Python matplotlib.pyplot.imshow() Examples
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code examples of matplotlib.pyplot.imshow().
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Example #1
Source File: movie.py From kvae with MIT License | 10 votes |
def save_frames(images, filename): num_sequences, n_steps, w, h = images.shape fig = plt.figure() im = plt.imshow(combine_multiple_img(images[:, 0]), cmap=plt.cm.get_cmap('Greys'), interpolation='none') plt.axis('image') def updatefig(*args): im.set_array(combine_multiple_img(images[:, args[0]])) return im, ani = animation.FuncAnimation(fig, updatefig, interval=500, frames=n_steps) # Either avconv or ffmpeg need to be installed in the system to produce the videos! try: writer = animation.writers['avconv'] except KeyError: writer = animation.writers['ffmpeg'] writer = writer(fps=3) ani.save(filename, writer=writer) plt.close(fig)
Example #2
Source File: visualise_att_maps_epoch.py From Attention-Gated-Networks with MIT License | 7 votes |
def plotNNFilter(units, figure_id, interp='bilinear', colormap=cm.jet, colormap_lim=None): plt.ion() filters = units.shape[2] n_columns = round(math.sqrt(filters)) n_rows = math.ceil(filters / n_columns) + 1 fig = plt.figure(figure_id, figsize=(n_rows*3,n_columns*3)) fig.clf() for i in range(filters): ax1 = plt.subplot(n_rows, n_columns, i+1) plt.imshow(units[:,:,i].T, interpolation=interp, cmap=colormap) plt.axis('on') ax1.set_xticklabels([]) ax1.set_yticklabels([]) plt.colorbar() if colormap_lim: plt.clim(colormap_lim[0],colormap_lim[1]) plt.subplots_adjust(wspace=0, hspace=0) plt.tight_layout() # Epochs
Example #3
Source File: movie.py From kvae with MIT License | 7 votes |
def save_movie_to_frame(images, filename, idx=0, cmap='Blues'): # Collect to single image image = movie_to_frame(images[idx]) # Flip it # image = np.fliplr(image) # image = np.flipud(image) f = plt.figure(figsize=[12, 12]) plt.imshow(image, cmap=plt.cm.get_cmap(cmap), interpolation='none', vmin=0, vmax=1) plt.axis('image') plt.xticks([]) plt.yticks([]) plt.savefig(filename, format='png', bbox_inches='tight', dpi=80) plt.close(f)
Example #4
Source File: prod_basis.py From pyscf with Apache License 2.0 | 6 votes |
def generate_png_chess_dp_vertex(self): """Produces pictures of the dominant product vertex a chessboard convention""" import matplotlib.pylab as plt plt.ioff() dab2v = self.get_dp_vertex_doubly_sparse() for i, ab in enumerate(dab2v): fname = "chess-v-{:06d}.png".format(i) print('Matrix No.#{}, Size: {}, Type: {}'.format(i+1, ab.shape, type(ab)), fname) if type(ab) != 'numpy.ndarray': ab = ab.toarray() fig = plt.figure() ax = fig.add_subplot(1,1,1) ax.set_aspect('equal') plt.imshow(ab, interpolation='nearest', cmap=plt.cm.ocean) plt.colorbar() plt.savefig(fname) plt.close(fig)
Example #5
Source File: inference.py From mmdetection with Apache License 2.0 | 6 votes |
def show_result_pyplot(model, img, result, score_thr=0.3, fig_size=(15, 10)): """Visualize the detection results on the image. Args: model (nn.Module): The loaded detector. img (str or np.ndarray): Image filename or loaded image. result (tuple[list] or list): The detection result, can be either (bbox, segm) or just bbox. score_thr (float): The threshold to visualize the bboxes and masks. fig_size (tuple): Figure size of the pyplot figure. """ if hasattr(model, 'module'): model = model.module img = model.show_result(img, result, score_thr=score_thr, show=False) plt.figure(figsize=fig_size) plt.imshow(mmcv.bgr2rgb(img)) plt.show()
Example #6
Source File: test_mesh_io.py From simnibs with GNU General Public License v3.0 | 6 votes |
