def __init__(self):
self.camera = Camera.instance(width=224, height=224)
self.robot = Robot()
#Collision Avoidance
self.ca_model = torchvision.models.alexnet(pretrained=False)
self.ca_model.classifier[6] = torch.nn.Linear(
self.ca_model.classifier[6].in_features, 2)
self.ca_model.load_state_dict(torch.load('best_model.pth'))
self.device = torch.device('cuda')
self.ca_model = self.ca_model.to(self.device)
self.mean = torch.Tensor([0.485, 0.456, 0.406]).cuda().half()
self.std = torch.Tensor([0.229, 0.224, 0.225]).cuda().half()
self.normalize = torchvision.transforms.Normalize(self.mean, self.std)
#Road following support variables
self.angle = 0.0
self.angle_last = 0.0
# Instantiating the road following network.
self.rf_model = torchvision.models.resnet18(pretrained=False)
self.rf_model.fc = torch.nn.Linear(512, 2)
self.rf_model.load_state_dict(torch.load('best_steering_model_xy.pth'))
self.rf_model = self.rf_model.to(self.device)
self.rf_model = self.rf_model.eval().half()
self.speed_gain = 0.12
self.steering_gain = 0
self.steering_dgain = 0.1
self.steering_bias = 0.0
self.t_unit_dist = 0.04
self.starttime = 0
self.cumulative_angle = -1
self.pitstop = []
self.startposition = []
self.pathpoints = [[]]
self.proportionality_const = -1 # TODO : Add the proper value
def collision_avoidance():
global normalize, device, model, mean, camera, robot
i_frame = -1
model = torchvision.models.alexnet(pretrained=False)
model.classifier[6] = torch.nn.Linear(model.classifier[6].in_features, 2)
model.load_state_dict(torch.load('best_model.pth'))
device = torch.device('cuda')
model = model.to(device)
mean = 255.0 * np.array([0.485, 0.456, 0.406])
stdev = 255.0 * np.array([0.229, 0.224, 0.225])
camera = Camera.instance(width=224, height=224)
normalize = torchvision.transforms.Normalize(mean, stdev)
robot = Robot()
robot.stop()
now = time.time()
stop_time = now + 120
while time.time() < stop_time:
i_frame += 1
if i_frame % 2 == 0:
update({'new':
camera.value}) # we call the function once to intialize
#cv2.imshow(camera.value)
#camera.observe(update, names='value')
robot.stop()
camera.stop()
def main(vocab_path, settings_path):
slam = orbslam2.System(vocab_path, settings_path,
orbslam2.Sensor.MONOCULAR)
slam.set_use_viewer(True)
slam.initialize()
camera = Camera.instance(width=960,
height=540,
capture_width=1280,
capture_height=720)
print('-----')
print('Start processing sequence ...')
ts = 0
#cv2.namedWindow("test")
while (True):
img = camera.value.copy()
t1 = time.time()
slam.process_image_mono(img, float(ts))
t2 = time.time()
ttrack = t2 - t1
print("frame id:" + str(ts), ttrack)
time.sleep(0.01)
ts += 1
save_trajectory(slam.get_trajectory_points(), 'trajectory.txt')
slam.shutdown()
return 0
def start(self):
self.device = torch.device('cuda')
print('Loading model...')
# create model
self.model = torchvision.models.alexnet(pretrained=False)
self.model.classifier[6] = torch.nn.Linear(
self.model.classifier[6].in_features, 2)
self.model.load_state_dict(torch.load(self.collision_model))
self.model = self.model.to(self.device)
# create robot
self.robot = Robot()
print('Initializing camera...')
