Semi-Autonomous-Robot / Semi Autonomous Robot - ECE 4180

Code for controlling mbed hardware (LED's, motors), as well as code for the Raspberry Pi to run a Support Vector Machine that identifies objects using the Pi camera

Dependencies:   mbed Motordriver mbed-rtos PololuLedStrip

pi/main.py@3:a3ed7ff99772, 2019-12-06 (annotated)

Committer:
arogliero3
Date:
Fri Dec 06 00:58:02 2019 -0500
Revision:
3:a3ed7ff99772
Parent:
1:183a8efd562a 
update img6

Who changed what in which revision?

UserRevisionLine numberNew contents of line
arogliero3 1:183a8efd562a 1 import io
arogliero3 1:183a8efd562a 2 import time
arogliero3 1:183a8efd562a 3 from Classifier import ImageClassifier
arogliero3 1:183a8efd562a 4 import numpy as np
arogliero3 1:183a8efd562a 5 from PIL import Image
arogliero3 1:183a8efd562a 6 from joblib import dump, load
arogliero3 1:183a8efd562a 7 import picamera
arogliero3 1:183a8efd562a 8 import serial
arogliero3 1:183a8efd562a 9
arogliero3 1:183a8efd562a 10
arogliero3 1:183a8efd562a 11
arogliero3 1:183a8efd562a 12 print("Starting camera")
arogliero3 1:183a8efd562a 13 camera = picamera.PiCamera()
arogliero3 1:183a8efd562a 14 camera.resolution = (352, 240)
arogliero3 1:183a8efd562a 15 camera.color_effects = (128, 128) # turn camera to black and white
arogliero3 1:183a8efd562a 16
arogliero3 1:183a8efd562a 17 mbed = serial.Serial('/dev/ttyACM0', baudrate=9600)
arogliero3 1:183a8efd562a 18 time.sleep(1)
arogliero3 1:183a8efd562a 19
arogliero3 1:183a8efd562a 20 print("starting model training")
arogliero3 1:183a8efd562a 21
arogliero3 1:183a8efd562a 22 try:
arogliero3 1:183a8efd562a 23 img_clf = load('model.joblib')
arogliero3 1:183a8efd562a 24 except Exception as e:
arogliero3 1:183a8efd562a 25 img_clf = ImageClassifier()
arogliero3 1:183a8efd562a 26 # load images
arogliero3 1:183a8efd562a 27 (train_raw, train_labels) = img_clf.load_data_from_folder('./train/')
arogliero3 1:183a8efd562a 28 # convert images into features
arogliero3 1:183a8efd562a 29 train_data = img_clf.extract_image_features(train_raw)
arogliero3 1:183a8efd562a 30 # train model and test on training data
arogliero3 1:183a8efd562a 31 img_clf.train_classifier(train_data, train_labels)
arogliero3 1:183a8efd562a 32 # dump classifier into file for later use
arogliero3 1:183a8efd562a 33 dump(img_clf, 'model.joblib')
arogliero3 1:183a8efd562a 34
arogliero3 1:183a8efd562a 35 print("Model Trained")
arogliero3 1:183a8efd562a 36
arogliero3 1:183a8efd562a 37 while True:
arogliero3 1:183a8efd562a 38 stream = io.BytesIO()
arogliero3 1:183a8efd562a 39 camera.capture(stream, format="bmp", resize=(352, 240))
arogliero3 1:183a8efd562a 40 image = Image.open(stream)
arogliero3 1:183a8efd562a 41 data = np.array(image)
arogliero3 1:183a8efd562a 42 # Construct a numpy array from the stream
arogliero3 1:183a8efd562a 43 features = img_clf.extract_image_features([data])
arogliero3 1:183a8efd562a 44 label = img_clf.predict_labels(features)[0]
arogliero3 1:183a8efd562a 45 if label == "disco":
arogliero3 1:183a8efd562a 46 mbed.write("d")
arogliero3 1:183a8efd562a 47 elif label == "tornado":
arogliero3 1:183a8efd562a 48 mbed.write("t")
arogliero3 1:183a8efd562a 49
arogliero3 1:183a8efd562a 50