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import numpy as np
import cv2
import torch
# Note that the coordinates passed to the model must not exceed 256.
# xy range 256
def pdf2(sigma_matrix, grid):
"""Calculate PDF of the bivariate Gaussian distribution.
Args:
sigma_matrix (ndarray): with the shape (2, 2)
grid (ndarray): generated by :func:`mesh_grid`,
with the shape (K, K, 2), K is the kernel size.
Returns:
kernel (ndarrray): un-normalized kernel.
"""
inverse_sigma = np.linalg.inv(sigma_matrix)
kernel = np.exp(-0.5 * np.sum(np.dot(grid, inverse_sigma) * grid, 2))
return kernel
def mesh_grid(kernel_size):
"""Generate the mesh grid, centering at zero.
Args:
kernel_size (int):
Returns:
xy (ndarray): with the shape (kernel_size, kernel_size, 2)
xx (ndarray): with the shape (kernel_size, kernel_size)
yy (ndarray): with the shape (kernel_size, kernel_size)
"""
ax = np.arange(-kernel_size // 2 + 1.0, kernel_size // 2 + 1.0)
xx, yy = np.meshgrid(ax, ax)
xy = np.hstack(
(
xx.reshape((kernel_size * kernel_size, 1)),
yy.reshape(kernel_size * kernel_size, 1),
)
).reshape(kernel_size, kernel_size, 2)
return xy, xx, yy
def sigma_matrix2(sig_x, sig_y, theta):
"""Calculate the rotated sigma matrix (two dimensional matrix).
Args:
sig_x (float):
sig_y (float):
theta (float): Radian measurement.
Returns:
ndarray: Rotated sigma matrix.
"""
d_matrix = np.array([[sig_x**2, 0], [0, sig_y**2]])
u_matrix = np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]])
return np.dot(u_matrix, np.dot(d_matrix, u_matrix.T))
def bivariate_Gaussian(kernel_size, sig_x, sig_y, theta, grid=None, isotropic=True):
"""Generate a bivariate isotropic or anisotropic Gaussian kernel.
In the isotropic mode, only `sig_x` is used. `sig_y` and `theta` is ignored.
Args:
kernel_size (int):
sig_x (float):
sig_y (float):
theta (float): Radian measurement.
grid (ndarray, optional): generated by :func:`mesh_grid`,
with the shape (K, K, 2), K is the kernel size. Default: None
isotropic (bool):
Returns:
kernel (ndarray): normalized kernel.
"""
if grid is None:
grid, _, _ = mesh_grid(kernel_size)
if isotropic:
sigma_matrix = np.array([[sig_x**2, 0], [0, sig_x**2]])
else:
sigma_matrix = sigma_matrix2(sig_x, sig_y, theta)
kernel = pdf2(sigma_matrix, grid)
kernel = kernel / np.sum(kernel)
return kernel
size = 99
sigma = 10
blur_kernel = bivariate_Gaussian(size, sigma, sigma, 0, grid=None, isotropic=True)
blur_kernel = blur_kernel / blur_kernel[size // 2, size // 2]
canvas_width, canvas_height = 256, 256
def get_flow(points, optical_flow, video_len):
for i in range(video_len - 1):
p = points[i]
p1 = points[i + 1]
optical_flow[i + 1, p[1], p[0], 0] = p1[0] - p[0]
optical_flow[i + 1, p[1], p[0], 1] = p1[1] - p[1]
return optical_flow
def process_points(points, frames=49):
defualt_points = [[128, 128]] * frames
if len(points) < 2:
return defualt_points
elif len(points) >= frames:
skip = len(points) // frames
return points[::skip][: frames - 1] + points[-1:]
else:
insert_num = frames - len(points)
insert_num_dict = {}
interval = len(points) - 1
n = insert_num // interval
m = insert_num % interval
for i in range(interval):
insert_num_dict[i] = n
for i in range(m):
insert_num_dict[i] += 1
res = []
for i in range(interval):
insert_points = []
x0, y0 = points[i]
x1, y1 = points[i + 1]
delta_x = x1 - x0
delta_y = y1 - y0
for j in range(insert_num_dict[i]):
x = x0 + (j + 1) / (insert_num_dict[i] + 1) * delta_x
y = y0 + (j + 1) / (insert_num_dict[i] + 1) * delta_y
insert_points.append([int(x), int(y)])
res += points[i : i + 1] + insert_points
res += points[-1:]
return res
def read_points_from_list(traj_list, video_len=16, reverse=False):
points = []
for point in traj_list:
if isinstance(point, str):
x, y = point.strip().split(",")
else:
x, y = point[0], point[1]
points.append((int(x), int(y)))
if reverse:
points = points[::-1]
if len(points) > video_len:
skip = len(points) // video_len
points = points[::skip]
points = points[:video_len]
return points
def read_points_from_file(file, video_len=16, reverse=False):
with open(file, "r") as f:
lines = f.readlines()
points = []
for line in lines:
x, y = line.strip().split(",")
points.append((int(x), int(y)))
if reverse:
points = points[::-1]
if len(points) > video_len:
skip = len(points) // video_len
points = points[::skip]
points = points[:video_len]
return points
def process_traj(trajs_list, num_frames, video_size, device="cpu"):
if trajs_list and trajs_list[0] and (not isinstance(trajs_list[0][0], (list, tuple))):
tmp = trajs_list
trajs_list = [tmp]
optical_flow = np.zeros((num_frames, video_size[0], video_size[1], 2), dtype=np.float32)
processed_points = []
for traj_list in trajs_list:
points = read_points_from_list(traj_list, video_len=num_frames)
xy_range = 256
h, w = video_size
points = process_points(points, num_frames)
points = [[int(w * x / xy_range), int(h * y / xy_range)] for x, y in points]
optical_flow = get_flow(points, optical_flow, video_len=num_frames)
processed_points.append(points)
print(f"received {len(trajs_list)} trajectorie(s)")
for i in range(1, num_frames):
optical_flow[i] = cv2.filter2D(optical_flow[i], -1, blur_kernel)
optical_flow = torch.tensor(optical_flow).to(device)
return optical_flow, processed_points
def add_provided_traj(traj_name):
global traj_list
traj_list = PROVIDED_TRAJS[traj_name]
traj_str = [f"{traj}" for traj in traj_list]
return ", ".join(traj_str)
def scale_traj_list_to_256(traj_list, canvas_width, canvas_height):
scale_x = 256 / canvas_width
scale_y = 256 / canvas_height
scaled_traj_list = [[int(x * scale_x), int(y * scale_y)] for x, y in traj_list]
return scaled_traj_list |