config.py

"""
CAR CONFIG

This file is read by your car application's manage.py script to change the car
performance.

EXAMPLE
-----------
import dk
cfg = dk.load_config(config_path='~/mycar/config.py')
print(cfg.CAMERA_RESOLUTION)

"""


import os

#PATHS
CAR_PATH = PACKAGE_PATH = os.path.dirname(os.path.realpath(__file__))
DATA_PATH = os.path.join(CAR_PATH, 'data')
MODELS_PATH = os.path.join(CAR_PATH, 'models')

#VEHICLE
DRIVE_LOOP_HZ = 20      # the vehicle loop will pause if faster than this speed.
MAX_LOOPS = None        # the vehicle loop can abort after this many iterations, when given a positive integer.

#CAMERA
CAMERA_TYPE = "PICAM"   # (PICAM|WEBCAM|CVCAM|CSIC|V4L|D435|MOCK|IMAGE_LIST)
IMAGE_W = 160
IMAGE_H = 120
IMAGE_DEPTH = 3         # default RGB=3, make 1 for mono
CAMERA_FRAMERATE = DRIVE_LOOP_HZ
CAMERA_VFLIP = False
CAMERA_HFLIP = False
# For CSIC camera - If the camera is mounted in a rotated position, changing the below parameter will correct the output frame orientation
CSIC_CAM_GSTREAMER_FLIP_PARM = 0 # (0 => none , 4 => Flip horizontally, 6 => Flip vertically)

# For IMAGE_LIST camera
# PATH_MASK = "~/mycar/data/tub_1_20-03-12/*.jpg"

#9865, over rides only if needed, ie. TX2..
PCA9685_I2C_ADDR = 0x40     #I2C address, use i2cdetect to validate this number
PCA9685_I2C_BUSNUM = None   #None will auto detect, which is fine on the pi. But other platforms should specify the bus num.

#SSD1306_128_32
USE_SSD1306_128_32 = False    # Enable the SSD_1306 OLED Display
SSD1306_128_32_I2C_BUSNUM = 1 # I2C bus number

#DRIVETRAIN
#These options specify which chasis and motor setup you are using. Most are using SERVO_ESC.
#DC_STEER_THROTTLE uses HBridge pwm to control one steering dc motor, and one drive wheel motor
#DC_TWO_WHEEL uses HBridge pwm to control two drive motors, one on the left, and one on the right.
#SERVO_HBRIDGE_PWM use ServoBlaster to output pwm control from the PiZero directly to control steering, and HBridge for a drive motor.
#PIGPIO_PWM uses Raspberrys internal PWM
DRIVE_TRAIN_TYPE = "SERVO_ESC" # SERVO_ESC|DC_STEER_THROTTLE|DC_TWO_WHEEL|DC_TWO_WHEEL_L298N|SERVO_HBRIDGE_PWM|PIGPIO_PWM|MM1|MOCK

#STEERING
STEERING_CHANNEL = 1            #channel on the 9685 pwm board 0-15
STEERING_LEFT_PWM = 460         #pwm value for full left steering
STEERING_RIGHT_PWM = 290        #pwm value for full right steering

#STEERING FOR PIGPIO_PWM
STEERING_PWM_PIN = 13           #Pin numbering according to Broadcom numbers
STEERING_PWM_FREQ = 50          #Frequency for PWM
STEERING_PWM_INVERTED = False   #If PWM needs to be inverted

#THROTTLE
THROTTLE_CHANNEL = 0            #channel on the 9685 pwm board 0-15
THROTTLE_FORWARD_PWM = 500      #pwm value for max forward throttle
THROTTLE_STOPPED_PWM = 370      #pwm value for no movement
THROTTLE_REVERSE_PWM = 220      #pwm value for max reverse throttle

#THROTTLE FOR PIGPIO_PWM
THROTTLE_PWM_PIN = 18           #Pin numbering according to Broadcom numbers
THROTTLE_PWM_FREQ = 50          #Frequency for PWM
THROTTLE_PWM_INVERTED = False   #If PWM needs to be inverted

#DC_STEER_THROTTLE with one motor as steering, one as drive
#these GPIO pinouts are only used for the DRIVE_TRAIN_TYPE=DC_STEER_THROTTLE
HBRIDGE_PIN_LEFT = 18
HBRIDGE_PIN_RIGHT = 16
HBRIDGE_PIN_FWD = 15
HBRIDGE_PIN_BWD = 13

