我们从Python开源项目中，提取了以下50个代码示例，用于说明如何使用matplotlib.pyplot.gray()。
def plot_labeled_images_random(image_list, label_list, categories, n, title_str, ypixels, xpixels, seed, filename): random.seed(seed) index_sample = random.sample(range(len(image_list)), n) plt.figure(figsize=(2*n, 2)) #plt.suptitle(title_str) for i, ind in enumerate(index_sample): ax = plt.subplot(1, n, i + 1) plt.imshow(image_list[ind].reshape(ypixels, xpixels)) plt.gray() ax.set_title(categories[label_list[ind]], fontsize=20) ax.get_xaxis().set_visible(False); ax.get_yaxis().set_visible(False) if 1: pylab.savefig(filename, bbox_inches='tight') else: plt.show() # plot_unlabeled_images_random: plots unlabeled images at random
def plot_unlabeled_images_random(image_list, n, title_str, ypixels, xpixels, seed, filename): random.seed(seed) index_sample = random.sample(range(len(image_list)), n) plt.figure(figsize=(2*n, 2)) plt.suptitle(title_str) for i, ind in enumerate(index_sample): ax = plt.subplot(1, n, i + 1) plt.imshow(image_list[ind].reshape(ypixels, xpixels)) plt.gray() ax.get_xaxis().set_visible(False); ax.get_yaxis().set_visible(False) if 1: pylab.savefig(filename, bbox_inches='tight') else: plt.show() # plot_compare: given test images and their reconstruction, we plot them for visual comparison
def plot_compare(x_test, decoded_imgs, filename): n = 10 plt.figure(figsize=(2*n, 4)) for i in range(n): # display original ax = plt.subplot(2, n, i + 1) plt.imshow(x_test[i].reshape(28, 28)) plt.gray() ax.get_xaxis().set_visible(False) ax.get_yaxis().set_visible(False) # display reconstruction ax = plt.subplot(2, n, i + 1 + n) plt.imshow(decoded_imgs[i].reshape(28, 28)) plt.gray() ax.get_xaxis().set_visible(False) ax.get_yaxis().set_visible(False) if 1: pylab.savefig(filename, bbox_inches='tight') else: plt.show() # plot_img: plots greyscale image
def visualize_weights(W, outfile): # ????????? W = (W - np.min(W)) / (np.max(W) - np.min(W)) W *= 255.0 W = W.astype(np.int) pos = 1 for i in range(100): plt.subplot(10, 10, pos) plt.subplots_adjust(wspace=0, hspace=0) plt.imshow(W[i].reshape(28, 28)) plt.gray() plt.axis('off') pos += 1 plt.show() plt.savefig(outfile)
def test(): saver.restore(sess, FLAGS.save_dir+'/model.ckpt') batch_x, _ = mnist.test.next_batch(10) batch_p = sess.run(p, {x:batch_x}) P = np.zeros((0, n_in)) for i in range(10): P = np.concatenate([P, batch_x[i].reshape((1, n_in))], 0) for t in range(T): P = np.concatenate([P, batch_p[t][i].reshape((1, n_in))], 0) fig = plt.figure('reconstructed') plt.gray() plt.axis('off') plt.imshow(batchmat_to_tileimg(P, (28, 28), (10, T+1))) fig.savefig(FLAGS.save_dir+'/reconstructed.png') P = np.zeros((0, attunit.read_dim)) for t in range(T): batch_att = sess.run(tf.nn.sigmoid(x_att[t]), {x:batch_x}) P = np.concatenate([P, batch_att], 0) fig = plt.figure('attended') plt.gray() plt.axis('off') plt.imshow(batchmat_to_tileimg(P, (N, N), (10, T))) plt.show()
