Python keras.preprocessing.image.array_to_img() Examples
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Example #1
Source File: test_preprocessing.py From Keras-FCN with MIT License | 6 votes |
def test_pair_crop(crop_function): arr1 = np.random.random(500, 800) arr2 = np.random.random(500, 800) img1 = PILImage.fromarray(arr1) img2 = PILImage.fromarray(arr2) crop_width = img1.width / 5 crop_height = img1.height / 5 result1, result2 = crop_function(img_to_array(img1), img_to_array(img2), (crop_height, crop_width), 'channels_last') result1 = array_to_img(result1) result2 = array_to_img(result2) assert result1.width == crop_width == result2.width assert result2.height == crop_height == result2.height
Example #2
Source File: my_image.py From MachineLearning with Apache License 2.0 | 6 votes |
def next(self): # Keeps under lock only the mechanism which advances # the indexing of each batch. with self.lock: index_array, current_index, current_batch_size = next(self.index_generator) # The transformation of images is not under thread lock # so it can be done in parallel batch_x = np.zeros(tuple([current_batch_size] + list(self.image_size)), dtype=K.floatx()) for i, j in enumerate(index_array): x = scipy.misc.imread(self.x[j]) x = scipy.misc.imresize(x, self.image_size) x = self.image_data_generator.random_transform(x.astype(K.floatx())) x = self.image_data_generator.standardize(x) batch_x[i] = x if self.save_to_dir: for i in range(current_batch_size): img = image.array_to_img(batch_x[i], self.data_format, scale=True) fname = '{prefix}_{index}_{hash}.{format}'.format(prefix=self.save_prefix, index=current_index + i, hash=np.random.randint(1e4), format=self.save_format) img.save(os.path.join(self.save_to_dir, fname)) batch_y = self.y[index_array] return batch_x, batch_y
Example #3
Source File: utils.py From enet-keras with MIT License | 6 votes |
def resize(item, target_h, target_w, keep_aspect_ratio=False): """ Resizes an image to match target dimensions :type item: np.ndarray :type target_h: int :type target_w: int :param item: 3d numpy array or PIL.Image :param target_h: height in pixels :param target_w: width in pixels :param keep_aspect_ratio: If False then image is rescaled to smallest dimension and then cropped :return: 3d numpy array """ img = array_to_img(item, scale=False) if keep_aspect_ratio: img.thumbnail((target_w, target_w), PILImage.ANTIALIAS) img_resized = img else: img_resized = img.resize((target_w, target_h), resample=PILImage.NEAREST) # convert output img_resized = img_to_array(img_resized) img_resized = img_resized.astype(dtype=np.uint8) return img_resized
Example #4
Source File: datasets.py From DEC-keras with MIT License | 6 votes |
def extract_vgg16_features(x): from keras.preprocessing.image import img_to_array, array_to_img from keras.applications.vgg16 import preprocess_input, VGG16 from keras.models import Model # im_h = x.shape[1] im_h = 224 model = VGG16(include_top=True, weights='imagenet', input_shape=(im_h, im_h, 3)) # if flatten: # add_layer = Flatten() # else: # add_layer = GlobalMaxPool2D() # feature_model = Model(model.input, add_layer(model.output)) feature_model = Model(model.input, model.get_layer('fc1').output) print('extracting features...') x = np.asarray([img_to_array(array_to_img(im, scale=False).resize((im_h,im_h))) for im in x]) x = preprocess_input(x) # data - 127. #data/255.# features = feature_model.predict(x) print('Features shape = ', features.shape) return features
Example #5
Source File: KerasCallback.py From aetros-cli with MIT License | 5 votes |
def make_image_from_dense_softmax(self, neurons): from aetros.utils import array_to_img img = array_to_img(neurons.reshape((1, len(neurons), 1))) img = img.resize((9, len(neurons) * 8)) return img
Example #6
Source File: test_preprocessing.py From Keras-FCN with MIT License | 5 votes |
def test_crop(crop_function): arr = np.random.random(500, 800) img = PILImage.fromarray(arr) crop_width = img.width / 5 crop_height = img.height / 5 result = crop_function(img_to_array(img), (crop_height, crop_width), 'channels_last') result = array_to_img(result) assert result.width == crop_width assert result.height == crop_height
Example #7
Source File: predict.py From enet-keras with MIT License | 5 votes |
def run(segmenter, data): data_gen = data['data_gen'] num_instances = data['num_instances'] out_directory = os.path.realpath(data['dir_target']) keep_context = data['keep_context'] # dataset = getattr(datasets, data['dataset_name'])(**data) dataset = getattr(datasets, data['dataset_name']) for idx, image in enumerate(data_gen): if idx > 20: break print('Processing {} out of {}'.format(idx+1, num_instances), end='\r') pred_final, scores = predict(segmenter, image, h=dh, w=dw) # draw prediction as rgb pred_final = color_output_image(dataset.palette, pred_final[:, :, 0]) pred_final = array_to_img(pred_final) out_file = os.path.join( out_directory, '{}_{}_{}_out.png'.format( idx, keep_context, utils.basename_without_ext(pw))) sys.stdout.flush() if os.path.isfile(out_file): continue utils.ensure_dir(out_directory) print('Saving output to {}'.format(out_file)) pilimg = PILImage.fromarray(image.astype(np.uint8), mode='RGB') pilimg.save(out_file.replace('_out.png', '.png')) pred_final.save(out_file)
