solix

BroadcastReceiver mqtt_reciever = new BroadcastReceiver() {
        @Override
        public void onReceive(Context context, Intent intent) {
            String data = intent.getStringExtra("msg-sub");
            Log.d("MQTT", "Temperature"+ data);
        }
    };
    

//launch

solix

#from models/object_detection
python3 train.py --logstderr --train_dir=training/ --pipeline_config_path=data/ssd_mobilenet_v1_coco.config

solix

git filter-branch --index-filter 'git rm -r --cached --ignore-unmatch app/src/main/assets/yolo.pb' HEAD


OR



git filter-branch -f \                                                                                                                                                  [15:23:39]
--index-filter 'git rm --cached --ignore-unmatch app/src/main/assets/output_graph_opt.pb' HEAD

solix

 # training_gen = generator_batch(X_train)
    # val_gen = generator_batch(X_validation)
    datagen = ImageDataGenerator(
    featurewise_center=True,
    featurewise_std_normalization=True,
    rotation_range=20,
    width_shift_range=0.2,
    height_shift_range=0.2,
    horizontal_flip=True)

solix

datagen = ImageDataGenerator(
        featurewise_center=True, # set input mean to 0 over the dataset
        samplewise_center=False, # set each sample mean to 0
        featurewise_std_normalization=True, # divide inputs by std of the dataset
        samplewise_std_normalization=False, # divide each input by its std
        zca_whitening=False, # apply ZCA whitening
        rotation_range=20, # randomly rotate images in the range (degrees, 0 to 180)
        width_shift_range=0.2, # randomly shift images horizontally (fraction of total width)
        height_shift_range=0.2, # randomly shift images vertically (fraction of total height)
        horizontal_flip=True, # randomly flip images

solix

# Save 
dist_pickle = {}
dist_pickle["mtx"] = mtx
dist_pickle["dist"] = dist
pickle.dump( dist_pickle, open( filename, "wb" ) )


#load
#1.1 load pickle
calib_dist = pickle.load(open(filename,"rb"))

solix

def generator(features=X_train, labels=y_train, batch_size=FLAGS.batch_size):
 # Create empty arrays to contain batch of features and labels#
 batch_features = np.zeros((batch_size,160,320,3))
 print("batch_feature shape is {}".format(batch_features.shape))
 batch_labels = np.zeros((batch_size,1))
 while True:
   for i in range(batch_size):
     #choose random index in features
     index= random.choice(len(features),1)
     batch_features[i] = features[index]

solix

import pandas as pd
# type of reader is dataframe
reader = pd.read_csv('./data/example.csv', usecols=['c1','c2','c5','c10'] ) #choose csv file labels

#iterate through dataframe and get specific values
for index, row in reader.iterrows():
    
    print(row['c1'], row['c10'])

solix

# Here's how you would run the file 
# from the command line:  python feature_extraction.py --training_file vgg_cifar10_100_bottleneck_features_train.p
# --validation_file vgg_cifar10_bottleneck_features_validation.p

flags = tf.app.flags
FLAGS = flags.FLAGS

# command line flags
flags.DEFINE_string('training_file', '', "Bottleneck features training file (.p)")
flags.DEFINE_string('validation_file', '', "Bottleneck features validation file (.p)")

solix

from keras.datasets import cifar10

(X_train, y_train), (X_test, y_test) = cifar10.load_data()
# y_train.shape is 2d, (50000, 1). While Keras is smart enough to handle this
# it's a good idea to flatten the array.
y_train = y_train.reshape(-1)
y_test = y_test.reshape(-1)

assert(len(X_train) == len(y_train))
assert(len(X_test) == len(y_test))