Python keras.preprocessing.image.load_img() Examples

def main(self):
        self.logger.info('Will load keras model')
        model = ResNet50(weights='imagenet')
        self.logger.info('Keras model loaded')
        feature_list = []
        img_path_list = []
        for raw_file in self.inp.raw_files:
            media_path = raw_file.path
            file_list = os.listdir(media_path)
            total = float(len(file_list))
            for index, img_file in enumerate(file_list):
                img_path = os.path.join(media_path, img_file)
                img_path_list.append(img_path)
                img = image.load_img(img_path, target_size=(224, 224))
                x = keras_image.img_to_array(img)
                x = np.expand_dims(x, axis=0)
                x = preprocess_input(x)
                # extract features
                scores = model.predict(x)
                sim_class = np.argmax(scores)
                print('Scores {}\nSimClass: {}'.format(scores, sim_class))
                self.outp.request_annos(img_path, img_sim_class=sim_class)
                self.logger.info('Requested annotation for: {} (cluster: {})'.format(img_path, sim_class))
                self.update_progress(index*100/total) 

def preprocess_image_scale(image_path, img_size=None):
    '''
    Preprocess the image scaling it so that its larger size is max_size.
    This function preserves aspect ratio.
    '''
    img = load_img(image_path)
    if img_size:
        scale = float(img_size) / max(img.size)
        new_size = (int(np.ceil(scale * img.size[0])), int(np.ceil(scale * img.size[1])))
        img = img.resize(new_size, resample=Image.BILINEAR)
    img = img_to_array(img)
    img = np.expand_dims(img, axis=0)
    img = vgg16.preprocess_input(img)
    return img


# util function to convert a tensor into a valid image 

def get_data(dir):
    X_train, Y_train = [], []
    X_test, Y_test = [], []
    subfolders = sorted([file.path for file in os.scandir(dir) if file.is_dir()])
    for idx, folder in enumerate(subfolders):
        for file in sorted(os.listdir(folder)):
            img = load_img(folder+"/"+file, color_mode='grayscale')
            img = img_to_array(img).astype('float32')/255
            img = img.reshape(img.shape[0], img.shape[1],1)
            if idx < 35:
                X_train.append(img)
                Y_train.append(idx)
            else:
                X_test.append(img)
                Y_test.append(idx-35)

    X_train = np.array(X_train)
    X_test = np.array(X_test)
    Y_train = np.array(Y_train)
    Y_test = np.array(Y_test)
    return (X_train, Y_train), (X_test, Y_test) 

def predict(self, f, k=5, resize_mode='fill'):
        from keras.preprocessing import image
        from vergeml.img import resize_image

        filename = os.path.basename(f)

        if not os.path.exists(f):
            return dict(filename=filename, prediction=[])

        img = image.load_img(f)
        img = resize_image(img, self.image_size, self.image_size, 'antialias', resize_mode)

        x = image.img_to_array(img)
        x = np.expand_dims(x, axis=0)
        x = self.preprocess_input(x)
        preds = self.model.predict(x)
        pred = self._decode(preds, top=k)[0]
        prediction=[dict(probability=np.asscalar(perc), label=klass) for _, klass, perc in pred]

        return dict(filename=filename, prediction=prediction) 

def predict(imagepath, target_x, target_y, name, model):
    if imagepath.startswith('http://') or imagepath.startswith('https://') or imagepath.startswith('ftp://'):
        response = requests.get(imagepath)
        img = Image.open(BytesIO(response.content))
        img = img.resize((target_x, target_y))
    else:
        if not os.path.exists(imagepath):
            raise Exception('Input image file does not exist')
        img = image.load_img(imagepath, target_size=(target_x, target_y))

    x = image.img_to_array(img)
    x = np.expand_dims(x, axis=0)
    x = processInputImage(name, x)
    preds = decodePrediction(name, model.predict(x))
    result = []
    for p in preds[0]:
        result.append({"synset": p[0], "text": p[1], "prediction": float("{0:.2f}".format((p[2] * 100)))})