# create camera
self.camera = Camera.instance(width=224, height=224)
print('Running...')
self.camera.observe(self._update, names='value')
def kill(sig, frame):
print('Shutting down...')
self.camera.stop()
signal.signal(signal.SIGINT, kill)
self.camera.thread.join()
def __init__(self):
print('Setting up camera')
self.camera = Camera.instance(width=224, height=224)
print('Set up camera')
self.robot = Robot()
self.completed = False
# Collision Avoidance.
print('Loading CA model')
self.ca_model = torchvision.models.alexnet(pretrained=False)
self.ca_model.classifier[6] = torch.nn.Linear(
self.ca_model.classifier[6].in_features, 2)
#self.ca_model.load_state_dict(torch.load('best_model_nvidia.pth'))
self.device = torch.device('cuda')
self.ca_model = self.ca_model.to(self.device)
print('Loaded CA model')
self.mean = 255.0 * torch.Tensor(np.array([0.485, 0.456, 0.406
])).cuda().half()
self.std = 255.0 * torch.Tensor(np.array([0.485, 0.456, 0.406
])).cuda().half()
self.normalize = torchvision.transforms.Normalize(self.mean, self.std)
# Road following support variables.
self.angle = 0.0
self.angle_last = 0.0
# Instantiating the road following network.
print('Loading RF model')
self.rf_model = torchvision.models.resnet18(pretrained=False)
self.rf_model.fc = torch.nn.Linear(512, 2)
self.rf_model.load_state_dict(torch.load('best_steering_model_xy.pth'))
self.rf_model = self.rf_model.to(self.device)
self.rf_model = self.rf_model.eval().half()
print('Loaded RF Model')
# Initializing additional variables.
self.speed_gain = 0.12
self.steering_gain = 0
self.steering_dgain = 0.1
self.steering_bias = 0.0
self.starttime = 0
self.cumulative_angle = -1
self.pitstop = []
self.startposition = []
self.start_num = -1
self.baton_callback = None
self.pathpoints_callback = None
self.pathpoints = [[]]
# Add proper value below.
self.proportionality_const = -1
self.image_widget = ipywidgets.Image()
self.previous_position = -1
traitlets.dlink((self.camera, 'value'), (self.image_widget, 'value'),
transform=bgr8_to_jpeg)
def __init__(self, width=640, height=480, saveFramesToFile=True):
super().__init__()
# Set camera frame size
self.width = width
self.height = height
# Initialization
# Camera instance
self.camera = Camera.instance(width=self.width, height=self.height)
# Region Of Interest
self.sceneWhole = None
self.sceneROI = None
# Captured frame
self.savedFrameWhole = None
self.savedFrameROI = None
# File numbering counter
self.fileNumberCount = 1000000000
# Flag whether to save to file or not
self.saveFramesToFile = saveFramesToFile
def cam_capture(fn):
global cam
global capidx
capidx += 1
path = "/home/jetbot/camerasnaps"
try:
os.makedirs(path)
except FileExistsError:
pass
except Exception as ex:
print("Exception creating directory '%s', error: %s" % (path, str(ex)))
return
if cam == None:
try:
print("Initializing camera...")
cam = Camera.instance(width=960, height=720)
print("\r\nCamera initialized!")
except Exception as ex:
sys.stderr.write("Exception initializing camera: " + str(ex))
cam = None
return
try:
fp = os.path.join(path, fn + '.jpg')
with open(fp, 'wb') as f:
f.write(bytes(imencode('.jpg', cam.value)[1]))
teensyadc.set_camera_led()
try:
uid = pwd.getpwnam("jetbot").pw_uid
gid = grp.getgrnam("jetbot").gr_gid
os.chown(fp, uid, gid)
except Exception as ex:
sys.stderr.write(
"Exception changing ownership of file '%s', error: %s" %
(fp, str(ex)))
except Exception as ex:
sys.stderr.write("Exception writing to file '%s', error: %s" %
(fp, str(ex)))
def face_main():
global face
global smile
global eye
camera_0 = Camera.instance(width=224, height=224)
face_seen = 1
face_haar_model = './data/haarcascade_frontalface_default.xml'
smile_haar_model = './data/haarcascade_smile.xml'
eye_haar_model = './data/haarcascade_eye.xml'
face = cv2.CascadeClassifier(face_haar_model)
smile = cv2.CascadeClassifier(smile_haar_model)
eye = cv2.CascadeClassifier(eye_haar_model)
while True:
frame_0 = camera_0.value
gray_0 = cv2.cvtColor(frame_0, cv2.COLOR_BGR2GRAY)
canvas_0 = detect(gray_0, frame_0, face_seen)
#cv2.imshow('Video', canvas_0)
if cv2.waitKey(1) & 0xff == ord('q'):
break
camera_0.stop()
cv2.destroyAllWindows()
import numpy as np
import cv2
import glob
from jetbot import Camera
cam = Camera.instance()
# termination criteria
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((6 * 7, 3), np.float32)
objp[:, :2] = np.mgrid[0:7, 0:6].T.reshape(-1, 2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d point in real world space
imgpoints = [] # 2d points in image plane.
