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 = 128  # 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
CAMERA_INDEX = (
    0  # used for 'WEBCAM' and 'CVCAM' when there is more than one camera connected
)
# 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)
)
BGR2RGB = False  # true to convert from BRG format to RGB format; requires opencv
SHOW_PILOT_IMAGE = (
    False  # show the image used to do the inference when in autopilot mode
)

# 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_ROTATION = 0  # 0 = text is right-side up, 1 = rotated 90 degrees clockwise, 2 = 180 degrees (flipped), 3 = 270 degrees
SSD1306_RESOLUTION = 1  # 1 = 128x32; 2 = 128x64

#
# DRIVE_TRAIN_TYPE
# These options specify which chasis and motor setup you are using.
# See Actuators documentation https://docs.donkeycar.com/parts/actuators/
# for a detailed explanation of each drive train type and it's configuration.
# Choose one of the following and then update the related configuration section:
#
# "PWM_STEERING_THROTTLE" uses two PWM output pins to control a steering servo and an ESC, as in a standard RC car.
# "MM1" Robo HAT MM1 board
# "SERVO_HBRIDGE_2PIN" Servo for steering and HBridge motor driver in 2pin mode for motor
# "SERVO_HBRIDGE_3PIN" Servo for steering and HBridge motor driver in 3pin mode for motor
# "DC_STEER_THROTTLE" uses HBridge pwm to control one steering dc motor, and one drive wheel motor
# "DC_TWO_WHEEL" uses HBridge in 2-pin mode to control two drive motors, one on the left, and one on the right.
# "DC_TWO_WHEEL_L298N" using HBridge in 3-pin mode to control two drive motors, one of the left and one on the right.
# "MOCK" no drive train.  This can be used to test other features in a test rig.
# "VESC" VESC Motor controller to set servo angle and duty cycle
# (deprecated) "SERVO_HBRIDGE_PWM" use ServoBlaster to output pwm control from the PiZero directly to control steering,
#                                  and HBridge for a drive motor.
# (deprecated) "PIGPIO_PWM" uses Raspberrys internal PWM
# (deprecated) "I2C_SERVO" uses PCA9685 servo controller to control a steering servo and an ESC, as in a standard RC car
#
DRIVE_TRAIN_TYPE = "PWM_STEERING_THROTTLE"

#
# PWM_STEERING_THROTTLE
#
# Drive train for RC car with a steering servo and ESC.
# Uses a PwmPin for steering (servo) and a second PwmPin for throttle (ESC)
# Base PWM Frequence is presumed to be 60hz; use PWM_xxxx_SCALE to adjust pulse with for non-standard PWM frequencies
#
PWM_STEERING_THROTTLE = {
    "PWM_STEERING_PIN": "PCA9685.1:40.1",  # PWM output pin for steering servo
    "PWM_STEERING_SCALE": 1.0,  # used to compensate for PWM frequency differents from 60hz; NOT for adjusting steering range
    "PWM_STEERING_INVERTED": False,  # True if hardware requires an inverted PWM pulse
    "PWM_THROTTLE_PIN": "PCA9685.1:40.0",  # PWM output pin for ESC
    "PWM_THROTTLE_SCALE": 1.0,  # used to compensate for PWM frequence differences from 60hz; NOT for increasing/limiting speed
    "PWM_THROTTLE_INVERTED": False,  # True if hardware requires an inverted PWM pulse
    "STEERING_LEFT_PWM": 460,  # pwm value for full left steering
    "STEERING_RIGHT_PWM": 290,  # pwm value for full right steering
    "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
}

#
# I2C_SERVO (deprecated in favor of PWM_STEERING_THROTTLE)
#
STEERING_CHANNEL = 1  # (deprecated) 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
THROTTLE_CHANNEL = 0  # (deprecated) 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

#
# PIGPIO_PWM (deprecated in favor of PWM_STEERING_THROTTLE)
#
STEERING_PWM_PIN = 13  # (deprecated) Pin numbering according to Broadcom numbers
STEERING_PWM_FREQ = 50  # Frequency for PWM
STEERING_PWM_INVERTED = False  # If PWM needs to be inverted
THROTTLE_PWM_PIN = 18  # (deprecated) Pin numbering according to Broadcom numbers
THROTTLE_PWM_FREQ = 50  # Frequency for PWM
THROTTLE_PWM_INVERTED = False  # If PWM needs to be inverted