    return json.loads(jsonpickle.encode(result, unpicklable=False)) 

def create_test_data(self):
		# 测试集生成npy
		i = 0
		print('-' * 30)
		print('Creating test images...')
		print('-' * 30)
		imgs = glob.glob(self.test_path + "/*." + self.img_type)           # ../data_set/train
		print(len(imgs))
		imgdatas = np.ndarray((len(imgs), self.out_rows, self.out_cols, 1), dtype=np.uint8)
		for imgname in imgs:
			midname = imgname[imgname.rindex("/") + 1:]   # 图像的名字
			img = load_img(self.test_path + "/" + midname, grayscale=True)   # 转换为灰度图
			img = img_to_array(img)
			imgdatas[i] = img
			if i % 100 == 0:
				print('Done: {0}/{1} images'.format(i, len(imgs)))
			i += 1
		print('loading done', imgdatas.shape)
		np.save(self.npy_path + '/imgs_test.npy', imgdatas)            # 将30张训练集和30张label生成npy数据
		# np.save(self.npy_path + '/imgs_mask_train.npy', imglabels)
		print('Saving to .npy files done.') 

def data_loader(q, ):
    for bi in batch_indices:
        start, end = bi
        x_batch = []
        filenames_batch = filenames[start:end]

        for filename in filenames_batch:
            imgs = []
            for d in dirs:
                img = img_to_array(load_img(os.path.join(d, filename), grayscale=True))
                imgs.append(np.squeeze(img))
            x_batch.append(np.array(imgs).transpose((1, 2, 0)))
        q.put((filenames_batch, np.array(x_batch)))

    for gpu in gpus:
        q.put((None, None)) 

def extract_features(filename, model, model_type):
	if model_type == 'inceptionv3':
		from keras.applications.inception_v3 import preprocess_input
		target_size = (299, 299)
	elif model_type == 'vgg16':
		from keras.applications.vgg16 import preprocess_input
		target_size = (224, 224)
	# Loading and resizing image
	image = load_img(filename, target_size=target_size)
	# Convert the image pixels to a numpy array
	image = img_to_array(image)
	# Reshape data for the model
	image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))
	# Prepare the image for the CNN Model model
	image = preprocess_input(image)
	# Pass image into model to get encoded features
	features = model.predict(image, verbose=0)
	return features

# Load the tokenizer 

def load_image_pixels(filename, shape):
	# load the image to get its shape
	image = load_img(filename)
	width, height = image.size
	# load the image with the required size
	image = load_img(filename, target_size=shape)
	# convert to numpy array
	image = img_to_array(image)
	# scale pixel values to [0, 1]
	image = image.astype('float32')
	image /= 255.0
	# add a dimension so that we have one sample
	image = expand_dims(image, 0)
	return image, width, height

# get all of the results above a threshold 

def predict(img_dir, model):
    img_files = []
    for root, dirs, files in os.walk(img_dir, topdown=False):
        for name in files:
            img_files.append(os.path.join(root, name))
    img_files = sorted(img_files)

    y_pred = []
    y_test = []

    for img_path in tqdm(img_files):
        # print(img_path)
        img = image.load_img(img_path, target_size=(224, 224))
        x = image.img_to_array(img)
        preds = model.predict(x[None, :, :, :])
        decoded = decode_predictions(preds, top=1)
        pred_label = decoded[0][0][0]
        # print(pred_label)
        y_pred.append(pred_label)
        tokens = img_path.split(os.pathsep)
        class_id = int(tokens[-2])
        # print(str(class_id))
        y_test.append(class_id)