#images = glob.glob('./chessboard_img/*.jpg')
#for fname in images:
while True:
img = cam.value
#img = cv2.imread(fname)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Find the chess board corners
ret, corners = cv2.findChessboardCorners(gray, (7, 6), None)
print(ret)
#3 seconds
# Great! We've now defined our pre-processing function which can convert images from the camera format to the neural network input format.
#
# Now, let's start and display our camera. You should be pretty familiar with this by now. We'll also create a slider that will display the
# probability that the robot is blocked.
# In[5]:
print('16 second traitlets')
import traitlets
from IPython.display import display
import ipywidgets.widgets as widgets
from jetbot import Camera, bgr8_to_jpeg
camera = Camera.instance(width=224, height=224)
image = widgets.Image(format='jpeg', width=224, height=224)
blocked_slider = widgets.FloatSlider(description='blocked',
min=0.0,
max=1.0,
orientation='vertical')
#DMF Added Next Line
gpu_slider = widgets.FloatSlider(description="GPU%",
min=0.0,
max=1.0,
orientation='vertical')
camera_link = traitlets.dlink((camera, 'value'), (image, 'value'),
transform=bgr8_to_jpeg)
display(widgets.HBox([image, blocked_slider, gpu_slider]))
model_roadfollow.fc = torch.nn.Linear(512, 2)
model_roadfollow.load_state_dict(torch.load(steering_model_path))
model_dinodet = torchvision.models.resnet18(pretrained=False)
model_dinodet.fc = torch.nn.Linear(512, 6)
model_dinodet.load_state_dict(torch.load(dino_detect_model_path))
device = torch.device('cuda')
model = model_roadfollow.to(device)
model = model_roadfollow.eval().half()
model_dinodet = model_dinodet.to(device)
model_dinodet = model_dinodet.eval()
camera = Camera.instance(width=int(rospy.get_param("image_width")),
height=int(rospy.get_param("image_height")))
from jetbot import Robot
robot = Robot()
speed_gain_slider = float(rospy.get_param("speed"))
steering_gain_slider = float(rospy.get_param("steering_gain"))
steering_dgain_slider = float(rospy.get_param("steering_d_gain"))
steering_bias_slider = float(rospy.get_param("steering_bias"))
angle = 0.0
angle_last = 0.0
prev_class = -1
except Exception as e:
rospy.loginfo("Error occured:", str(e))
dino_names = rospy.get_param("dinosaurs")
def trigger_speech(trigger_type):
'''
Respond to the user command, or trigger
- "greeting": The user says hi to J-Bot and she responds about general weather information
- "weather": The user asks about the weather, J-Bot responds with the weather information
- "air pollution": The user asks about the air quality, J-Bot responds with the air quality information
- "camera": The user asks for the opinion about the outfit, J-Bot responds according to the condition outside
whether the outfit is suitable
Args:
trigger_type: (str) Command by the user
Returns:
(str) What J-Bot tells the user according to what the user asks
'''
# Get the information about the weather and air quality
d = get_outside_condition()
temp = int(d['temperature'])
highest_temp_forecast = 0
highest_temp_time = 0
forecasted_weather = ""
forecasted_time = ""
current_w = d['weather']['detail']
is_all_day = True
forecasted_data = d['forecast']
air_quality = d['air_condition']['level']
count_equal = 0
for f in forecasted_data:
# Get the highest temperature for the day
if highest_temp_forecast < f['temperature']:
highest_temp_forecast = f['temperature']
highest_temp_time = f['time']
if current_w == f['weather']['detail']:
