当我将帕斯卡数据集转换为 tfrecord 时出现“KeyError：face”

您可以使用以下方法将更宽的数据集转换为 TFRecords。1.您需要创建一个config.py文件。# TrainingTRAIN_WIDER_PATH = "widerface/WIDER_train/"#ValidationVAL_WIDER_PATH = "widerface/WIDER_val/"#TestingTEST_WIDER_PATH = "widerface/WIDER_test/"# Ground TruthGROUND_TRUTH_PATH = "widerface/wider_face_split/"# OutputOUTPUT_PATH = "datasets/widerface/TF_data/"  生成 TFRecords 的代码（create_tf_record.py）。这是代码：import tensorflow as tfimport numpyimport cv2import osimport hashlibimport configfrom utils import dataset_utildef parse_test_example(f, images_path):    height = None # Image height    width = None # Image width    filename = None # Filename of the image. Empty if image is not from file    encoded_image_data = None # Encoded image bytes    image_format = b'jpeg' # b'jpeg' or b'png'    filename = f.readline().rstrip()    if not filename:        raise IOError()    filepath = os.path.join(images_path, filename)    image_raw = cv2.imread(filepath)    encoded_image_data = open(filepath, "rb").read()    key = hashlib.sha256(encoded_image_data).hexdigest()    height, width, channel = image_raw.shape    tf_example = tf.train.Example(features=tf.train.Features(feature={        'image/height': dataset_util.int64_feature(int(height)),        'image/width': dataset_util.int64_feature(int(width)),        'image/filename': dataset_util.bytes_feature(filename.encode('utf-8')),        'image/source_id': dataset_util.bytes_feature(filename.encode('utf-8')),        'image/key/sha256': dataset_util.bytes_feature(key.encode('utf8')),        'image/encoded': dataset_util.bytes_feature(encoded_image_data),        'image/format': dataset_util.bytes_feature('jpeg'.encode('utf8')),        }))    return tf_exampledef parse_example(f, images_path):    height = None # Image height    width = None # Image width    filename = None # Filename of the image. Empty if image is not from file    encoded_image_data = None # Encoded image bytes    image_format = b'jpeg' # b'jpeg' or b'png'    xmins = [] # List of normalized left x coordinates in bounding box (1 per box)    xmaxs = [] # List of normalized right x coordinates in bounding box (1 per box)    ymins = [] # List of normalized top y coordinates in bounding box (1 per box)    ymaxs = [] # List of normalized bottom y coordinates in bounding box (1 per box)    classes_text = [] # List of string class name of bounding box (1 per box)    classes = [] # List of integer class id of bounding box (1 per box)    poses = []    truncated = []    difficult_obj = []    filename = f.readline().rstrip()    if not filename:        raise IOError()    filepath = os.path.join(images_path, filename)    image_raw = cv2.imread(filepath)    encoded_image_data = open(filepath, "rb").read()    key = hashlib.sha256(encoded_image_data).hexdigest()    height, width, channel = image_raw.shape    face_num = int(f.readline().rstrip())    if not face_num:      face_num += 1        # raise Exception()    for i in range(face_num):        annot = f.readline().rstrip().split()        if not annot:            raise Exception()        # WIDER FACE DATASET CONTAINS SOME ANNOTATIONS WHAT EXCEEDS THE IMAGE BOUNDARY        if(float(annot[2]) > 25.0):            if(float(annot[3]) > 30.0):                xmins.append( max(0.005, (float(annot[0]) / width) ) )                ymins.append( max(0.005, (float(annot[1]) / height) ) )                xmaxs.append( min(0.995, ((float(annot[0]) + float(annot[2])) / width) ) )                ymaxs.append( min(0.995, ((float(annot[1]) + float(annot[3])) / height) ) )                classes_text.append(b'face')                classes.append(1)                poses.append("front".encode('utf8'))                truncated.append(int(0))    tf_example = tf.train.Example(features=tf.train.Features(feature={        'image/height': dataset_util.int64_feature(int(height)),        'image/width': dataset_util.int64_feature(int(width)),        'image/filename': dataset_util.bytes_feature(filename.encode('utf-8')),        'image/source_id': dataset_util.bytes_feature(filename.encode('utf-8')),        'image/key/sha256': dataset_util.bytes_feature(key.encode('utf8')),        'image/encoded': dataset_util.bytes_feature(encoded_image_data),        'image/format': dataset_util.bytes_feature('jpeg'.encode('utf8')),        'image/object/bbox/xmin': dataset_util.float_list_feature(xmins),        'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs),        'image/object/bbox/ymin': dataset_util.float_list_feature(ymins),        'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs),        'image/object/class/text': dataset_util.bytes_list_feature(classes_text),        'image/object/class/label': dataset_util.int64_list_feature(classes),        'image/object/difficult': dataset_util.int64_list_feature(int(0)),        'image/object/truncated': dataset_util.int64_list_feature(truncated),        'image/object/view': dataset_util.bytes_list_feature(poses),        }))    return tf_exampledef run(images_path, description_file, output_path, no_bbox=False):    f = open(description_file)    writer = tf.python_io.TFRecordWriter(output_path)    i = 0    print("Processing {}".format(images_path))    while True:        try:            if no_bbox:                tf_example = parse_test_example(f, images_path)            else:                tf_example = parse_example(f, images_path)            writer.write(tf_example.SerializeToString())            i += 1        except IOError:            break        except Exception:            raise    writer.close()    print("Correctly created record for {} images\n".format(i))def main(unused_argv):    # Training    if config.TRAIN_WIDER_PATH is not None:        images_path = os.path.join(config.TRAIN_WIDER_PATH, "images")        description_file = os.path.join(config.GROUND_TRUTH_PATH, "wider_face_train_bbx_gt.txt")        output_path = os.path.join(config.OUTPUT_PATH, "train.tfrecord")        run(images_path, description_file, output_path)    # Validation    if config.VAL_WIDER_PATH is not None:        images_path = os.path.join(config.VAL_WIDER_PATH, "images")        description_file = os.path.join(config.GROUND_TRUTH_PATH, "wider_face_val_bbx_gt.txt")        output_path = os.path.join(config.OUTPUT_PATH, "val.tfrecord")        run(images_path, description_file, output_path)    # Testing. This set does not contain bounding boxes, so the tfrecord will contain images only    if config.TEST_WIDER_PATH is not None:        images_path = os.path.join(config.TEST_WIDER_PATH, "images")        description_file = os.path.join(config.GROUND_TRUTH_PATH, "wider_face_test_filelist.txt")        output_path = os.path.join(config.OUTPUT_PATH, "test.tfrecord")        run(images_path, description_file, output_path, no_bbox=True)if __name__ == '__main__':    tf.app.run()  运行create_tf_record.py生成 TFRecord 文件。python create_tf_record.py  希望这能回答你的问题，快乐学习！

当我将帕斯卡数据集转换为 tfrecord 时出现“KeyError：face”

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