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‎generate_tfrecord.py

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from __future__ import division
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from __future__ import print_function
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from __future__ import absolute_import
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import os
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import io
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import pandas as pd
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import tensorflow as tf
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from PIL import Image
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from object_detection.utils import dataset_util
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from collections import namedtuple, OrderedDict
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flags = tf.app.flags
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flags.DEFINE_string('csv_input', '', 'Path to the CSV input')
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flags.DEFINE_string('output_path', '', 'Path to output TFRecord')
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flags.DEFINE_string('image_dir', '', 'Path to images')
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FLAGS = flags.FLAGS
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def class_text_to_int(row_label):
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if row_label == 'Raspberry_Pi_3':
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return 1
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elif row_label == 'Arduino_Nano':
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return 2
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elif row_label == 'ESP8266':
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return 3
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elif row_label == 'Heltec_ESP32_Lora':
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return 4
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else:
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return None
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def split(df, group):
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data = namedtuple('data', ['filename', 'object'])
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gb = df.groupby(group)
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return [data(filename, gb.get_group(x)) for filename, x in zip(gb.groups.keys(), gb.groups)]
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def create_tf_example(group, path):
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with tf.gfile.GFile(os.path.join(path, '{}'.format(group.filename)), 'rb') as fid:
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encoded_jpg = fid.read()
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encoded_jpg_io = io.BytesIO(encoded_jpg)
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image = Image.open(encoded_jpg_io)
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width, height = image.size
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filename = group.filename.encode('utf8')
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image_format = b'jpg'
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xmins = []
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xmaxs = []
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ymins = []
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ymaxs = []
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classes_text = []
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classes = []
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for index, row in group.object.iterrows():
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xmins.append(row['xmin'] / width)
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xmaxs.append(row['xmax'] / width)
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ymins.append(row['ymin'] / height)
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ymaxs.append(row['ymax'] / height)
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classes_text.append(row['class'].encode('utf8'))
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classes.append(class_text_to_int(row['class']))
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tf_example = tf.train.Example(features=tf.train.Features(feature={
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'image/height': dataset_util.int64_feature(height),
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'image/width': dataset_util.int64_feature(width),
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'image/filename': dataset_util.bytes_feature(filename),
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'image/source_id': dataset_util.bytes_feature(filename),
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'image/encoded': dataset_util.bytes_feature(encoded_jpg),
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'image/format': dataset_util.bytes_feature(image_format),
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'image/object/bbox/xmin': dataset_util.float_list_feature(xmins),
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'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs),
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'image/object/bbox/ymin': dataset_util.float_list_feature(ymins),
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'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs),
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'image/object/class/text': dataset_util.bytes_list_feature(classes_text),
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'image/object/class/label': dataset_util.int64_list_feature(classes),
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}))
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return tf_example
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def main(_):
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writer = tf.python_io.TFRecordWriter(FLAGS.output_path)
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path = os.path.join(FLAGS.image_dir)
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examples = pd.read_csv(FLAGS.csv_input)
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grouped = split(examples, 'filename')
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for group in grouped:
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tf_example = create_tf_example(group, path)
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writer.write(tf_example.SerializeToString())
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writer.close()
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output_path = os.path.join(os.getcwd(), FLAGS.output_path)
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print('Successfully created the TFRecords: {}'.format(output_path))
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if __name__ == '__main__':
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tf.app.run()

‎images/test/IMG_20181228_102636.jpg

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‎images/test/IMG_20181228_102636.xml

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<annotation>
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<folder>microcontroller object detection smaller</folder>
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<filename>IMG_20181228_102636.jpg</filename>
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<path>C:\Users\Gilbert\Desktop\Local_Programming\Datasets\Own\microcontroller object detection smaller\IMG_20181228_102636.jpg</path>
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<source>
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<database>Unknown</database>
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</source>
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<size>
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<width>800</width>
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<height>600</height>
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<depth>3</depth>
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</size>
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<segmented>0</segmented>
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<object>
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<name>ESP8266</name>
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<pose>Unspecified</pose>
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<truncated>0</truncated>
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<difficult>0</difficult>
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<bndbox>
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<xmin>316</xmin>
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<ymin>205</ymin>
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<xmax>465</xmax>
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<ymax>313</ymax>
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</bndbox>
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</object>
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</annotation>

‎images/test/IMG_20181228_102641.jpg

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‎images/test/IMG_20181228_102641.xml

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<annotation>
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<folder>microcontroller object detection smaller</folder>
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<filename>IMG_20181228_102641.jpg</filename>
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<path>C:\Users\Gilbert\Desktop\Local_Programming\Datasets\Own\microcontroller object detection smaller\IMG_20181228_102641.jpg</path>
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<source>
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<database>Unknown</database>
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</source>
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<size>
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<width>800</width>
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<height>600</height>
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<depth>3</depth>
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</size>
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<segmented>0</segmented>
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<object>
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<name>Heltec_ESP32_Lora</name>
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<pose>Unspecified</pose>
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<truncated>0</truncated>
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<difficult>0</difficult>
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<bndbox>
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<xmin>401</xmin>
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<ymin>251</ymin>
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<xmax>553</xmax>
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<ymax>405</ymax>
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</bndbox>
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</object>
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</annotation>

‎images/test/IMG_20181228_102658.jpg

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‎images/test/IMG_20181228_102658.xml

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<annotation>
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<folder>microcontroller object detection smaller</folder>
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<filename>IMG_20181228_102658.jpg</filename>
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<path>C:\Users\Gilbert\Desktop\Local_Programming\Datasets\Own\microcontroller object detection smaller\IMG_20181228_102658.jpg</path>
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<source>
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<database>Unknown</database>
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</source>
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<size>
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<width>800</width>
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<height>600</height>
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<depth>3</depth>
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</size>
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<segmented>0</segmented>
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<object>
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<name>Arduino_Nano</name>
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<pose>Unspecified</pose>
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<truncated>0</truncated>
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<difficult>0</difficult>
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<bndbox>
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<xmin>184</xmin>
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<ymin>130</ymin>
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<xmax>618</xmax>
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<ymax>330</ymax>
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</bndbox>
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</object>
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</annotation>

‎images/test/IMG_20181228_102706.jpg

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‎images/test/IMG_20181228_102706.xml

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<annotation>
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<folder>microcontroller object detection smaller</folder>
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<filename>IMG_20181228_102706.jpg</filename>
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<path>C:\Users\Gilbert\Desktop\Local_Programming\Datasets\Own\microcontroller object detection smaller\IMG_20181228_102706.jpg</path>
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<source>
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<database>Unknown</database>
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</source>
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<size>
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<width>800</width>
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<height>600</height>
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<depth>3</depth>
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</size>
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<segmented>0</segmented>
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<object>
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<name>Raspberry_Pi_3</name>
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<pose>Unspecified</pose>
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<truncated>0</truncated>
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<difficult>0</difficult>
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<bndbox>
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<xmin>233</xmin>
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<ymin>137</ymin>
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<xmax>640</xmax>
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<ymax>472</ymax>
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</bndbox>
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</object>
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</annotation>

‎images/test/IMG_20181228_102745.jpg

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