#DC_TWO_WHEEL - with two wheels as drive, left and right.
#these GPIO pinouts are only used for the DRIVE_TRAIN_TYPE=DC_TWO_WHEEL
HBRIDGE_PIN_LEFT_FWD = 18
HBRIDGE_PIN_LEFT_BWD = 16
HBRIDGE_PIN_RIGHT_FWD = 15
HBRIDGE_PIN_RIGHT_BWD = 13


#ODOMETRY
HAVE_ODOM = False                   # Do you have an odometer/encoder
ENCODER_TYPE = 'GPIO'            # What kind of encoder? GPIO|Arduino|Astar
MM_PER_TICK = 12.7625               # How much travel with a single tick, in mm. Roll you car a meter and divide total ticks measured by 1,000
ODOM_PIN = 13                        # if using GPIO, which GPIO board mode pin to use as input
ODOM_DEBUG = False                  # Write out values on vel and distance as it runs

# #LIDAR
USE_LIDAR = False
LIDAR_TYPE = 'RP' #(RP|YD)
LIDAR_LOWER_LIMIT = 90 # angles that will be recorded. Use this to block out obstructed areas on your car, or looking backwards. Note that for the RP A1M8 Lidar, "0" is in the direction of the motor
LIDAR_UPPER_LIMIT = 270


# #RC CONTROL
USE_RC = False
STEERING_RC_GPIO = 26
THROTTLE_RC_GPIO = 20
DATA_WIPER_RC_GPIO = 19

#DC_TWO_WHEEL_L298N - with two wheels as drive, left and right.
#these GPIO pinouts are only used for the DRIVE_TRAIN_TYPE=DC_TWO_WHEEL_L298N
HBRIDGE_L298N_PIN_LEFT_FWD = 16
HBRIDGE_L298N_PIN_LEFT_BWD = 18
HBRIDGE_L298N_PIN_LEFT_EN = 22

HBRIDGE_L298N_PIN_RIGHT_FWD = 15
HBRIDGE_L298N_PIN_RIGHT_BWD = 13
HBRIDGE_L298N_PIN_RIGHT_EN = 11


#TRAINING
# The default AI framework to use. Choose from (tensorflow|pytorch)
DEFAULT_AI_FRAMEWORK = 'tensorflow'

#The DEFAULT_MODEL_TYPE will choose which model will be created at training time. This chooses
#between different neural network designs. You can override this setting by passing the command
#line parameter --type to the python manage.py train and drive commands.
# tensorflow models: (linear|categorical|tflite_linear|tensorrt_linear)
# pytorch models: (resnet18)
DEFAULT_MODEL_TYPE = 'linear'
BATCH_SIZE = 128                #how many records to use when doing one pass of gradient decent. Use a smaller number if your gpu is running out of memory.
TRAIN_TEST_SPLIT = 0.8          #what percent of records to use for training. the remaining used for validation.
MAX_EPOCHS = 100                #how many times to visit all records of your data
SHOW_PLOT = True                #would you like to see a pop up display of final loss?
VERBOSE_TRAIN = True            #would you like to see a progress bar with text during training?
USE_EARLY_STOP = True           #would you like to stop the training if we see it's not improving fit?
EARLY_STOP_PATIENCE = 5         #how many epochs to wait before no improvement
MIN_DELTA = .0005               #early stop will want this much loss change before calling it improved.
PRINT_MODEL_SUMMARY = True      #print layers and weights to stdout
OPTIMIZER = None                #adam, sgd, rmsprop, etc.. None accepts default
LEARNING_RATE = 0.001           #only used when OPTIMIZER specified
LEARNING_RATE_DECAY = 0.0       #only used when OPTIMIZER specified
SEND_BEST_MODEL_TO_PI = False   #change to true to automatically send best model during training
CACHE_IMAGES = True             #keep images in memory. will speed succesive epochs, but crater if not enough mem.

PRUNE_CNN = False               #This will remove weights from your model. The primary goal is to increase performance.
PRUNE_PERCENT_TARGET = 75       # The desired percentage of pruning.
PRUNE_PERCENT_PER_ITERATION = 20 # Percenge of pruning that is perform per iteration.
PRUNE_VAL_LOSS_DEGRADATION_LIMIT = 0.2 # The max amout of validation loss that is permitted during pruning.
PRUNE_EVAL_PERCENT_OF_DATASET = .05  # percent of dataset used to perform evaluation of model.