def test(): saver.restore(sess, FLAGS.save_dir+'/model.ckpt') batch_x, _ = mnist.test.next_batch(100) fig = plt.figure('original') plt.gray() plt.axis('off') plt.imshow(batchmat_to_tileimg(batch_x, (height, width), (10, 10))) fig.savefig(FLAGS.save_dir+'/original.png') fig = plt.figure('reconstructed') plt.gray() plt.axis('off') p_recon = sess.run(p, {x:batch_x, is_train:False}) plt.imshow(batchmat_to_tileimg(p_recon, (height, width), (10, 10))) fig.savefig(FLAGS.save_dir+'/reconstructed.png') p_gen = sess.run(p, {z:np.random.normal(size=(100, n_lat)), is_train:False}) I_gen = batchmat_to_tileimg(p_gen, (height, width), (10, 10)) fig = plt.figure('generated') plt.gray() plt.axis('off') plt.imshow(I_gen) fig.savefig(FLAGS.save_dir+'/generated.png') plt.show()
def test(): saver.restore(sess, FLAGS.save_dir+'/model.ckpt') batch_x = test_x[0:100] fig = plt.figure('original') plt.gray() plt.axis('off') plt.imshow(batchmat_to_tileimg(batch_x, (height, width), (10, 10))) fig.savefig(FLAGS.save_dir+'/original.png') fig = plt.figure('reconstructed') plt.gray() plt.axis('off') p_recon = sess.run(p, {x:batch_x}) plt.imshow(batchmat_to_tileimg(p_recon, (height, width), (10, 10))) fig.savefig(FLAGS.save_dir+'/reconstructed.png') p_gen = sess.run(p, {z:np.random.normal(size=(100, n_lat))}) I_gen = batchmat_to_tileimg(p_gen, (height, width), (10, 10)) fig = plt.figure('generated') plt.gray() plt.axis('off') plt.imshow(I_gen) fig.savefig(FLAGS.save_dir+'/generated.png') plt.show()
def test(): saver.restore(sess, FLAGS.save_dir+'/model.ckpt') batch_x = test_x[0:100] fig = plt.figure('original') plt.gray() plt.axis('off') plt.imshow(batchmat_to_tileimg(batch_x, (height, width), (10, 10))) fig.savefig(FLAGS.save_dir+'/original.png') fig = plt.figure('reconstructed') plt.gray() plt.axis('off') p_recon = sess.run(p, {x:batch_x, is_training:False}) plt.imshow(batchmat_to_tileimg(p_recon, (height, width), (10, 10))) fig.savefig(FLAGS.save_dir+'/reconstructed.png') p_gen = sess.run(p, {z:np.random.normal(size=(100, n_lat)), is_training:False}) I_gen = batchmat_to_tileimg(p_gen, (height, width), (10, 10)) fig = plt.figure('generated') plt.gray() plt.axis('off') plt.imshow(I_gen) fig.savefig(FLAGS.save_dir+'/generated.png') plt.show()
def plot_examples(nade, dataset, shape, name, rows=5, cols=10): #Show some samples images = list() for row in xrange(rows): for i in xrange(cols): nade.setup_n_orderings(n=1) sample = dataset.sample_data(1)[0].T dens = nade.logdensity(sample) images.append((sample, dens)) images.sort(key=lambda x: -x[1]) plt.figure(figsize=(0.5*cols,0.5*rows), dpi=100) plt.gray() for row in xrange(rows): for col in xrange(cols): i = row*cols+col sample, dens = images[i] plt.subplot(rows, cols, i+1) plot_sample(np.resize(sample, np.prod(shape)).reshape(shape), shape, origin="upper") plt.subplots_adjust(left=0.01, right=0.99, top=0.99, bottom=0.01, hspace=0.04, wspace=0.04) type_1_font() plt.savefig(os.path.join(DESTINATION_PATH, name))
def plot_samples(nade, shape, name, rows=5, cols=10): #Show some samples images = list() for row in xrange(rows): for i in xrange(cols): nade.setup_n_orderings(n=1) sample = nade.sample(1)[:,0] dens = nade.logdensity(sample[:, np.newaxis]) images.append((sample, dens)) images.sort(key=lambda x: -x[1]) plt.figure(figsize=(0.5*cols,0.5*rows), dpi=100) plt.gray() for row in xrange(rows): for col in xrange(cols): i = row*cols+col sample, dens = images[i] plt.subplot(rows, cols, i+1) plot_sample(np.resize(sample, np.prod(shape)).reshape(shape), shape, origin="upper") plt.subplots_adjust(left=0.01, right=0.99, top=0.99, bottom=0.01, hspace=0.04, wspace=0.04) type_1_font() plt.savefig(os.path.join(DESTINATION_PATH, name)) #plt.show()
