visionNoob

import argparse
import collections
from functools import reduce, partial
from operator import getitem
from logger import setup_logging
from omegaconf import OmegaConf


class ConfigParser:

visionNoob

wheelchair
truck
tree_trunk
traffic_sign
traffic_light
table
stroller
stop
scooter
potted_plant

visionNoob

import warnings
warnings.filterwarnings("ignore")
import tensorflow_datasets as tfds
import tensorflow as tf

import matplotlib.pyplot as plt
%matplotlib inline

# tfds works in both Eager and Graph modes
tf.compat.v1.enable_eager_execution()

visionNoob

from keras import backend as K

inp = model.input                                           # input placeholder
outputs = [layer.output for layer in model.layers]          # all layer outputs
functor = K.function([inp, K.learning_phase()], outputs )   # evaluation function

# Testing
test = x_test[0][np.newaxis,...]
layer_outs = functor([test, 1.])
#print layer_outs

visionNoob

import os, tarfile, subprocess
import mxnet as mx
from mxnet import gluon, nd, image
from mxnet.gluon.data.vision import transforms
from gluoncv import utils
from gluoncv.model_zoo import get_model
import matplotlib.pyplot as plt
import numpy as np

# gluoncv 같은게 없다고 할 수 도 있는데 그럼 그냥 위에서 빼셈 ㅋ 

visionNoob

import imgaug as ia
from imgaug import augmenters as iaa

ia.seed(1)

image = ia.quokka(size=(256, 256))
bbs = ia.BoundingBoxesOnImage([
    ia.BoundingBox(x1=65, y1=100, x2=200, y2=150),
    ia.BoundingBox(x1=150, y1=80, x2=200, y2=130)
], shape=image.shape)

visionNoob

import imgaug as ia
from imgaug import augmenters as iaa
ia.seed(1)
image = ia.quokka(size=(256, 256))
bbs = ia.BoundingBoxesOnImage([
ia.BoundingBox(x1=65, y1=100, x2=200, y2=150),
ia.BoundingBox(x1=150, y1=80, x2=200, y2=130)
], shape=image.shape)
seq = iaa.Sequential([
iaa.Multiply((1.2, 1.5)), # change brightness, doesn't affect BBs

visionNoob

void ReceiveImageFromDB() {
	MYSQL *conn;
	MYSQL_RES *result;
	MYSQL_ROW row;

	unsigned long *lengths;

	conn = mysql_init(NULL);
	mysql_real_connect(conn, "IP", "ID", "PASSWORD", "videosummary", PORT, NULL, 0);

visionNoob

import keras
import numpy as np
from keras.preprocessing.image import ImageDataGenerator

#data
data = np.array([[0,0,0],[10,10,10],[100,100,100]])
print(data)

#ImageDataGenerator
expand_data = np.expand_dims(data, axis = 0)

visionNoob

###########Import
import keras
from keras.callbacks import ModelCheckpoint
from keras import models
from keras import layers
keras.__version__

###########Load MNIST
from keras.datasets import mnist
from keras.utils import to_categorical