self.matrix = -np.ones((self.height, self.width))
时间: 2023-11-16 19:04:23 浏览: 36
As an AI language model, I can understand that the given code initializes a matrix of size `height` x `width` with all elements as -1 using the NumPy library.
The `np` in the code refers to the NumPy library which is used for scientific computing in Python.
The `height` and `width` variables are parameters that determine the dimensions of the matrix.
The `-1` in the code represents that all the elements in the matrix are initialized as -1.
The resulting matrix can be used for various purposes such as image processing, machine learning, and data analysis.
相关问题
程序执行提示AttributeError: 'point_cloud_generator' object has no attribute 'widthself',优化程序class point_cloud_generator(): def __init__(self, rgb_file, depth_file, save_ply, camera_intrinsics=[784.0, 779.0, 649.0, 405.0]): 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 # depth[depth==65535]=0 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.widthself.height] data_ply[1] = -self.Y.T.reshape(-1)[:self.widthself.height] data_ply[2] = -self.Z.T.reshape(-1)[:self.widthself.height] img = np.array(self.rgb, dtype=np.uint8) data_ply[3] = img[:, :, 0:1].reshape(-1)[:self.widthself.height] data_ply[4] = img[:, :, 1:2].reshape(-1)[:self.widthself.height] data_ply[5] = img[:, :, 2:3].reshape(-1)[:self.widthself.height] self.data_ply = data_ply t2 = time.time() print('calcualte 3d point cloud Done.', t2 - t1)
在代码中有两处出现了 `self.widthself.height`,这应该是想要写成 `self.width * self.height`。你需要修改这两处,如下所示:
```
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]
```
这样就可以避免出现 `AttributeError: 'point_cloud_generator' object has no attribute 'widthself'` 的错误了。
程序运行提示ValueError: could not broadcast input array from shape (921600,) into shape (307200,),优化程序 def compute(self): t1 = time.time() depth = np.asarray(self.depth, dtype=np.uint16).T # depth[depth==65535]=0 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) data_ply[1] = -self.Y.T.reshape(-1) data_ply[2] = -self.Z.T.reshape(-1) img = np.array(self.rgb, dtype=np.uint8) data_ply[3] = img[:, :, 0:1].reshape(-1) data_ply[4] = img[:, :, 1:2].reshape(-1) data_ply[5] = img[:, :, 2:3].reshape(-1) self.data_ply = data_ply t2 = time.time() print('calcualte 3d point cloud Done.', t2 - t1)
根据报错信息,可以看出是在数据维度转换的过程中出现了问题。具体来说,`self.X.T.reshape(-1)`、`-self.Y.T.reshape(-1)` 和 `-self.Z.T.reshape(-1)` 这三行代码中,将二维数组转换成了一维数组,并且数组长度发生了变化。其中,`self.X`、`self.Y` 和 `self.Z` 都是形状为 `(self.width, self.height)` 的二维数组,转换成一维数组之后,长度应该是 `self.width * self.height`。但是由于错误提示中提到的维度不匹配问题,可以猜测有些数据的长度没有正确计算。
为了解决这个问题,可以在调用 `reshape` 函数之前,先打印出相应数组的形状,确保长度计算正确。同时,也可以使用 `flatten` 函数代替 `reshape(-1)`,这样可以保证不改变数组的长度和维度。修改后的代码如下:
```
data_ply[0] = self.X.T.flatten()
data_ply[1] = -self.Y.T.flatten()
data_ply[2] = -self.Z.T.flatten()
```
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