# Region of interst cropping
# only supported in Categorical and Linear models.
# If these crops values are too large, they will cause the stride values to become negative and the model with not be valid.
ROI_CROP_TOP = 0                    #the number of rows of pixels to ignore on the top of the image
ROI_CROP_BOTTOM = 0                 #the number of rows of pixels to ignore on the bottom of the image


#Model transfer options
#When copying weights during a model transfer operation, should we freeze a certain number of layers
#to the incoming weights and not allow them to change during training?
FREEZE_LAYERS = False               #default False will allow all layers to be modified by training
NUM_LAST_LAYERS_TO_TRAIN = 7        #when freezing layers, how many layers from the last should be allowed to train?

#WEB CONTROL
WEB_CONTROL_PORT = int(os.getenv("WEB_CONTROL_PORT", 8887))  # which port to listen on when making a web controller
WEB_INIT_MODE = "user"              # which control mode to start in. one of user|local_angle|local. Setting local will start in ai mode.

#JOYSTICK
USE_JOYSTICK_AS_DEFAULT = False      #when starting the manage.py, when True, will not require a --js option to use the joystick
JOYSTICK_MAX_THROTTLE = 0.5         #this scalar is multiplied with the -1 to 1 throttle value to limit the maximum throttle. This can help if you drop the controller or just don't need the full speed available.
JOYSTICK_STEERING_SCALE = 1.0       #some people want a steering that is less sensitve. This scalar is multiplied with the steering -1 to 1. It can be negative to reverse dir.
AUTO_RECORD_ON_THROTTLE = True      #if true, we will record whenever throttle is not zero. if false, you must manually toggle recording with some other trigger. Usually circle button on joystick.
CONTROLLER_TYPE = 'xbox'            #(ps3|ps4|xbox|nimbus|wiiu|F710|rc3|MM1|custom) custom will run the my_joystick.py controller written by the `donkey createjs` command
USE_NETWORKED_JS = False            #should we listen for remote joystick control over the network?
NETWORK_JS_SERVER_IP = None         #when listening for network joystick control, which ip is serving this information
JOYSTICK_DEADZONE = 0.01            # when non zero, this is the smallest throttle before recording triggered.
JOYSTICK_THROTTLE_DIR = -1.0         # use -1.0 to flip forward/backward, use 1.0 to use joystick's natural forward/backward
USE_FPV = False                     # send camera data to FPV webserver
JOYSTICK_DEVICE_FILE = "/dev/input/js0" # this is the unix file use to access the joystick.

#For the categorical model, this limits the upper bound of the learned throttle
#it's very IMPORTANT that this value is matched from the training PC config.py and the robot.py
#and ideally wouldn't change once set.
MODEL_CATEGORICAL_MAX_THROTTLE_RANGE = 0.5

#RNN or 3D
SEQUENCE_LENGTH = 3             #some models use a number of images over time. This controls how many.

#IMU
HAVE_IMU = False                #when true, this add a Mpu6050 part and records the data. Can be used with a
IMU_SENSOR = 'mpu6050'          # (mpu6050|mpu9250)
IMU_DLP_CONFIG = 0              # Digital Lowpass Filter setting (0:250Hz, 1:184Hz, 2:92Hz, 3:41Hz, 4:20Hz, 5:10Hz, 6:5Hz)

#SOMBRERO
HAVE_SOMBRERO = False           #set to true when using the sombrero hat from the Donkeycar store. This will enable pwm on the hat.

#ROBOHAT MM1
HAVE_ROBOHAT = False            # set to true when using the Robo HAT MM1 from Robotics Masters.  This will change to RC Control.
MM1_STEERING_MID = 1500         # Adjust this value if your car cannot run in a straight line
MM1_MAX_FORWARD = 2000          # Max throttle to go fowrward. The bigger the faster
MM1_STOPPED_PWM = 1500
MM1_MAX_REVERSE = 1000          # Max throttle to go reverse. The smaller the faster
MM1_SHOW_STEERING_VALUE = False
# Serial port 
# -- Default Pi: '/dev/ttyS0'
# -- Jetson Nano: '/dev/ttyTHS1'
# -- Google coral: '/dev/ttymxc0'
# -- Windows: 'COM3', Arduino: '/dev/ttyACM0'
# -- MacOS/Linux:please use 'ls /dev/tty.*' to find the correct serial port for mm1 
#  eg.'/dev/tty.usbmodemXXXXXX' and replace the port accordingly
MM1_SERIAL_PORT = '/dev/ttyS0'  # Serial Port for reading and sending MM1 data.