def inpaint_digits_(dataset, shape, model, n_examples = 5, delete_shape = (10,10), n_samples = 5, name = "inpaint_digits"): #Load a few digits from the test dataset (as rows) data = dataset.sample_data(1000)[0] #data = data[[1,12,17,81,88,102],:] data = data[range(20,40),:] n_examples = data.shape[0] #Plot it all matplotlib.rcParams.update({'font.size': 8}) plt.figure(figsize=(5,5), dpi=100) plt.gray() cols = 2 + n_samples for row in xrange(n_examples): # Original plt.subplot(n_examples, cols, row*cols+1) plot_sample(data[row,:], shape, origin="upper") plt.subplots_adjust(left=0.01, right=0.99, top=0.95, bottom=0.01, hspace=0.40, wspace=0.04) plt.savefig(os.path.join(DESTINATION_PATH, "kk.pdf"))
def plot_imgs_and_reconstructions(imgs, reconstructions, n=10, shape=(28,28)): plt.figure(figsize=(20, 4)) for i in range(n): # display original ax = plt.subplot(2, n, i + 1) plt.imshow(imgs[i].reshape(shape)) if len(shape) == 2: plt.gray() ax.get_xaxis().set_visible(False) ax.get_yaxis().set_visible(False) # display reconstruction ax = plt.subplot(2, n, i + 1 + n) plt.imshow(reconstructions[i].reshape(shape)) if len(shape) == 2: plt.gray() ax.get_xaxis().set_visible(False) ax.get_yaxis().set_visible(False) plt.show() # Back to the top!
def save_ims(filename, ims, dpi=100): n, c, w, h = ims.shape x_plots = math.ceil(math.sqrt(n)) y_plots = x_plots if n % x_plots == 0 else x_plots - 1 plt.figure(figsize=(w*x_plots/dpi, h*y_plots/dpi), dpi=dpi) for i, im in enumerate(ims): plt.subplot(y_plots, x_plots, i+1) if c == 1: plt.imshow(im[0]) else: plt.imshow(im.transpose((1, 2, 0))) plt.axis('off') plt.gca().set_xticks([]) plt.gca().set_yticks([]) plt.gray() plt.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=0, hspace=0) plt.savefig(filename, dpi=dpi*2, facecolor='black') plt.clf() plt.close()
def save_ims(filename, ims, dpi=100): n, c, w, h = ims.shape x_plots = math.ceil(math.sqrt(n)) y_plots = x_plots if n % x_plots == 0 else x_plots - 1 plt.figure(figsize=(w*x_plots/dpi, h*y_plots/dpi), dpi=dpi) for i, im in enumerate(ims): plt.subplot(y_plots, x_plots, i+1) if c == 1: plt.imshow(im[0]) else: raise NotImplementedError plt.axis('off') plt.gca().set_xticks([]) plt.gca().set_yticks([]) plt.gray() plt.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=0, hspace=0) plt.savefig(filename, dpi=dpi*2, facecolor='black') plt.clf() plt.close()
def plot_figures(figures, nrows=1, ncols=1, titles=False): """Plot a dictionary of figures. Parameters ---------- figures : <title, figure> dictionary ncols : number of columns of subplots wanted in the display nrows : number of rows of subplots wanted in the figure """ fig, axeslist = plt.subplots(ncols=ncols, nrows=nrows) for ind, title in enumerate(sorted(figures.keys(), key=lambda s: int(s[3:]))): axeslist.ravel()[ind].imshow(figures[title], cmap=plt.gray()) if titles: axeslist.ravel()[ind].set_title(title) for ind in range(nrows*ncols): axeslist.ravel()[ind].set_axis_off() if titles: plt.tight_layout() plt.show()