Example #8
Source File: predict.py From AdvancedEAST with MIT License | 5 votes |
def cut_text_line(geo, scale_ratio_w, scale_ratio_h, im_array, img_path, s): geo /= [scale_ratio_w, scale_ratio_h] p_min = np.amin(geo, axis=0) p_max = np.amax(geo, axis=0) min_xy = p_min.astype(int) max_xy = p_max.astype(int) + 2 sub_im_arr = im_array[min_xy[1]:max_xy[1], min_xy[0]:max_xy[0], :].copy() for m in range(min_xy[1], max_xy[1]): for n in range(min_xy[0], max_xy[0]): if not point_inside_of_quad(n, m, geo, p_min, p_max): sub_im_arr[m - min_xy[1], n - min_xy[0], :] = 255 sub_im = image.array_to_img(sub_im_arr, scale=False) sub_im.save(img_path + '_subim%d.jpg' % s)
Example #9
Source File: data.py From U-net-segmentation with GNU General Public License v2.0 | 5 votes |
def Augmentation(self): # 读入3通道的train和label, 分别转换成矩阵, 然后将label的第一个通道放在train的第2个通处, 做数据增强 print("运行 Augmentation") """ Start augmentation..... """ trains = self.train_imgs labels = self.label_imgs path_train = self.train_path path_label = self.label_path path_merge = self.merge_path imgtype = self.img_type path_aug_merge = self.aug_merge_path print(len(trains), len(labels)) if len(trains) != len(labels) or len(trains) == 0 or len(trains) == 0: print("trains can't match labels") return 0 for i in range(len(trains)): img_t = load_img(path_train + "/" + str(i) + "." + imgtype) # 读入train img_l = load_img(path_label + "/" + str(i) + "." + imgtype) # 读入label x_t = img_to_array(img_t) # 转换成矩阵 x_l = img_to_array(img_l) x_t[:, :, 2] = x_l[:, :, 0] # 把label当做train的第三个通道 img_tmp = array_to_img(x_t) img_tmp.save(path_merge + "/" + str(i) + "." + imgtype) # 保存合并后的图像 img = x_t img = img.reshape((1,) + img.shape) # 改变shape(1, 512, 512, 3) savedir = path_aug_merge + "/" + str(i) # 存储合并增强后的图像 if not os.path.lexists(savedir): os.mkdir(savedir) self.doAugmentate(img, savedir, str(i)) # 数据增强
Example #10
Source File: KerasCallback.py From aetros-cli with MIT License | 5 votes |
def make_image(self, data): from keras.preprocessing.image import array_to_img try: if len(data.shape) == 2: # grayscale image, just add once channel data = data.reshape((data.shape[0], data.shape[1], 1)) image = array_to_img(data) except Exception: return None # image = image.resize((128, 128)) return image
Example #11
Source File: KerasCallback.py From aetros-cli with MIT License | 5 votes |
def make_image_from_dense(self, neurons): from aetros.utils import array_to_img cols = int(math.ceil(math.sqrt(len(neurons)))) even_length = cols * cols diff = even_length - len(neurons) if diff > 0: neurons = np.append(neurons, np.zeros(diff, dtype=neurons.dtype)) img = array_to_img(neurons.reshape((1, cols, cols))) img = img.resize((cols * 8, cols * 8)) return img
Example #12
Source File: utils_backdoor.py From backdoor with MIT License | 5 votes |
def dump_image(x, filename, format): img = image.array_to_img(x, scale=False) img.save(filename, format) return
Example #13
Source File: data_Keras.py From U-net with MIT License | 5 votes |
def augmentation(self): # 读入3通道的train和label, 分别转换成矩阵, 然后将label的第一个通道放在train的第2个通处, 做数据增强 print("运行 Augmentation") # Start augmentation..... trains = self.train_imgs labels = self.label_imgs path_train = self.train_path path_label = self.label_path path_merge = self.merge_path imgtype = self.img_type path_aug_merge = self.aug_merge_path print('%d images \n%d labels' % (len(trains), len(labels))) if len(trains) != len(labels) or len(trains) == 0 or len(trains) == 0: print("trains can't match labels") return 0 if not os.path.lexists(path_merge): os.mkdir(path_merge) if not os.path.lexists(path_aug_merge): os.mkdir(path_aug_merge) for i in range(len(trains)): img_t = load_img(path_train + "/" + str(i) + "." + imgtype) # 读入train img_l = load_img(path_label + "/" + str(i) + "." + imgtype) # 读入label x_t = img_to_array(img_t) # 转换成矩阵 x_l = img_to_array(img_l) x_t[:, :, 2] = x_l[:, :, 0] # 把label当做train的第三个通道 img_tmp = array_to_img(x_t) img_tmp.save(path_merge + "/" + str(i) + "." + imgtype) # 保存合并后的图像 img = x_t img = img.reshape((1,) + img.shape) # 改变shape(1, 512, 512, 3) savedir = path_aug_merge + "/" + str(i) # 存储合并增强后的图像 if not os.path.lexists(savedir): os.mkdir(savedir) print("running %d doAugmenttaion" % i) self.do_augmentate(img, savedir, str(i)) # 数据增强
Example #14
Source File: data.py From detect-cell-edge-use-unet with GNU General Public License v2.0 | 5 votes |
def Augmentation(self): # 读入3通道的train和label, 分别转换成矩阵, 然后将label的第一个通道放在train的第2个通处, 做数据增强 print("运行 Augmentation") """ Start augmentation..... """ trains = self.train_imgs labels = self.label_imgs path_train = self.train_path path_label = self.label_path path_merge = self.merge_path imgtype = self.img_type path_aug_merge = self.aug_merge_path print(len(trains), len(labels)) if len(trains) != len(labels) or len(trains) == 0 or len(trains) == 0: print("trains can't match labels") return 0 for i in range(len(trains)): img_t = load_img(path_train + "/" + str(i) + "." + imgtype) # 读入train img_l = load_img(path_label + "/" + str(i) + "." + imgtype) # 读入label x_t = img_to_array(img_t) # 转换成矩阵 x_l = img_to_array(img_l) x_t[:, :, 2] = x_l[:, :, 0] # 把label当做train的第三个通道 img_tmp = array_to_img(x_t) img_tmp.save(path_merge + "/" + str(i) + "." + imgtype) # 保存合并后的图像 img = x_t img = img.reshape((1,) + img.shape) # 改变shape(1, 512, 512, 3) savedir = path_aug_merge + "/" + str(i) # 存储合并增强后的图像 if not os.path.lexists(savedir): os.mkdir(savedir) self.doAugmentate(img, savedir, str(i)) # 数据增强