    return y_pred, y_test 

def extract_features(path, model_type):
	if model_type == 'inceptionv3':
		from keras.applications.inception_v3 import preprocess_input
		target_size = (299, 299)
	elif model_type == 'vgg16':
		from keras.applications.vgg16 import preprocess_input
		target_size = (224, 224)
	# Get CNN Model from model.py
	model = CNNModel(model_type)
	features = dict()
	# Extract features from each photo
	for name in tqdm(os.listdir(path)):
		# Loading and resizing image
		filename = path + name
		image = load_img(filename, target_size=target_size)
		# Convert the image pixels to a numpy array
		image = img_to_array(image)
		# Reshape data for the model
		image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))
		# Prepare the image for the CNN Model model
		image = preprocess_input(image)
		# Pass image into model to get encoded features
		feature = model.predict(image, verbose=0)
		# Store encoded features for the image
		image_id = name.split('.')[0]
		features[image_id] = feature
	return features 

def preprocess_image_crop(image_path, img_size):
    '''
    Preprocess the image scaling it so that its smaller size is img_size.
    The larger size is then cropped in order to produce a square image.
    '''
    img = load_img(image_path)
    scale = float(img_size) / min(img.size)
    new_size = (int(np.ceil(scale * img.size[0])), int(np.ceil(scale * img.size[1])))
    # print('old size: %s,new size: %s' %(str(img.size), str(new_size)))
    img = img.resize(new_size, resample=Image.BILINEAR)
    img = img_to_array(img)
    crop_h = img.shape[0] - img_size
    crop_v = img.shape[1] - img_size
    img = img[crop_h:img_size+crop_h, crop_v:img_size+crop_v, :]
    img = np.expand_dims(img, axis=0)
    img = vgg16.preprocess_input(img)
    return img

# util function to open, resize and format pictures into appropriate tensors 

def helper_test(model):
    img_path = "../examples/dog.jpg"
    new_model = to_heatmap(model)

    # Loading the image
    img = image.load_img(img_path, target_size=(800, 800))
    x = image.img_to_array(img)
    x = np.expand_dims(x, axis=0)
    x = preprocess_input(x)

    out = new_model.predict(x)

    s = "n02084071"  # Imagenet code for "dog"
    ids = synset_to_dfs_ids(s)
    heatmap = out[0]
    if K.image_data_format() == 'channels_first':
        heatmap = heatmap[ids]
        heatmap = np.sum(heatmap, axis=0)
    else:
        heatmap = heatmap[:, :, ids]
        heatmap = np.sum(heatmap, axis=2)
    print(heatmap.shape)
    assert heatmap.shape[0] == heatmap.shape[1]
    K.clear_session() 

def create_test_data(self):

        # 测试集生成npy
        i = 0
        print('-' * 30)
        print('Creating training images...')
        print('-' * 30)
        imgs = glob.glob(self.test_path + "/*." + self.img_type)           # deform/train
        print(len(imgs))
        imgdatas = np.ndarray((len(imgs), self.out_rows, self.out_cols, 1), dtype=np.uint8)
        for imgname in imgs:
            midname = imgname[imgname.rindex("/") + 1:]   # 图像的名字
            img = load_img(self.test_path + "/" + midname, grayscale=True)   # 转换为灰度图
            img = img_to_array(img)
            imgdatas[i] = img
            if i % 100 == 0:
                print('Done: {0}/{1} images'.format(i, len(imgs)))
            i += 1
        print('loading done', imgdatas.shape)
        np.save(self.npy_path + '/imgs_test.npy', imgdatas)            # 将30张训练集和30张label生成npy数据
        # np.save(self.npy_path + '/imgs_mask_train.npy', imglabels)
        print('Saving to .npy files done.') 

def load_images_for_keras(self, img_path, target_size=(224, 224)):

        features = []
        filenames = sorted(os.listdir(img_path))

        for filename in filenames:

            img = image.load_img(os.path.join(img_path, filename), target_size=target_size)
            img = image.img_to_array(img)
            img = np.expand_dims(img, axis=0)
            img = preprocess_input(img)

            feature = self.model.predict(img)

            if img is not None:
                features.append(feature)

        return features 

def _load_img_pair(self, idx, load_from_memory):
        """Get a pair of images with index idx."""
        if load_from_memory:
            a = self.a[idx]
            b = self.b[idx]
            return a, b

        fname = self.filenames[idx]

        a = load_img(os.path.join(self.a_dir, fname),
                     grayscale=self.is_a_grayscale,
                     target_size=self.target_size)
        b = load_img(os.path.join(self.b_dir, fname),
                     grayscale=self.is_b_grayscale,
                     target_size=self.target_size)

        a = img_to_array(a, self.dim_ordering)
        b = img_to_array(b, self.dim_ordering)

        return a, b 

def _compute_stats(self, mean = None, std = None):
        """ Computes channel-wise mean and standard deviation of all images in the dataset.
        
