nvidia--tensorrt/samples/python/efficientnet/image_batcher.py

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import os
import sys

import numpy as np
from PIL import Image


class ImageBatcher:
    """
    Creates batches of pre-processed images.
    """

    def __init__(
        self,
        input,
        shape,
        dtype,
        max_num_images=None,
        exact_batches=False,
        preprocessor="V2",
    ):
        """
        :param input: The input directory to read images from.
        :param shape: The tensor shape of the batch to prepare, either in NCHW or NHWC format.
        :param dtype: The (numpy) datatype to cast the batched data to.
        :param max_num_images: The maximum number of images to read from the directory.
        :param exact_batches: This defines how to handle a number of images that is not an exact multiple of the batch
        size. If false, it will pad the final batch with zeros to reach the batch size. If true, it will *remove* the
        last few images in excess of a batch size multiple, to guarantee batches are exact (useful for calibration).
        :param preprocessor: Set the preprocessor to use, V1 or V2, depending on which network is being used.
        """
        # Find images in the given input path
        input = os.path.realpath(input)
        self.images = []

        extensions = [".jpg", ".jpeg", ".png", ".bmp"]

        def is_image(path):
            return (
                os.path.isfile(path) and os.path.splitext(path)[1].lower() in extensions
            )

        if os.path.isdir(input):
            self.images = [
                os.path.join(input, f)
                for f in os.listdir(input)
                if is_image(os.path.join(input, f))
            ]
            self.images.sort()
        elif os.path.isfile(input):
            if is_image(input):
                self.images.append(input)
        self.num_images = len(self.images)
        if self.num_images < 1:
            print("No valid {} images found in {}".format("/".join(extensions), input))
            sys.exit(1)

        # Handle Tensor Shape
        self.dtype = dtype
        self.shape = shape
        assert len(self.shape) == 4
        self.batch_size = shape[0]
        assert self.batch_size > 0
        self.format = None
        self.width = -1
        self.height = -1
        if self.shape[1] == 3:
            self.format = "NCHW"
            self.height = self.shape[2]
            self.width = self.shape[3]
        elif self.shape[3] == 3:
            self.format = "NHWC"
            self.height = self.shape[1]
            self.width = self.shape[2]
        assert all([self.format, self.width > 0, self.height > 0])

        # Adapt the number of images as needed
        if max_num_images and 0 < max_num_images < len(self.images):
            self.num_images = max_num_images
        if exact_batches:
            self.num_images = self.batch_size * (self.num_images // self.batch_size)
        if self.num_images < 1:
            print("Not enough images to create batches")
            sys.exit(1)
        self.images = self.images[0 : self.num_images]

        # Subdivide the list of images into batches
        self.num_batches = 1 + int((self.num_images - 1) / self.batch_size)
        self.batches = []
        for i in range(self.num_batches):
            start = i * self.batch_size
            end = min(start + self.batch_size, self.num_images)
            self.batches.append(self.images[start:end])

        # Indices
        self.image_index = 0
        self.batch_index = 0

        self.preprocessor = preprocessor

    def preprocess_image(self, image_path):
        """
        The image preprocessor loads an image from disk and prepares it as needed for batching. This includes cropping,
        resizing, normalization, data type casting, and transposing.
        This Image Batcher implements two algorithms:
        * V2: The algorithm for EfficientNet V2, as defined in automl/efficientnetv2/preprocessing.py.
        * V1: The algorithm for EfficientNet V1, aka "Legacy", as defined in automl/efficientnetv2/preprocess_legacy.py.
        :param image_path: The path to the image on disk to load.
        :return: A numpy array holding the image sample, ready to be contacatenated into the rest of the batch.
        """

        def pad_crop(image):
            """
            A subroutine to implement padded cropping. This will create a center crop of the image, padded by 32 pixels.
            :param image: The PIL image object
            :return: The PIL image object already padded and cropped.
            """
            # Assume square images
            assert self.height == self.width
            width, height = image.size
            ratio = self.height / (self.height + 32)
            crop_size = int(ratio * min(height, width))
            y = (height - crop_size) // 2
            x = (width - crop_size) // 2
            return image.crop((x, y, x + crop_size, y + crop_size))

        image = Image.open(image_path)
        image = image.convert(mode="RGB")
        if self.preprocessor == "V2":
            # For EfficientNet V2: Bilinear Resize and [-1,+1] Normalization
            if self.height < 320:
                # Padded crop only on smaller sizes
                image = pad_crop(image)
            image = image.resize((self.width, self.height), resample=Image.BILINEAR)
            image = np.asarray(image, dtype=self.dtype)
            image = (image - 128.0) / 128.0
        elif self.preprocessor == "V1":
            # For EfficientNet V1: Padded Crop, Bicubic Resize, and [0,1] Normalization
            # (Mean subtraction and Std Dev scaling will be part of the graph, so not done here)
            image = pad_crop(image)
            image = image.resize((self.width, self.height), resample=Image.BICUBIC)
            image = np.asarray(image, dtype=self.dtype)
            image = image / 255.0
        elif self.preprocessor == "V1MS":
            # For EfficientNet V1: Padded Crop, Bicubic Resize, and [0,1] Normalization
            # Mean subtraction and Std dev scaling are applied as a pre-processing step outside the graph.
            image = pad_crop(image)
            image = image.resize((self.width, self.height), resample=Image.BICUBIC)
            image = np.asarray(image, dtype=self.dtype)
            image = image - np.asarray([123.68, 116.28, 103.53])
            image = image / np.asarray([58.395, 57.120, 57.375])
        else:
            print("Preprocessing method {} not supported".format(self.preprocessor))
            sys.exit(1)
        if self.format == "NCHW":
            image = np.transpose(image, (2, 0, 1))
        return image

    def get_batch(self):
        """
        Retrieve the batches. This is a generator object, so you can use it within a loop as:
        for batch, images in batcher.get_batch():
           ...
        Or outside of a batch with the next() function.
        :return: A generator yielding two items per iteration: a numpy array holding a batch of images, and the list of
        paths to the images loaded within this batch.
        """
        for i, batch_images in enumerate(self.batches):
            batch_data = np.zeros(self.shape, dtype=self.dtype)
            for i, image in enumerate(batch_images):
                self.image_index += 1
                batch_data[i] = self.preprocess_image(image)
            self.batch_index += 1
            yield batch_data, batch_images