Example #15
Source File: image_test.py From DeepLearning_Wavelet-LSTM with MIT License | 4 votes |
def test_img_utils(self): height, width = 10, 8 # Test th data format x = np.random.random((3, height, width)) img = image.array_to_img(x, data_format='channels_first') assert img.size == (width, height) x = image.img_to_array(img, data_format='channels_first') assert x.shape == (3, height, width) # Test 2D x = np.random.random((1, height, width)) img = image.array_to_img(x, data_format='channels_first') assert img.size == (width, height) x = image.img_to_array(img, data_format='channels_first') assert x.shape == (1, height, width) # Test tf data format x = np.random.random((height, width, 3)) img = image.array_to_img(x, data_format='channels_last') assert img.size == (width, height) x = image.img_to_array(img, data_format='channels_last') assert x.shape == (height, width, 3) # Test 2D x = np.random.random((height, width, 1)) img = image.array_to_img(x, data_format='channels_last') assert img.size == (width, height) x = image.img_to_array(img, data_format='channels_last') assert x.shape == (height, width, 1) # Test invalid use case with pytest.raises(ValueError): x = np.random.random((height, width)) # not 3D img = image.array_to_img(x, data_format='channels_first') with pytest.raises(ValueError): x = np.random.random((height, width, 3)) img = image.array_to_img(x, data_format='channels') # unknown data_format with pytest.raises(ValueError): x = np.random.random((height, width, 5)) # neither RGB nor gray-scale img = image.array_to_img(x, data_format='channels_last') with pytest.raises(ValueError): x = np.random.random((height, width, 3)) img = image.img_to_array(x, data_format='channels') # unknown data_format with pytest.raises(ValueError): x = np.random.random((height, width, 5, 3)) # neither RGB nor gray-scale img = image.img_to_array(x, data_format='channels_last')
Example #16
Source File: coco_extract_labels.py From enet-keras with MIT License | 4 votes |
def extract_coco_labels(target_dir): kwargs = { 'h': 512, 'w': 512, 'batch_size': 2, 'root_dir': 'data', 'dataset_name': 'mscoco', 'data_type': 'train2017', 'sample_size': 0.01, 'instance_mode': False, 'keep_context': 0.25, 'merge_annotations': True, 'cover_gaps': True, 'resize_mode': 'stretch', } dataset = datasets.MSCOCO(**kwargs) for idx, res in enumerate(dataset.flow()): if not res: status = 'Skip' else: # convert label to rgb img, mask = res[0], res[1] rgb_label = one_hot_to_rgb(mask, dataset.PALETTE) # extract target filename filename_no_ext = os.path.splitext(img['file_name'])[0] lbl_path = os.path.join( target_dir, kwargs['data_type'], 'labels', '{}.png'.format(filename_no_ext) ) # convert array to PIL Image and save image to disk in png format (lossless) label = array_to_img(rgb_label) label.save(lbl_path) status = 'OK' msg = 'Processed {}/{} items. Status: {}'.format(idx + 1, dataset.num_items, status) print(msg, end='\r') sys.stdout.flush()
Example #17
Source File: cifar10_eval.py From DenseNet-Cifar10 with MIT License | 4 votes |
def eval_model(): model = createDenseNet(nb_classes=nb_classes,img_dim=img_dim,depth=densenet_depth, growth_rate = densenet_growth_rate) model.load_weights(check_point_file) optimizer = Adam() model.compile(loss='categorical_crossentropy',optimizer=optimizer,metrics=['accuracy']) label_list_path = 'datasets/cifar-10-batches-py/batches.meta' keras_dir = os.path.expanduser(os.path.join('~', '.keras')) datadir_base = os.path.expanduser(keras_dir) if not os.access(datadir_base, os.W_OK): datadir_base = os.path.join('/tmp', '.keras') label_list_path = os.path.join(datadir_base, label_list_path) with open(label_list_path, mode='rb') as f: labels = pickle.load(f) (x_train,y_train),(x_test,y_test) = cifar10.load_data() x_test = x_test.astype('float32') x_test /= 255 y_test= keras.utils.to_categorical(y_test, nb_classes) test_datagen = getDataGenerator(train_phase=False) test_datagen = test_datagen.flow(x_test,y_test,batch_size = batch_size,shuffle=False) # Evaluate model with test data set and share sample prediction results evaluation = model.evaluate_generator(test_datagen, steps=x_test.shape[0] // batch_size, workers=4) print('Model Accuracy = %.2f' % (evaluation[1])) counter = 0 figure = plt.figure() plt.subplots_adjust(left=0.1,bottom=0.1, right=0.9, top=0.9,hspace=0.5, wspace=0.3) for x_batch,y_batch in test_datagen: predict_res = model.predict_on_batch(x_batch) for i in range(batch_size): actual_label = labels['label_names'][np.argmax(y_batch[i])] predicted_label = labels['label_names'][np.argmax(predict_res[i])] if actual_label != predicted_label: counter += 1 pics_raw = x_batch[i] pics_raw *= 255 pics = array_to_img(pics_raw) ax = plt.subplot(25//5, 5, counter) ax.axis('off') ax.set_title(predicted_label) plt.imshow(pics) if counter >= 25: plt.savefig("./wrong_predicted.jpg") break if counter >= 25: break print("Everything seems OK...")
