First commit

drive.py

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+import argparse
+import base64
+from datetime import datetime
+import os
+
+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 model_from_json
+from preprocess import process_image
+
+sio = socketio.Server()
+app = Flask(__name__)
+model = None
+prev_image_array = None
+
+
+@sio.on('telemetry')
+def telemetry(sid, 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)
+    img = process_image(image_array)
+    steering_angle = float(model.predict(img[None, :, :, :], batch_size=1))
+    throttle = 0.1
+    # print(steering_angle, throttle)
+    send_control(steering_angle, throttle)
+
+    # save frame
+    timestamp = datetime.utcnow().strftime('%Y_%m_%d_%H_%M_%S_%f')[:-3]
+    if args.image_folder != '':
+        image.save('{}/{}.jpg'.format(args.image_folder, timestamp))
+
+
+@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 definition json. Model weights 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()
+    with open(args.model, 'r') as jfile:
+        # NOTE: if you saved the file by calling json.dump(model.to_json(), ...)
+        # then you will have to call:
+        #
+        #   model = model_from_json(json.loads(jfile.read()))\
+        #
+        # instead.
+        model = model_from_json(jfile.read())
+
+    model.compile("adam", "mse")
+    weights_file = args.model.replace('json', 'h5')
+    model.load_weights(weights_file)
+
+    if args.image_folder != '':
+        print("Creating image folder at {}".format(args.image_folder))
+        os.makedirs(args.image_folder)
+        print("This run will be recorded ...")
+    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)

video.py

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+from moviepy.editor import ImageSequenceClip
+import argparse
+
+
+def main():
+    parser = argparse.ArgumentParser(description='Create driving video.')
+    parser.add_argument(
+        'image_folder',
+        type=str,
+        default='',
+        help='Path to image folder. The video will be created from these images.'
+    )
+    parser.add_argument(
+        '--fps',
+        type=int,
+        default=60,
+        help='FPS (Frames per second) setting for the video.')
+    args = parser.parse_args()
+
+    video_file = args.image_folder + '.mp4'
+    print("Creating video {}, FPS={}".format(video_file, args.fps))
+    clip = ImageSequenceClip(args.image_folder, fps=args.fps)
+    clip.write_videofile(video_file)
+
+
+if __name__ == '__main__':
+    main()