#LOGGING
HAVE_CONSOLE_LOGGING = True
LOGGING_LEVEL = 'INFO'          # (Python logging level) 'NOTSET' / 'DEBUG' / 'INFO' / 'WARNING' / 'ERROR' / 'FATAL' / 'CRITICAL'
LOGGING_FORMAT = '%(message)s'  # (Python logging format - https://docs.python.org/3/library/logging.html#formatter-objects

#TELEMETRY
HAVE_MQTT_TELEMETRY = False
TELEMETRY_DONKEY_NAME = 'my_robot1234'
TELEMETRY_MQTT_TOPIC_TEMPLATE = 'donkey/%s/telemetry'
TELEMETRY_MQTT_JSON_ENABLE = False
TELEMETRY_MQTT_BROKER_HOST = 'broker.hivemq.com'
TELEMETRY_MQTT_BROKER_PORT = 1883
TELEMETRY_PUBLISH_PERIOD = 1
TELEMETRY_LOGGING_ENABLE = True
TELEMETRY_LOGGING_LEVEL = 'INFO' # (Python logging level) 'NOTSET' / 'DEBUG' / 'INFO' / 'WARNING' / 'ERROR' / 'FATAL' / 'CRITICAL'
TELEMETRY_LOGGING_FORMAT = '%(message)s'  # (Python logging format - https://docs.python.org/3/library/logging.html#formatter-objects
TELEMETRY_DEFAULT_INPUTS = 'pilot/angle,pilot/throttle,recording'
TELEMETRY_DEFAULT_TYPES = 'float,float'

# PERF MONITOR
HAVE_PERFMON = False

#RECORD OPTIONS
RECORD_DURING_AI = False        #normally we do not record during ai mode. Set this to true to get image and steering records for your Ai. Be careful not to use them to train.
AUTO_CREATE_NEW_TUB = False     #create a new tub (tub_YY_MM_DD) directory when recording or append records to data directory directly

#LED
HAVE_RGB_LED = False            #do you have an RGB LED like https://www.amazon.com/dp/B07BNRZWNF
LED_INVERT = False              #COMMON ANODE? Some RGB LED use common anode. like https://www.amazon.com/Xia-Fly-Tri-Color-Emitting-Diffused/dp/B07MYJQP8B

#LED board pin number for pwm outputs
#These are physical pinouts. See: https://www.raspberrypi-spy.co.uk/2012/06/simple-guide-to-the-rpi-gpio-header-and-pins/
LED_PIN_R = 12
LED_PIN_G = 10
LED_PIN_B = 16

#LED status color, 0-100
LED_R = 0
LED_G = 0
LED_B = 1

#LED Color for record count indicator
REC_COUNT_ALERT = 1000          #how many records before blinking alert
REC_COUNT_ALERT_CYC = 15        #how many cycles of 1/20 of a second to blink per REC_COUNT_ALERT records
REC_COUNT_ALERT_BLINK_RATE = 0.4 #how fast to blink the led in seconds on/off

#first number is record count, second tuple is color ( r, g, b) (0-100)
#when record count exceeds that number, the color will be used
RECORD_ALERT_COLOR_ARR = [ (0, (1, 1, 1)),
            (3000, (5, 5, 5)),
            (5000, (5, 2, 0)),
            (10000, (0, 5, 0)),
            (15000, (0, 5, 5)),
            (20000, (0, 0, 5)), ]


#LED status color, 0-100, for model reloaded alert
MODEL_RELOADED_LED_R = 100
MODEL_RELOADED_LED_G = 0
MODEL_RELOADED_LED_B = 0


#BEHAVIORS
#When training the Behavioral Neural Network model, make a list of the behaviors,
#Set the TRAIN_BEHAVIORS = True, and use the BEHAVIOR_LED_COLORS to give each behavior a color
TRAIN_BEHAVIORS = False
BEHAVIOR_LIST = ['Left_Lane', "Right_Lane"]
BEHAVIOR_LED_COLORS =[ (0, 10, 0), (10, 0, 0) ] #RGB tuples 0-100 per chanel

#Localizer
#The localizer is a neural network that can learn to predice it's location on the track.
#This is an experimental feature that needs more developement. But it can currently be used
#to predict the segement of the course, where the course is divided into NUM_LOCATIONS segments.
TRAIN_LOCALIZER = False
NUM_LOCATIONS = 10
BUTTON_PRESS_NEW_TUB = False #when enabled, makes it easier to divide our data into one tub per track length if we make a new tub on each X button press.

