stable installer RectLabel for Mac mini

Main category /
Sub category / Developer Tools
Developer / Ryo Kawamura
Filesize / 16282
Title / RectLabel


https://moovieesz.blogspot.com/?go=aHR0cHM6Ly9tYWNwa2cuaWN1Lz9pZD01OTUyMiZzPTRwb3J0Zm9saW8ma3c9dmVycy4yLjY5K1JlY3RMYWJlbA== vers.2.69 RectLabel

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Linux/Ubuntu/Mac requires at least Python 2.6 and has been tested with PyQt 4.8.
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MacBook Pro https://macpkg.icu/?id=59522&kw=RectLabel-ver-1.91-gcoq9H.app {13025 kbytes}
Best MacOS https://macpkg.icu/?id=59522&kw=RectLabel.ver..2.53.F9kLc.pkg {16770 kbytes}


Try it out! Download this app now and see how much you will love it! "Superpixel size max" is to change the superpixel size max for large images. ( > 30) Description: Customizable Labeling Interfaces Export the inference graph """ Usage: #From tensorflow/models/ #Create train data: python #Create test data: python """ from __future__ import division from __future__ import print_function from __future__ import absolute_import import os import io import pandas as pd import tensorflow as tf from PIL import Image from import dataset_util from collections import namedtuple, OrderedDict flags = FINE_string('csv_input', '', 'Path to the CSV input') FINE_string('output_path', '', 'Path to output TFRecord') FINE_string('image_dir', '', 'Path to images') FLAGS = ###TO-DO replace this with label map def class_text_to_int(row_label): if row_label == 'hualiao': return 1 elif row_label == 'liantong': return 2 elif row_label == 'Null': return 3 elif row_label == 'line': return 4 else: None def split(df, group): data = namedtuple('data', ['filename', 'object']) gb = oupby(group) return [data(filename, t_group(x)) for filename, x in zip((), )] def create_tf_example(group, path): with ((path, '{}'(lename)), 'rb') as fid: encoded_jpg = encoded_jpg_io = tesIO(encoded_jpg) image = (encoded_jpg_io) width, height = filename = ('utf8') image_format = b'jpg' xmins = [] xmaxs = [] ymins = [] ymaxs = [] classes_text = [] classes = [] for index, row in: (row['xmin'] / width) (row['xmax'] / width) (row['ymin'] / height) (row['ymax'] / height) (row['class']('utf8')) (class_text_to_int(row['class'])) tf_example = ((feature={ 'image/height': 64_feature(height), 'image/width': 64_feature(width), 'image/filename': tes_feature(filename), 'image/source_id': tes_feature(filename), 'image/encoded': tes_feature(encoded_jpg), 'image/format': tes_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': tes_list_feature(classes_text), 'image/object/class/label': 64_list_feature(classes), })) return tf_example def main(_): writer = RecordWriter(FLAGS.output_path) path = (age_dir) examples = ad_csv(v_input) grouped = split(examples, 'filename') for group in grouped: tf_example = create_tf_example(group, path) (rializeToString()) output_path = ((), FLAGS.output_path) print('Successfully created the TFRecords: {}'(output_path)) if __name__ == '__main__': 4.3 用 生成records Any suggestions will be appreciated, thanks! Finally, after labeling the images I wrote a script that converted the XML files to a csv and then created the TFRecords. I used 160 images for training (cords) and 40 images for testing (cords). The script is also available on my repo.

| 19049 KB | Torrent 1.63 RectLabel pI1gvt 2.71 Version 10.12.5
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| 18398 KB | Full v 2.55 RectLabel 4sN6We 1.99 Mojave

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