Example #18
Source File: data_input.py From DenseNet-Cifar10 with MIT License | 4 votes |
def testDataGenerator(pics_num): """visualize the pics after data augmentation Args: pics_num: the number of pics you want to observe return: None """ print("Now, we are testing data generator......") (x_train,y_train),(x_test,y_test) = cifar10.load_data() x_train = x_train.astype('float32') y_train = keras.utils.to_categorical(y_train, 10) # Load label names to use in prediction results label_list_path = 'datasets/cifar-10-batches-py/batches.meta' keras_dir = os.path.expanduser(os.path.join('~', '.keras')) datadir_base = os.path.expanduser(keras_dir) if not os.access(datadir_base, os.W_OK): datadir_base = os.path.join('/tmp', '.keras') label_list_path = os.path.join(datadir_base, label_list_path) with open(label_list_path, mode='rb') as f: labels = pickle.load(f) datagen = getDataGenerator(train_phase=True) """ x_batch is a [-1,row,col,channel] np array y_batch is a [-1,labels] np array """ figure = plt.figure() plt.subplots_adjust(left=0.1,bottom=0.1, right=0.9, top=0.9,hspace=0.5, wspace=0.3) for x_batch,y_batch in datagen.flow(x_train,y_train,batch_size = pics_num): for i in range(pics_num): pics_raw = x_batch[i] pics = array_to_img(pics_raw) ax = plt.subplot(pics_num//5, 5, i+1) ax.axis('off') ax.set_title(labels['label_names'][np.argmax(y_batch[i])]) plt.imshow(pics) plt.savefig("./processed_data.jpg") break print("Everything seems OK...")
Example #19
Source File: image_test.py From DeepLearning_Wavelet-LSTM with MIT License | 4 votes |
def test_load_img(self, tmpdir): filename = str(tmpdir / 'image.png') original_im_array = np.array(255 * np.random.rand(100, 100, 3), dtype=np.uint8) original_im = image.array_to_img(original_im_array, scale=False) original_im.save(filename) # Test that loaded image is exactly equal to original. loaded_im = image.load_img(filename) loaded_im_array = image.img_to_array(loaded_im) assert loaded_im_array.shape == original_im_array.shape assert np.all(loaded_im_array == original_im_array) loaded_im = image.load_img(filename, grayscale=True) loaded_im_array = image.img_to_array(loaded_im) assert loaded_im_array.shape == (original_im_array.shape[0], original_im_array.shape[1], 1) # Test that nothing is changed when target size is equal to original. loaded_im = image.load_img(filename, target_size=(100, 100)) loaded_im_array = image.img_to_array(loaded_im) assert loaded_im_array.shape == original_im_array.shape assert np.all(loaded_im_array == original_im_array) loaded_im = image.load_img(filename, grayscale=True, target_size=(100, 100)) loaded_im_array = image.img_to_array(loaded_im) assert loaded_im_array.shape == (original_im_array.shape[0], original_im_array.shape[1], 1) # Test down-sampling with bilinear interpolation. loaded_im = image.load_img(filename, target_size=(25, 25)) loaded_im_array = image.img_to_array(loaded_im) assert loaded_im_array.shape == (25, 25, 3) loaded_im = image.load_img(filename, grayscale=True, target_size=(25, 25)) loaded_im_array = image.img_to_array(loaded_im) assert loaded_im_array.shape == (25, 25, 1) # Test down-sampling with nearest neighbor interpolation. loaded_im_nearest = image.load_img(filename, target_size=(25, 25), interpolation="nearest") loaded_im_array_nearest = image.img_to_array(loaded_im_nearest) assert loaded_im_array_nearest.shape == (25, 25, 3) assert np.any(loaded_im_array_nearest != loaded_im_array) # Check that exception is raised if interpolation not supported. loaded_im = image.load_img(filename, interpolation="unsupported") with pytest.raises(ValueError): loaded_im = image.load_img(filename, target_size=(25, 25), interpolation="unsupported")
Example #20
Source File: image_test.py From DeepLearning_Wavelet-LSTM with MIT License | 4 votes |
def test_img_utils(self): height, width = 10, 8 # Test th data format x = np.random.random((3, height, width)) img = image.array_to_img(x, data_format='channels_first') assert img.size == (width, height) x = image.img_to_array(img, data_format='channels_first') assert x.shape == (3, height, width) # Test 2D x = np.random.random((1, height, width)) img = image.array_to_img(x, data_format='channels_first') assert img.size == (width, height) x = image.img_to_array(img, data_format='channels_first') assert x.shape == (1, height, width) # Test tf data format x = np.random.random((height, width, 3)) img = image.array_to_img(x, data_format='channels_last') assert img.size == (width, height) x = image.img_to_array(img, data_format='channels_last') assert x.shape == (height, width, 3) # Test 2D x = np.random.random((height, width, 1)) img = image.array_to_img(x, data_format='channels_last') assert img.size == (width, height) x = image.img_to_array(img, data_format='channels_last') assert x.shape == (height, width, 1) # Test invalid use case with pytest.raises(ValueError): x = np.random.random((height, width)) # not 3D img = image.array_to_img(x, data_format='channels_first') with pytest.raises(ValueError): x = np.random.random((height, width, 3)) img = image.array_to_img(x, data_format='channels') # unknown data_format with pytest.raises(ValueError): x = np.random.random((height, width, 5)) # neither RGB nor gray-scale img = image.array_to_img(x, data_format='channels_last') with pytest.raises(ValueError): x = np.random.random((height, width, 3)) img = image.img_to_array(x, data_format='channels') # unknown data_format with pytest.raises(ValueError): x = np.random.random((height, width, 5, 3)) # neither RGB nor gray-scale img = image.img_to_array(x, data_format='channels_last')