        If `mean` and `std` arguments are given, they will just be stored instead of being re-computed.

        The channel order of both is always "RGB", independent of `color_mode`.
        """
        
        if mean is None:
            mean = 0
            for fn in tqdm(self.train_img_files, desc = 'Computing channel mean'):
                mean += np.mean(np.asarray(load_img(fn), dtype=np.float64), axis = (0,1))
            mean /= len(self.train_img_files)
            print('Channel-wise mean:               {}'.format(mean))
        self.mean = np.asarray(mean, dtype=np.float32)
        if (mean is None) or (std is None):
            std = 0
            for fn in tqdm(self.train_img_files, desc = 'Computing channel variance'):
                std += np.mean((np.asarray(load_img(fn), dtype=np.float64) - self.mean) ** 2, axis = (0,1))
            std = np.sqrt(std / (len(self.train_img_files) - 1))
            print('Channel-wise standard deviation: {}'.format(std))
        self.std = np.asarray(std, dtype=np.float32) 

def test_01_image_classifier_with_image_as_input(self):
        
        cnn_pmml = KerasToPmml(self.model_final,model_name="MobileNetImage",description="Demo",\
            copyright="Internal User",dataSet='image',predictedClasses=['dogs','cats'])
        cnn_pmml.export(open('2classMBNet.pmml', "w"), 0)

        img = image.load_img('nyoka/tests/resizedCat.png')
        img = img_to_array(img)
        img = preprocess_input(img)
        imgtf = np.expand_dims(img, axis=0)
        model_pred=self.model_final.predict(imgtf)
        model_preds = {'dogs':model_pred[0][0],'cats':model_pred[0][1]}

        model_name  = self.adapa_utility.upload_to_zserver('2classMBNet.pmml')

        predictions, probabilities = self.adapa_utility.score_in_zserver(model_name, 'nyoka/tests/resizedCat.png','DN')
  
        self.assertEqual(abs(probabilities['cats'] - model_preds['cats']) < 0.00001, True)
        self.assertEqual(abs(probabilities['dogs'] - model_preds['dogs']) < 0.00001, True) 

def predict(imagePath):
    img = load_img(imagePath)
    img = img_to_array(img)
    output = img.copy()
    # make prediction
    results = rcnn.detect([img], verbose=0)
    r = results[0]
    for (box, score) in zip(r['rois'], r['scores']):
          # filter out weak detections
          if score < 0.5:
               continue
          label = "{}: {:.2f}".format('table', score)
          cv2.rectangle(output, (box[1], box[0]), (box[3], box[2]),(0, 255, 0), 2)
          cv2.putText(output, label, (box[1], box[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
    cv2.imwrite("prediction.jpg", output)
    return r['rois'] 

def display_heatmap(new_model, img_path, ids, preprocessing=None):
    # The quality is reduced.
    # If you have more than 8GB of RAM, you can try to increase it.
    img = image.load_img(img_path, target_size=(800, 1280))
    x = image.img_to_array(img)
    x = np.expand_dims(x, axis=0)
    if preprocessing is not None:
        x = preprocess_input(x)

    out = new_model.predict(x)

    heatmap = out[0]  # Removing batch axis.

    if K.image_data_format() == 'channels_first':
        heatmap = heatmap[ids]
        if heatmap.ndim == 3:
            heatmap = np.sum(heatmap, axis=0)
    else:
        heatmap = heatmap[:, :, ids]
        if heatmap.ndim == 3:
            heatmap = np.sum(heatmap, axis=2)

    plt.imshow(heatmap, interpolation="none")
    plt.show() 

def _load_image(self, filename, target_size = None, randzoom = False):
        """ Loads an image file.

