基於object_detection API的車牌檢測 （二）

一、數據集格式轉換

我的數據集一共有400張車牌圖片，其中訓練集360張，測試集40張，分別放在D:\PythonNotebook\models-master\research\object_detection\images下的train和test文件夾，因為labellmg生成的是xml格式的文件，所以需要將xml文件轉換成csv文件，再轉換成tfrecord格式文件，其中xml文件轉換成csv文件代碼如下：

import os
import glob
import pandas as pd
import xml.etree.ElementTree as ET

path = 'images/test'

def xml_to_csv(path):
    xml_list = []
    for xml_file in glob.glob(path + '/*.xml'):
        tree = ET.parse(xml_file)
        root = tree.getroot()
        for member in root.findall('object'):
            value = (root.find('filename').text,
                     int(root.find('size')[0].text),
                     int(root.find('size')[1].text),
                     member[0].text,
                     int(member[4][0].text),
                     int(member[4][1].text),
                     int(member[4][2].text),
                     int(member[4][3].text)
                     )
            xml_list.append(value)
    column_name = ['filename', 'width', 'height', 'class', 'xmin', 'ymin', 'xmax', 'ymax']
    xml_df = pd.DataFrame(xml_list, columns=column_name)
    return xml_df


def main():
    image_path = path
    xml_df = xml_to_csv(path)
    xml_df.to_csv('data/plate_test_labels.csv', index=None)
    print('Successfully converted xml to csv.')

保存在D:\PythonNotebook\models-master\research\object_detection文件夾下為xml_to_csv.py，由csv文件轉換成tf_record文件代碼為generate_tfrecord.py，代碼也保存在D:\PythonNotebook\models-master\research\object_detection下

import os
import io
import pandas as pd
import tensorflow as tf

from PIL import Image
from object_detection.utils import dataset_util
from collections import namedtuple, OrderedDict


# TO-DO replace this with label map
def class_text_to_int(row_label):
    if row_label == 'plate':
        return 1
    else:
        None


def split(df, group):
    data = namedtuple('data', ['filename', 'object'])
    gb = df.groupby(group)
    print('split')
    return [data(filename, gb.get_group(x)) for filename, x in zip(gb.groups.keys(), gb.groups)]


def create_tf_example(group, path):
    with tf.gfile.GFile(os.path.join(path, '{}'.format(group.filename)), 'rb') as fid:
        encoded_jpg = fid.read()
    encoded_jpg_io = io.BytesIO(encoded_jpg)
    image = Image.open(encoded_jpg_io)
    width, height = image.size
    print(width)

    filename = group.filename.encode('utf8')
    image_format = b'jpg'
    xmins = []
    xmaxs = []
    ymins = []
    ymaxs = []
    classes_text = []
    classes = []

    for index, row in group.object.iterrows():
        xmins.append(row['xmin'] / width)
        xmaxs.append(row['xmax'] / width)
        ymins.append(row['ymin'] / height)
        ymaxs.append(row['ymax'] / height)
        classes_text.append(row['class'].encode('utf8'))
        classes.append(class_text_to_int(row['class']))

    tf_example = tf.train.Example(features=tf.train.Features(feature={
        'image/height': dataset_util.int64_feature(height),
        'image/width': dataset_util.int64_feature(width),
        'image/filename': dataset_util.bytes_feature(filename),
        'image/source_id': dataset_util.bytes_feature(filename),
        'image/encoded': dataset_util.bytes_feature(encoded_jpg),
        'image/format': dataset_util.bytes_feature(image_format),
        '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),
    }))
    return tf_example


def main():
    writer = tf.python_io.TFRecordWriter('data/test.record')
    path = 'images/test'
    examples = pd.read_csv('data/plate_test_labels.csv')
    grouped = split(examples, 'filename')
    for group in grouped:
        tf_example = create_tf_example(group, path)
        writer.write(tf_example.SerializeToString())

    writer.close()
    output_path = 'data/test.record'
    print('Successfully created the TFRecords: {}'.format(output_path))


if __name__ == '__main__':
    main()

然后分別執行這兩段代碼就可以了，我將csv文件以及tfrecord文件都放在了object_detection/data下了。當然我這里都只是對測試集進行了格式轉換，對於訓練集只需要將代碼里所有的test改成train就可以了。

二、選擇模型和配置文件、

在tensorflow的物體檢測模型界面選擇你的模型，這里我用的是ssd_mobilenet_v1_coco.config模型，點擊ssd_mobilenet_v1_coco.config進入代碼界面，將代碼拷貝下來，在D:\PythonNotebook\models-master\research\object_detection下新建一個文件夾training，在training文件夾下新建一個文本文檔，命名為ssd_mobilenet_v1_coco.config，將剛才拷貝的代碼復制到里面，並修改一些代碼：

1、input_path改為自己的train.tfrecord以及test.tfrecord路徑，注意不要把test跟train弄反了；

2、將 num_classes 按照實際情況更改，本文中是1；

3、batch_size 原本是24改為1或者2，因為電腦內存的原因；

4、fine_tune_checkpoint: "ssd_mobilenet_v1_coco_11_06_2017/model.ckpt"
  from_detection_checkpoint: true這兩行刪除。

5、eval_config中的num_examples改為40，對應你的測試集樣本數。

6、在object_detection/data文件夾下隨便拷貝一個.pbtxt文件副本命名為plate.pbtxt，將里面的代碼改成如下：

item {
name: "plate"
id: 1
}

代表label的名稱以及對應的數字，將label_map_path設置為對應的plate.pbtxt路徑，我這里是data/plate.pbtxt。

整個配置文件如下：

# SSD with Mobilenet v1 configuration for MSCOCO Dataset.
# Users should configure the fine_tune_checkpoint field in the train config as
# well as the label_map_path and input_path fields in the train_input_reader and
# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
# should be configured.

