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import time
import numpy as np
import xml.etree.ElementTree as ET
from jax import jit, vmap
import jax.numpy as jnp
def rot_x(o):
return jnp.array([
[1, 0, 0],
[0, jnp.cos(o), jnp.sin(o)],
[0, -jnp.sin(o), jnp.cos(o)]])
def rot_y(p):
return jnp.array([
[jnp.cos(p), 0, -jnp.sin(p)],
[0, 1, 0],
[jnp.sin(p), 0, jnp.cos(p)]])
def rot_z(k):
return jnp.array([
[jnp.cos(k), jnp.sin(k), 0],
[-jnp.sin(k), jnp.cos(k), 0],
[0, 0, 1]])
def rot_zyx(o, p, k):
return rot_z(k) @ rot_y(p) @ rot_x(o)
def rms_dict(_s):
R = rot_zyx(_s['omega'], _s['phi'], _s['kappa'])
M = jnp.array([_s['X'], _s['Y'], _s['Z']])
S = jnp.array([_s['Xs'], _s['Ys'], _s['Zs']])
RMS = R @ (M - S)
return RMS
def xy_frame(_s):
RMS = rms_dict(_s)
m = - RMS / RMS[2]
x, y, _ = m
z = -RMS[2]
return x, y, z
def corr_dist_agi(x, y, _s):
rc = x ** 2 + y ** 2
dr = 1 + _s['k1'] * rc + _s['k2'] * rc ** 2 + _s['k3'] * rc ** 3 + _s['k4'] * rc ** 4 + _s['k5'] * rc ** 5
drx = x * dr
dry = y * dr
#
dtx = _s['p1'] * (rc + 2 * x ** 2) + 2 * _s['p2'] * x * y * (1 + _s['p3'] * rc + _s['p4'] * rc ** 2)
dty = _s['p2'] * (rc + 2 * y ** 2) + 2 * _s['p1'] * x * y * (1 + _s['p3'] * rc + _s['p4'] * rc ** 2)
xp = drx + dtx
yp = dry + dty
#
fx = _s['width'] * 0.5 + _s['cx'] + xp * _s['f'] + xp * _s['b1'] + yp * _s['b2']
fy = _s['height'] * 0.5 + _s['cy'] + yp * _s['f']
return fx, fy
def f_frame_agi(_s):
x, y, z = xy_frame(_s)
w_2 = _s['width'] / _s['f'] / 2
h_2 = _s['height'] / _s['f'] / 2
ins = (x >= -w_2) & (x < w_2) & (y >= -h_2) & (y < h_2) & (z > 0)
y = -y # to match agisoft convention
fx, fy = corr_dist_agi(x, y, _s)
return fx, fy, z, ins
def read_camera_file(filepath, offset):
data = {}
with open(filepath, 'r') as file:
for line in file:
if line.startswith("#"):
continue
values = line.strip().split()
if len(values) < 16:
continue
photo_id = values[0]
data[photo_id] = {
"Xs": float(values[1]) - offset[0],
"Ys": float(values[2]) - offset[1],
"Zs": float(values[3]) - offset[2],
"omega": np.radians(float(values[4])),
"phi": np.radians(float(values[5])),
"kappa": np.radians(float(values[6])),
}
return data
def parse_calibration_xml(file_path):
tree = ET.parse(file_path)
root = tree.getroot()
# Initialize
calibration_data = {
#'projection': 'frame',
'width': 0,
'height': 0,
'f': 0.0,
'cx': 0.0,
'cy': 0.0,
'k1': 0.0,
'k2': 0.0,
'k3': 0.0,
'k4': 0.0,
'k5': 0.0,
'p1': 0.0,
'p2': 0.0,
'p3': 0.0,
'p4': 0.0,
'b1': 0.0,
'b2': 0.0,
#'date': '' # empty
}
for element in root:
if element.tag in calibration_data:
if element.tag in ['projection', 'date']:
continue
elif element.tag in ['width', 'height']:
calibration_data[element.tag] = int(element.text)
else:
calibration_data[element.tag] = float(element.text)
return calibration_data
def get_pixel_values(image, i, j, in_bounds, nb_classes=10):
"""
Retrieve pixel values from `image` at specified `coords`, marking out-of-bounds
coordinates with `jnp.nan`. Uses only `jax.numpy` operations.
Parameters:
image (jnp.ndarray): 2D image array.
fx, fy (jnp.ndarray): 2 arrays of shape (N, 1) representing i and j coordinates.
Returns:
jnp.ndarray: Array of pixel values with `jnp.nan` for out-of-bounds coordinates.
"""
# Initialize pixel values with NaNs
pixel_values = jnp.full((i.shape[0], nb_classes), jnp.nan)
values = image[j[in_bounds], i[in_bounds]]
pixel_values = pixel_values.at[in_bounds].set(image[j[in_bounds], i[in_bounds]])
pixel_values = pixel_values.at[in_bounds].set(values)
return pixel_values
def compute_depth_map(i, j, z, depth_map, buffer_size, threshold=0.05):
height, width = depth_map.shape
# Define buffer
offsets = jnp.arange(-buffer_size, buffer_size + 1) # Range from -3 to 3
# Create all combinations of offsets to form a square neighborhood
di, dj = jnp.meshgrid(offsets, offsets, indexing='ij') # du, dv are (7, 7) arrays
di = di.ravel() # Flatten to 1D
dj = dj.ravel()
# Compute the neighborhood coordinates for each point
neighbor_i = (i[:, None] + di).clip(0, width - 1) # (N, 49), clipped to image bounds
neighbor_j = (j[:, None] + dj).clip(0, height - 1) # (N, 49), clipped to image bounds
neighbor_depths = jnp.repeat(z[:, None], len(di), axis=1) # Repeat depths to match neighborhood shape
# Flatten everything for efficient indexing
neighbor_i = neighbor_i.ravel()
neighbor_j = neighbor_j.ravel()
neighbor_depths = neighbor_depths.ravel()
# Use scatter_min to update depth_image efficiently
depth_map = depth_map.at[neighbor_j, neighbor_i].min(neighbor_depths)
# Compute visibility map by comparing depth_map with z value
visibility = jnp.abs(depth_map[j, i] - z) <= threshold
return depth_map, visibility
def project_classes_into_image(i, j, classes, img_classes, buffer_size):
height, width = img_classes.shape
# Define buffer
offsets = jnp.arange(-buffer_size, buffer_size + 1) # Range from -3 to 3
# Create all combinations of offsets to form a square neighborhood
di, dj = jnp.meshgrid(offsets, offsets, indexing='ij') # du, dv are (7, 7) arrays
di = di.ravel() # Flatten to 1D
dj = dj.ravel()
# Compute the neighborhood coordinates for each point
neighbor_i = (i[:, None] + di).clip(0, width - 1) # (N, 49), clipped to image bounds
neighbor_j = (j[:, None] + dj).clip(0, height - 1) # (N, 49), clipped to image bounds
neighbor_classes = jnp.repeat(classes[:, None], len(di), axis=1) # Repeat depths to match neighborhood shape
# Flatten everything for efficient indexing
neighbor_i = neighbor_i.ravel()
neighbor_j = neighbor_j.ravel()
neighbor_classes = neighbor_classes.ravel()
# Use scatter_min to update depth_image efficiently
img_classes = img_classes.at[neighbor_j, neighbor_i].set(neighbor_classes)
return img_classes
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