        # Arguments:

        - filename: The path of the image file.

        - target_size: Int or tuple of ints. Specifies the target size which the image will be resized to.
                       If a single int is given, it specifies the size of the smaller side of the image and the aspect ratio will be retained.
                       If set to -1, the image won't be resized.
                       If set to None, the default_target_size passed to the constructor will be used.
                       The actual size may be modified further is `randzoom` is True.
        
        - randzoom: If True and `self.randzoom_range` is not None, random zooming will be applied.
                    If `self.randzoom_range` is given as floats defining a range relative to the image size,
                    `target_size` will be used as reference if it is not None, otherwise the original image size.
        
        # Returns:
            the image as PIL image.
        """

        img = load_img(filename)
        if target_size is None:
            target_size = self.default_target_size
        
        if (target_size > 0) or (randzoom and (self.randzoom_range is not None)):
            if target_size <= 0:
                target_size = img.size
            if randzoom and (self.randzoom_range is not None):
                if isinstance(self.randzoom_range[0], float):
                    target_size = np.round(np.array(target_size) * np.random.uniform(self.randzoom_range[0], self.randzoom_range[1])).astype(int).tolist()
                else:
                    target_size = np.random.randint(self.randzoom_range[0], self.randzoom_range[1])
            if isinstance(target_size, int):
                target_size = (target_size, round(img.size[1] * (target_size / img.size[0]))) if img.size[0] < img.size[1] else (round(img.size[0] * (target_size / img.size[1])), target_size)
            img = img.resize(target_size, PIL.Image.BILINEAR)
        
        return img 

def get_all_images(image_names, path_voc):
    images = []
    for j in range(np.size(image_names)):
        image_name = image_names[0][j]
        string = path_voc + '/JPEGImages/' + image_name + '.jpg'
        images.append(image.load_img(string, False))
    return images 

def _load_images(image_dir, filenames):
    LOGGER.debug('Loading %s images from %s', len(filenames), image_dir)
    images = []
    for filename in filenames:
        filename = os.path.join(image_dir, filename)

        image = load_img(filename)
        image = image.resize(tuple(INPUT_SHAPE[0:2]))
        image = img_to_array(image)
        image = image / 255.0  # Quantize images.
        images.append(image)

    return np.array(images) 

def data_loader(q, ):
    for start in tqdm(range(0, len(filenames), batch_size)):
        x_batch = []
        end = min(start + batch_size, len(filenames))
        filenames_batch = filenames[start:end]

        for filename in filenames_batch:
            img = load_img(filename)

            stacked_channels = []
            for i in range(args.stacked_channels):
                channel_path = os.path.join(args.stacked_channels_dir,
                                            str(i),
                                            filename.split('/')[-1].replace('.jpg', '.png'))
                stacked_channel = load_img(channel_path, grayscale=True)
                stacked_channels.append(stacked_channel)
            stacked_img = np.dstack((img, *stacked_channels))

            x_batch.append(img_to_array(stacked_img))


        x_batch = preprocess_input(np.array(x_batch, np.float32), mode=args.preprocessing_function)
        if args.pred_tta:
            x_batch = do_tta(x_batch, args.pred_tta)
        padded_x = np.zeros((batch_size, 1280, 1920, args.stacked_channels + 3))
        padded_x[:, :, 1:-1, :] = x_batch
        q.put((filenames_batch, padded_x))

    for gpu in gpus:
        q.put((None, None)) 

def deprocess(path):
    img_path = path
    img = load_img(img_path, target_size=(299, 299))
    x = img_to_array(img)
    return x 

def load_image(path):
    img_path = path
    img = load_img(img_path, target_size=(299, 299))
    x = img_to_array(img)
    x = np.expand_dims(x, axis=0)
    x = preprocess_input(x)
    return x 

