def evaluate(self):
self.max_alt = 0
self.max_sun_alt = -2**30
self.min_range = 2**30
self.eclipsed = 0
self.measure_points = 0
for date, obj in tracker.sat_stepper(self.observer, self.obj, self.start, self.end, 10.0 / 86400):
sun.compute(self.observer)
self.measure_points += 1
if obj.eclipsed:
self.eclipsed += 1
if obj.alt > self.max_alt:
self.max_alt = obj.alt
if sun.alt > self.max_sun_alt:
self.max_sun_alt = sun.alt
if obj.range < self.min_range:
self.min_range = obj.range
self.shadow_ratio = self.eclipsed * 100 / self.measure_points
total = criteria.Score()
total.add(Pass.shadow_crit, self.shadow_ratio)
total.add(Pass.max_alt_crit, rad2deg(self.max_alt))
total.add(Pass.max_sun_alt_crit, rad2deg(self.max_sun_alt))
self.score = total.score()
def inverse_kinematics(x, y, l1, l2):
solve = []
# theta2存在两个解
D = (x**2 + y**2 - l1**2 - l2**2) / (2 * l1 * l2)
S1 = np.sqrt(1 - D**2)
S2 = -S1
a_theta2 = arctan2(S1, D)
b_theta2 = arctan2(S2, D)
# 计算theta1
kesi = np.arctan2(y, x)
a_theta1 = kesi - arcsin(l2 * sin(pi - a_theta2) / np.sqrt(x**2 + y**2))
b_theta1 = kesi - arcsin(l2 * sin(pi - b_theta2) / np.sqrt(x**2 + y**2))
solve.append((utils.rad2deg(a_theta1), utils.rad2deg(a_theta2)))
solve.append((utils.rad2deg(b_theta1), utils.rad2deg(b_theta2)))
return solve
def main():
# points to be followed
pts = [(-6, -7), (-6, 0), (-4, 6), (0, 5), (0, -2), (-2, -6), (3, -5),
(3, 6), (6, 4)]
# generate PATH so the vectors are pointing at each other
PATH = []
for i in range(len(pts) - 1):
dx = pts[i + 1][0] - pts[i][0]
dy = pts[i + 1][1] - pts[i][1]
theta = math.atan2(dy, dx)
PATH.append((pts[i][0], pts[i][1], utils.rad2deg(theta)))
PATH.append((pts[-1][0], pts[-1][1], 0))
# or you can also manually set the angles:
# PATH = [(-5,5,90),(-5,5,-90),(1,4,180), (5,4,0), (6,-3,90), (4,-4,-40),(-2,0,240), \
# (-6, -7, 160), (-7,-1,80)]
# or just generate a random route:
# PATH = []
# for _ in range(10):
# PATH.append((rd.randint(-7,7), rd.randint(-7,7), rd.randint(0,359)))
# init turtle
tesla = turtle.Turtle()
tesla.speed(0) # 0: fast; 1: slow, 8.4: cool
tesla.shape('arrow')
tesla.resizemode('user')
tesla.shapesize(1, 1)
# draw vectors representing points in PATH
for pt in PATH:
draw.goto(tesla, pt)
draw.vec(tesla)
# draw all routes found
tesla.speed(0)
for i in range(len(PATH) - 1):
paths = rs.get_all_paths(PATH[i], PATH[i + 1])
for path in paths:
draw.set_random_pencolor(tesla)
draw.goto(tesla, PATH[i])
draw.draw_path(tesla, path)
# draw shortest route
tesla.pencolor(1, 0, 0)
tesla.pensize(3)
tesla.speed(10)
draw.goto(tesla, PATH[0])
path_length = 0
for i in range(len(PATH) - 1):
path = rs.get_optimal_path(PATH[i], PATH[i + 1])
path_length += rs.path_length(path)
draw.draw_path(tesla, path)
print("Shortest path length: {} px.".format(int(draw.scale(path_length))))
turtle.done()
def meters_and_degrees_error(xy, theta, xy_predicted, theta_predicted):
# q1 = q / np.linalg.norm(q)
# q2 = q_predicted / np.linalg.norm(q_predicted)
# d = np.abs(np.sum(np.multiply(q1, q2)))
# orientation_error = 2 * np.arccos(d) * 180 / np.pi
diff = np.abs(theta - theta_predicted)
orientation_error = rad2deg(min(np.pi - diff, diff))
position_error = np.linalg.norm(xy - xy_predicted)
return position_error, orientation_error
def joint_command_cb(self, joint_desired):
""" Transform subscribed joint command to dynamixel byte information."""