#DonkeyGym
#Only on Ubuntu linux, you can use the simulator as a virtual donkey and
#issue the same python manage.py drive command as usual, but have them control a virtual car.
#This enables that, and sets the path to the simualator and the environment.
#You will want to download the simulator binary from: https://github.com/tawnkramer/donkey_gym/releases/download/v18.9/DonkeySimLinux.zip
#then extract that and modify DONKEY_SIM_PATH.
DONKEY_GYM = True
DONKEY_SIM_PATH = "/Applications/donkey_sim.app/Contents/MacOS/sdsim" #"/home/tkramer/projects/sdsandbox/sdsim/build/DonkeySimLinux/donkey_sim.x86_64" when racing on virtual-race-league use "remote", or user "remote" when you want to start the sim manually first.
DONKEY_GYM_ENV_NAME = "donkey-generated-track-v0" # ("donkey-generated-track-v0"|"donkey-generated-roads-v0"|"donkey-warehouse-v0"|"donkey-avc-sparkfun-v0")
GYM_CONF = { "body_style" : "donkey", "body_rgb" : (128, 128, 128), "car_name" : "car", "font_size" : 100} # body style(donkey|bare|car01) body rgb 0-255
GYM_CONF["racer_name"] = "Your Name"
GYM_CONF["country"] = "Place"
GYM_CONF["bio"] = "I race robots."

SIM_HOST = "127.0.0.1"              # when racing on virtual-race-league use host "trainmydonkey.com"
SIM_ARTIFICIAL_LATENCY = 0          # this is the millisecond latency in controls. Can use useful in emulating the delay when useing a remote server. values of 100 to 400 probably reasonable.

# Save info from Simulator (pln)
SIM_RECORD_LOCATION = False
SIM_RECORD_GYROACCEL= False
SIM_RECORD_VELOCITY = False

#publish camera over network
#This is used to create a tcp service to pushlish the camera feed
PUB_CAMERA_IMAGES = False

#When racing, to give the ai a boost, configure these values.
AI_LAUNCH_DURATION = 0.0            # the ai will output throttle for this many seconds
AI_LAUNCH_THROTTLE = 0.0            # the ai will output this throttle value
AI_LAUNCH_ENABLE_BUTTON = 'R2'      # this keypress will enable this boost. It must be enabled before each use to prevent accidental trigger.
AI_LAUNCH_KEEP_ENABLED = False      # when False ( default) you will need to hit the AI_LAUNCH_ENABLE_BUTTON for each use. This is safest. When this True, is active on each trip into "local" ai mode.

#Scale the output of the throttle of the ai pilot for all model types.
AI_THROTTLE_MULT = 1.0              # this multiplier will scale every throttle value for all output from NN models

#Path following
PATH_FILENAME = "donkey_path.pkl"   # the path will be saved to this filename
PATH_SCALE = 5.0                    # the path display will be scaled by this factor in the web page
PATH_OFFSET = (0, 0)                # 255, 255 is the center of the map. This offset controls where the origin is displayed.
PATH_MIN_DIST = 0.3                 # after travelling this distance (m), save a path point
PID_P = -10.0                       # proportional mult for PID path follower
PID_I = 0.000                       # integral mult for PID path follower
PID_D = -0.2                        # differential mult for PID path follower
PID_THROTTLE = 0.2                  # constant throttle value during path following
SAVE_PATH_BTN = "cross"             # joystick button to save path
RESET_ORIGIN_BTN = "triangle"       # joystick button to press to move car back to origin

# Intel Realsense D435 and D435i depth sensing camera
REALSENSE_D435_RGB = True       # True to capture RGB image
REALSENSE_D435_DEPTH = True     # True to capture depth as image array
REALSENSE_D435_IMU = False      # True to capture IMU data (D435i only)
REALSENSE_D435_ID = None        # serial number of camera or None if you only have one camera (it will autodetect)

# Stop Sign Detector
STOP_SIGN_DETECTOR = False
STOP_SIGN_MIN_SCORE = 0.2
STOP_SIGN_SHOW_BOUNDING_BOX = True