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:
_ctx_print_all("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:
_ctx_print_all("Object location is '{}'.".format(location))
return location
def detect_stop_signs(frame, annotate=True, verbose=True):
"""
Detect stop signs inside of `frame`, and annotate each stop sign.
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 STOPSIGNDETECTOR
try:
STOPSIGNDETECTOR
except NameError:
from auto.models import StopSignDetector
STOPSIGNDETECTOR = StopSignDetector()
if verbose:
_ctx_print_all("Instantiated a StopSignDetector object!")
rects = STOPSIGNDETECTOR.detect(frame, annotate=annotate)
n = len(rects)
if verbose:
_ctx_print_all("Found {} stop sign{}.".format(n,
's' if n != 1 else ''))
return rects
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
if IS_VIRTUAL:
PEDESTRIANDETECTOR = PedestrianDetector(hitThreshold=-1.5)
else:
PEDESTRIANDETECTOR = PedestrianDetector()
if verbose:
_ctx_print_all("Instantiated a PedestrianDetector object!")
rects = PEDESTRIANDETECTOR.detect(frame, annotate=annotate)
n = len(rects)
if verbose:
_ctx_print_all("Found {} pedestrian{}.".format(n,
's' if n != 1 else ''))
return rects
def pause(sec=1.0, verbose=True):
"""
Pause the car's code for `sec` seconds.
"""
if verbose:
_ctx_print_all("Pausing for {} seconds.".format(sec))
time.sleep(sec)
def stream(frame, to_console=True, to_labs=False, verbose=False):
"""
Stream the given `frame` (a numpy ndarray) to your device's
console _and_ (optionally) to your `labs` account to be shown
in your browser.
The `frame` parameter must be a numpy ndarray with one of the
following shapes:
- (h, w, 3) meaning a single 3-channel RGB image of size `w`x`h`
- (h, w, 1) meaning a single 1-channel gray image of size `w`x`h`
- (h, w) meaning a single 1-channel gray image of size `w`x`h`
"""
if frame is None:
if to_console:
console.clear_image()
if to_labs:
send_message_to_labs({'base64_img': ''})
return
# Publish the uncompressed frame to the console UI.
if to_console:
if frame.ndim == 3:
if frame.shape[2] == 3:
pass # all good
elif frame.shape[2] == 1:
pass # all good
else:
raise Exception("invalid number of channels")
elif frame.ndim == 2:
frame = np.expand_dims(frame, axis=2)
assert frame.ndim == 3 and frame.shape[2] == 1
else:
raise Exception(f"invalid frame ndarray ndim: {frame.ndim}")
height, width, channels = frame.shape
aspect_ratio = width / height
if aspect_ratio != OPTIMAL_ASPECT_RATIO:
final_frame = _add_white_bars(frame)
height, width, channels = final_frame.shape
else:
final_frame = frame
shape = [width, height, channels]
rect = [0, 0, 0, 0]
console.stream_image(rect, shape, final_frame.tobytes())
# Encode the frame and publish to the network connection.
if to_labs:
base64_img = base64_encode_image(frame)
send_message_to_labs({'base64_img': base64_img})
if verbose:
h, w = frame.shape[:2]
_ctx_print_all("Streamed frame of size {}x{}.".format(w, h))
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:
_ctx_print_all("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:
_ctx_print_all("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:
_ctx_print_all(
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 close_global_camera(verbose=False):
"""
Close and delete the global camera object.
"""
global GLOBAL_CAMERA
try:
GLOBAL_CAMERA # <-- just to see if it exists
if verbose:
auto._ctx_print_all("Closing the global camera object...")
release(GLOBAL_CAMERA._camera)
del GLOBAL_CAMERA
except NameError:
# There is no global camera, so nothing needs to be done.
pass
def global_camera(verbose=False):
"""
Creates (for the first call) or retrieves (for later calls) the
global camera object. This is a convenience function to facilitate
quickly and easily creating and retrieving a camera singleton.
"""
global GLOBAL_CAMERA
try:
GLOBAL_CAMERA
except NameError:
caps = list_caps()
if 'Camera' not in caps:
raise AttributeError("This device does not have a Camera.")
camera = acquire('Camera')
GLOBAL_CAMERA = _CameraRGB(camera)
if verbose:
auto._ctx_print_all("Instantiated a global camera object!")
return GLOBAL_CAMERA
def capture(num_frames=1, verbose=False):
"""
Capture `num_frames` frames from the (global) camera and return
them as a numpy ndarray.
This is a convenience function to make the most common use-case simpler.