#
# SERVO_HBRIDGE_2PIN
# - configures a steering servo and an HBridge in 2pin mode (2 pwm pins)
# - Servo takes a standard servo PWM pulse between 1 millisecond (fully reverse)
#   and 2 milliseconds (full forward) with 1.5ms being neutral.
# - the motor is controlled by two pwm pins,
#   one for forward and one for backward (reverse).
# - the pwm pin produces a duty cycle from 0 (completely LOW)
#   to 1 (100% completely high), which is proportional to the
#   amount of power delivered to the motor.
# - in forward mode, the reverse pwm is 0 duty_cycle,
#   in backward mode, the forward pwm is 0 duty cycle.
# - both pwms are 0 duty cycle (LOW) to 'detach' motor and
#   and glide to a stop.
# - both pwms are full duty cycle (100% HIGH) to brake
#
# Pin specifier string format:
# - use RPI_GPIO for RPi/Nano header pin output
#   - use BOARD for board pin numbering
#   - use BCM for Broadcom GPIO numbering
#   - for example "RPI_GPIO.BOARD.18"
# - use PIPGIO for RPi header pin output using pigpio server
#   - must use BCM (broadcom) pin numbering scheme
#   - for example, "PIGPIO.BCM.13"
# - use PCA9685 for PCA9685 pin output
#   - include colon separated I2C channel and address
#   - for example "PCA9685.1:40.13"
# - RPI_GPIO, PIGPIO and PCA9685 can be mixed arbitrarily,
#   although it is discouraged to mix RPI_GPIO and PIGPIO.
#
SERVO_HBRIDGE_2PIN = {
    "FWD_DUTY_PIN": "RPI_GPIO.BOARD.18",  # provides forward duty cycle to motor
    "BWD_DUTY_PIN": "RPI_GPIO.BOARD.16",  # provides reverse duty cycle to motor
    "PWM_STEERING_PIN": "RPI_GPIO.BOARD.33",  # provides servo pulse to steering servo
    "PWM_STEERING_SCALE": 1.0,  # used to compensate for PWM frequency differents from 60hz; NOT for adjusting steering range
    "PWM_STEERING_INVERTED": False,  # True if hardware requires an inverted PWM pulse
    "STEERING_LEFT_PWM": 460,  # pwm value for full left steering (use `donkey calibrate` to measure value for your car)
    "STEERING_RIGHT_PWM": 290,  # pwm value for full right steering (use `donkey calibrate` to measure value for your car)
}

#
# SERVO_HBRIDGE_3PIN
# - configures a steering servo and an HBridge in 3pin mode (2 ttl pins, 1 pwm pin)
# - Servo takes a standard servo PWM pulse between 1 millisecond (fully reverse)
#   and 2 milliseconds (full forward) with 1.5ms being neutral.
# - the motor is controlled by three pins,
#   one ttl output for forward, one ttl output
#   for backward (reverse) enable and one pwm pin
#   for motor power.
# - the pwm pin produces a duty cycle from 0 (completely LOW)
#   to 1 (100% completely high), which is proportional to the
#   amount of power delivered to the motor.
# - in forward mode, the forward pin  is HIGH and the
#   backward pin is LOW,
# - in backward mode, the forward pin is LOW and the
#   backward pin is HIGH.
# - both forward and backward pins are LOW to 'detach' motor
#   and glide to a stop.
# - both forward and backward pins are HIGH to brake
#
# Pin specifier string format:
# - use RPI_GPIO for RPi/Nano header pin output
#   - use BOARD for board pin numbering
#   - use BCM for Broadcom GPIO numbering
#   - for example "RPI_GPIO.BOARD.18"
# - use PIPGIO for RPi header pin output using pigpio server
#   - must use BCM (broadcom) pin numbering scheme
#   - for example, "PIGPIO.BCM.13"
# - use PCA9685 for PCA9685 pin output
#   - include colon separated I2C channel and address
#   - for example "PCA9685.1:40.13"
# - RPI_GPIO, PIGPIO and PCA9685 can be mixed arbitrarily,
#   although it is discouraged to mix RPI_GPIO and PIGPIO.
#
SERVO_HBRIDGE_3PIN = {
    "FWD_PIN": "RPI_GPIO.BOARD.18",  # ttl pin, high enables motor forward
    "BWD_PIN": "RPI_GPIO.BOARD.16",  # ttl pin, high enables motor reverse
    "DUTY_PIN": "RPI_GPIO.BOARD.35",  # provides duty cycle to motor
    "PWM_STEERING_PIN": "RPI_GPIO.BOARD.33",  # provides servo pulse to steering servo
    "PWM_STEERING_SCALE": 1.0,  # used to compensate for PWM frequency differents from 60hz; NOT for adjusting steering range
    "PWM_STEERING_INVERTED": False,  # True if hardware requires an inverted PWM pulse
    "STEERING_LEFT_PWM": 460,  # pwm value for full left steering (use `donkey calibrate` to measure value for your car)
    "STEERING_RIGHT_PWM": 290,  # pwm value for full right steering (use `donkey calibrate` to measure value for your car)
}

