File size: 3,173 Bytes
ea66a99 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | import torch
import os
from PIL import Image, ImageDraw, ImageFont
from matplotlib import pyplot as plt
from matplotlib import font_manager
plt.rcParams['font.family'] = ['DejaVu Sans']
import numpy as np
import copy
@torch.no_grad()
def render_training_image(scene, gaussians, viewpoints, render_func, pipe, background, stage, iteration, time_now, dataset_type):
def render(gaussians, viewpoint, path, scaling, cam_type):
# scaling_copy = gaussians._scaling
render_pkg = render_func(viewpoint, gaussians, pipe, background, stage=stage, cam_type=cam_type)
label1 = f"stage:{stage},iter:{iteration}"
times = time_now/60
if times < 1:
end = "min"
else:
end = "mins"
label2 = "time:%.2f" % times + end
image = render_pkg["render"]
depth = render_pkg["depth"]
if dataset_type == "PanopticSports":
gt_np = viewpoint['image'].permute(1,2,0).cpu().numpy()
else:
gt_np = viewpoint.original_image.permute(1,2,0).cpu().numpy()
image_np = image.permute(1, 2, 0).cpu().numpy() # (H, W, 3)
depth_np = depth.permute(1, 2, 0).cpu().numpy()
depth_np /= depth_np.max()
depth_np = np.repeat(depth_np, 3, axis=2)
image_np = np.concatenate((gt_np, image_np, depth_np), axis=1)
image_with_labels = Image.fromarray((np.clip(image_np,0,1) * 255).astype('uint8'))
draw1 = ImageDraw.Draw(image_with_labels)
font_path = font_manager.findfont("DejaVu Sans")
try:
font = ImageFont.truetype(str(font_path), size=40)
except OSError:
font = ImageFont.load_default()
text_color = (255, 0, 0)
label1_position = (10, 10)
label2_position = (image_with_labels.width - 100 - len(label2) * 10, 10)
draw1.text(label1_position, label1, fill=text_color, font=font)
draw1.text(label2_position, label2, fill=text_color, font=font)
image_with_labels.save(path)
render_base_path = os.path.join(scene.model_path, f"{stage}_render")
point_cloud_path = os.path.join(render_base_path,"pointclouds")
image_path = os.path.join(render_base_path,"images")
if not os.path.exists(os.path.join(scene.model_path, f"{stage}_render")):
os.makedirs(render_base_path)
if not os.path.exists(point_cloud_path):
os.makedirs(point_cloud_path)
if not os.path.exists(image_path):
os.makedirs(image_path)
for idx in range(len(viewpoints)):
image_save_path = os.path.join(image_path,f"{iteration}_{idx}.jpg")
render(gaussians,viewpoints[idx],image_save_path,scaling = 1,cam_type=dataset_type)
pc_mask = gaussians.get_opacity
pc_mask = pc_mask > 0.1
def visualize_and_save_point_cloud(point_cloud, R, T, filename):
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
R = R.T
T = -R.dot(T)
transformed_point_cloud = np.dot(R, point_cloud) + T.reshape(-1, 1)
ax.scatter(transformed_point_cloud[0], transformed_point_cloud[1], transformed_point_cloud[2], c='g', marker='o')
ax.axis("off")
plt.savefig(filename)
|