def imshow(self): (_, _), (x_test_in, x_test) = self.Data x_test_in, x_test = update2(x_test_in, x_test) autoencoder = self.autoencoder decoded_imgs = autoencoder.predict(x_test_in) n = 10 plt.figure(figsize=(20, 4)) for i in range(n): # display original ax = plt.subplot(2, n, i + 1) plt.imshow(x_test[i].reshape(28, 28)) plt.gray() ax.get_xaxis().set_visible(False) ax.get_yaxis().set_visible(False) # display reconstruction ax = plt.subplot(2, n, i + n + 1) plt.imshow(decoded_imgs[i].reshape(28, 28)) plt.gray() ax.get_xaxis().set_visible(False) ax.get_yaxis().set_visible(False) plt.show()
def display_example(x,i): # imsize=42*42 # observation = x[2:imsize+2].reshape([42,42]) # observation2 = x[imsize+2:].reshape([42,42]) # print(observation.shape) # Plot the grid x = x.reshape(42,42) plt.imshow(x) plt.gray() #plt.show() plt.savefig('/tmp/catastrophe/frame_{}.png'.format(i))
def plot_img(img, title_str, fignum): plt.plot(fignum), plt.imshow(img, cmap='gray') plt.title(title_str), plt.xticks([]), plt.yticks([]) fignum += 1 # move onto next figure number plt.show() return fignum # read image
def imshow(image): """ show a [-1.0, 1.0] image """ plt.imshow(to_range(image), cmap=plt.gray())
def pltShowImageGray(image_file): image_gray = cv2.imread(image_file, 0) plt.title('image') plt.gray() plt.imshow(image_gray) plt.axis('off') plt.show()
def showImageGray(image_file): image_gray = cv2.imread(image_file, 0) plt.title('Gray') plt.gray() plt.imshow(image_gray) plt.axis('off') plt.show() # HSV????????
def showImageHSV(image_file): image_bgr = cv2.imread(image_file) image_hsv = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2HSV) H = image_hsv[:, :, 0] S = image_hsv[:, :, 1] V = image_hsv[:, :, 2] plt.subplot(1, 3, 1) plt.title('Hue') plt.gray() plt.imshow(H) plt.axis('off') plt.subplot(1, 3, 2) plt.title('Saturation') plt.gray() plt.imshow(S) plt.axis('off') plt.subplot(1, 3, 3) plt.title('Value') plt.gray() plt.imshow(V) plt.axis('off') plt.show() # Lab????????
def showImageLab(image_file): image_bgr = cv2.imread(image_file) image_Lab = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2LAB) L = image_Lab[:, :, 0] a = image_Lab[:, :, 1] b = image_Lab[:, :, 2] plt.subplot(1, 3, 1) plt.title('L') plt.gray() plt.imshow(L) plt.axis('off') plt.subplot(1, 3, 2) plt.title('a') plt.gray() plt.imshow(a) plt.axis('off') plt.subplot(1, 3, 3) plt.title('b') plt.gray() plt.imshow(b) plt.axis('off') plt.show()
def visualization_mnist(x_data,n=10): plt.figure(figsize=(20, 4)) for i in range(n): # display digit ax = plt.subplot(1, n, i+1) plt.imshow(x_data[i].reshape(28, 28)) plt.gray() ax.get_xaxis().set_visible(False) ax.get_yaxis().set_visible(False) plt.show()
def show_pane(top_digits, bottom_digits): """Displays two rows of digits on the screen. """ all_digits = top_digits + bottom_digits fig, axes = plt.subplots(nrows = 2, ncols = int(len(all_digits)/2)) for axis, digit in zip(axes.reshape(-1), all_digits): axis.imshow(digit, interpolation='nearest', cmap=plt.gray()) axis.axis('off') plt.show()