i = 0
while i < 4:
self.q_desired[i] = joint_desired.data[i]
dxl_command = int(
rad2deg(self.q_desired[i]) / self.cfg["DXL_RESOLUTION"] +
self.cfg["DXL_POS_OFFSET"])
if dxl_command > self.cfg["CW_LIMIT"]:
dxl_command = self.cfg["CW_LIMIT"]
elif dxl_command < self.cfg["CCW_LIMIT"]:
dxl_command = self.cfg["CCW_LIMIT"]
self.dxl_goal_position[i] = [
DXL_LOBYTE(DXL_LOWORD(dxl_command)),
DXL_HIBYTE(DXL_LOWORD(dxl_command)),
DXL_LOBYTE(DXL_HIWORD(dxl_command)),
DXL_HIBYTE(DXL_HIWORD(dxl_command)),
]
i += 1
def __str__(self, form: str = "r") -> "ComplexNumber":
i = float(re.sub(r"^[+\-]", "", str(self.i)))
s = "-" if self.i < 0 else "+"
d = hideDecimal(round(self.distance, 2))
a = hideDecimal(round(rad2deg(self.angle), 2))
if (form == "r"):
space = (" " if self.r and i else "")
r = (hideDecimal(self.r) if self.r else "") + space
i = (s if (r or s == "-") and i else
"") + space + (hideDecimal(i) + self.sign if i else "")
return r + i
elif (form == "a"):
return f"{d} / {a}°"
elif (form == "p"):
return f"{d} * (cos({a}°) + {self.sign} * sin({a}°))"
elif (form == "e"):
return f"{d} * e ^ ({self.sign} * {a}°)"
else:
raise TypeError(
"Unknown number form. Supported r = rectangular, a = angle, p = polar, e = exponential"
)
frame = np.int0(frame)
# Get the 2 main angles of the frame
angles = ut.get_angles(frame)
rect2 = (rect[0], rect[1], angles[-1])
img_croped = ut.crop_min_area_rect(color, rect2)
cv2.imshow("Cropped", img_croped)
cv2.waitKey(0)
# As we scale the image, we need to scale back the contour to the original image
frame_orig = list(
map(lambda x: ut.rescale(x, size_ini, size_end), frame))
# Save data to export
frames.append([ut.rad2deg(angles[-1]), frame_orig])
# Save the data in a pickle file
ut.bbox_to_pkl(frames, fname='frames', folder='pkl')
"""
################################## TASK 3: Filter keypoints ####################################
"""
# Check for training database file
if not os.path.exists(PICKLE_MUSEUM_DATASET):
logger.info("Creating pickle database for museum dataset...")
db_museum = ut.create_db(TRAIN_MUSEUM_DIR, FEATURES, candidates)
ut.save_db(db_museum, PICKLE_MUSEUM_DATASET)
else:
logger.info("Reading pickle database for museum dataset...")
