savan77

- name: ocr-detector
    inputs:
      artifacts:
      - name: tsrc
        path: /mnt/src/train
        git:
          repo: 'https://github.com/onepanelio/LicensePlateOcr.git'
      - git:
          repo: https://github.com/tensorflow/models.git
        name: src

savan77

- name: license-detector
    inputs:
      artifacts:
      - name: src
        path: /mnt/src
        git:
          repo: "https://github.com/onepanelio/LicensePlateOcr.git"
      - name: data
        path: /mnt/data/datasets/
        s3:

savan77

arguments:
  parameters:
  - name: source
    value: https://github.com/tensorflow/models.git
    displayName: Model source code
    type: hidden
    visibility: private

  - name: trainingsource
    value: https://github.com/onepanelio/LicensePlateOcr.git

savan77

def run(checkpoint, batch_size, dataset_name, image_path_pattern, annotations):
  images_placeholder, endpoints = create_model(batch_size,
                                               dataset_name)
  session_creator = monitored_session.ChiefSessionCreator(
      checkpoint_filename_with_path=checkpoint)
  count = 0
  width, height = get_dataset_image_size(dataset_name)
  with monitored_session.MonitoredSession(
          session_creator=session_creator) as sess:
    for path,boxes in annotations.items():

savan77

python train.py \
    --dataset_name=custom \
    --batch_size=1 \
    --train_log_dir=/mnt/output/ \
    --checkpoint=model.ckpt-399731 \
    --max_number_of_steps=110000

savan77

def get_crops(args):
    tree = ET.parse(args.xml_file)
    root = tree.getroot()
    count = 0

    if not os.path.exists(args.output_dir):
        os.makedirs(args.output_dir)

    for image in root.iter('image'):
       

savan77

scores = detection_graph.get_tensor_by_name('detection_scores:0')
classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
(boxes, scores, classes, num_detections) = sess.run([boxes, scores, classes, num_detections], feed_dict={image_tensor: image_np_expanded})

result = []
for i in range(len(classes[0])):
    if scores[0][i] >= 0.5:
        xmin, ymin, xmax, ymax = _normalize_box(boxes[0][i], width, height)
        label = CLASSES[classes[0][i]]

savan77

#Source code with the blog post at http://monik.in/a-noobs-guide-to-implementing-rnn-lstm-using-tensorflow/
import numpy as np
import random
from random import shuffle
import tensorflow as tf

# from tensorflow.models.rnn import rnn_cell
# from tensorflow.models.rnn import rnn

##Create Dataset##

savan77

import tensorflow as tf

#create a variable
#Synatx - tf.Variable(<initial-value>, name="")

x = tf.Variable(3, name="x")
y = tf.Variable(4, name="y")

#add x,y
c = tf.add(x,y)