def test(): saver.restore(sess, FLAGS.save_dir+'/model.ckpt') batch_x = test_x[0:100] batch_p = sess.run(p, {x:batch_x}) P = np.zeros((0, n_in)) for i in range(10): P = np.concatenate([P, batch_x[i].reshape((1, n_in))], 0) for t in range(T): P = np.concatenate([P, batch_p[t][i].reshape((1, n_in))], 0) fig = plt.figure('reconstructed') plt.gray() plt.axis('off') plt.imshow(batchmat_to_tileimg(P, (height, width), (10, T+1))) fig.savefig(FLAGS.save_dir+'/reconstructed.png') plt.show()
def test(): saver.restore(sess, FLAGS.save_dir+'/model.ckpt') batch_x, _ = mnist.test.next_batch(batch_size) fig = plt.figure('original') plt.gray() plt.axis('off') plt.imshow(batchmat_to_tileimg(batch_x, (height, width), (10, 10))) fig.savefig(FLAGS.save_dir+'/original.png') fig = plt.figure('reconstructed') plt.gray() plt.axis('off') p_recon = sess.run(p, {x:batch_x}) plt.imshow(batchmat_to_tileimg(p_recon, (height, width), (10, 10))) fig.savefig(FLAGS.save_dir+'/reconstructed.png') batch_w = np.zeros((n_fac*n_fac, n_fac)) for i in range(n_fac): batch_w[i*n_fac:(i+1)*n_fac, i] = 1.0 batch_z = np.random.normal(size=(n_fac*n_fac, n_lat)) p_gen = sess.run(p, {w:batch_w, z:batch_z}) I_gen = batchmat_to_tileimg(p_gen, (height, width), (n_fac, n_fac)) fig = plt.figure('generated') plt.gray() plt.axis('off') plt.imshow(I_gen) fig.savefig(FLAGS.save_dir+'/generated.png') fig = plt.figure('factor activation heatmap') hist = np.zeros((10, n_fac)) for i in range(mnist.test.num_examples): batch_x, batch_y = mnist.test.next_batch(batch_size) batch_w = sess.run(w, {x:batch_x}) for i in range(batch_size): hist[batch_y[i], batch_w[i] > 0] += 1 sns.heatmap(hist) fig.savefig(FLAGS.save_dir+'/feature_activation.png') plt.show()
def test(): saver.restore(sess, FLAGS.save_dir+'/model.ckpt') batch_x, _ = mnist.test.next_batch(batch_size) fig = plt.figure('original') plt.gray() plt.axis('off') plt.imshow(batchmat_to_tileimg(batch_x, (height, width), (10, 10))) fig.savefig(FLAGS.save_dir+'/original.png') fig = plt.figure('reconstructed') plt.gray() plt.axis('off') p_recon, batch_w = sess.run([p, w], {x:batch_x}) plt.imshow(batchmat_to_tileimg(p_recon, (height, width), (10, 10))) fig.savefig(FLAGS.save_dir+'/reconstructed.png') batch_w = np.zeros((n_fac*n_fac, n_fac)) for i in range(n_fac): batch_w[i*n_fac:(i+1)*n_fac, i] = 1.0 batch_z = np.random.normal(size=(n_fac*n_fac, n_lat)) p_gen = sess.run(p, {w:batch_w, z:batch_z}) I_gen = batchmat_to_tileimg(p_gen, (height, width), (n_fac, n_fac)) fig = plt.figure('generated') plt.gray() plt.axis('off') plt.imshow(I_gen) fig.savefig(FLAGS.save_dir+'/generated.png') plt.show()
def test(): saver.restore(sess, FLAGS.save_dir+'/model.ckpt') np.random.shuffle(test_x) ind = ((test_y==3).nonzero())[0][0:10] batch_x = test_x[ind] batch_x_att, batch_x_hat, batch_pk, batch_p = \ sess.run([x_att, x_hat, pk, p], {x:batch_x}) A = np.zeros((0, N*N)) for i in range(10): for k in range(K): A = np.concatenate([A, batch_x_att[k][i].reshape((1, N*N))], 0) fig = plt.figure('attended') plt.gray() plt.axis('off') plt.imshow(batchmat_to_tileimg(A, (N, N), (10, K))) fig.savefig(FLAGS.save_dir+'/attended.png') P = np.zeros((0, n_in)) for i in range(10): P = np.concatenate([P, batch_x[i].reshape((1, n_in))], 0) for k in range(K): P = np.concatenate([P, batch_pk[k][i].reshape((1, n_in))], 0) P = np.concatenate([P, batch_p[i].reshape((1, n_in))]) fig = plt.figure('reconstructed') plt.gray() plt.axis('off') plt.imshow(batchmat_to_tileimg(P, (height, width), (10, K+2))) fig.savefig(FLAGS.save_dir+'/reconstructed.png') plt.show()