#
# DRIVETRAIN_TYPE == "SERVO_HBRIDGE_PWM" (deprecated in favor of SERVO_HBRIDGE_2PIN)
# - configures a steering servo and an HBridge in 2pin mode (2 pwm pins)
# - Uses ServoBlaster library, which is NOT installed by default, so
#   you will need to install it to make this work.
# - Servo takes a standard servo PWM pulse between 1 millisecond (fully reverse)
#   and 2 milliseconds (full forward) with 1.5ms being neutral.
# - the motor is controlled by two pwm pins,
#   one for forward and one for backward (reverse).
# - the pwm pins produce a duty cycle from 0 (completely LOW)
#   to 1 (100% completely high), which is proportional to the
#   amount of power delivered to the motor.
# - in forward mode, the reverse pwm is 0 duty_cycle,
#   in backward mode, the forward pwm is 0 duty cycle.
# - both pwms are 0 duty cycle (LOW) to 'detach' motor and
#   and glide to a stop.
# - both pwms are full duty cycle (100% HIGH) to brake
#
HBRIDGE_PIN_FWD = 18  # provides forward duty cycle to motor
HBRIDGE_PIN_BWD = 16  # provides reverse duty cycle to motor
STEERING_CHANNEL = 0  # PCA 9685 channel for steering control
STEERING_LEFT_PWM = 460  # pwm value for full left steering (use `donkey calibrate` to measure value for your car)
STEERING_RIGHT_PWM = 290  # pwm value for full right steering (use `donkey calibrate` to measure value for your car)

# VESC controller, primarily need to change VESC_SERIAL_PORT  and VESC_MAX_SPEED_PERCENT
VESC_MAX_SPEED_PERCENT = 0.2  # Max speed as a percent of the actual speed
VESC_SERIAL_PORT = "/dev/ttyACM0"  # Serial device to use for communication. Can check with ls /dev/tty*
VESC_HAS_SENSOR = True  # Whether or not the bldc motor is using a hall effect sensor
VESC_START_HEARTBEAT = True  # Whether or not to automatically start the heartbeat thread that will keep commands alive.
VESC_BAUDRATE = (
    115200  # baudrate for the serial communication. Shouldn't need to change this.
)
VESC_TIMEOUT = 0.05  # timeout for the serial communication
VESC_STEERING_SCALE = 0.5  # VESC accepts steering inputs from 0 to 1. Joystick is usually -1 to 1. This changes it to -0.5 to 0.5
VESC_STEERING_OFFSET = 0.5  # VESC accepts steering inputs from 0 to 1. Coupled with above change we move Joystick to 0 to 1