count_equal += 1
elif (is_all_day == True) and (current_w != f['weather']['detail']):
forecasted_weather = f['weather']['detail']
forecasted_time = f['time']
is_all_day = False
# If the highest forecasted temperature is lower than the current weather, assign current temperature as forecasted highest temperature
if highest_temp_forecast <= temp:
highest_temp_forecast = temp
# Call greeting context, weather context (temperature included), air pollution context
if trigger_type == "greeting":
return greeting_context() + " " + weather_context(
temp, highest_temp_forecast, highest_temp_time, forecasted_weather,
forecasted_time, forecasted_data, current_w, is_all_day,
count_equal) + " " + air_context(d['air_condition']['level'])
# Call weather context
elif trigger_type == "weather":
return weather_context(temp, highest_temp_forecast, highest_temp_time,
forecasted_weather, forecasted_time,
forecasted_data, current_w, is_all_day,
count_equal)
# Call air pollution context
elif trigger_type == 'air pollution':
return air_context(air_quality)
# Check the user's outfit and respond accordingly
elif trigger_type == 'camera':
# Initiate the camera
camera = Camera.instance(width=224, height=224)
camera.start()
image = widgets.Image(format='jpeg', width=224, height=224)
camera_link = traitlets.dlink((camera, 'value'), (image, 'value'),
transform=bgr8_to_jpeg)
image_path = 'temp.jpg'
# Saving the camera image locally
with open(image_path, 'wb') as f:
f.write(image.value)
camera.stop()
# Classify the upper and lower outfits of the user
top, bot = detectClothes(image_path, clothe_model)
print(top, bot)
f.close()
return recommend_clothes(highest_temp_forecast, forecasted_weather,
air_quality, top, bot)
logging.config.dictConfig(logging_config)
logger = logging.getLogger(__name__)
# init camera
logger.info('initialize camera')
camera_width = 600
camera_height = 600
model_width = 300
model_height = 300
xscale = model_width * (camera_width / model_width)
yscale = model_height * (camera_height / model_height)
#camera = Camera.instance(width=camera_width, height=camera_height, capture_width=3280, capture_height=2464) # W = 3280 H = 2464 1920 x 1080 1280 x 720
camera = Camera.instance(
width=camera_width,
height=camera_height,
capture_width=1920,
capture_height=1080) # W = 3280 H = 2464 1920 x 1080 1280 x 720
cat_count = 0
seconds_between_pics = 1.0
v2_coco_labels_to_capture = [16, 17, 18, 91, 92]
# create save directory
image_dir = 'dataset/cats'
# we have this "try/except" statement because these next functions can throw an error if the directories exist already
try:
os.makedirs(image_dir)
except FileExistsError:
logger.info('image directories not created because they already exist')
from jetbot import Camera
width = 640
height = 480
camera = Camera.instance(width=width, height=height)
while True:
print(camera.value[width // 2][height // 2][0])
# otherwsie steer towards target
else:
# move robot forward and steer proportional target's x-distance from center
center = detection_center(det)
robot.set_motors(
float(speed_widget.value + turn_gain_widget.value * center[0]),
float(speed_widget.value - turn_gain_widget.value * center[0]))
# update image widget
image_widget.value = bgr8_to_jpeg(image)
model = ObjectDetector(
'/home/dewald/projects/frameworks/nvidia/deepstream_sdk_v4.0.2_jetson/samples/models/SSD-Mobilenet-v2/ssd_mobilenet_v2_coco.engine'
)
camera = Camera.instance(width=300, height=300)
detections = model(camera.value)