Example #21
Source File: image_test.py From DeepLearning_Wavelet-LSTM with MIT License | 4 votes |
def test_load_img(self, tmpdir): filename = str(tmpdir / 'image.png') original_im_array = np.array(255 * np.random.rand(100, 100, 3), dtype=np.uint8) original_im = image.array_to_img(original_im_array, scale=False) original_im.save(filename) # Test that loaded image is exactly equal to original. loaded_im = image.load_img(filename) loaded_im_array = image.img_to_array(loaded_im) assert loaded_im_array.shape == original_im_array.shape assert np.all(loaded_im_array == original_im_array) loaded_im = image.load_img(filename, grayscale=True) loaded_im_array = image.img_to_array(loaded_im) assert loaded_im_array.shape == (original_im_array.shape[0], original_im_array.shape[1], 1) # Test that nothing is changed when target size is equal to original. loaded_im = image.load_img(filename, target_size=(100, 100)) loaded_im_array = image.img_to_array(loaded_im) assert loaded_im_array.shape == original_im_array.shape assert np.all(loaded_im_array == original_im_array) loaded_im = image.load_img(filename, grayscale=True, target_size=(100, 100)) loaded_im_array = image.img_to_array(loaded_im) assert loaded_im_array.shape == (original_im_array.shape[0], original_im_array.shape[1], 1) # Test down-sampling with bilinear interpolation. loaded_im = image.load_img(filename, target_size=(25, 25)) loaded_im_array = image.img_to_array(loaded_im) assert loaded_im_array.shape == (25, 25, 3) loaded_im = image.load_img(filename, grayscale=True, target_size=(25, 25)) loaded_im_array = image.img_to_array(loaded_im) assert loaded_im_array.shape == (25, 25, 1) # Test down-sampling with nearest neighbor interpolation. loaded_im_nearest = image.load_img(filename, target_size=(25, 25), interpolation="nearest") loaded_im_array_nearest = image.img_to_array(loaded_im_nearest) assert loaded_im_array_nearest.shape == (25, 25, 3) assert np.any(loaded_im_array_nearest != loaded_im_array) # Check that exception is raised if interpolation not supported. loaded_im = image.load_img(filename, interpolation="unsupported") with pytest.raises(ValueError): loaded_im = image.load_img(filename, target_size=(25, 25), interpolation="unsupported")
Example #22
Source File: image_test.py From DeepLearning_Wavelet-LSTM with MIT License | 4 votes |
def test_img_utils(self): height, width = 10, 8 # Test th data format x = np.random.random((3, height, width)) img = image.array_to_img(x, data_format='channels_first') assert img.size == (width, height) x = image.img_to_array(img, data_format='channels_first') assert x.shape == (3, height, width) # Test 2D x = np.random.random((1, height, width)) img = image.array_to_img(x, data_format='channels_first') assert img.size == (width, height) x = image.img_to_array(img, data_format='channels_first') assert x.shape == (1, height, width) # Test tf data format x = np.random.random((height, width, 3)) img = image.array_to_img(x, data_format='channels_last') assert img.size == (width, height) x = image.img_to_array(img, data_format='channels_last') assert x.shape == (height, width, 3) # Test 2D x = np.random.random((height, width, 1)) img = image.array_to_img(x, data_format='channels_last') assert img.size == (width, height) x = image.img_to_array(img, data_format='channels_last') assert x.shape == (height, width, 1) # Test invalid use case with pytest.raises(ValueError): x = np.random.random((height, width)) # not 3D img = image.array_to_img(x, data_format='channels_first') with pytest.raises(ValueError): x = np.random.random((height, width, 3)) img = image.array_to_img(x, data_format='channels') # unknown data_format with pytest.raises(ValueError): x = np.random.random((height, width, 5)) # neither RGB nor gray-scale img = image.array_to_img(x, data_format='channels_last') with pytest.raises(ValueError): x = np.random.random((height, width, 3)) img = image.img_to_array(x, data_format='channels') # unknown data_format with pytest.raises(ValueError): x = np.random.random((height, width, 5, 3)) # neither RGB nor gray-scale img = image.img_to_array(x, data_format='channels_last')
Example #23
Source File: image_test.py From DeepLearning_Wavelet-LSTM with MIT License | 4 votes |
