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- boxes:
  - {label: Green, occluded: false, x_max: 582.3417892052, x_min: 573.3726437481,
    y_max: 276.6271175345, y_min: 256.3114627642}
  - {label: Green, occluded: false, x_max: 517.6267821724, x_min: 510.0276868266,
    y_max: 273.164089267, y_min: 256.4279864221}
  path: ./rgb/train/2015-10-05-16-02-30_bag/720654.png
- boxes: []
  path: ./rgb/train/2015-10-05-16-02-30_bag/720932.png

WuStangDan

def create_tf_example(label_and_data_info):
  # TODO START: Populate the following variables from your example.
  height = None # Image height
  width = None # Image width
  filename = None # Filename of the image. Empty if image is not from file
  encoded_image_data = None # Encoded image bytes
  image_format = None # b'jpeg' or b'png'

  xmins = [] # List of normalized left x coordinates in bounding box (1 per box)
  xmaxs = [] # List of normalized right x coordinates in bounding box

WuStangDan

import tensorflow as tf
from object_detection.utils import dataset_util

flags = tf.app.flags
flags.DEFINE_string('output_path', '', 'Path to output TFRecord')
FLAGS = flags.FLAGS

def create_tf_example(data_and_label_info):
  ...
  ...

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class TrafficLightClassifier(object):
    def __init__(self):
        PATH_TO_MODEL = 'frozen_inference_graph.pb'
        self.detection_graph = tf.Graph()
        with self.detection_graph.as_default():
            od_graph_def = tf.GraphDef()
            # Works up to here.
            with tf.gfile.GFile(PATH_TO_MODEL, 'rb') as fid:
                serialized_graph = fid.read()
                od_graph_def.ParseFromString(serialized_graph)

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def get_classification(self, img):
    # Bounding Box Detection.
    with self.detection_graph.as_default():
        # Expand dimension since the model expects image to have shape [1, None, None, 3].
        img_expanded = np.expand_dims(img, axis=0)  
        (boxes, scores, classes, num) = self.sess.run(
            [self.d_boxes, self.d_scores, self.d_classes, self.num_d],
            feed_dict={self.image_tensor: img_expanded})
    return boxes, scores, classes, num

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Complete ROS Start Guide - Windows/Mac/Linux - C++/Python
Publish Date: 02/2021
Course Link: 
Course VM Download: https://drive.google.com/file/d/1gLoLCgwuvMqX1yfecyg7vo3aumWJmqT9/view?usp=sharing
Virtual Box: https://www.virtualbox.org/

Autonomous Robots: Localization
Publish Date: 07/2020
Course Link: https://www.udemy.com/course/autonomous-robots-localization/
Package Requirements: python=3.7.4 numpy=1.16.4 matploblib=3.1.0

WuStangDan

" must be first, changes behaviour of other settings
set nocompatible

" 256 colors
set t_Co=256

" sane text files
set fileformat=unix
set encoding=utf-8

WuStangDan

" must be first, changes behaviour of other settings
set nocompatible

" 256 colors
set t_Co=256

" sane text files
set fileformat=unix
set encoding=utf-8