def siamese_network(input_image):
#    print('Inside Siamese')
    base_model = VGG16(weights='imagenet', include_top=False)
    model = Model(inputs=base_model.input, outputs=base_model.get_layer('block5_pool').output)
    #print model.summary()
    img_path = input_image
    img = image.load_img(img_path, target_size=(224, 224))
    x = image.img_to_array(img)
    x = np.expand_dims(x, axis=0)
    x = preprocess_input(x)

    feature = model.predict(x)
    return feature 

def layman(input_image):
    img_path = input_image
    img = image.load_img(img_path, target_size=(50, 50))
    x = image.img_to_array(img)
    x = np.expand_dims(x, axis=0)
    x = preprocess_input(x)
    
    return x 

def val_images_to_array(img_path, source_path, img_dim, categories):

    array_path = os.path.join(source_path, 'array')
    shutil.rmtree(array_path,ignore_errors=True)
    os.makedirs(array_path)

    print('Iterating over all categories: ', categories)
    category_lengths = []
    for category_idx, category in enumerate(categories):
        print('categories:', category)
        category_path = os.path.join(img_path, category)
        img_files = sorted(os.listdir(category_path))
        category_lengths.append(len(img_files))
        for img_idx, img_file in tqdm(enumerate(img_files)):
            this_img_path = os.path.join(category_path, img_file)
            img = load_img(this_img_path, target_size=(img_dim, img_dim))

            img_name = '{}-img-{}-{}'.format(img_idx, category, category_idx)
            label_name = '{}-label-{}-{}'.format(img_idx, category, category_idx)

            label = np.eye(len(categories), dtype = np.float32)[category_idx]

            img_array_path = os.path.join(array_path, img_name)
            img_label_path = os.path.join(array_path, label_name)

            np.save(img_array_path, img)
            np.save(img_label_path, label)
    category_lengths = np.array(category_lengths) / sum(category_lengths)
    category_lengths = list(category_lengths / max(category_lengths))
    category_rounds = {cat: min(int(np.round(1 / l)), 10) for cat, l in zip(categories, category_lengths)}
    return category_rounds 

def predict(path, model_path, index_file_path, MainUI):
    try:
        result_string = " Detected Object : Probability \n \n"

        # Making check to load Model
        if (MainUI.resnet_model_loaded == False):
            wx.CallAfter(pub.sendMessage, "report101", message="Loading ResNet model for the first time. This may take a few minutes or less than a minute. Please wait. \nLoading.....")
            model = ResNet50(include_top=True, weights="imagenet", model_path=model_path)
            wx.CallAfter(pub.sendMessage, "report101", message="ResNet model loaded.. Picture about to be processed.. \nLoading......")
            MainUI.model_collection_resnet.append(model)  # Loading model if not loaded yet
            MainUI.resnet_model_loaded = True
        else:
            wx.CallAfter(pub.sendMessage, "report101", message="Retrieving loaded model. \nLoading........")
            model = MainUI.model_collection_resnet[0]  # Getting Model from model array if loaded before
            wx.CallAfter(pub.sendMessage, "report101", message="ResNet model loaded.. Picture about to be processed.. \nLoading......")

        # Image prediction processing
        target_image = image.load_img(path, grayscale=False, target_size=(224, 224))
        target_image = image.img_to_array(target_image, data_format="channels_last")
        target_image = np.expand_dims(target_image, axis=0)

        target_image = preprocess_input(target_image, data_format="channels_last")
        wx.CallAfter(pub.sendMessage, "report101", message="Picture is transformed for prediction. \nLoading........")
        prediction = model.predict(x=target_image, steps=1)
        wx.CallAfter(pub.sendMessage, "report101", message="Picture prediction is done. Sending in results. \nLoading......")

        # Retrieving prediction result and sending it back to the thread
        prediction_result = decode_predictions(prediction, top=10, index_file_path=index_file_path)

        for results in prediction_result:
            countdown = 0
            for result in results:
                countdown += 1
                result_string += "(" + str(countdown) + ") " + str(result[1]) + " : " + str(100 * result[2])[
                                                                                        0:4] + "% \n"

        return result_string
    except Exception as e:
        return getattr(e, "message", repr(e))