def test_interpolate_grid_elmdata_dicontinuous(self, sphere3_msh): data = sphere3_msh.elm.tag1 f = mesh_io.ElementData(data, mesh=sphere3_msh) n = (200, 130, 1) affine = np.array([[1, 0, 0, -100.1], [0,-1, 0, 65.1], [0, 0, 1, 0], [0, 0, 0, 1]], dtype=float) interp = f.interpolate_to_grid(n, affine, method='linear', continuous=False) ''' import matplotlib.pyplot as plt plt.figure() plt.imshow(np.squeeze(interp)) plt.colorbar() plt.show() ''' assert np.allclose(interp[6:10, 65, 0], 5, atol=1e-1) assert np.allclose(interp[11:15, 65, 0], 4, atol=1e-1) assert np.allclose(interp[16:100, 65, 0], 3, atol=1e-1)
Example #7
Source File: visualise_fmaps.py From Attention-Gated-Networks with MIT License | 6 votes |
def plotNNFilter(units, figure_id, interp='bilinear', colormap=cm.jet, colormap_lim=None): plt.ion() filters = units.shape[2] n_columns = round(math.sqrt(filters)) n_rows = math.ceil(filters / n_columns) + 1 fig = plt.figure(figure_id, figsize=(n_rows*3,n_columns*3)) fig.clf() for i in range(filters): ax1 = plt.subplot(n_rows, n_columns, i+1) plt.imshow(units[:,:,i].T, interpolation=interp, cmap=colormap) plt.axis('on') ax1.set_xticklabels([]) ax1.set_yticklabels([]) plt.colorbar() if colormap_lim: plt.clim(colormap_lim[0],colormap_lim[1]) plt.subplots_adjust(wspace=0, hspace=0) plt.tight_layout() # Load options
Example #8
Source File: utils.py From deep-learning-note with MIT License | 6 votes |
def show(image): """ Render a given numpy.uint8 2D array of pixel data. """ plt.imshow(image, cmap='gray') plt.show()
Example #9
Source File: dataset.py From neural-combinatorial-optimization-rl-tensorflow with MIT License | 6 votes |
def visualize_sampling(self, permutations): max_length = len(permutations[0]) grid = np.zeros([max_length,max_length]) # initialize heatmap grid to 0 transposed_permutations = np.transpose(permutations) for t, cities_t in enumerate(transposed_permutations): # step t, cities chosen at step t city_indices, counts = np.unique(cities_t,return_counts=True,axis=0) for u,v in zip(city_indices, counts): grid[t][u]+=v # update grid with counts from the batch of permutations # plot heatmap fig = plt.figure() rcParams.update({'font.size': 22}) ax = fig.add_subplot(1,1,1) ax.set_aspect('equal') plt.imshow(grid, interpolation='nearest', cmap='gray') plt.colorbar() plt.title('Sampled permutations') plt.ylabel('Time t') plt.xlabel('City i') plt.show()
Example #10
Source File: test_mesh_io.py From simnibs with GNU General Public License v3.0 | 6 votes |
def test_interpolate_grid_elmdata_linear(self, sphere3_msh): data = sphere3_msh.elements_baricenters().value[:, 0] f = mesh_io.ElementData(data, mesh=sphere3_msh) n = (130, 130, 1) affine = np.array([[1, 0, 0, -65], [0, 1, 0, -65], [0, 0, 1, 0], [0, 0, 0, 1]], dtype=float) X, _ = np.meshgrid(np.arange(130), np.arange(130), indexing='ij') interp = f.interpolate_to_grid(n, affine, method='linear', continuous=True) ''' import matplotlib.pyplot as plt plt.figure() plt.imshow(np.squeeze(interp)) plt.colorbar() plt.show() ''' assert np.allclose(interp[:, :, 0], X - 64.5, atol=1)
Example #11
Source File: test_mesh_io.py From simnibs with GNU General Public License v3.0 | 6 votes |
def test_interpolate_grid_rotate_nodedata(self, sphere3_msh): data = np.zeros(sphere3_msh.nodes.nr) b = sphere3_msh.nodes.node_coord.copy() f = mesh_io.NodeData(data, mesh=sphere3_msh) # Assign quadrant numbers f.value[(b[:, 0] >= 0) * (b[:, 1] >= 0)] = 1. f.value[(b[:, 0] <= 0) * (b[:, 1] >= 0)] = 2. f.value[(b[:, 0] <= 0) * (b[:, 1] <= 0)] = 3. f.value[(b[:, 0] >= 0) * (b[:, 1] <= 0)] = 4. n = (200, 200, 1) affine = np.array([[np.cos(np.pi/4.), np.sin(np.pi/4.), 0, -141], [-np.sin(np.pi/4.), np.cos(np.pi/4.), 0, 0], [0, 0, 1, .5], [0, 0, 0, 1]], dtype=float) interp = f.interpolate_to_grid(n, affine) ''' import matplotlib.pyplot as plt plt.imshow(np.squeeze(interp), interpolation='nearest') plt.colorbar() plt.show() ''' assert np.isclose(interp[190, 100, 0], 4) assert np.isclose(interp[100, 190, 0], 1) assert np.isclose(interp[10, 100, 0], 2) assert np.isclose(interp[100, 10, 0], 3)