def test(): saver.restore(sess, FLAGS.save_dir+'/model.ckpt') batch_x = test_x[0:100] fig = plt.figure('original') plt.gray() plt.axis('off') plt.imshow(batchmat_to_tileimg(batch_x, (height, width), (10, 10))) fig.savefig(FLAGS.save_dir+'/original.png') fig = plt.figure('reconstructed') plt.gray() plt.axis('off') p_recon = sess.run(p, {x:batch_x}) plt.imshow(batchmat_to_tileimg(p_recon, (height, width), (10, 10))) fig.savefig(FLAGS.save_dir+'/reconstructed.png') batch_w = np.zeros((n_fac*n_fac, n_fac)) for i in range(n_fac): batch_w[i*n_fac:(i+1)*n_fac, i] = 1.0 batch_z = np.random.normal(size=(n_fac*n_fac, n_lat)) p_gen = sess.run(p, {w:batch_w, z:batch_z}) I_gen = batchmat_to_tileimg(p_gen, (height, width), (n_fac, n_fac)) fig = plt.figure('generated') plt.gray() plt.axis('off') plt.imshow(I_gen) fig.savefig(FLAGS.save_dir+'/generated.png') """ fig = plt.figure('factor activation heatmap') hist = np.zeros((10, n_fac)) for i in range(len(test_x)): batch_x = test_x[i*batch_size:(i+1)*batch_size] batch_w = sess.run(w, {x:batch_x}) for i in range(batch_size): hist[batch_y[i], batch_w[i] > 0] += 1 sns.heatmap(hist) fig.savefig(FLAGS.save_dir+'/feature_activation.png') """ plt.show()
def test(): saver.restore(sess, FLAGS.save_dir+'/model.ckpt') fig = plt.figure('generated') batch_z = np.random.uniform(-1, 1, [100, 100]) batch_y = np.zeros((100, 10)) for i in range(10): batch_y[i*10:(i+1)*10,i] = i gen = sess.run(fake, {z:batch_z, y:batch_y, is_train:False}) plt.gray() plt.axis('off') plt.imshow(batchimg_to_tileimg(gen, (10, 10))) fig.savefig(FLAGS.save_dir+'/genereated.png') plt.show()
def test(): saver.restore(sess, FLAGS.save_dir+'/model.ckpt') batch_x, batch_y = mnist.test.next_batch(100) """ fig = plt.figure('original') plt.gray() plt.axis('off') plt.imshow(batchmat_to_tileimg(batch_x, (height, width), (10, 10))) fig.savefig(FLAGS.save_dir+'/original.png') fig = plt.figure('reconstructed') plt.gray() plt.axis('off') p_recon = sess.run(p_x, {x:batch_x, y:batch_y}) plt.imshow(batchmat_to_tileimg(p_recon, (height, width), (10, 10))) fig.savefig(FLAGS.save_dir+'/reconstructed.png') """ batch_z = np.random.normal(size=(100, 50)) batch_y = np.zeros((100, 10)) for i in range(10): batch_y[i*10:(i+1)*10, i] = 1.0 fig = plt.figure('generated') plt.gray() plt.axis('off') p_gen = sess.run(p_x, {z:batch_z, y:batch_y, is_train:False}) plt.imshow(batchmat_to_tileimg(p_gen, (height, width), (10, 10))) fig.savefig(FLAGS.save_dir+'/generated.png') plt.show()
def test(): saver.restore(sess, FLAGS.save_dir+'/model.ckpt') fig = plt.figure('generated') gen = sess.run(fake, {z:np.random.normal(size=(100, 128)), is_train:False}) plt.gray() plt.axis('off') plt.imshow(batchimg_to_tileimg(gen, (10, 10))) fig.savefig(FLAGS.save_dir+'/genereated.png') plt.show()
def test(): saver.restore(sess, FLAGS.save_dir+'/model.ckpt') fig = plt.figure('generated') batch_z = np.random.normal(size=(100, 128)) batch_y = np.zeros((100, 10)) for i in range(10): batch_y[10*i:10*(i+1), i] = i gen = sess.run(fake, {z:batch_z, y:batch_y, is_train:False}) plt.gray() plt.axis('off') plt.imshow(batchimg_to_tileimg(gen, (10, 10))) fig.savefig(FLAGS.save_dir+'/genereated.png') plt.show()
