def pause(duration=1.0, verbose=True):
"""
Pause the car's code for `duration` seconds.
"""
if verbose:
print("Pausing for {} seconds.".format(duration))
time.sleep(duration)
def object_location(object_list, frame_shape, verbose=True):
"""
Calculate the location of the largest object in `object_list`.
Returns one of: 'frame_left', 'frame_right', 'frame_center', None
"""
if not object_list:
if verbose:
print("Object location is None.")
return None
import numpy as np
areas = [w * h for x, y, w, h in object_list]
i = np.argmax(areas)
nearest = object_list[i]
x, y, w, h = nearest
x_center = x + w / 2.
if x_center < frame_shape[1] / 3.:
location = 'frame_left'
elif x_center < 2 * frame_shape[1] / 3.:
location = 'frame_center'
else:
location = 'frame_right'
if verbose:
print("Object location is '{}'.".format(location))
return location
def object_size(object_list, frame_shape, verbose=True):
"""
Calculate the ratio of the nearest object's area to the frame's area.
"""
if not object_list:
if verbose:
print("Object area is 0.")
return 0.0
areas = [w * h for x, y, w, h in object_list]
ratio = max(areas) / (frame_shape[0] * frame_shape[1])
if verbose:
print("Object area is {}.".format(ratio))
return ratio
def capture(num_frames=1, verbose=True):
"""
Capture `num_frames` frames from the car's camera and return
them as a numpy ndarray.
"""
MAX_FRAMES = 4
if num_frames > MAX_FRAMES:
print(
f"Warning: You may capture at most {MAX_FRAMES} frames with this function."
)
num_frames = MAX_FRAMES
from auto import camera
return camera.capture(num_frames, verbose)
def right(duration=1.0, verbose=True):
"""
Drive the car forward and right for `duration` seconds.
"""
from car import motors
if duration > 5.0:
print(
"Error: The duration exceeds 5 seconds; will reset to 5 seconds.")
duration = 5.0
if verbose:
print("Driving right for {} seconds.".format(duration))
if duration <= 0.0:
return
motors.drive(-45.0, motors.CAR_THROTTLE_FORWARD_SAFE_SPEED, duration)
def left(duration=1.0, verbose=True):
"""
Drive the car forward and left for `duration` seconds.
"""
from car import motors
if duration > 5.0:
print(
"Error: The duration exceeds 5 seconds; will reset to 5 seconds.")
duration = 5.0
if verbose:
print("Driving left for {} seconds.".format(duration))
if duration <= 0.0:
return
motors.left(duration)
def reverse(duration=1.0, verbose=True):
"""
Drive the car in reverse for `duration` seconds.
"""
from car import motors
if duration > 5.0:
print(
"Error: The duration exceeds 5 seconds; will reset to 5 seconds.")
duration = 5.0
if verbose:
print("Driving in reverse for {} seconds.".format(duration))
if duration <= 0.0:
return
motors.straight(motors.CAR_THROTTLE_REVERSE_SAFE_SPEED,
duration,
invert_output=True)
def classify_color(frame, annotate=True, verbose=True):
"""
Classify the center region of `frame` as having either primarily "red",
"yellow", or "green, or none of those ("background").
The `frame` parameter must be a numpy array containing an RGB image.
Returns a string representing the color found in the center of the
image, one of "red", "yellow", "green", or "background".
"""
global COLORCLASSIFIER
try:
COLORCLASSIFIER
except NameError:
from auto.models import ColorClassifier
COLORCLASSIFIER = ColorClassifier()
if verbose:
print("Instantiated a ColorClassifier object!")
p1, p2, classific = COLORCLASSIFIER.classify(frame, annotate=annotate)
if verbose:
print("Classified color as '{}'.".format(classific))
return classific
def detect_pedestrians(frame, annotate=True, verbose=True):
"""
Detect pedestrians inside of `frame`, and annotate each pedestrian.
The `frame` parameter must be an image as a numpy array either containing
3-channel RGB values _or_ 1-channel gray values.
Returns a list of rectangles, where each rectangle is a 4-tuple of:
(x, y, width, height)
"""
global PEDESTRIANDETECTOR
try:
PEDESTRIANDETECTOR
except NameError:
from auto.models import PedestrianDetector
PEDESTRIANDETECTOR = PedestrianDetector()
if verbose:
print("Instantiated a PedestrianDetector object!")
rects = PEDESTRIANDETECTOR.detect(frame, annotate=annotate)
n = len(rects)
if verbose:
print("Found {} pedestrian{}.".format(n, 's' if n != 1 else ''))
return rects
def detect_faces(frame, annotate=True, verbose=True):
"""
Detect faces inside of `frame`, and annotate each face.
The `frame` parameter must be an image as a numpy array either containing
3-channel RGB values _or_ 1-channel gray values.
Returns a list of rectangles, where each rectangle is a 4-tuple of:
(x, y, width, height)
"""
global FACEDETECTOR
try:
FACEDETECTOR
except NameError:
from auto.models import FaceDetector
FACEDETECTOR = FaceDetector()
if verbose:
print("Instantiated a FaceDetector object!")
faces = FACEDETECTOR.detect(frame, annotate=annotate)
n = len(faces)
if verbose:
print("Found {} face{}.".format(n, 's' if n != 1 else ''))
return faces
