drive.py

import argparse
import base64
from datetime import datetime
import os
import shutil
import cv2

import numpy as np
import socketio
import eventlet
import eventlet.wsgi
from PIL import Image
from flask import Flask
from io import BytesIO

from keras.models import load_model
import h5py
from keras import __version__ as keras_version
from collections import deque
import operator

sio = socketio.Server()
app = Flask(__name__)
model = None
prev_image_array = None


class SimplePIController:
    def __init__(self, Kp, Ki):
        self.Kp = Kp
        self.Ki = Ki
        self.set_point = 0.
        self.error = 0.
        self.integral = 0.

    def set_desired(self, desired):
        self.set_point = desired

    def update(self, measurement):
        # proportional error
        self.error = self.set_point - measurement

        # integral error
        self.integral += self.error

        return self.Kp * self.error + self.Ki * self.integral

# my computer is occationally too slow (especially when recording), which 
# causes a large integral windup that then causes severe speed oscillisions
# reducing both gains seems to fix. 
# When restarting a run, integral windup also happens, so should have reset 
# controller values everytime a new run is started

controller = SimplePIController(0.05, 0.001)
set_speed = 9
controller.set_desired(set_speed)

steering_history = deque([0.0,0.0,0.0,0.0,0.0,0.0])
steering_weights = [0.5,0.5,0.5,1.0,1.5,1.8]

@sio.on('telemetry')
def telemetry(sid, data):
    if data:
        # The current steering angle of the car
        steering_angle = data["steering_angle"]
        # The current throttle of the car
        throttle = data["throttle"]
        # The current speed of the car
        speed = data["speed"]
        # The current image from the center camera of the car
        imgString = data["image"]
        image = Image.open(BytesIO(base64.b64decode(imgString)))
        
        image_array = np.asarray(image) 
        
        # resize to the appropriate input size
        image_array = cv2.resize(image_array,(160, 80), interpolation = cv2.INTER_CUBIC)
        
        # get prediction                        
        steering_angle = float(model.predict(image_array[None, :, :, :], batch_size=1))
        
        # rather than use the predicted steering motion, we use a weighted moving average        
        steering_history.popleft()
        steering_history.append(steering_angle)        
        
        steering_history2 = list(steering_history)        
        ind_max, val_max = max(enumerate(steering_history2), key=operator.itemgetter(1))
        ind_min, val_min = min(enumerate(steering_history2), key=operator.itemgetter(1))        
        # remove a potential outlier
        if(abs(val_max)>abs(val_min)):
            steering_history2[ind_max] = 0
        else:
            steering_history2[ind_min] = 0

        # the moving average introduces a time delay into the system
        # we can attempt to compensate for this by exagerating the input
        steering_angle_MA = np.dot(steering_history2,steering_weights)/(3.5)
        
        throttle = controller.update(float(speed))

#        print(steering_angle, throttle)
        send_control(steering_angle_MA, throttle)

        # save frame
        if args.image_folder != '':
            timestamp = datetime.utcnow().strftime('%Y_%m_%d_%H_%M_%S_%f')[:-3]
            image_filename = os.path.join(args.image_folder, timestamp)
            image.save('{}.jpg'.format(image_filename))
    else:
        # NOTE: DON'T EDIT THIS.
        sio.emit('manual', data={}, skip_sid=True)


@sio.on('connect')
def connect(sid, environ):
    print("connect ", sid)
    send_control(0, 0)


def send_control(steering_angle, throttle):
    sio.emit(
        "steer",
        data={
            'steering_angle': steering_angle.__str__(),
            'throttle': throttle.__str__()
        },
        skip_sid=True)


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='Remote Driving')
    parser.add_argument(
        'model',
        type=str,
        help='Path to model h5 file. Model should be on the same path.'
    )
    parser.add_argument(
        'image_folder',
        type=str,
        nargs='?',
        default='',
        help='Path to image folder. This is where the images from the run will be saved.'
    )
    args = parser.parse_args()

    # check that model Keras version is same as local Keras version
    f = h5py.File(args.model, mode='r')
    model_version = f.attrs.get('keras_version')
    keras_version = str(keras_version).encode('utf8')

    if model_version != keras_version:
        print('You are using Keras version ', keras_version,
              ', but the model was built using ', model_version)

    model = load_model(args.model)

    if args.image_folder != '':
        print("Creating image folder at {}".format(args.image_folder))
        if not os.path.exists(args.image_folder):
            os.makedirs(args.image_folder)
        else:
            shutil.rmtree(args.image_folder)
            os.makedirs(args.image_folder)
        print("RECORDING THIS RUN ...")
    else:
        print("NOT RECORDING THIS RUN ...")

    # wrap Flask application with engineio's middleware
    app = socketio.Middleware(sio, app)

    # deploy as an eventlet WSGI server
    eventlet.wsgi.server(eventlet.listen(('', 4567)), app)