#
# DC_STEER_THROTTLE with one motor as steering, one as drive
# - uses L298N type motor controller in two pin wiring
#   scheme utilizing two pwm pins per motor; one for
#   forward(or right) and one for reverse (or left)
#
# GPIO pin configuration for the DRIVE_TRAIN_TYPE=DC_STEER_THROTTLE
# - use RPI_GPIO for RPi/Nano header pin output
#   - use BOARD for board pin numbering
#   - use BCM for Broadcom GPIO numbering
#   - for example "RPI_GPIO.BOARD.18"
# - use PIPGIO for RPi header pin output using pigpio server
#   - must use BCM (broadcom) pin numbering scheme
#   - for example, "PIGPIO.BCM.13"
# - use PCA9685 for PCA9685 pin output
#   - include colon separated I2C channel and address
#   - for example "PCA9685.1:40.13"
# - RPI_GPIO, PIGPIO and PCA9685 can be mixed arbitrarily,
#   although it is discouraged to mix RPI_GPIO and PIGPIO.
#
DC_STEER_THROTTLE = {
    "LEFT_DUTY_PIN": "RPI_GPIO.BOARD.18",  # pwm pin produces duty cycle for steering left
    "RIGHT_DUTY_PIN": "RPI_GPIO.BOARD.16",  # pwm pin produces duty cycle for steering right
    "FWD_DUTY_PIN": "RPI_GPIO.BOARD.15",  # pwm pin produces duty cycle for forward drive
    "BWD_DUTY_PIN": "RPI_GPIO.BOARD.13",  # pwm pin produces duty cycle for reverse drive
}

#
# DC_TWO_WHEEL pin configuration
# - configures L298N_HBridge_2pin driver
# - two wheels as differential drive, left and right.
# - each wheel is controlled by two pwm pins,
#   one for forward and one for backward (reverse).
# - each pwm pin produces a duty cycle from 0 (completely LOW)
#   to 1 (100% completely high), which is proportional to the
#   amount of power delivered to the motor.
# - in forward mode, the reverse pwm is 0 duty_cycle,
#   in backward mode, the forward pwm is 0 duty cycle.
# - both pwms are 0 duty cycle (LOW) to 'detach' motor and
#   and glide to a stop.
# - both pwms are full duty cycle (100% HIGH) to brake
#
# Pin specifier string format:
# - use RPI_GPIO for RPi/Nano header pin output
#   - use BOARD for board pin numbering
#   - use BCM for Broadcom GPIO numbering
#   - for example "RPI_GPIO.BOARD.18"
# - use PIPGIO for RPi header pin output using pigpio server
#   - must use BCM (broadcom) pin numbering scheme
#   - for example, "PIGPIO.BCM.13"
# - use PCA9685 for PCA9685 pin output
#   - include colon separated I2C channel and address
#   - for example "PCA9685.1:40.13"
# - RPI_GPIO, PIGPIO and PCA9685 can be mixed arbitrarily,
#   although it is discouraged to mix RPI_GPIO and PIGPIO.
#
DC_TWO_WHEEL = {
    "LEFT_FWD_DUTY_PIN": "RPI_GPIO.BOARD.18",  # pwm pin produces duty cycle for left wheel forward
    "LEFT_BWD_DUTY_PIN": "RPI_GPIO.BOARD.16",  # pwm pin produces duty cycle for left wheel reverse
    "RIGHT_FWD_DUTY_PIN": "RPI_GPIO.BOARD.15",  # pwm pin produces duty cycle for right wheel forward
    "RIGHT_BWD_DUTY_PIN": "RPI_GPIO.BOARD.13",  # pwm pin produces duty cycle for right wheel reverse
}

