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import os
import cv2
import h5py
import imageio
import argparse
import numpy as np
from dataclasses import dataclass
MAIN_BGR_H = 384
MAIN_BGR_W = 480
ELEVATION_H = 200
ELEVATION_W = 200
@dataclass
class ExcavatorModel:
name: str = ''
# body
boom_len: float = 0.0
arm_len: float = 0.0
operating_arm_height: float = 0.0
boom_init_angle: float = 0.0
arm_init_angle: float = 0.0
arm_offset_x: float = 0.0
arm_offset_y: float = 0.0
# bucket
bucket_length: float = 0.0
bucket_width: float = 0.0
# elevation resolution
meter_per_pixel_u: float = 0.0
meter_per_pixel_v: float = 0.0
class ExcavatorKinematics(object):
def __init__(self, excavator: str):
self.model = self._get_model(excavator)
def _get_model(self, excavator: str) -> ExcavatorModel:
exc = ExcavatorModel(name=excavator)
if excavator == '75':
exc.boom_len = 3.6957
exc.arm_len = 1.62233
exc.operating_arm_height = 1.4
exc.boom_init_angle = 1.0
exc.arm_init_angle = 1.5
exc.arm_offset_x = 0.0
exc.arm_offset_y = -0.1
exc.bucket_length = 0.8
exc.bucket_width = 0.6
exc.meter_per_pixel_u = 0.1
exc.meter_per_pixel_v = 0.1
elif excavator in ('490', '306'):
exc.boom_len = 6.670
exc.arm_len = 2.90746
exc.operating_arm_height = 2.46
exc.boom_init_angle = 1.0
exc.arm_init_angle = 1.5
exc.arm_offset_x = 0.0
exc.arm_offset_y = 0.0
exc.bucket_length = 1.5
exc.bucket_width = 1.2
exc.meter_per_pixel_u = 0.2
exc.meter_per_pixel_v = 0.2
else:
raise NotImplementedError
return exc
def fk(self, joints: np.ndarray) -> np.ndarray:
assert joints.shape[1] == 4
boom = joints[:, 0]
arm = joints[:, 1]
x = self.model.boom_len * np.sin(boom + self.model.boom_init_angle) + \
self.model.arm_len * np.sin(np.pi - boom - self.model.boom_init_angle - arm - self.model.arm_init_angle)
z = self.model.boom_len * np.cos(boom + self.model.boom_init_angle) - \
self.model.arm_len * np.cos(np.pi - boom - self.model.boom_init_angle - arm - self.model.arm_init_angle)
y = np.zeros_like(x)
xyz = np.stack([x, y, z], axis=1)
return xyz
def resize_with_aspect_ratio(
image: np.ndarray,
width: int=None,
height: int=None
) -> np.ndarray:
if width is None and height is None:
return image
h, w = image.shape[:2]
ratio = min(height / h, width / w)
new_h, new_w = int(h * ratio), int(w * ratio)
# keep ratio resize
resized = cv2.resize(image, (new_w, new_h))
# pad size
pad_top = (height - new_h) // 2
pad_bottom = height - new_h - pad_top
pad_left = (width - new_w) // 2
pad_right = width - new_w - pad_left
# pad with zero
padded = cv2.copyMakeBorder(
resized, pad_top, pad_bottom, pad_left, pad_right, cv2.BORDER_CONSTANT, value=[0, 0, 0])
return padded
def traj_visualization(
excavator: str,
data_path: str,
out_dir: str,
fmt: str='mp4',
fps: int=30
) -> None:
# load data
with h5py.File(data_path) as f:
elevations = np.array(f['observations']['images']['elevation'])
joints = np.array(f['observations']['qpos'])
mains = np.array(f['observations']['images']['main'])
assert elevations.shape[0] == joints.shape[0]
# forward kinemetics
exc = ExcavatorKinematics(excavator)
xyz = exc.fk(joints)
px = (xyz[:, 0] / exc.model.meter_per_pixel_u).astype(np.int32)
bucket_half_u = int(exc.model.bucket_length / 2 / exc.model.meter_per_pixel_u)
bucket_half_v = int(exc.model.bucket_width / 2 / exc.model.meter_per_pixel_v)
# visualization
frames = []
for i in range(elevations.shape[0]):
# end pose
traj_elevation = elevations[i].copy()
center_u = ELEVATION_W // 2
center_v = ELEVATION_H // 2 - px[i]
traj_elevation[center_v - bucket_half_v: center_v + bucket_half_v,
center_u - bucket_half_u: center_u + bucket_half_u] = (255, 0, 0)
# elevation
traj_elevation = resize_with_aspect_ratio(traj_elevation, height=MAIN_BGR_H, width=MAIN_BGR_W)
# main bgr
traj_main = mains[i].copy()[..., ::-1] # BGR -> RGB
traj_main = resize_with_aspect_ratio(traj_main, height=MAIN_BGR_H, width=MAIN_BGR_W)
traj_img = np.concatenate([traj_main, traj_elevation], axis=1)
frames.append(traj_img)
# save
os.makedirs(out_dir, exist_ok=True)
imageio.mimsave(f"{out_dir}/{data_path.split('/')[-1].split('.')[0]}.{fmt}", frames, fps=fps)
if __name__=="__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--excavator", type=str, required=True, help='excavator name')
parser.add_argument("--data_path", type=str, required=True, help='path to the h5 file')
parser.add_argument("--out_dir", type=str, required=True, help='path the output directory')
parser.add_argument("--format", type=str, required=False, default='mp4', help='format of the output file, mp4 or gif are supported')
parser.add_argument("--fps", type=int, required=False, default=30, help='FPS of the output file')
args = parser.parse_args()
traj_visualization(args.excavator, args.data_path, args.out_dir, args.format, args.fps) |