print(detections)
collision_model = torchvision.models.alexnet(pretrained=False)
collision_model.classifier[6] = torch.nn.Linear(
collision_model.classifier[6].in_features, 2)
collision_model.load_state_dict(
torch.load('/home/dewald/projects/jetbot/jetbot/best_model.pth'))
device = torch.device('cuda')
collision_model = collision_model.to(device)
mean = 255.0 * np.array([0.485, 0.456, 0.406])
stdev = 255.0 * np.array([0.229, 0.224, 0.225])
normalize = torchvision.transforms.Normalize(mean, stdev)
# Robot Movement
import os
import cv2
from jetbot import Camera
dataset_path = "jetbot_dataset_960test2/"
image_folder = "rgb/"
if not os.path.exists(dataset_path + image_folder):
os.makedirs(dataset_path + image_folder)
file = open(dataset_path + "rgb.txt", "w")
camera = Camera.instance(width=960,
height=540,
capture_width=1280,
capture_height=720)
iid = 0
while (True):
img = camera.value
cv2.imshow("test", img)
fname = str(iid).zfill(4) + ".jpg"
cv2.imwrite(dataset_path + image_folder + fname, img)
# Write file
file.write(str(iid) + " " + image_folder + fname + "\n")
iid += 1
k = cv2.waitKey(100)
if k == ord('q'):
camera.stop()
break
from jetson import inference
# ## General Setups
# If GPU is available, we will use it for efficiency
if torch.cuda.is_available():
device = torch.device("cuda")
else:
device = torch.device("cpu")
if __name__ == '__main__':
print(device)
# Instanciate camera with defined size.
camwidth, camheight = 320, 240 #224,224
cam = Camera.instance(width=camwidth, height=camheight)
try:
print("Camspecs are to specifications: ",
((cam.width == camwidth) and (cam.height == camheight)))
print(cam.width, cam.height)
except NameError:
pass
# Instanciate Camera
robot = Robot()
robot.stop(
) # Sometimes the robot start driving when instantiated, so this is in case it does this.
def geometric_average(l: list):
x = torch.from_numpy(x).float()
x = normalize(x)
x = x.to(device)
x = x[None, ...]
return x
#カメラ起動、画面表示(pyではwidgetが使えないため代替案検討)
update_bar(1, 'start and display our camera')
import traitlets
#from IPython.display import display
#import ipywidgets.widgets as widgets
from jetbot import Camera, bgr8_to_jpeg
camera = Camera.instance(width=300, height=224, fps=15)
#image = widgets.Image(format='jpeg', width=224, height=224)
#blocked_slider = widgets.FloatSlider(description='blocked', min=0.0, max=1.0, orientation='vertical')
#camera_link = traitlets.dlink((camera, 'value'), (image, 'value'), transform=bgr8_to_jpeg)
#display(widgets.HBox([image, blocked_slider]))
#robotインスタンス生成
update_bar(1, 'create our robot instance')
from jetbot import Robot
robot = Robot()
"""
#tkinter生成
import tkinter
"""
import imutils
from PIL import Image
from jetbot import Camera, Robot
import time
face_cascade = cv2.CascadeClassifier('./data/haarcascade_frontalface_default.xml')
KNOWN_FACES_DIR = 'known_faces'
#UNKNOWN_FACES_DIR = 'unknown_faces' trengs kun for bilder
TOLERANCE = 1 #kan endres
FRAME_THICKNESS = 3
FONT_THICKNESS = 2
MODEL = 'cnn' # default: 'hog', other one can be 'cnn' - CUDA accelerated (if available) deep-learning pretrained model
#video = cv2.VideoCapture(0)
video = Camera.instance()
# Returns (R, G, B) from name
def name_to_color(name):
# Take 3 first letters, tolower()
# lowercased character ord() value rage is 97 to 122, substract 97, multiply by 8
color = [(ord(c.lower())-97)*8 for c in name[:3]]
return color
print('Loading known faces...')
known_faces = []
known_names = []