#
# DC_TWO_WHEEL_L298N pin configuration
# - configures L298N_HBridge_3pin driver
# - two wheels as differential drive, left and right.
# - each wheel is controlled by three pins,
#   one ttl output for forward, one ttl output
#   for backward (reverse) enable and one pwm pin
#   for motor power.
# - the pwm pin produces a duty cycle from 0 (completely LOW)
#   to 1 (100% completely high), which is proportional to the
#   amount of power delivered to the motor.
# - in forward mode, the forward pin  is HIGH and the
#   backward pin is LOW,
# - in backward mode, the forward pin is LOW and the
#   backward pin is HIGH.
# - both forward and backward pins are LOW to 'detach' motor
#   and glide to a stop.
# - both forward and backward pins are HIGH to brake
#
# GPIO pin configuration for the DRIVE_TRAIN_TYPE=DC_TWO_WHEEL_L298N
# - use RPI_GPIO for RPi/Nano header pin output
#   - use BOARD for board pin numbering
#   - use BCM for Broadcom GPIO numbering
#   - for example "RPI_GPIO.BOARD.18"
# - use PIPGIO for RPi header pin output using pigpio server
#   - must use BCM (broadcom) pin numbering scheme
#   - for example, "PIGPIO.BCM.13"
# - use PCA9685 for PCA9685 pin output
#   - include colon separated I2C channel and address
#   - for example "PCA9685.1:40.13"
# - RPI_GPIO, PIGPIO and PCA9685 can be mixed arbitrarily,
#   although it is discouraged to mix RPI_GPIO and PIGPIO.
#
DC_TWO_WHEEL_L298N = {
    "LEFT_FWD_PIN": "RPI_GPIO.BOARD.16",  # TTL output pin enables left wheel forward
    "LEFT_BWD_PIN": "RPI_GPIO.BOARD.18",  # TTL output pin enables left wheel reverse
    "LEFT_EN_DUTY_PIN": "RPI_GPIO.BOARD.22",  # PWM pin generates duty cycle for left motor speed
    "RIGHT_FWD_PIN": "RPI_GPIO.BOARD.15",  # TTL output pin enables right wheel forward
    "RIGHT_BWD_PIN": "RPI_GPIO.BOARD.13",  # TTL output pin enables right wheel reverse
    "RIGHT_EN_DUTY_PIN": "RPI_GPIO.BOARD.11",  # PWM pin generates duty cycle for right wheel speed
}

# 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

# TFMINI
HAVE_TFMINI = False
TFMINI_SERIAL_PORT = (
    "/dev/serial0"  # tfmini serial port, can be wired up or use usb/serial adapter
)

# 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 = (
    0.0005  # 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
)
CREATE_TF_LITE = True  # automatically create tflite model in training
CREATE_TENSOR_RT = False  # automatically create tensorrt model in training
SAVE_MODEL_AS_H5 = False  # if old keras format should be used instead of savedmodel
CACHE_IMAGES = True  # if images are cached in training for speed up

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 = (
    0.05  # percent of dataset used to perform evaluation of model.
)

