没有GPU，优化程序class point_cloud_generator(): def init(self, rgb_file, depth_file, save_ply, camera_intrinsics=[312.486, 243.928, 382.363, 382.363]): self.rgb_file = rgb_file self.depth_file = depth_file self.save_ply = save_ply self.rgb = cv2.imread(rgb_file) self.depth = cv2.imread(self.depth_file, -1) print("your depth image shape is:", self.depth.shape) self.width = self.rgb.shape[1] self.height = self.rgb.shape[0] self.camera_intrinsics = camera_intrinsics self.depth_scale = 1000 def compute(self): t1 = time.time() depth = np.asarray(self.depth, dtype=np.uint16).T self.Z = depth / self.depth_scale fx, fy, cx, cy = self.camera_intrinsics X = np.zeros((self.width, self.height)) Y = np.zeros((self.width, self.height)) for i in range(self.width): X[i, :] = np.full(X.shape[1], i) self.X = ((X - cx / 2) * self.Z) / fx for i in range(self.height): Y[:, i] = np.full(Y.shape[0], i) self.Y = ((Y - cy / 2) * self.Z) / fy data_ply = np.zeros((6, self.width * self.height)) data_ply[0] = self.X.T.reshape(-1)[:self.width * self.height] data_ply[1] = -self.Y.T.reshape(-1)[:self.width * self.height] data_ply[2] = -self.Z.T.reshape(-1)[:self.width * self.height] img = np.array(self.rgb, dtype=np.uint8) data_ply[3] = img[:, :, 0:1].reshape(-1)[:self.width * self.height] data_ply[4] = img[:, :, 1:2].reshape(-1)[:self.width * self.height] data_ply[5] = img[:, :, 2:3].reshape(-1)[:self.width * self.height] self.data_ply = data_ply t2 = time.time() print('calcualte 3d point cloud Done.', t2 - t1) def write_ply(self): start = time.time() float_formatter = lambda x: "%.4f" % x points = [] for i in self.data_ply

It that the code is generating a point cloud from an RGB-D image pair. Since you mentioned that you do not have a GPU, one possible optimization could be to use the `numba` library to speed up the computation. Here is how you can modify the code to use `numba`:

1. Import the `numba` library by adding the following line at the top of your code:

   import numba

2. Add the `@numba.jit(nopython=True)` decorator to the `compute` method to enable `numba` JIT compilation:

   @numba.jit(nopython=True)
   def compute(self):
       # rest of the code

3. Modify the `for` loops in the `compute` method to use `numba`'s `prange` function to parallelize the computation across multiple CPU cores:

   for i in numba.prange(self.width):
       # rest of the code

   for i in numba.prange(self.height):
       # rest of the code

4. Add the `@numba.jit(nopython=True)` decorator to the `write_ply` method as well, since it is also computationally intensive:

   @numba.jit(nopython=True)
   def write_ply(self):
       # rest of the code

With these modifications, the `compute` and `write_ply` methods should run faster. However, keep in mind that `numba`'s JIT compilation can take some time during the first function call, so subsequent calls should be faster. Also, note that `numba`'s `prange` function only works with integer indices, so you may need to convert floating-point indices to integers before using them in the loops.