def test_load_img(self, tmpdir): filename = str(tmpdir / 'image.png') original_im_array = np.array(255 * np.random.rand(100, 100, 3), dtype=np.uint8) original_im = image.array_to_img(original_im_array, scale=False) original_im.save(filename) # Test that loaded image is exactly equal to original. loaded_im = image.load_img(filename) loaded_im_array = image.img_to_array(loaded_im) assert loaded_im_array.shape == original_im_array.shape assert np.all(loaded_im_array == original_im_array) loaded_im = image.load_img(filename, grayscale=True) loaded_im_array = image.img_to_array(loaded_im) assert loaded_im_array.shape == (original_im_array.shape[0], original_im_array.shape[1], 1) # Test that nothing is changed when target size is equal to original. loaded_im = image.load_img(filename, target_size=(100, 100)) loaded_im_array = image.img_to_array(loaded_im) assert loaded_im_array.shape == original_im_array.shape assert np.all(loaded_im_array == original_im_array) loaded_im = image.load_img(filename, grayscale=True, target_size=(100, 100)) loaded_im_array = image.img_to_array(loaded_im) assert loaded_im_array.shape == (original_im_array.shape[0], original_im_array.shape[1], 1) # Test down-sampling with bilinear interpolation. loaded_im = image.load_img(filename, target_size=(25, 25)) loaded_im_array = image.img_to_array(loaded_im) assert loaded_im_array.shape == (25, 25, 3) loaded_im = image.load_img(filename, grayscale=True, target_size=(25, 25)) loaded_im_array = image.img_to_array(loaded_im) assert loaded_im_array.shape == (25, 25, 1) # Test down-sampling with nearest neighbor interpolation. loaded_im_nearest = image.load_img(filename, target_size=(25, 25), interpolation="nearest") loaded_im_array_nearest = image.img_to_array(loaded_im_nearest) assert loaded_im_array_nearest.shape == (25, 25, 3) assert np.any(loaded_im_array_nearest != loaded_im_array) # Check that exception is raised if interpolation not supported. loaded_im = image.load_img(filename, interpolation="unsupported") with pytest.raises(ValueError): loaded_im = image.load_img(filename, target_size=(25, 25), interpolation="unsupported")
Example #24
Source File: image_test.py From DeepLearning_Wavelet-LSTM with MIT License | 4 votes |
def test_img_utils(self): height, width = 10, 8 # Test th data format x = np.random.random((3, height, width)) img = image.array_to_img(x, data_format='channels_first') assert img.size == (width, height) x = image.img_to_array(img, data_format='channels_first') assert x.shape == (3, height, width) # Test 2D x = np.random.random((1, height, width)) img = image.array_to_img(x, data_format='channels_first') assert img.size == (width, height) x = image.img_to_array(img, data_format='channels_first') assert x.shape == (1, height, width) # Test tf data format x = np.random.random((height, width, 3)) img = image.array_to_img(x, data_format='channels_last') assert img.size == (width, height) x = image.img_to_array(img, data_format='channels_last') assert x.shape == (height, width, 3) # Test 2D x = np.random.random((height, width, 1)) img = image.array_to_img(x, data_format='channels_last') assert img.size == (width, height) x = image.img_to_array(img, data_format='channels_last') assert x.shape == (height, width, 1) # Test invalid use case with pytest.raises(ValueError): x = np.random.random((height, width)) # not 3D img = image.array_to_img(x, data_format='channels_first') with pytest.raises(ValueError): x = np.random.random((height, width, 3)) img = image.array_to_img(x, data_format='channels') # unknown data_format with pytest.raises(ValueError): x = np.random.random((height, width, 5)) # neither RGB nor gray-scale img = image.array_to_img(x, data_format='channels_last') with pytest.raises(ValueError): x = np.random.random((height, width, 3)) img = image.img_to_array(x, data_format='channels') # unknown data_format with pytest.raises(ValueError): x = np.random.random((height, width, 5, 3)) # neither RGB nor gray-scale img = image.img_to_array(x, data_format='channels_last')
Example #25
Source File: image_test.py From DeepLearning_Wavelet-LSTM with MIT License | 4 votes |
def test_load_img(self, tmpdir): filename = str(tmpdir / 'image.png') original_im_array = np.array(255 * np.random.rand(100, 100, 3), dtype=np.uint8) original_im = image.array_to_img(original_im_array, scale=False) original_im.save(filename) # Test that loaded image is exactly equal to original. loaded_im = image.load_img(filename) loaded_im_array = image.img_to_array(loaded_im) assert loaded_im_array.shape == original_im_array.shape assert np.all(loaded_im_array == original_im_array) loaded_im = image.load_img(filename, grayscale=True) loaded_im_array = image.img_to_array(loaded_im) assert loaded_im_array.shape == (original_im_array.shape[0], original_im_array.shape[1], 1) # Test that nothing is changed when target size is equal to original. loaded_im = image.load_img(filename, target_size=(100, 100)) loaded_im_array = image.img_to_array(loaded_im) assert loaded_im_array.shape == original_im_array.shape assert np.all(loaded_im_array == original_im_array) loaded_im = image.load_img(filename, grayscale=True, target_size=(100, 100)) loaded_im_array = image.img_to_array(loaded_im) assert loaded_im_array.shape == (original_im_array.shape[0], original_im_array.shape[1], 1) # Test down-sampling with bilinear interpolation. loaded_im = image.load_img(filename, target_size=(25, 25)) loaded_im_array = image.img_to_array(loaded_im) assert loaded_im_array.shape == (25, 25, 3) loaded_im = image.load_img(filename, grayscale=True, target_size=(25, 25)) loaded_im_array = image.img_to_array(loaded_im) assert loaded_im_array.shape == (25, 25, 1) # Test down-sampling with nearest neighbor interpolation. loaded_im_nearest = image.load_img(filename, target_size=(25, 25), interpolation="nearest") loaded_im_array_nearest = image.img_to_array(loaded_im_nearest) assert loaded_im_array_nearest.shape == (25, 25, 3) assert np.any(loaded_im_array_nearest != loaded_im_array) # Check that exception is raised if interpolation not supported. loaded_im = image.load_img(filename, interpolation="unsupported") with pytest.raises(ValueError): loaded_im = image.load_img(filename, target_size=(25, 25), interpolation="unsupported")
