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import os
import torch
import numpy as np
# visualization
import matplotlib
import matplotlib.cm as cm
import matplotlib.pyplot as plt
matplotlib.use("Agg")
from PIL import Image
# GIF
import imageio.v2 as imageio
# customized
import sys
sys.path.append(".")
from lib.constants import *
from lib.camera_helper import polar_to_xyz
def visualize_quad_mask(mask_image_dir, quad_mask_tensor, view_idx, view_score, device):
quad_mask_tensor = quad_mask_tensor.unsqueeze(-1).repeat(1, 1, 1, 3)
quad_mask_image_tensor = torch.zeros_like(quad_mask_tensor)
for idx in PALETTE:
selected = quad_mask_tensor[quad_mask_tensor == idx].reshape(-1, 3)
selected = torch.FloatTensor(PALETTE[idx]).to(device).unsqueeze(0).repeat(selected.shape[0], 1)
quad_mask_image_tensor[quad_mask_tensor == idx] = selected.reshape(-1)
quad_mask_image_np = quad_mask_image_tensor[0].cpu().numpy().astype(np.uint8)
quad_mask_image = Image.fromarray(quad_mask_image_np).convert("RGB")
quad_mask_image.save(os.path.join(mask_image_dir, "{}_quad_{:.5f}.png".format(view_idx, view_score)))
def visualize_outputs(output_dir, init_image_dir, mask_image_dir, inpainted_image_dir, num_views):
# subplot settings
num_col = 3
num_row = 1
subplot_size = 4
summary_image_dir = os.path.join(output_dir, "summary")
os.makedirs(summary_image_dir, exist_ok=True)
# graph settings
print("=> visualizing results...")
for view_idx in range(num_views):
plt.switch_backend("agg")
fig = plt.figure(dpi=100)
fig.set_size_inches(subplot_size * num_col, subplot_size * (num_row + 1))
fig.set_facecolor('white')
# rendering
plt.subplot2grid((num_row, num_col), (0, 0))
plt.imshow(Image.open(os.path.join(init_image_dir, "{}.png".format(view_idx))))
plt.text(0, 0, "Rendering", fontsize=16, color='black', backgroundcolor='white')
plt.axis('off')
# mask
plt.subplot2grid((num_row, num_col), (0, 1))
plt.imshow(Image.open(os.path.join(mask_image_dir, "{}_project.png".format(view_idx))))
plt.text(0, 0, "Project Mask", fontsize=16, color='black', backgroundcolor='white')
plt.set_cmap(cm.Greys_r)
plt.axis('off')
# inpainted
plt.subplot2grid((num_row, num_col), (0, 2))
plt.imshow(Image.open(os.path.join(inpainted_image_dir, "{}.png".format(view_idx))))
plt.text(0, 0, "Inpainted", fontsize=16, color='black', backgroundcolor='white')
plt.axis('off')
plt.savefig(os.path.join(summary_image_dir, "{}.png".format(view_idx)), bbox_inches="tight")
fig.clf()
# generate GIF
images = [imageio.imread(os.path.join(summary_image_dir, "{}.png".format(view_idx)))for view_idx in range(num_views)]
imageio.mimsave(os.path.join(summary_image_dir, "output.gif"), images, duration=1)
print("=> done!")