def test(): saver.restore(sess, FLAGS.save_dir+'/model.ckpt') test_acc = 0. for i in range(n_test_batches): batch_x, batch_y = mnist.test.next_batch(batch_size) batch_acc = sess.run(accuracy, {x:batch_x, y:batch_y}) test_acc += batch_acc test_acc /= n_test_batches print 'test acc %f\n' % (test_acc) batch_x, batch_y = mnist.test.next_batch(100) fig = plt.figure('original') plt.gray() plt.axis('off') plt.imshow(batchmat_to_tileimg(batch_x, (height, width), (10, 10))) fig.savefig(FLAGS.save_dir+'/original.png') fig = plt.figure('reconstructed') plt.gray() plt.axis('off') p_recon = sess.run(p, {x:batch_x}) plt.imshow(batchimg_to_tileimg(p_recon, (10, 10))) fig.savefig(FLAGS.save_dir+'/reconstructed.png') p_gen = sess.run(p, {z:np.random.normal(size=(100, n_lat))}) fig = plt.figure('generated') plt.gray() plt.axis('off') plt.imshow(batchimg_to_tileimg(p_gen, (10, 10))) fig.savefig(FLAGS.save_dir+'/generated.png') plt.show()
def show(self): """Plot the image as a b&w plot""" plt.imshow(self.pixels)#, interpolation='nearest') plt.axis([0,self.pixels[0].size,0,self.pixels.size/self.pixels[0].size]) plt.gray() #plt.draw()
def plot_templates(templates, epoch): n = 10 templates = templates.reshape((28,28,n)) plt.figure(figsize=(16, 8)) for i in range(n): ax = plt.subplot(2, 5, i+1) plt.imshow(templates[:, :, i]) plt.gray() ax.get_xaxis().set_visible(False) ax.get_yaxis().set_visible(False) plot_name = "./frames/mnist/regression/templates_"+str(epoch)+".png" if not os.path.exists(os.path.dirname(plot_name)): os.makedirs(os.path.dirname(plot_name)) plt.savefig(plot_name)
def draw_digit(data, n, row, col, title): import matplotlib.pyplot as plt size = 28 plt.subplot(row, col, n) Z = data.reshape(size,size) # convert from vector to 28x28 matrix Z = Z[::-1,:] # flip vertical plt.xlim(0,28) plt.ylim(0,28) plt.pcolor(Z) plt.title("title=%s"%(title), size=8) plt.gray() plt.tick_params(labelbottom="off") plt.tick_params(labelleft="off")
def plot_circle_in_micrograph(micrograph_2d, coordinate, particle_size, filename, color = 'white'): """plot the particle circle in micrograph image Based on the coordinate of particle, plot circles of the particles in the micrograph. And save the ploted image in filename. Args: micrograph_2d: numpy.array,it is a 2D numpy array. coordinate: list, it is a 2D list, the shape is (num_particle, 2). particle_size: int, the value of the particle size filename: the filename of the image to be save. color: define the color of the circle Raises: pass """ micrograph_2d = micrograph_2d.reshape(micrograph_2d.shape[0], micrograph_2d.shape[1]) fig = plt.figure() ax = fig.add_subplot(111) plt.axis('off') plt.gray() plt.imshow(micrograph_2d) radius = particle_size/2 i = 0 while True: if i >= len(coordinate): break coordinate_x = coordinate[i][0] coordinate_y = coordinate[i][1] cir1 = Circle(xy = (coordinate_x, coordinate_y), radius = radius, alpha = 0.5, color = color, fill = False) ax.add_patch(cir1) # extract the particles i = i + 1 plt.savefig(filename)
