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import os
import sys
PPF_PATH = "/mnt/prev_nas/qhy_1/GenSpace/osdsynth/external/PerspectiveFields"
sys.path.append(PPF_PATH) # This is needed for the following imports in this file
PPF_PATH_ABS = os.path.abspath(PPF_PATH)
import copy
import os
import cv2
import numpy as np
import torch
from perspective2d import PerspectiveFields
from perspective2d.utils import draw_from_r_p_f_cx_cy, draw_perspective_fields
def create_rotation_matrix(
roll: float,
pitch: float,
yaw: float,
degrees: bool = False,
) -> np.ndarray:
r"""Create rotation matrix from extrinsic parameters
Args:
roll (float): camera rotation about camera frame z-axis
pitch (float): camera rotation about camera frame x-axis
yaw (float): camera rotation about camera frame y-axis
Returns:
np.ndarray: rotation R_z @ R_x @ R_y
"""
if degrees:
roll = np.radians(roll)
pitch = np.radians(pitch)
yaw = np.radians(yaw)
# calculate rotation about the x-axis
R_x = np.array(
[
[1.0, 0.0, 0.0],
[0.0, np.cos(pitch), np.sin(pitch)],
[0.0, -np.sin(pitch), np.cos(pitch)],
]
)
# calculate rotation about the y-axis
R_y = np.array(
[
[np.cos(yaw), 0.0, -np.sin(yaw)],
[0.0, 1.0, 0.0],
[np.sin(yaw), 0.0, np.cos(yaw)],
]
)
# calculate rotation about the z-axis
R_z = np.array(
[
[np.cos(roll), np.sin(roll), 0.0],
[-np.sin(roll), np.cos(roll), 0.0],
[0.0, 0.0, 1.0],
]
)
return R_z @ R_x @ R_y
def resize_fix_aspect_ratio(img, field, target_width=None, target_height=None):
height = img.shape[0]
width = img.shape[1]
if target_height is None:
factor = target_width / width
elif target_width is None:
factor = target_height / height
else:
factor = max(target_width / width, target_height / height)
if factor == target_width / width:
target_height = int(height * factor)
else:
target_width = int(width * factor)
img = cv2.resize(img, (target_width, target_height))
for key in field:
if key not in ["up", "lati"]:
continue
tmp = field[key].numpy()
transpose = len(tmp.shape) == 3
if transpose:
tmp = tmp.transpose(1, 2, 0)
tmp = cv2.resize(tmp, (target_width, target_height))
if transpose:
tmp = tmp.transpose(2, 0, 1)
field[key] = torch.tensor(tmp)
return img, field
def run_perspective_fields_model(model, image_bgr):
pred = model.inference(img_bgr=image_bgr)
field = {
"up": pred["pred_gravity_original"].cpu().detach(),
"lati": pred["pred_latitude_original"].cpu().detach(),
}
img, field = resize_fix_aspect_ratio(image_bgr[..., ::-1], field, 640)
# Draw perspective field from ParamNet predictions
param_vis = draw_from_r_p_f_cx_cy(
img,
pred["pred_roll"].item(),
pred["pred_pitch"].item(),
pred["pred_general_vfov"].item(),
pred["pred_rel_cx"].item(),
pred["pred_rel_cy"].item(),
"deg",
up_color=(0, 1, 0),
).astype(np.uint8)
param_vis = cv2.cvtColor(param_vis, cv2.COLOR_RGB2BGR)
param = {
"roll": pred["pred_roll"].cpu().item(),
"pitch": pred["pred_pitch"].cpu().item(),
}
return param_vis, param
def get_perspective_fields_model(cfg, device):
MODEL_ID = "Paramnet-360Cities-edina-centered"
# MODEL_ID = 'Paramnet-360Cities-edina-uncentered'
# MODEL_ID = 'PersNet_Paramnet-GSV-centered'
# MODEL_ID = 'PersNet_Paramnet-GSV-uncentered'
# MODEL_ID = 'PersNet-360Cities'
# feel free to test with uncentered or centered depending on your data
PerspectiveFields.versions()
pf_model = PerspectiveFields(MODEL_ID).eval().cuda()
return pf_model
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