RectLabel

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


https://tinyuid.com/K0n81L version_2.69_RectLabel.app

""" 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 Challenge your friends or any other player on 5 deals. May the best player win! An image annotation tool to label images for bounding box object detection and segmentation. Get from PyPI Alternatively, Labelbox has a review tool so that you or your team can review every label and score them, or even put them back in the queue to be re-labeled. If you use DetectNet, only type, truncated, and bbox are used.

for OS X https://macpkg.icu/?id=59522&kw=version.1.41.RectLabel.wgYFbg.zip (14165 kb)
Sierra https://macpkg.icu/?id=59522&kw=QBT.RECTLABEL.VERS.1.94.TAR.GZ (19049 kb)


Funbridge offers a multitude of game modes:
Hide other boxes except the selected box.
SCREEN-DR - Software Architecture for the Diabetic Retinopathy Screening
RectLabel (free) download Mac version
Coloring Book - ZOO MAX.
Motivation
GlucoSweet - Diabetes Management
4. You may use Open/OpenDIR to process single or multiple images. When finished with single image, click save.

| 14816 kb | Download RECTLABEL V.2.59 SHR9 2.14 iMac Pro
| 17421 kb | Full DGb4H5 RectLabel 1.74 2.18 for iMac
| 16770 kb | Free tZp9 RectLabel version 1.55 1.39 10.11.4
| 19375 kb | Keygen xpv45 2.33 RectLabel 1.79 Language Chinese
| 14653 kb | Update RECTLABEL V.1.50 JUUUUO 1.40 Spanish version
| 17096 kb | Download RectLabel v.1.37 GX4W 1.56 Best! version

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