Example #26
Source File: image_test.py From DeepLearning_Wavelet-LSTM with MIT License | 4 votes |
def test_img_utils(self): height, width = 10, 8 # Test th data format x = np.random.random((3, height, width)) img = image.array_to_img(x, data_format='channels_first') assert img.size == (width, height) x = image.img_to_array(img, data_format='channels_first') assert x.shape == (3, height, width) # Test 2D x = np.random.random((1, height, width)) img = image.array_to_img(x, data_format='channels_first') assert img.size == (width, height) x = image.img_to_array(img, data_format='channels_first') assert x.shape == (1, height, width) # Test tf data format x = np.random.random((height, width, 3)) img = image.array_to_img(x, data_format='channels_last') assert img.size == (width, height) x = image.img_to_array(img, data_format='channels_last') assert x.shape == (height, width, 3) # Test 2D x = np.random.random((height, width, 1)) img = image.array_to_img(x, data_format='channels_last') assert img.size == (width, height) x = image.img_to_array(img, data_format='channels_last') assert x.shape == (height, width, 1) # Test invalid use case with pytest.raises(ValueError): x = np.random.random((height, width)) # not 3D img = image.array_to_img(x, data_format='channels_first') with pytest.raises(ValueError): x = np.random.random((height, width, 3)) img = image.array_to_img(x, data_format='channels') # unknown data_format with pytest.raises(ValueError): x = np.random.random((height, width, 5)) # neither RGB nor gray-scale img = image.array_to_img(x, data_format='channels_last') with pytest.raises(ValueError): x = np.random.random((height, width, 3)) img = image.img_to_array(x, data_format='channels') # unknown data_format with pytest.raises(ValueError): x = np.random.random((height, width, 5, 3)) # neither RGB nor gray-scale img = image.img_to_array(x, data_format='channels_last')
Example #27
Source File: image_test.py From DeepLearning_Wavelet-LSTM with MIT License | 4 votes |
def test_img_utils(self): height, width = 10, 8 # Test th data format x = np.random.random((3, height, width)) img = image.array_to_img(x, data_format='channels_first') assert img.size == (width, height) x = image.img_to_array(img, data_format='channels_first') assert x.shape == (3, height, width) # Test 2D x = np.random.random((1, height, width)) img = image.array_to_img(x, data_format='channels_first') assert img.size == (width, height) x = image.img_to_array(img, data_format='channels_first') assert x.shape == (1, height, width) # Test tf data format x = np.random.random((height, width, 3)) img = image.array_to_img(x, data_format='channels_last') assert img.size == (width, height) x = image.img_to_array(img, data_format='channels_last') assert x.shape == (height, width, 3) # Test 2D x = np.random.random((height, width, 1)) img = image.array_to_img(x, data_format='channels_last') assert img.size == (width, height) x = image.img_to_array(img, data_format='channels_last') assert x.shape == (height, width, 1) # Test invalid use case with pytest.raises(ValueError): x = np.random.random((height, width)) # not 3D img = image.array_to_img(x, data_format='channels_first') with pytest.raises(ValueError): x = np.random.random((height, width, 3)) img = image.array_to_img(x, data_format='channels') # unknown data_format with pytest.raises(ValueError): x = np.random.random((height, width, 5)) # neither RGB nor gray-scale img = image.array_to_img(x, data_format='channels_last') with pytest.raises(ValueError): x = np.random.random((height, width, 3)) img = image.img_to_array(x, data_format='channels') # unknown data_format with pytest.raises(ValueError): x = np.random.random((height, width, 5, 3)) # neither RGB nor gray-scale img = image.img_to_array(x, data_format='channels_last')
Example #28
Source File: image_test.py From DeepLearning_Wavelet-LSTM with MIT License | 4 votes |
def test_load_img(self, tmpdir): filename = str(tmpdir / 'image.png') original_im_array = np.array(255 * np.random.rand(100, 100, 3), dtype=np.uint8) original_im = image.array_to_img(original_im_array, scale=False) original_im.save(filename) # Test that loaded image is exactly equal to original. loaded_im = image.load_img(filename) loaded_im_array = image.img_to_array(loaded_im) assert loaded_im_array.shape == original_im_array.shape assert np.all(loaded_im_array == original_im_array) loaded_im = image.load_img(filename, grayscale=True) loaded_im_array = image.img_to_array(loaded_im) assert loaded_im_array.shape == (original_im_array.shape[0], original_im_array.shape[1], 1) # Test that nothing is changed when target size is equal to original. loaded_im = image.load_img(filename, target_size=(100, 100)) loaded_im_array = image.img_to_array(loaded_im) assert loaded_im_array.shape == original_im_array.shape assert np.all(loaded_im_array == original_im_array) loaded_im = image.load_img(filename, grayscale=True, target_size=(100, 