Example #12
Source File: my.py From 3D-HourGlass-Network with MIT License | 6 votes |
def test_heatmaps(heatmaps,img,i): heatmaps=heatmaps.numpy() #heatmaps=np.squeeze(heatmaps) heatmaps=heatmaps[:,:64,:] heatmaps=heatmaps.transpose(1,2,0) print('heatmap inside shape is',heatmaps.shape) ## print('----------------here') ## print(heatmaps.shape) img=img.numpy() #img=np.squeeze(img) img=img.transpose(1,2,0) img=cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # print('heatmaps',heatmaps.shape) heatmaps = cv2.resize(heatmaps,(0,0), fx=4,fy=4) # print('heatmapsafter',heatmaps.shape) for j in range(0, 16): heatmap = heatmaps[:,:,j] heatmap = heatmap.reshape((256,256,1)) heatmapimg = np.array(heatmap * 255, dtype = np.uint8) heatmap = cv2.applyColorMap(heatmapimg, cv2.COLORMAP_JET) heatmap = heatmap/255 plt.imshow(img) plt.imshow(heatmap, alpha=0.5) plt.show() #plt.savefig('hmtestpadh36'+str(i)+js[j]+'.png')
Example #13
Source File: test_mesh_io.py From simnibs with GNU General Public License v3.0 | 6 votes |
def test_interpolate_grid_const_nn(self, sphere3_msh): data = sphere3_msh.elm.tag1 f = mesh_io.ElementData(data, mesh=sphere3_msh) n = (200, 10, 1) affine = np.array([[1, 0, 0, -100.5], [0, 1, 0, -5], [0, 0, 1, 0], [0, 0, 0, 1]], dtype=float) interp = f.interpolate_to_grid(n, affine, method='assign') ''' import matplotlib.pyplot as plt plt.imshow(np.squeeze(interp)) plt.colorbar() plt.show() assert False ''' assert np.isclose(interp[100, 5, 0], 3) assert np.isclose(interp[187, 5, 0], 4) assert np.isclose(interp[193, 5, 0], 5) assert np.isclose(interp[198, 5, 0], 0)
Example #14
Source File: massachusetts_road_segm.py From Recipes with MIT License | 6 votes |
def plot_some_results(pred_fn, test_generator, n_images=10): fig_ctr = 0 for data, seg in test_generator: res = pred_fn(data) for d, s, r in zip(data, seg, res): plt.figure(figsize=(12, 6)) plt.subplot(1, 3, 1) plt.imshow(d.transpose(1,2,0)) plt.title("input patch") plt.subplot(1, 3, 2) plt.imshow(s[0]) plt.title("ground truth") plt.subplot(1, 3, 3) plt.imshow(r) plt.title("segmentation") plt.savefig("road_segmentation_result_%03.0f.png"%fig_ctr) plt.close() fig_ctr += 1 if fig_ctr > n_images: break
Example #15
Source File: movie.py From kvae with MIT License | 6 votes |
def save_movies_to_frame(images, filename, cmap='Blues'): # Binarize images # images[images > 0] = 1. # Grid images images = np.swapaxes(images, 1, 0) images = np.array([combine_multiple_img(image) for image in images]) # Collect to single image image = movie_to_frame(images) f = plt.figure(figsize=[12, 12]) plt.imshow(image, cmap=plt.cm.get_cmap(cmap), interpolation='none', vmin=0, vmax=1) plt.axis('image') plt.savefig(filename, format='png', bbox_inches='tight', dpi=80) plt.close(f)
Example #16
Source File: malware.py From trees with Apache License 2.0 | 6 votes |
def classify(self, features, show=False): recs, _ = features.shape result_shape = (features.shape[0], len(self.root)) scores = np.zeros(result_shape) print scores.shape R = Record(np.arange(recs, dtype=int), features) for i, T in enumerate(self.root): for idxs, result in classify(T, R): for idx in idxs.indexes(): scores[idx, i] = float(result[0]) / sum(result.values()) if show: plt.cla() plt.clf() plt.close() plt.imshow(scores, cmap=plt.cm.gray) plt.title('Scores matrix') plt.savefig(r"../scratch/tree_scores.png", bbox_inches='tight') return scores
Example #17
Source File: demo.py From RingNet with MIT License | 6 votes |