"""
camera = global_camera(verbose)
if num_frames > 1:
frames = np.array([frame for _, frame in zip(range(num_frames), camera.stream())])
if verbose:
auto._ctx_print_all("Captured {} frames.".format(num_frames))
return frames
else:
frame = camera.capture()
if verbose:
auto._ctx_print_all("Captured 1 frame.")
return frame
def reverse(sec=None, cm=None, verbose=True):
"""
Drive the car in reverse for `sec` seconds or `cm` centimeters (passing in both
will raise an error). If neither is passed in, the car will drive for 1 second.
"""
from car import motors
if sec is not None and cm is not None:
_ctx_print_all(
"Error: Please specify duration (sec) OR distance (cm) - not both."
)
return
if sec is None and cm is None:
sec = 1.0
if sec is not None:
if sec > 5.0:
_ctx_print_all(
"Error: The duration (sec) exceeds 5 seconds; will reset to 5 seconds."
)
sec = 5.0
if sec <= 0.0:
return
if verbose:
_ctx_print_all("Driving in reverse for {} seconds.".format(sec))
if cm is not None:
if cm > 300.0:
_ctx_print_all(
"Error: The distance (cm) exceeds 300 cm (~10 feet); will reset to 300 cm."
)
cm = 300.0
if cm <= 0.0:
return
if verbose:
_ctx_print_all("Driving in reverse for {} centimeters.".format(cm))
motors.straight(motors.safe_reverse_throttle(),
sec,
cm,
invert_output=True)
def plot(frames, also_stream=True, verbose=False):
"""
Stitch together the given `frames` (a numpy nd-array) into a single nd-array.
If running in a notebook then the PIL image will be returned (and displayed).
This function by default also streams the image to your `labs` account.
The `frames` parameter must be a numpy ndarray with one of the
following shapes:
- (n, h, w, 3) meaning `n` 3-channel RGB images of size `w`x`h`
- (n, h, w, 1) meaning `n` 1-channel gray images of size `w`x`h`
- (h, w, 3) meaning a single 3-channel RGB image of size `w`x`h`
- (h, w, 1) meaning a single 1-channel gray image of size `w`x`h`
- (h, w) meaning a single 1-channel gray image of size `w`x`h`
"""
# Ensure the proper shape of `frames`.
if frames.ndim == 4:
pass
elif frames.ndim == 3:
frames = np.expand_dims(frames, axis=0)
elif frames.ndim == 2:
frames = np.expand_dims(frames, axis=2)
frames = np.expand_dims(frames, axis=0)
else:
raise Exception("invalid frames ndarray ndim")
if frames.shape[3] != 3 and frames.shape[3] != 1:
raise Exception("invalid number of channels")
if verbose:
n = frames.shape[0]
_ctx_print_all("Plotting {} frame{}...".format(n,
's' if n != 1 else ''))
montage = _create_montage(frames)
if also_stream:
stream(montage, to_labs=True, verbose=False)
return PIL.Image.fromarray(np.squeeze(montage)) if _in_notebook() else None
def right(sec=None, deg=None, verbose=True):
"""
Drive the car forwad and right for `sec` seconds or `deg` degrees (passing in both
will raise an error). If neither is passed in, the car will drive for 1 second.
"""
from car import motors
if sec is not None and deg is not None:
_ctx_print_all(
"Error: Please specify duration (sec) OR degrees (deg) - not both."
)
return
if sec is None and deg is None:
sec = 1.0
if sec is not None:
if sec > 5.0:
_ctx_print_all(
"Error: The duration (sec) exceeds 5 seconds; will reset to 5 seconds."
)
sec = 5.0
if sec <= 0.0:
return
if verbose:
_ctx_print_all("Driving right for {} seconds.".format(sec))
if deg is not None:
if deg > 360.0:
_ctx_print_all(
"Error: The degrees (deg) exceeds 360; will reset to 360.")
deg = 360.0
if deg <= 0.0:
return
if verbose:
_ctx_print_all("Driving right for {} degrees.".format(deg))
motors.drive(-45.0, motors.safe_forward_throttle(), sec, deg)
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:
_ctx_print_all("Instantiated a ColorClassifier object!")
p1, p2, classific = COLORCLASSIFIER.classify(frame, annotate=annotate)
if verbose:
_ctx_print_all("Classified color as '{}'.".format(classific))
return classific
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:
_ctx_print_all("Instantiated a FaceDetector object!")
faces = FACEDETECTOR.detect(frame, annotate=annotate)
n = len(faces)
if verbose:
_ctx_print_all("Found {} face{}.".format(n, 's' if n != 1 else ''))
return faces