model {
  ssd {
    num_classes: 1
    box_coder {
      faster_rcnn_box_coder {
        y_scale: 10.0
        x_scale: 10.0
        height_scale: 5.0
        width_scale: 5.0
      }
    }
    matcher {
      argmax_matcher {
        matched_threshold: 0.5
        unmatched_threshold: 0.5
        ignore_thresholds: false
        negatives_lower_than_unmatched: true
        force_match_for_each_row: true
      }
    }
    similarity_calculator {
      iou_similarity {
      }
    }
    anchor_generator {
      ssd_anchor_generator {
        num_layers: 6
        min_scale: 0.2
        max_scale: 0.95
        aspect_ratios: 1.0
        aspect_ratios: 2.0
        aspect_ratios: 0.5
        aspect_ratios: 3.0
        aspect_ratios: 0.3333
      }
    }
    image_resizer {
      fixed_shape_resizer {
        height: 300
        width: 300
      }
    }
    box_predictor {
      convolutional_box_predictor {
        min_depth: 0
        max_depth: 0
        num_layers_before_predictor: 0
        use_dropout: false
        dropout_keep_probability: 0.8
        kernel_size: 1
        box_code_size: 4
        apply_sigmoid_to_scores: false
        conv_hyperparams {
          activation: RELU_6,
          regularizer {
            l2_regularizer {
              weight: 0.00004
            }
          }
          initializer {
            truncated_normal_initializer {
              stddev: 0.03
              mean: 0.0
            }
          }
          batch_norm {
            train: true,
            scale: true,
            center: true,
            decay: 0.9997,
            epsilon: 0.001,
          }
        }
      }
    }
    feature_extractor {
      type: 'ssd_mobilenet_v1'
      min_depth: 16
      depth_multiplier: 1.0
      conv_hyperparams {
        activation: RELU_6,
        regularizer {
          l2_regularizer {
            weight: 0.00004
          }
        }
        initializer {
          truncated_normal_initializer {
            stddev: 0.03
            mean: 0.0
          }
        }
        batch_norm {
          train: true,
          scale: true,
          center: true,
          decay: 0.9997,
          epsilon: 0.001,
        }
      }
    }
    loss {
      classification_loss {
        weighted_sigmoid {
        }
      }
      localization_loss {
        weighted_smooth_l1 {
        }
      }
      hard_example_miner {
        num_hard_examples: 3000
        iou_threshold: 0.99
        loss_type: CLASSIFICATION
        max_negatives_per_positive: 3
        min_negatives_per_image: 0
      }
      classification_weight: 1.0
      localization_weight: 1.0
    }
    normalize_loss_by_num_matches: true
    post_processing {
      batch_non_max_suppression {
        score_threshold: 1e-8
        iou_threshold: 0.6
        max_detections_per_class: 100
        max_total_detections: 100
      }
      score_converter: SIGMOID
    }
  }
}

train_config: {
  batch_size: 2
  optimizer {
    rms_prop_optimizer: {
      learning_rate: {
        exponential_decay_learning_rate {
          initial_learning_rate: 0.004
          decay_steps: 800720
          decay_factor: 0.95
        }
      }
      momentum_optimizer_value: 0.9
      decay: 0.9
      epsilon: 1.0
    }
  }
  # Note: The below line limits the training process to 200K steps, which we
  # empirically found to be sufficient enough to train the pets dataset. This
  # effectively bypasses the learning rate schedule (the learning rate will
  # never decay). Remove the below line to train indefinitely.
  num_steps: 200000
  data_augmentation_options {
    random_horizontal_flip {
    }
  }
  data_augmentation_options {
    ssd_random_crop {
    }
  }
}

train_input_reader: {
  tf_record_input_reader {
    input_path: "data/train.record"
  }
  label_map_path: "data/plate.pbtxt"
}

eval_config: {
  num_examples: 40
  # Note: The below line limits the evaluation process to 10 evaluations.
  # Remove the below line to evaluate indefinitely.
  max_evals: 10
}

eval_input_reader: {
  tf_record_input_reader {
    input_path: "data/test.record"
  }
  label_map_path: "data/plate.pbtxt"
  shuffle: false
  num_readers: 1
}

接下來就是訓練模型了

三、模型訓練

打開Anaconda Prompt，定位到object_detection文件夾路徑，我的是D:\PythonNotebook\models-master\research\object_detection，輸入如下命令：

python model_main.py --logtostderr --train_dir=training/ --pipeline_config_path=training/ssd_mobilenet_v1_coco.config

期間遇到很多問題，從網上查找了各種解決方法，匯總如下：

1、No modul named pycocotools

原生態的pycocotools是不支持windows系統的，但是有大神已經改寫了代碼支持windows版本，鏈接在此，下載后解壓，打開Anaconda Prompt，定位到你的解壓路徑下的cocoapi-master\PythonAPI ，輸入命令：python setup.py install即可安裝成功。

2、TypeError: can’t pickle dict_values objects”

找到前面拷貝到Anaconda安裝路徑里面的object_detection文件夾，我的路徑是D:\Anaconda3\Lib\site-packages\object_detection，打開model_lib.py文件，修改其中的代碼：

在category_index.values()前加上list()即可。

 再次運行python model_main.py --logtostderr --train_dir=training/ --pipeline_config_path=training/ssd_mobilenet_v1_coco.config發現已經可以訓練了。

部分參考博主dy_guox，在此表示感謝。