#
# Augmentations and Transformations
#
# - Augmentations are changes to the image that are only applied during
#   training and are applied randomly to create more variety in the data.
#   Available augmentations are:
#   - BRIGHTNESS  - modify the image brightness. See [albumentations](https://albumentations.ai/docs/api_reference/augmentations/transforms/#albumentations.augmentations.transforms.RandomBrightnessContrast)
#   - BLUR        - blur the image. See [albumentations](https://albumentations.ai/docs/api_reference/augmentations/blur/transforms/#albumentations.augmentations.blur.transforms.Blur)
#
# - Transformations are changes to the image that apply both in
#   training and at inference.  They are always applied and in
#   the configured order.  Available image transformations are:
#   - Apply a mask to the image:
#     - 'CROP'      - apply rectangular mask to borders of image
#     - 'TRAPEZE'   - apply a trapezoidal mask to image
#   - Apply an enhancement to the image
#     - 'CANNY'     - apply canny edge detection
#     - 'BLUR'      - blur the image
#   - resize the image
#     - 'RESIZE'    - resize to given pixel width and height
#     - 'SCALE'     - resize by given scale factor
#   - change the color space of the image
#     - 'RGB2BGR'   - change color model from RGB to BGR
#     - 'BGR2RGB'   - change color model from BGR to RGB
#     - 'RGB2HSV'   - change color model from RGB to HSV
#     - 'HSV2RGB'   - change color model from HSV to RGB
#     - 'BGR2HSV'   - change color model from BGR to HSV
#     - 'HSV2BGR'   - change color model from HSV to BGR
#     - 'RGB2GRAY'  - change color model from RGB to greyscale
#     - 'BGR2GRAY'  - change color model from BGR to greyscale
#     - 'HSV2GRAY'  - change color model from HSV to greyscale
#     - 'GRAY2RGB'  - change color model from greyscale to RGB
#     - 'GRAY2BGR'  - change color model from greyscale to BGR
#
# You can create custom tranformations and insert them into the pipeline.
# - Use a tranformer label that beings with `CUSTOM`, like `CUSTOM_CROP`
#   and add that to the TRANSFORMATIONS or POST_TRANFORMATIONS list.
#   So for the custom crop example, that might look like this;
#   `POST_TRANSFORMATIONS = ['CUSTOM_CROP']`
# - Set configuration properties for the module and class that
#   implement your custom transformation.
#   - The module config will begin with the transformer label
#     and end with `_MODULE`, like `CUSTOM_CROP_MODULE`.  It's value is
#     the absolute file path to the python file that has the transformer
#     class.  For instance, if you called the file
#     `my_custom_transformer.py` and put in in the root of
#     your `mycar` folder, next to `myconfig.py`, then you would add
#     the following to your myconfig.py file (keeping with the crop example);
#     `CUSTOM_CROP_MODULE = "/home/pi/mycar/my_custom_transformer.py"`
#     The actual path will depend on what OS you are using and what
#     your user name is.
#   - The class config will begin with the transformer label and end with `_CLASS`,
#     like `CUSTOM_CROP_CLASS`.  So if your class is called `CustomCropTransformer`
#     the you would add the following property to your `myconfig.py` file:
#     `CUSTOM_CROP_CLASS = "CustomCropTransformer"`
# - Your custom class' constructor will take in the Config object to
#   it it's constructor.  So you can add whatever configuration properties
#   you need to your myconfig.py, then read them in the constructor.
#   You can name the properties anything you want, but it is good practice
#   to prefix them with the custom tranformer label so they don't conflict
#   with any other config and so it is way to see what they go with.
#   For instance, in the custom crop example, we would want the border
#   values, so that could look like;
#   ```
#   CUSTOM_CROP_TOP = 45    # rows to ignore on the top of the image
#   CUSTOM_CROP_BOTTOM = 5  # rows ignore on the bottom of the image
#   CUSTOM_CROP_RIGHT = 10  # pixels to ignore on the right of the image
#   CUSTOM_CROP_LEFT = 10   # pixels to ignore on the left of the image
#   ```
# - Your custom class must have a `run` method that takes an image and
#   returns an image.  It is in this method where you will implement your
#   transformation logic.
# - For example, a custom crop that did a blur after the crop might look like;
#   ```
#   from donkeycar.parts.cv import ImgCropMask, ImgSimpleBlur
#
#   class CustomCropTransformer:
#       def __init__(self, config) -> None:
#           self.top = config.CUSTOM_CROP_TOP
#           self.bottom = config.CUSTOM_CROP_BOTTOM
#           self.left = config.CUSTOM_CROP_LEFT
#           self.right = config.CUSTOM_CROP_RIGHT
#           self.crop = ImgCropMask(self.left, self.top, self.right, self.bottom)
#           self.blur = ImgSimpleBlur()
#
#       def run(self, image):
#           image = self.crop.run(image)
#           return self.blur.run(image)
#   ```
#
AUGMENTATIONS = []  # changes to image only applied in training to create
# more variety in the data.
TRANSFORMATIONS = []  # changes applied _before_ training augmentations,
# such that augmentations are applied to the transformed image,
POST_TRANSFORMATIONS = []  # transformations applied _after_ training augmentations,
# such that changes are applied to the augmented image

# Settings for brightness and blur, use 'MULTIPLY' and/or 'BLUR' in
# AUGMENTATIONS
AUG_BRIGHTNESS_RANGE = 0.2  # this is interpreted as [-0.2, 0.2]
AUG_BLUR_RANGE = (0, 3)

