust_gamma(color.gray2rgb(gray_img), 0.65) sliced_image = image.copy() red_multiplier = [1, 0.2, 0.2] yellow_multiplier = [1, 1, 0.25] green_multiplier = [0.35, 0.75, 0.25] blue_multiplier = [0, 0.25, 0.9] print str(len(ones)) print str(len(twos)) print str(len(threes)) print str(len(fours)) # change colors of segmented classes for i in xrange(len(ones)): sliced_image[ones[i][0]][ones[i][1]] = red_multiplier for i in xrange(len(twos)): sliced_image[twos[i][0]][twos[i][1]] = green_multiplier for i in xrange(len(threes)): sliced_image[threes[i][0]][threes[i][1]] = blue_multiplier for i in xrange(len(fours)): sliced_image[fours[i][0]][fours[i][1]] = yellow_multiplier #if show=True show the prediction if show: print 'Showing...' io.imshow(sliced_image) plt.show() #save the prediction print 'Saving...' try: mkdir_p('./predictions/') io.imsave('./predictions/' + os.path.basename(filepath_image) + '.png', sliced_image) print 'prediction saved.' except: io.imsave('./predictions/' + os.path.basename(filepath_image) + '.png', sliced_image) print 'prediction saved.'
def save_segmented_image(self, index, test_img, save=False): """ Creates an image of original brain with segmentation overlay :param index: index of image to save :param test_img: filepath to test image for segmentation, including file extension :param save: If true, shows output image. (defaults to False) :return: if show is True, shows image of segmentation results if show is false, returns segmented image. """ segmentation = self.predict_image(test_img) img_mask = np.pad(segmentation, (16, 16), mode='edge') ones = np.argwhere(img_mask == 1) twos = np.argwhere(img_mask == 2) threes = np.argwhere(img_mask == 3) fours = np.argwhere(img_mask == 4) test_im = mpimg.imread(test_img).astype('float') test_back = rgb2gray(test_im).reshape(5, 216, 160)[-2] # overlay = mark_boundaries(test_back, img_mask) gray_img = img_as_float(test_back) # adjust gamma of image image = adjust_gamma(color.gray2rgb(gray_img), 0.65) sliced_image = image.copy() red_multiplier = [1, 0.2, 0.2] yellow_multiplier = [1, 1, 0.25] green_multiplier = [0.35, 0.75, 0.25] blue_multiplier = [0, 0.25, 0.9] # change colors of segmented classes for i in xrange(len(ones)): sliced_image[ones[i][0]][ones[i][1]] = red_multiplier for i in xrange(len(twos)): sliced_image[twos[i][0]][twos[i][1]] = green_multiplier for i in xrange(len(threes)): sliced_image[threes[i][0]][threes[i][1]] = blue_multiplier for i in xrange(len(fours)): sliced_image[fours[i][0]][fours[i][1]] = yellow_multiplier if save: try: mkdir_p('./results/') io.imsave('./results/result' + '_' + str(index) + '.png', sliced_image) except: io.imsave('./results/result' + '_' + str(index) + '.png', sliced_image) else: return sliced_image