def preprocess_image(img_path): img = io.imread(img_path) if np.max(img.shape[:2]) != config.img_size: print('Resizing so the max image size is %d..' % config.img_size) scale = (float(config.img_size) / np.max(img.shape[:2])) else: scale = 1.0#scaling_factor center = np.round(np.array(img.shape[:2]) / 2).astype(int) # image center in (x,y) center = center[::-1] crop, proc_param = img_util.scale_and_crop(img, scale, center, config.img_size) # import ipdb; ipdb.set_trace() # Normalize image to [-1, 1] # plt.imshow(crop/255.0) # plt.show() crop = 2 * ((crop / 255.) - 0.5) return crop, proc_param, img
Example #18
Source File: visualise_attention.py From Attention-Gated-Networks with MIT License | 6 votes |
def plotNNFilterOverlay(input_im, units, figure_id, interp='bilinear', colormap=cm.jet, colormap_lim=None, title='', alpha=0.8): plt.ion() filters = units.shape[2] fig = plt.figure(figure_id, figsize=(5,5)) fig.clf() for i in range(filters): plt.imshow(input_im[:,:,0], interpolation=interp, cmap='gray') plt.imshow(units[:,:,i], interpolation=interp, cmap=colormap, alpha=alpha) plt.axis('off') plt.colorbar() plt.title(title, fontsize='small') if colormap_lim: plt.clim(colormap_lim[0],colormap_lim[1]) plt.subplots_adjust(wspace=0, hspace=0) plt.tight_layout() # plt.savefig('{}/{}.png'.format(dir_name,time.time())) ## Load options
Example #19
Source File: plot.py From TaskBot with GNU General Public License v3.0 | 6 votes |
def plot_attention(sentences, attentions, labels, **kwargs): fig, ax = plt.subplots(**kwargs) im = ax.imshow(attentions, interpolation='nearest', vmin=attentions.min(), vmax=attentions.max()) plt.colorbar(im, shrink=0.5, ticks=[0, 1]) plt.setp(ax.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor") ax.set_yticks(range(len(labels))) ax.set_yticklabels(labels, fontproperties=getChineseFont()) # Loop over data dimensions and create text annotations. for i in range(attentions.shape[0]): for j in range(attentions.shape[1]): text = ax.text(j, i, sentences[i][j], ha="center", va="center", color="b", size=10, fontproperties=getChineseFont()) ax.set_title("Attention Visual") fig.tight_layout() plt.show()
Example #20
Source File: visualise_attention.py From Attention-Gated-Networks with MIT License | 6 votes |
def plotNNFilter(units, figure_id, interp='bilinear', colormap=cm.jet, colormap_lim=None, title=''): plt.ion() filters = units.shape[2] n_columns = round(math.sqrt(filters)) n_rows = math.ceil(filters / n_columns) + 1 fig = plt.figure(figure_id, figsize=(n_rows*3,n_columns*3)) fig.clf() for i in range(filters): ax1 = plt.subplot(n_rows, n_columns, i+1) plt.imshow(units[:,:,i].T, interpolation=interp, cmap=colormap) plt.axis('on') ax1.set_xticklabels([]) ax1.set_yticklabels([]) plt.colorbar() if colormap_lim: plt.clim(colormap_lim[0],colormap_lim[1]) plt.subplots_adjust(wspace=0, hspace=0) plt.tight_layout() plt.suptitle(title)
Example #21
Source File: test.py From Chinese-Character-and-Calligraphic-Image-Processing with MIT License | 6 votes |
def test(self): list_ = os.listdir("./maps/val/") nums_file = list_.__len__() saver = tf.train.Saver(tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, "generator")) saver.restore(self.sess, "./save_para/model.ckpt") rand_select = np.random.randint(0, nums_file) INPUTS_CONDITION = np.zeros([1, self.img_h, self.img_w, 3]) INPUTS = np.zeros([1, self.img_h, self.img_w, 3]) img = np.array(Image.open(self.path + list_[rand_select])) img_h, img_w = img.shape[0], img.shape[1] INPUTS_CONDITION[0] = misc.imresize(img[:, img_w//2:], [self.img_h, self.img_w]) / 127.5 - 1.0 INPUTS[0] = misc.imresize(img[:, :img_w//2], [self.img_h, self.img_w]) / 127.5 - 1.0 [fake_img] = self.sess.run([self.inputs_fake], feed_dict={self.inputs_condition: INPUTS_CONDITION}) out_img = np.concatenate((INPUTS_CONDITION[0], fake_img[0], INPUTS[0]), axis=1) Image.fromarray(np.uint8((out_img + 1.0)*127.5)).save("./results/1.jpg") plt.imshow(np.uint8((out_img + 1.0)*127.5)) plt.grid("off") plt.axis("off") plt.show()
