face_recognition/face_detect/vision/datasets/voc_dataset.py

import logging
import os
import pathlib
import xml.etree.ElementTree as ET
import h5py
import cv2
import numpy as np
import lmdb
from .caffe_pb2 import *

class VOCDataset:

    def __init__(self, root, transform=None, target_transform=None, is_test=False, keep_difficult=False, label_file=None):
        """Dataset for VOC data.
        Args:
            root: the root of the VOC2007 or VOC2012 dataset, the directory contains the following sub-directories:
                Annotations, ImageSets, JPEGImages, SegmentationClass, SegmentationObject.
        """
        self.root = "D:/test"
        self.transform = transform
        self.target_transform = target_transform
        if is_test:
            image_sets_file = self.root + '/test.txt'
        else:
            image_sets_file = self.root + '/test.txt'
        self.ids = ['1.hdf5']#VOCDataset._read_image_ids(image_sets_file)
        self.keep_difficult = keep_difficult

        # if the labels file exists, read in the class names
        label_file_name = self.root + "labels.txt"

        if os.path.isfile(label_file_name):
            class_string = ""
            with open(label_file_name, 'r') as infile:
                for line in infile:
                    class_string += line.rstrip()

            # classes should be a comma separated list

            classes = class_string.split(',')
            # prepend BACKGROUND as first class
            classes.insert(0, 'BACKGROUND')
            classes = [elem.replace(" ", "") for elem in classes]
            self.class_names = tuple(classes)
            logging.info("VOC Labels read from file: " + str(self.class_names))

        else:
            logging.info("No labels file, using default VOC classes.")
            self.class_names = ('BACKGROUND',
                                'face')

        self.class_dict = {class_name: i for i, class_name in enumerate(self.class_names)}

    # def __getitem__(self, index):
    #     image_id = self.ids[index]
    #     boxes, labels, is_difficult = self._get_annotation(image_id)
    #     if not self.keep_difficult:
    #         boxes = boxes[is_difficult == 0]
    #         labels = labels[is_difficult == 0]
    #     image = self._read_image(image_id)
    #     if self.transform:
    #         image, boxes, labels = self.transform(image, boxes, labels)
    #     if self.target_transform:
    #         boxes, labels = self.target_transform(boxes, labels)
    #     return image, boxes, labels

    def __getitem__(self, index):
        num_per_shared = 3
        file_idx = index // num_per_shared
        idx_in_file = index % num_per_shared
        hdf_path = os.path.join(self.root, self.ids[file_idx])
        with h5py.File(hdf_path, 'r') as f:
            boxes = f[str(idx_in_file) + '_boxes']
            is_difficult = f[str(idx_in_file) + '_difficult']
            image = f[str(idx_in_file) + '_image']
            labels = f[str(idx_in_file) + 'labels']

        if not self.keep_difficult:
            boxes = boxes[is_difficult == 0]
            labels = labels[is_difficult == 0]
        if self.transform:
            image, boxes, labels = self.transform(image, boxes, labels)
        if self.target_transform:
            boxes, labels = self.target_transform(boxes, labels)

        return image, boxes, labels

    def get_image(self, index):
        image_id = self.ids[index]
        image = self._read_image(image_id)
        if self.transform:
            image, _ = self.transform(image)
        return image

    def get_annotation(self, index):
        image_id = self.ids[index]
        return image_id, self._get_annotation(image_id)

    def __len__(self):
        total = 0
        # for file in self.ids:
        #     hdf_path = os.path.join(self.root, file)
        #     f = h5py.File(hdf_path, 'r')
        #     total += len(f.keys())
        return total // 4

    @staticmethod
    def _read_image_ids(image_sets_file):
        ids = []
        with open(image_sets_file) as f:
            for line in f:
                ids.append(line.rstrip())
        return ids

    def _get_annotation(self, image_id):
        annotation_file = self.root / f"Annotations/{image_id}.xml"
        objects = ET.parse(annotation_file).findall("object")
        boxes = []
        labels = []
        is_difficult = []
        for object in objects:
            class_name = object.find('name').text.lower().strip()
            # we're only concerned with clases in our list
            if class_name in self.class_dict:
                bbox = object.find('bndbox')

                # VOC dataset format follows Matlab, in which indexes start from 0
                x1 = float(bbox.find('xmin').text) - 1
                y1 = float(bbox.find('ymin').text) - 1
                x2 = float(bbox.find('xmax').text) - 1
                y2 = float(bbox.find('ymax').text) - 1
                boxes.append([x1, y1, x2, y2])

                labels.append(self.class_dict[class_name])
                is_difficult_str = object.find('difficult').text
                is_difficult.append(int(is_difficult_str) if is_difficult_str else 0)

        return (np.array(boxes, dtype=np.float32),
                np.array(labels, dtype=np.int64),
                np.array(is_difficult, dtype=np.uint8))

    def _read_image(self, image_id):
        image_file = self.root / f"JPEGImages/{image_id}.jpg"
        image = cv2.imread(str(image_file))
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        return image