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test_render_window.py
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import torch
import os,sys
import json
from tqdm import tqdm
import numpy as np
from lib.models.street_gaussian_model import StreetGaussianModel
from lib.models.street_gaussian_renderer import StreetGaussianRenderer, StreetGaussianRendererLite
from lib.datasets.dataset import Dataset
from lib.models.scene import Scene
from lib.utils.general_utils import safe_state
from lib.config import cfg
from lib.visualizers.base_visualizer import BaseVisualizer as Visualizer
from lib.visualizers.street_gaussian_visualizer import StreetGaussianVisualizer, StreetGaussianVisualizerLite
import time
import copy
from lib.utils.camera_utils import Camera
from scipy.spatial.transform import Rotation
import tkinter as tk
from PIL import Image, ImageTk
import matplotlib.pyplot as plt
from PyQt5.QtWidgets import QApplication, QWidget, QLabel
from PyQt5.QtGui import QPixmap, QImage
from PyQt5 import QtCore
def render_sets():
cfg.render.save_image = True
cfg.render.save_video = False
with torch.no_grad():
dataset = Dataset()
gaussians = StreetGaussianModel(dataset.scene_info.metadata)
scene = Scene(gaussians=gaussians, dataset=dataset)
renderer = StreetGaussianRenderer()
times = []
if not cfg.eval.skip_train:
save_dir = os.path.join(cfg.model_path, 'train', "ours_{}".format(scene.loaded_iter))
visualizer = Visualizer(save_dir)
cameras = scene.getTrainCameras()
for idx, camera in enumerate(tqdm(cameras, desc="Rendering Training View")):
torch.cuda.synchronize()
start_time = time.time()
result = renderer.render(camera, gaussians)
torch.cuda.synchronize()
end_time = time.time()
times.append((end_time - start_time) * 1000)
visualizer.visualize(result, camera)
if not cfg.eval.skip_test:
save_dir = os.path.join(cfg.model_path, 'test', "ours_{}".format(scene.loaded_iter))
visualizer = Visualizer(save_dir)
cameras = scene.getTestCameras()
for idx, camera in enumerate(tqdm(cameras, desc="Rendering Testing View")):
torch.cuda.synchronize()
start_time = time.time()
result = renderer.render(camera, gaussians)
torch.cuda.synchronize()
end_time = time.time()
times.append((end_time - start_time) * 1000)
visualizer.visualize(result, camera)
print(times)
print('average rendering time: ', sum(times[1:]) / len(times[1:]))
def render_trajectory():
# cfg.render.save_image = False
# cfg.render.save_video = True
cfg.render.save_image = True
cfg.render.save_video = False
app = QApplication(sys.argv)
window = QWidget()
window.setWindowFlags(QtCore.Qt.FramelessWindowHint) # Remove title bar and toolbar
# Set the window size, for example, 800x600
# window.showFullScreen() # Show in full screen
# window.show()
window.resize(1600, 1066)
window.move(1920, 0) # can send the pic to the next screen (which goes into the rdk)
label = QLabel(window)
label.setGeometry(0, 0, window.width(), window.height()) # Set label size to window size
window.show()
with torch.no_grad():
dataset = Dataset()
gaussians = StreetGaussianModel(dataset.scene_info.metadata)
scene = Scene(gaussians=gaussians, dataset=dataset)
renderer = StreetGaussianRendererLite()
# save_dir = os.path.join(cfg.model_path, 'trajectory', "ours_{}".format(scene.loaded_iter))
# visualizer = StreetGaussianVisualizerLite(save_dir)
train_cameras = scene.getTrainCameras()
test_cameras = scene.getTestCameras()
cameras = train_cameras + test_cameras
cameras = list(sorted(cameras, key=lambda x: x.id))
len_cameras = len(cameras)
# for idx in range(90, len_cameras+10):
for idx in range(len_cameras):
start_time = time.perf_counter()
if idx < len_cameras:
cam_sample = cameras[idx]
else:
cam_orig = copy.deepcopy(cameras[-1])
fake_idx = idx - len_cameras + 1
# cam_orig.T[0] = cam_orig.T[0] - 0.1*fake_idx
# cam_orig.T[1] = cam_orig.T[1] + 0.1*fake_idx
# 物体前进的距离 x
Rt = cam_orig.R.transpose()
# r = Rotation.from_matrix(Rt)
# # 获取欧拉角
# euler_angles = r.as_euler('xyz', degrees=True)
# print(" camera xyz angle: ", euler_angles)
# x = 0.5 # 举例
x = 0.0 # 举例
# 从 T1 的旋转矩阵中提取前进方向的单位向量，这里是 z 轴负方向
# direction_vector = -Rt[:, 0] # 假设物体沿 z 轴负方向前进
direction_vector = np.array([0.0, 0.0, -1.0])
# 计算前进向量
delta_p = x * direction_vector
# # 更新 T1 的平移向量
# new_translation = fake_idx * delta_p
# print(" new T: ", new_translation)
cam_orig.T = cam_orig.T + fake_idx * delta_p
if cam_orig.K.is_cuda:
K = cam_orig.K.cpu()
K_array = K.detach().numpy()
cam_sample = Camera(
id=cam_orig.id,
R=cam_orig.R,
T=cam_orig.T,
FoVx=cam_orig.FoVx,
FoVy=cam_orig.FoVy,
K=K_array,
image=cam_orig.original_image,
image_name=cam_orig.image_name,
metadata=cam_orig.meta
)
cam_sample.ego_pose = cam_orig.ego_pose
cam_sample.extrinsic = cam_orig.extrinsic
cam_sample.id = cam_orig.id + fake_idx
cam_sample.meta['frame'] = cam_orig.meta['frame'] + fake_idx
cam_sample.meta['frame_idx'] = cam_orig.meta['frame_idx'] + fake_idx
cam_sample.meta['timestamp'] = cam_orig.meta['timestamp'] - fake_idx*0.1
cam_sample.image_name = '000%s_0' % cam_sample.meta['frame']
# print("#### idx: ", idx)
# # print(" camera.R: ", cam_sample.R)
# # print(" camera.R T: ", cam_sample.R.transpose())
#
# # print(" direction_vector: ", direction_vector)
# print(" camera.T: ", cam_sample.T)
# # print(" new T: ", new_translation)
# print(" camera.timestamp: ", cam_sample.meta['timestamp'])
result = renderer.render_all(cam_sample, gaussians)['rgb']
rgb = (result.detach().cpu().numpy().transpose(1, 2, 0) * 255).astype(np.uint8)
# Convert numpy array to QImage
height, width, channel = rgb.shape
print(height)
print(width)
bytes_per_line = 3 * width
q_image = QImage(rgb.tobytes(), width, height, bytes_per_line, QImage.Format_RGB888)
pixmap = QPixmap.fromImage(q_image)
label.setPixmap(pixmap)
label.show()
app.processEvents() # Update the GUI
end_time = time.perf_counter()
print(f" running time：{end_time - start_time}秒")
# sys.exit(app.exec_())
# visualizer.visualize(result, cam_sample)
if __name__ == "__main__":
print("Rendering " + cfg.model_path)
safe_state(cfg.eval.quiet)
try:
if cfg.mode == 'evaluate':
render_sets()
elif cfg.mode == 'trajectory':
render_trajectory()
else:
raise NotImplementedError()
except Exception as e:
print(f"An error occurred: {e}")
import traceback
traceback.print_exc()