wehub-resource-sync
223 lines
9.0 KiB
Python
223 lines
9.0 KiB
Python
#
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# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import os
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import sys
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import numpy as np
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from PIL import Image
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try:
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from detectron2.config import get_cfg
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except ImportError:
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print("Could not import Detectron 2 modules. Maybe you did not install Detectron 2")
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print(
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"Please install Detectron 2, check https://github.com/facebookresearch/detectron2/blob/main/INSTALL.md"
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)
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sys.exit(1)
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class ImageBatcher:
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"""
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Creates batches of pre-processed images.
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"""
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def __init__(
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self,
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input,
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shape,
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dtype,
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max_num_images=None,
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exact_batches=False,
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config_file=None,
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):
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"""
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:param input: The input directory to read images from.
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:param shape: The tensor shape of the batch to prepare, either in NCHW or NHWC format.
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:param dtype: The (numpy) datatype to cast the batched data to.
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:param max_num_images: The maximum number of images to read from the directory.
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:param exact_batches: This defines how to handle a number of images that is not an exact multiple of the batch
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size. If false, it will pad the final batch with zeros to reach the batch size. If true, it will *remove* the
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last few images in excess of a batch size multiple, to guarantee batches are exact (useful for calibration).
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:param config_file: The path pointing to the Detectron 2 yaml file which describes the model.
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"""
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def det2_setup(config_file):
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"""
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Create configs and perform basic setups.
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"""
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cfg = get_cfg()
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if config_file is not None:
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cfg.merge_from_file(config_file)
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cfg.freeze()
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return cfg
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# Set up Detectron 2 model configuration.
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self.det2_cfg = det2_setup(config_file)
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# Extract min and max dimensions for testing.
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self.min_size_test = self.det2_cfg.INPUT.MIN_SIZE_TEST
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self.max_size_test = self.det2_cfg.INPUT.MAX_SIZE_TEST
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# Find images in the given input path.
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input = os.path.realpath(input)
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self.images = []
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extensions = [".jpg", ".jpeg", ".png", ".bmp", ".ppm"]
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def is_image(path):
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return (
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os.path.isfile(path) and os.path.splitext(path)[1].lower() in extensions
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)
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if os.path.isdir(input):
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self.images = [
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os.path.join(input, f)
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for f in os.listdir(input)
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if is_image(os.path.join(input, f))
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]
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self.images.sort()
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elif os.path.isfile(input):
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if is_image(input):
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self.images.append(input)
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self.num_images = len(self.images)
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if self.num_images < 1:
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print("No valid {} images found in {}".format("/".join(extensions), input))
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sys.exit(1)
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# Handle Tensor Shape.
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self.dtype = dtype
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self.shape = shape
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assert len(self.shape) == 4
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self.batch_size = shape[0]
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assert self.batch_size > 0
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self.format = None
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self.width = -1
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self.height = -1
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if self.shape[1] == 3:
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self.format = "NCHW"
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self.height = self.shape[2]
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self.width = self.shape[3]
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elif self.shape[3] == 3:
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self.format = "NHWC"
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self.height = self.shape[1]
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self.width = self.shape[2]
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assert all([self.format, self.width > 0, self.height > 0])
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# Adapt the number of images as needed.
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if max_num_images and 0 < max_num_images < len(self.images):
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self.num_images = max_num_images
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if exact_batches:
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self.num_images = self.batch_size * (self.num_images // self.batch_size)
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if self.num_images < 1:
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print("Not enough images to create batches")
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sys.exit(1)
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self.images = self.images[0 : self.num_images]
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# Subdivide the list of images into batches.
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self.num_batches = 1 + int((self.num_images - 1) / self.batch_size)
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self.batches = []
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for i in range(self.num_batches):
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start = i * self.batch_size
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end = min(start + self.batch_size, self.num_images)
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self.batches.append(self.images[start:end])
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# Indices.
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self.image_index = 0
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self.batch_index = 0
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def preprocess_image(self, image_path):
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"""
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The image preprocessor loads an image from disk and prepares it as needed for batching. This includes padding,
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resizing, normalization, data type casting, and transposing.
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This Image Batcher implements one algorithm for now:
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* Resizes and pads the image to fit the input size.
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:param image_path: The path to the image on disk to load.
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:return: Two values: A numpy array holding the image sample, ready to be contacatenated into the rest of the
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batch, and the resize scale used, if any.
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"""
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def resize_pad(image, pad_color=(0, 0, 0)):
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"""
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A subroutine to implement padding and resizing. This will resize the image to fit fully within the input
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size, and pads the remaining bottom-right portions with the value provided.
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:param image: The PIL image object
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:pad_color: The RGB values to use for the padded area. Default: Black/Zeros.
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:return: Two values: The PIL image object already padded and cropped, and the resize scale used.
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"""
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# Get characteristics.
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width, height = image.size
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# Replicates behavior of ResizeShortestEdge augmentation.
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size = self.min_size_test * 1.0
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pre_scale = size / min(height, width)
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if height < width:
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newh, neww = size, pre_scale * width
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else:
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newh, neww = pre_scale * height, size
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# If delta between min and max dimensions is so that max sized dimension reaches self.max_size_test
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# before min dimension reaches self.min_size_test, keeping the same aspect ratio. We still need to
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# maintain the same aspect ratio and keep max dimension at self.max_size_test.
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if max(newh, neww) > self.max_size_test:
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pre_scale = self.max_size_test * 1.0 / max(newh, neww)
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newh = newh * pre_scale
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neww = neww * pre_scale
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neww = int(neww + 0.5)
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newh = int(newh + 0.5)
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# Scaling factor for normalized box coordinates scaling in post-processing.
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scaling = max(newh / height, neww / width)
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# Padding.
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image = image.resize((neww, newh), resample=Image.BILINEAR)
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pad = Image.new("RGB", (self.width, self.height))
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pad.paste(pad_color, [0, 0, self.width, self.height])
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pad.paste(image)
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return pad, scaling
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scale = None
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image = Image.open(image_path)
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image = image.convert(mode="RGB")
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# Pad with mean values of COCO dataset, since padding is applied before actual model's
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# preprocessor steps (Sub, Div ops), we need to pad with mean values in order to reverse
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# the effects of Sub and Div, so that padding after model's preprocessor will be with actual 0s.
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image, scale = resize_pad(image, (124, 116, 104))
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image = np.asarray(image, dtype=np.float32)
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# Change HWC -> CHW.
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image = np.transpose(image, (2, 0, 1))
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# Change RGB -> BGR.
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return image[[2, 1, 0]], scale
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def get_batch(self):
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"""
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Retrieve the batches. This is a generator object, so you can use it within a loop as:
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for batch, images in batcher.get_batch():
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...
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Or outside of a batch with the next() function.
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:return: A generator yielding three items per iteration: a numpy array holding a batch of images, the list of
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paths to the images loaded within this batch, and the list of resize scales for each image in the batch.
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"""
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for i, batch_images in enumerate(self.batches):
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batch_data = np.zeros(self.shape, dtype=self.dtype)
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batch_scales = [None] * len(batch_images)
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for i, image in enumerate(batch_images):
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self.image_index += 1
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batch_data[i], batch_scales[i] = self.preprocess_image(image)
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self.batch_index += 1
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yield batch_data, batch_images, batch_scales
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