db_museum = ut.get_db(PICKLE_MUSEUM_DATASET)
self.shadow_ratio = self.eclipsed * 100 / self.measure_points
total = criteria.Score()
total.add(Pass.shadow_crit, self.shadow_ratio)
total.add(Pass.max_alt_crit, rad2deg(self.max_alt))
total.add(Pass.max_sun_alt_crit, rad2deg(self.max_sun_alt))
self.score = total.score()
def shadow_ratio(self):
return
if __name__ == '__main__':
from data import reto, iss
sun = ephem.Sun()
observer = reto
sat = iss
for start, end in risings(observer, sat, ephem.now(), ephem.now() + 14):
p = Pass(sat, observer, start, end)
if p.score > 70:
start = localtime(p.start)
end = localtime(p.end)
print "%s - %s: sh=%i alt=%i sun=%i score=%i%%" % \
(start.strftime("%Y-%m-%d %H:%M:%S"), end.strftime("%H:%M:%S"), \
p.shadow_ratio, rad2deg(p.max_alt), rad2deg(p.max_sun_alt), p.score)
from utils import rad2deg
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
from data import reto, iss
lats = []
longs = []
start = ephem.now()
for date, o in tracker.sat_stepper(reto, iss, start, start+ 1. / 24):
lats.append(rad2deg(o.sublat))
longs.append(rad2deg(o.sublong))
# set up orthographic map projection with
# perspective of satellite looking down at 50N, 100W.
# use low resolution coastlines.
# don't plot features that are smaller than 1000 square km.
map = Basemap(projection='robin',lat_0=50,lon_0=-100,
resolution='l',area_thresh=1000.)
# draw coastlines, country boundaries, fill continents.
map.drawcoastlines()
map.drawcountries()
map.fillcontinents(color='coral')
# draw the edge of the map projection region (the projection limb)
def update(self, id, x, y, theta):
lat, lng = PosePlotter.utm2latlng(x, y)
pose = self.poses[id]
lats, lngs = pose["lats"], pose["lngs"]
data = pose["data"]
# label = pose["label"]
data.set_visible(True)
if self.trajectory:
lats.append(lat)
lngs.append(lng)
i = len(lats)
data.set_xdata([lngs[: (i + 1)]])
data.set_ydata([lats[: (i + 1)]])
else:
marker_reference = pose["marker"]
new_marker = marker_reference.transformed(matplotlib.transforms.Affine2D().rotate_deg(rad2deg(theta)))
data.set_marker(new_marker)
data.set_xdata([lng])
data.set_ydata([lat])
default = 1
)
args = args_parser.parse_args()
UPDATE_INTERVAL = args.update_interval
plotter = PosePlotter(update_interval = UPDATE_INTERVAL, trajectory = False)
plotter.register("GT", "red")
# plotter.register("x", "blue") # TEST
data = PerceptionCarDatasetMerged(
"PerceptionCarDataset",
"PerceptionCarDataset2",
mode = "visualize",
preprocess = None,
augment = False
)
for image, pose in data:
x, y, theta = PerceptionCarDataset.unnormalize(*pose)
lat, lng = PosePlotter.utm2latlng(x, y)
azimuth = rad2deg(-theta.item())
print(lat, lng, azimuth)
plotter.update("GT", x, y, theta)
plotter.draw()
# plotter.update("x", x + 10, y + 10, theta + 3.14) # TEST
image_cv = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
cv2.imshow("front/center", image_cv)
cv2.waitKey(1)
plotter = PosePlotter(update_interval=0.05, trajectory=True)
dataset_path = "PerceptionCarDataset"
os.makedirs(dataset_path, exist_ok=True)
# Write origin to a file
origin_path = os.path.join(dataset_path, "origin.txt")
with open(origin_path, "wt", encoding="utf-8") as origin_file:
x, y, z = images_db.origin.numpy()
print(x, y, z, file=origin_file)
for i, (image, R_camera, T_camera, R_final_inv, T_final_inv, origin,
image_path) in enumerate(images_loader):
a1, a2, theta = euler_from_matrix(R_camera[0][0])
if visualize:
azimuth = rad2deg(-theta)
X = origin + T_camera
x, y, z = X[0][0][0].numpy()
lat, lng = PosePlotter.utm2latlng(x, y)
print(lat, lng, azimuth)
plotter.update(x, y)
show_image(image, "Image")
cv2.waitKey(1)
else:
# Save the image as JPEG
directory, image_name = os.path.split(image_path[0])
images_directory, _ = os.path.split(directory)
final_directory, _ = os.path.split(images_directory)
jpeg_image_name = image_name.replace(".png", ".jpeg")
dest_path = os.path.join(dataset_path, final_directory)
os.makedirs(dest_path, exist_ok=True)