Post by prohhogelora on May 11, 2019 1:12:18 GMT -8
Main category \
Sub category \ Developer Tools
Developer \ Ryo Kawamura
Filesize \ 16282
Title \ RectLabel
◊ goolnk.com/qXPgm6
○ 2.69 RectLabel
I have finished labelling all my images using Matlab Image labeler tool. I applied the trained model on a video that I found on you’ve watched the video, you’ll see that not every raccoon is detected or there are some misclassifications. This is logical as we only trained the model on a small dataset. To create a more generalized and robust Raccoon detector that is, for example, also able to the detect the most famous raccoon on earth which is Rocket Raccoon from the Guardians of the Galaxy, we just need much more data. That’s just one of the limitations of AI right now! --pipeline_config_path \ python object_detection/dataset_tools/ \ --train_image_dir="${TRAIN_IMAGE_DIR}" \ --val_image_dir="${VAL_IMAGE_DIR}" \ --train_annotations_file="${TRAIN_ANNOTATIONS_FILE}" \ --val_annotations_file="${VAL_ANNOTATIONS_FILE}" \ --output_dir="${OUTPUT_DIR}" \ --include_masks FINE_string('train_image_dir', '', 'Full path to training images directory.') FINE_string('val_image_dir', '', 'Full path to validation images directory.') FINE_string('train_annotations_file', '', 'Full path to training annotations JSON file.') FINE_string('val_annotations_file', '', 'Full path to validation annotations JSON file.') FINE_string('output_dir', '/tmp/', 'Full path to output data directory.') FINE_boolean('include_masks', False, 'If you train Mask-RCNN, add --include_masks otherwise you can remove it. "segmentation" is expected to be RLE format.') (ii) brew install libxml2 For your convenience, I have put together a complete list of objects that are detectable with the COCO models:
Site:
Version to MacOS macpkg.icu/?id=59522&kw=kYd-RectLabel-v-1.67.app
Featured! version macpkg.icu/?id=59522&kw=BYESL-VERS-2.07-RECTLABEL.PKG
num_examples:56(测试集图片数)
SCREEN-DR - Software Architecture for the Diabetic Retinopathy Screening
Create point
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The label would be added to the label table on the right.
""" 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
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•Very use to use with 3 steps: Add video file - Select output format - Convert video
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Download RectLabel 1.69 RPYq0J 1.20 Best to Mac mini
Free RECTLABEL VERSION 2.54 JTEWZ6 2.10 Featured! version
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Recomended for MacOS FUSE_for_macOS_3.7.1_CvY.dmg | 8386 kbytes | 3.7.0