Python image.array_to_img方法代码示例

# 需要导入模块: from keras.preprocessing import image [as 别名]
# 或者: from keras.preprocessing.image import array_to_img [as 别名]
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 

# 需要导入模块: from keras.preprocessing import image [as 别名]
# 或者: from keras.preprocessing.image import array_to_img [as 别名]
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 

# 需要导入模块: from keras.preprocessing import image [as 别名]
# 或者: from keras.preprocessing.image import array_to_img [as 别名]
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 

# 需要导入模块: from keras.preprocessing import image [as 别名]
# 或者: from keras.preprocessing.image import array_to_img [as 别名]
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 

# 需要导入模块: from keras.preprocessing import image [as 别名]
# 或者: from keras.preprocessing.image import array_to_img [as 别名]
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 

# 需要导入模块: from keras.preprocessing import image [as 别名]
# 或者: from keras.preprocessing.image import array_to_img [as 别名]
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 

# 需要导入模块: from keras.preprocessing import image [as 别名]
# 或者: from keras.preprocessing.image import array_to_img [as 别名]
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 

# 需要导入模块: from keras.preprocessing import image [as 别名]
# 或者: from keras.preprocessing.image import array_to_img [as 别名]
def dump_image(x, filename, format):
    img = image.array_to_img(x, scale=False)
    img.save(filename, format)
    return 

# 需要导入模块: from keras.preprocessing import image [as 别名]
# 或者: from keras.preprocessing.image import array_to_img [as 别名]
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 

# 需要导入模块: from keras.preprocessing import image [as 别名]
# 或者: from keras.preprocessing.image import array_to_img [as 别名]
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 

# 需要导入模块: from keras.preprocessing import image [as 别名]
# 或者: from keras.preprocessing.image import array_to_img [as 别名]
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) 

# 需要导入模块: from keras.preprocessing import image [as 别名]
# 或者: from keras.preprocessing.image import array_to_img [as 别名]
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) 

# 需要导入模块: from keras.preprocessing import image [as 别名]
# 或者: from keras.preprocessing.image import array_to_img [as 别名]
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 

# 需要导入模块: from keras.preprocessing import image [as 别名]
# 或者: from keras.preprocessing.image import array_to_img [as 别名]
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...") 

# 需要导入模块: from keras.preprocessing import image [as 别名]
# 或者: from keras.preprocessing.image import array_to_img [as 别名]
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...")