haoxi911

========== SHIPSTATION ORDERS ==========
Found 66454 shipped orders in ShipStation...

============== DEAR SALES ==============
Found 66442 sales in DEAR systems...

===== DUPLICATE SHIPSTATION ORDERS =====
+-----------+--------------+---------------------+---------+
| Order ID  | Order Number | Order Date          | Status  |
+-----------+--------------+---------------------+---------+

haoxi911

{
    "orderId": 686882714,
    "orderNumber": "9999616949",
    "orderKey": "1849507",
    "orderDate": "2018-04-16T00:00:00.0000000",
    "createDate": "2022-11-09T15:15:09.9970000",
    "modifyDate": "2022-11-09T15:15:10.7400000",
    "paymentDate": "2018-04-16T00:00:00.0000000",
    "shipByDate": null,
    "orderStatus": "awaiting_shipment",

haoxi911

-------------------------------------
Translated Report (Full Report Below)
-------------------------------------

Process:               Electron [28492]
Path:                  /Users/USER/*/Electron.app/Contents/MacOS/Electron
Identifier:            com.github.Electron
Version:               17.1.0 (17.1.0)
Code Type:             X86-64 (Native)
Parent Process:        node [28489]

haoxi911

from PIL import Image     
import os       
path = os.getcwd()
for folder in os.listdir(path):
  if os.path.isdir(os.path.join(path, folder)):
    for file in os.listdir(os.path.join(path, folder)):
      extension = file.split('.')[-1]
      if extension == 'jpg':
        fileLoc = os.path.join(path, folder)+'/'+file
        img = Image.open(fileLoc)

haoxi911

58266	dog
58432	not_dog

haoxi911

n04507155	1300	umbrella
n03633091	1300	ladle
n03982430	1300	pool table, billiard table, snooker table
n01877812	1300	wallaby, brush kangaroo
n04486054	1300	triumphal arch
n02840245	1300	binder, ring-binder
n04065272	1300	recreational vehicle, RV, R.V.
n01843065	1300	jacamar
n03028079	1300	church, church building
n04330267	1300	stove

haoxi911

bazel-bin/tensorflow/contrib/lite/toco/toco \
--input_file=/home/ubuntu/pps_output/output_graph.pb \
--output_file=/home/ubuntu/pps_output/output_graph.tflite \
--input_arrays=Placeholder \
--output_arrays=final_result \
--inference_type=FLOAT \
--input_shapes=1,224,224,3 \
--input_format=TENSORFLOW_GRAPHDEF \
--output_format=TFLITE 

haoxi911

import os
import tfcoreml as tf_converter

tf_model_path = os.path.join(os.getcwd(), 'output_graph.pb')
mlmodel_path = os.path.join(os.getcwd(), 'output_graph.mlmodel')
mlmodel = tf_converter.convert(
    tf_model_path = tf_model_path,
    mlmodel_path = mlmodel_path,
    output_feature_names = ['final_result:0'],
    input_name_shape_dict = {'input:0':[1,224,224,3]},

haoxi911

# Train
cd ~/tensorflow/
source ./bin/activate
python tensorflow/tensorflow/examples/image_retraining/retrain.py \
--image_dir $(pwd)/datasets/oxford-iiit-pet/images/ \
--learning_rate=0.001 \
--testing_percentage=20 \
--validation_percentage=20 \
--train_batch_size=32 \
--validation_batch_size=-1 \

haoxi911

# /bin/sh
# this script categorize image files by moving them into subfolders
# using the name of animals.

for f in *.jpg; do
    name=`echo "$f"|sed 's/ -.*//'`
    dir=`echo "${name%_*}"|tr '_' ' '| tr '[A-Z]' '[a-z]'`
    mkdir -p "$dir"
    mv "$f" "$dir"
done