100)) loaded_im_array = image.img_to_array(loaded_im) assert loaded_im_array.shape == (original_im_array.shape[0], original_im_array.shape[1], 1) # Test down-sampling with bilinear interpolation. loaded_im = image.load_img(filename, target_size=(25, 25)) loaded_im_array = image.img_to_array(loaded_im) assert loaded_im_array.shape == (25, 25, 3) loaded_im = image.load_img(filename, grayscale=True, target_size=(25, 25)) loaded_im_array = image.img_to_array(loaded_im) assert loaded_im_array.shape == (25, 25, 1) # Test down-sampling with nearest neighbor interpolation. loaded_im_nearest = image.load_img(filename, target_size=(25, 25), interpolation="nearest") loaded_im_array_nearest = image.img_to_array(loaded_im_nearest) assert loaded_im_array_nearest.shape == (25, 25, 3) assert np.any(loaded_im_array_nearest != loaded_im_array) # Check that exception is raised if interpolation not supported. loaded_im = image.load_img(filename, interpolation="unsupported") with pytest.raises(ValueError): loaded_im = image.load_img(filename, target_size=(25, 25), interpolation="unsupported")
Example #29
Source File: image_test.py From DeepLearning_Wavelet-LSTM with MIT License | 4 votes |
def test_img_utils(self): height, width = 10, 8 # Test th data format x = np.random.random((3, height, width)) img = image.array_to_img(x, data_format='channels_first') assert img.size == (width, height) x = image.img_to_array(img, data_format='channels_first') assert x.shape == (3, height, width) # Test 2D x = np.random.random((1, height, width)) img = image.array_to_img(x, data_format='channels_first') assert img.size == (width, height) x = image.img_to_array(img, data_format='channels_first') assert x.shape == (1, height, width) # Test tf data format x = np.random.random((height, width, 3)) img = image.array_to_img(x, data_format='channels_last') assert img.size == (width, height) x = image.img_to_array(img, data_format='channels_last') assert x.shape == (height, width, 3) # Test 2D x = np.random.random((height, width, 1)) img = image.array_to_img(x, data_format='channels_last') assert img.size == (width, height) x = image.img_to_array(img, data_format='channels_last') assert x.shape == (height, width, 1) # Test invalid use case with pytest.raises(ValueError): x = np.random.random((height, width)) # not 3D img = image.array_to_img(x, data_format='channels_first') with pytest.raises(ValueError): x = np.random.random((height, width, 3)) img = image.array_to_img(x, data_format='channels') # unknown data_format with pytest.raises(ValueError): x = np.random.random((height, width, 5)) # neither RGB nor gray-scale img = image.array_to_img(x, data_format='channels_last') with pytest.raises(ValueError): x = np.random.random((height, width, 3)) img = image.img_to_array(x, data_format='channels') # unknown data_format with pytest.raises(ValueError): x = np.random.random((height, width, 5, 3)) # neither RGB nor gray-scale img = image.img_to_array(x, data_format='channels_last')
Example #30
Source File: image_test.py From DeepLearning_Wavelet-LSTM with MIT License | 4 votes |
def test_load_img(self, tmpdir): filename = str(tmpdir / 'image.png') original_im_array = np.array(255 * np.random.rand(100, 100, 3), dtype=np.uint8) original_im = image.array_to_img(original_im_array, scale=False) original_im.save(filename) # Test that loaded image is exactly equal to original. loaded_im = image.load_img(filename) loaded_im_array = image.img_to_array(loaded_im) assert loaded_im_array.shape == original_im_array.shape assert np.all(loaded_im_array == original_im_array) loaded_im = image.load_img(filename, grayscale=True) loaded_im_array = image.img_to_array(loaded_im) assert loaded_im_array.shape == (original_im_array.shape[0], original_im_array.shape[1], 1) # Test that nothing is changed when target size is equal to original. loaded_im = image.load_img(filename, target_size=(100, 100)) loaded_im_array = image.img_to_array(loaded_im) assert loaded_im_array.shape == original_im_array.shape assert np.all(loaded_im_array == original_im_array) loaded_im = image.load_img(filename, grayscale=True, target_size=(100, 100)) loaded_im_array = image.img_to_array(loaded_im) assert loaded_im_array.shape == (original_im_array.shape[0], original_im_array.shape[1], 1) # Test down-sampling with bilinear interpolation. loaded_im = image.load_img(filename, target_size=(25, 25)) loaded_im_array = image.img_to_array(loaded_im) assert loaded_im_array.shape == (25, 25, 3) loaded_im = image.load_img(filename, grayscale=True, target_size=(25, 25)) loaded_im_array = image.img_to_array(loaded_im) assert loaded_im_array.shape == (25, 25, 1) # Test down-sampling with nearest neighbor interpolation. loaded_im_nearest = image.load_img(filename, target_size=(25, 25), interpolation="nearest") loaded_im_array_nearest = image.img_to_array(loaded_im_nearest) assert loaded_im_array_nearest.shape == (25, 25, 3) assert np.any(loaded_im_array_nearest != loaded_im_array) # Check that exception is raised if interpolation not supported. loaded_im = image.load_img(filename, interpolation="unsupported") with pytest.raises(ValueError): loaded_im = image.load_img(filename, target_size=(25, 25), interpolation="unsupported")