def visualize_principle_viewpoints(output_dir, dist_list, elev_list, azim_list):
theta_list = [e for e in azim_list]
phi_list = [90 - e for e in elev_list]
DIST = dist_list[0]
xyz_list = [polar_to_xyz(theta, phi, DIST) for theta, phi in zip(theta_list, phi_list)]
xyz_np = np.array(xyz_list)
color_np = np.array([[0, 0, 0]]).repeat(xyz_np.shape[0], 0)
fig = plt.figure()
ax = plt.axes(projection='3d')
SCALE = 0.8
ax.set_xlim((-DIST, DIST))
ax.set_ylim((-DIST, DIST))
ax.set_zlim((-SCALE * DIST, SCALE * DIST))
ax.scatter(xyz_np[:, 0], xyz_np[:, 2], xyz_np[:, 1], s=100, c=color_np, depthshade=True, label="Principle views")
ax.scatter([0], [0], [0], c=[[1, 0, 0]], s=100, depthshade=True, label="Object center")
# draw hemisphere
# theta inclination angle
# phi azimuthal angle
n_theta = 50 # number of values for theta
n_phi = 200 # number of values for phi
r = DIST #radius of sphere
# theta, phi = np.mgrid[0.0:0.5*np.pi:n_theta*1j, 0.0:2.0*np.pi:n_phi*1j]
theta, phi = np.mgrid[0.0:1*np.pi:n_theta*1j, 0.0:2.0*np.pi:n_phi*1j]
x = r*np.sin(theta)*np.cos(phi)
y = r*np.sin(theta)*np.sin(phi)
z = r*np.cos(theta)
ax.plot_surface(x, y, z, rstride=1, cstride=1, alpha=0.25, linewidth=1)
# Make the grid
ax.quiver(
xyz_np[:, 0],
xyz_np[:, 2],
xyz_np[:, 1],
-xyz_np[:, 0],
-xyz_np[:, 2],
-xyz_np[:, 1],
normalize=True,
length=0.3
)
ax.set_xlabel('X Label')
ax.set_ylabel('Z Label')
ax.set_zlabel('Y Label')
ax.view_init(30, 35)
ax.legend()
plt.show()
plt.savefig(os.path.join(output_dir, "principle_viewpoints.png"))
def visualize_refinement_viewpoints(output_dir, selected_view_ids, dist_list, elev_list, azim_list):
theta_list = [azim_list[i] for i in selected_view_ids]
phi_list = [90 - elev_list[i] for i in selected_view_ids]
DIST = dist_list[0]
xyz_list = [polar_to_xyz(theta, phi, DIST) for theta, phi in zip(theta_list, phi_list)]
xyz_np = np.array(xyz_list)
color_np = np.array([[0, 0, 0]]).repeat(xyz_np.shape[0], 0)
fig = plt.figure()
ax = plt.axes(projection='3d')
SCALE = 0.8
ax.set_xlim((-DIST, DIST))
ax.set_ylim((-DIST, DIST))
ax.set_zlim((-SCALE * DIST, SCALE * DIST))
ax.scatter(xyz_np[:, 0], xyz_np[:, 2], xyz_np[:, 1], c=color_np, depthshade=True, label="Refinement views")
ax.scatter([0], [0], [0], c=[[1, 0, 0]], s=100, depthshade=True, label="Object center")
# draw hemisphere
# theta inclination angle
# phi azimuthal angle
n_theta = 50 # number of values for theta
n_phi = 200 # number of values for phi
r = DIST #radius of sphere
# theta, phi = np.mgrid[0.0:0.5*np.pi:n_theta*1j, 0.0:2.0*np.pi:n_phi*1j]
theta, phi = np.mgrid[0.0:1*np.pi:n_theta*1j, 0.0:2.0*np.pi:n_phi*1j]
x = r*np.sin(theta)*np.cos(phi)
y = r*np.sin(theta)*np.sin(phi)
z = r*np.cos(theta)
ax.plot_surface(x, y, z, rstride=1, cstride=1, alpha=0.25, linewidth=1)
# Make the grid
ax.quiver(
xyz_np[:, 0],
xyz_np[:, 2],
xyz_np[:, 1],
-xyz_np[:, 0],
-xyz_np[:, 2],
-xyz_np[:, 1],
normalize=True,
length=0.3
)
ax.set_xlabel('X Label')
ax.set_ylabel('Z Label')
ax.set_zlabel('Y Label')
ax.view_init(30, 35)
ax.legend()
plt.show()
plt.savefig(os.path.join(output_dir, "refinement_viewpoints.png"))
fig.clear()
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