Example #22
Source File: test_mesh_io.py From simnibs with GNU General Public License v3.0 | 6 votes |
def test_interpolate_grid_rotate_nn(self, sphere3_msh): data = np.zeros(sphere3_msh.elm.nr) b = sphere3_msh.elements_baricenters().value f = mesh_io.ElementData(data, mesh=sphere3_msh) # Assign quadrant numbers f.value[(b[:, 0] > 0) * (b[:, 1] > 0)] = 1. f.value[(b[:, 0] < 0) * (b[:, 1] > 0)] = 2. f.value[(b[:, 0] < 0) * (b[:, 1] < 0)] = 3. f.value[(b[:, 0] > 0) * (b[:, 1] < 0)] = 4. n = (200, 200, 1) affine = np.array([[np.cos(np.pi/4.), np.sin(np.pi/4.), 0, -141], [-np.sin(np.pi/4.), np.cos(np.pi/4.), 0, 0], [0, 0, 1, .5], [0, 0, 0, 1]], dtype=float) interp = f.interpolate_to_grid(n, affine, method='assign') ''' import matplotlib.pyplot as plt plt.imshow(np.squeeze(interp)) plt.colorbar() plt.show() ''' assert np.isclose(interp[190, 100, 0], 4) assert np.isclose(interp[100, 190, 0], 1) assert np.isclose(interp[10, 100, 0], 2) assert np.isclose(interp[100, 10, 0], 3)
Example #23
Source File: movie.py From kvae with MIT License | 5 votes |
def save_true_generated_frames(true, generated, filename): num_sequences, n_steps, w, h = true.shape # Background is 0, foreground as 1 true = np.copy(true[:16]) true[true > 0.1] = 1 # Set foreground be near 0.5 generated = generated * .5 # Background is 1, foreground is near 0.5 generated = 1 - generated[:16, :n_steps] # Subtract true from generated so background is 1, true foreground is 0, # and generated foreground is around 0.5 images = generated - true # images[images > 0.5] = 1. fig = plt.figure() im = plt.imshow(combine_multiple_img(images[:, 0]), cmap=plt.cm.get_cmap('gist_heat'), interpolation='none', vmin=0, vmax=1) plt.axis('image') def updatefig(*args): im.set_array(combine_multiple_img(images[:, args[0]])) return im, ani = animation.FuncAnimation(fig, updatefig, interval=500, frames=n_steps) try: writer = animation.writers['avconv'] except KeyError: writer = animation.writers['ffmpeg'] writer = writer(fps=3) ani.save(filename, writer=writer) plt.close(fig)
Example #24
Source File: utils.py From DPC with MIT License | 5 votes |
def plot_mat(self, path, dictionary=None, annotate=False): plt.figure(dpi=600) plt.imshow(self.mat, cmap=plt.cm.jet, interpolation=None, extent=(0.5, np.shape(self.mat)[0]+0.5, np.shape(self.mat)[1]+0.5, 0.5)) width, height = self.mat.shape if annotate: for x in range(width): for y in range(height): plt.annotate(str(int(self.mat[x][y])), xy=(y+1, x+1), horizontalalignment='center', verticalalignment='center', fontsize=8) if dictionary is not None: plt.xticks([i+1 for i in range(width)], [dictionary[i] for i in range(width)], rotation='vertical') plt.yticks([i+1 for i in range(height)], [dictionary[i] for i in range(height)]) plt.xlabel('Ground Truth') plt.ylabel('Prediction') plt.colorbar() plt.tight_layout() plt.savefig(path, format='svg') plt.clf() # for i in range(width): # if np.sum(self.mat[i,:]) != 0: # self.precision.append(self.mat[i,i] / np.sum(self.mat[i,:])) # if np.sum(self.mat[:,i]) != 0: # self.recall.append(self.mat[i,i] / np.sum(self.mat[:,i])) # print('Average Precision: %0.4f' % np.mean(self.precision)) # print('Average Recall: %0.4f' % np.mean(self.recall))
Example #25
Source File: data_augmentation.py From 3D-R2N2 with MIT License | 5 votes |
def test(fn): import matplotlib.pyplot as plt cfg.TRAIN.RANDOM_CROP = True im = Image.open(fn) im = np.asarray(im)[:, :, :3] imt = image_transform(im, 10, 10) plt.imshow(imt) plt.show()