# "CROP" Transformation
# Apply mask to borders of the image
# defined by a rectangle.
# If these crops values are too large, they will cause the stride values to
# become negative and the model with not be valid.
# # # # # # # # # # # # #
# xxxxxxxxxxxxxxxxxxxxx #
# xxxxxxxxxxxxxxxxxxxxx #
# xx                 xx # top
# xx                 xx #
# xx                 xx #
# xxxxxxxxxxxxxxxxxxxxx # bottom
# xxxxxxxxxxxxxxxxxxxxx #
# # # # # # # # # # # # #
ROI_CROP_TOP = 45  # 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
ROI_CROP_RIGHT = 0  # the number of rows of pixels to ignore on the right of the image
ROI_CROP_LEFT = 0  # the number of rows of pixels to ignore on the left of the image

# "TRAPEZE" tranformation
# Apply mask to borders of image
# defined by a trapezoid.
# # # # # # # # # # # # # #
# xxxxxxxxxxxxxxxxxxxxxxx #
# xxxx ul     ur xxxxxxxx # min_y
# xxx             xxxxxxx #
# xx               xxxxxx #
# x                 xxxxx #
# ll                lr xx # max_y
# # # # # # # # # # # # # #
ROI_TRAPEZE_LL = 0
ROI_TRAPEZE_LR = 160
ROI_TRAPEZE_UL = 20
ROI_TRAPEZE_UR = 140
ROI_TRAPEZE_MIN_Y = 60
ROI_TRAPEZE_MAX_Y = 120

# "CANNY" Canny Edge Detection tranformation
CANNY_LOW_THRESHOLD = (
    60  # Canny edge detection low threshold value of intensity gradient
)
CANNY_HIGH_THRESHOLD = (
    110  # Canny edge detection high threshold value of intensity gradient
)
CANNY_APERTURE = (
    3  # Canny edge detect aperture in pixels, must be odd; choices=[3, 5, 7]
)

# "BLUR" transformation (not this is SEPARATE from the blur augmentation)
BLUR_KERNEL = 5  # blur kernel horizontal size in pixels
BLUR_KERNEL_Y = None  # blur kernel vertical size in pixels or None for square kernel
BLUR_GAUSSIAN = True  # blur is gaussian if True, simple if False

# "RESIZE" transformation
RESIZE_WIDTH = 160  # horizontal size in pixels
RESIZE_HEIGHT = 120  # vertical size in pixels

# "SCALE" transformation
SCALE_WIDTH = 1.0  # horizontal scale factor
SCALE_HEIGHT = None  # vertical scale factor or None to maintain aspect ratio

# 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|pigpio_rc|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 = 0.8

# 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_ADDRESS = (
    0x68  # if AD0 pin is pulled high them address is 0x69, otherwise it is 0x68
)
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.

# PIGPIO RC control
STEERING_RC_GPIO = 26
THROTTLE_RC_GPIO = 20
DATA_WIPER_RC_GPIO = 19
PIGPIO_STEERING_MID = (
    1500  # Adjust this value if your car cannot run in a straight line
)
PIGPIO_MAX_FORWARD = 2000  # Max throttle to go fowrward. The bigger the faster
PIGPIO_STOPPED_PWM = 1500
PIGPIO_MAX_REVERSE = 1000  # Max throttle to go reverse. The smaller the faster
PIGPIO_SHOW_STEERING_VALUE = False
PIGPIO_INVERT = False
PIGPIO_JITTER = 0.025  # threshold below which no signal is reported


# ROBOHAT MM1
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 predict its 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 = False
DONKEY_SIM_PATH = "path to sim"  # "/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
SIM_RECORD_LIDAR = False

# publish camera over network
# This is used to create a tcp service to publish 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
USE_CONSTANT_THROTTLE = False  # whether or not to use the constant throttle or variable throttle captured during path recording
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
STOP_SIGN_MAX_REVERSE_COUNT = 10  # How many times should the car reverse when detected a stop sign, set to 0 to disable reversing
STOP_SIGN_REVERSE_THROTTLE = -0.5  # Throttle during reversing when detected a stop sign

# FPS counter
SHOW_FPS = False
FPS_DEBUG_INTERVAL = (
    10  # the interval in seconds for printing the frequency info into the shell
)

# PI connection
PI_USERNAME = "pi"
PI_HOSTNAME = "donkeypi.local"