Example #26
Source File: run_mask.py From wechat_jump_end_to_end_train with MIT License | 5 votes |
def main(): # init conv net unet = UNet(3,1) if os.path.exists("./unet.pkl"): unet.load_state_dict(torch.load("./unet.pkl")) print("load unet") unet.cuda() cnn = CNNEncoder() if os.path.exists("./cnn.pkl"): cnn.load_state_dict(torch.load("./cnn.pkl")) print("load cnn") cnn.cuda() unet.eval() cnn.eval() print("load ok") while True: pull_screenshot("autojump.png") # obtain screen and save it to autojump.png image = Image.open('./autojump.png') set_button_position(image) image = preprocess(image) image = Variable(image.unsqueeze(0)).cuda() mask = unet(image) plt.imshow(mask.squeeze(0).squeeze(0).cpu().data.numpy(), cmap='hot', interpolation='nearest') plt.show() segmentation = image * mask press_time = cnn(segmentation) press_time = press_time.cpu().data[0].numpy() print(press_time) jump(press_time) time.sleep(random.uniform(0.6, 1.1))
Example #27
Source File: 14_cnn.py From pytorchTutorial with MIT License | 5 votes |
def imshow(img): img = img / 2 + 0.5 # unnormalize npimg = img.numpy() plt.imshow(np.transpose(npimg, (1, 2, 0))) plt.show() # get some random training images
Example #28
Source File: srgan.py From Keras-GAN with MIT License | 5 votes |
def sample_images(self, epoch): os.makedirs('images/%s' % self.dataset_name, exist_ok=True) r, c = 2, 2 imgs_hr, imgs_lr = self.data_loader.load_data(batch_size=2, is_testing=True) fake_hr = self.generator.predict(imgs_lr) # Rescale images 0 - 1 imgs_lr = 0.5 * imgs_lr + 0.5 fake_hr = 0.5 * fake_hr + 0.5 imgs_hr = 0.5 * imgs_hr + 0.5 # Save generated images and the high resolution originals titles = ['Generated', 'Original'] fig, axs = plt.subplots(r, c) cnt = 0 for row in range(r): for col, image in enumerate([fake_hr, imgs_hr]): axs[row, col].imshow(image[row]) axs[row, col].set_title(titles[col]) axs[row, col].axis('off') cnt += 1 fig.savefig("images/%s/%d.png" % (self.dataset_name, epoch)) plt.close() # Save low resolution images for comparison for i in range(r): fig = plt.figure() plt.imshow(imgs_lr[i]) fig.savefig('images/%s/%d_lowres%d.png' % (self.dataset_name, epoch, i)) plt.close()
Example #29
Source File: verification_code2text.py From TaiwanTrainVerificationCode2text with Apache License 2.0 | 5 votes |
def validation(test_path): file_path = 'success_vcode' os.chdir(PATH) if file_path not in os.listdir(): os.makedirs(file_path) if 'Windows' in platform.platform(): file_path = '{}\\{}\\'.format(PATH,'success_vcode') test_image_path = [file_path + i for i in os.listdir(file_path+'\\')] else: file_path = '{}/{}/'.format(PATH,'success_vcode') test_image_path = [file_path + i for i in os.listdir(file_path+'/')] sum_count = len(test_image_path) data_set = np.ndarray(( sum_count , 60, 200,3), dtype=np.uint8) i=0 #s = time.time() while( i < sum_count ): image_name = test_image_path[i] image = cv2.imread(image_name) data_set[i] = image i=i+1 if i%50 == 0: print('Processed {} of {}'.format(i, sum_count ) ) #-------------------------------------------------- real_labels = [] for text in test_image_path: if 'Windows' in platform.platform(): text = text.split('\\') else: text = text.split('/') text = text[len(text)-1] text_set = text.replace('.png','') real_labels.append(text_set) image = cv2.imread(image_name) plt.imshow(image) text = main(image) print(text)
Example #30
Source File: 15_transfer_learning.py From pytorchTutorial with MIT License | 5 votes |
def imshow(inp, title): """Imshow for Tensor.""" inp = inp.numpy().transpose((1, 2, 0)) inp = std * inp + mean inp = np.clip(inp, 0, 1) plt.imshow(inp) plt.title(title) plt.show() # Get a batch of training data