def compare(im1, im2): width, height = im1.shape cd_results = np.zeros((width, height)).astype(np.uint8) print('计算开始') for i in tqdm(range(width)): for j in range(height): if im2[i, j] == im1[i, j]: cd_results[i, j] = 169 elif im2[i, j] > im1[i, j]: cd_results[i, j] = 255 else: cd_results[i, j] = 0 print('计算完成') return cd_results.reshape(1, width, height)解释一下
时间: 2023-05-15 09:04:41 浏览: 47
这是一个比较两张图片的函数,输入参数为两张图片的像素矩阵 im1 和 im2。函数首先获取图片的宽度和高度,然后创建一个与图片大小相同的全零矩阵 cd_results。接着,函数遍历每个像素点,如果两张图片在该像素点的像素值相等,将 cd_results 对应位置的值设为 169;如果 im2 在该像素点的像素值大于 im1,将 cd_results 对应位置的值设为 255;否则,将 cd_results 对应位置的值设为 0。最后,函数将 cd_results 重新变形为一个三维数组并返回。函数中还包含了一个进度条 tqdm,用于显示计算进度。
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。
class KNearestNeighbor(object): def __init__(self): pass def train(self, X, y): self.X_train = X self.y_train = y def predict(self, X, k=1): num_test = X.shape[0] num_train = self.X_train.shape[0] dists = np.zeros((num_test, num_train)) d1 = -2 * np.dot(X, self.X_train.T) d2 = np.sum(np.square(X), axis=1, keepdims=True) d3 = np.sum(np.square(self.X_train), axis=1) dist = np.sqrt(d1 + d2 + d3) y_pred = np.zeros(num_test) for i in range(num_test): dist_k_min = np.argsort(dist[i])[:k] y_kclose = self.y_train[dist_k_min] y_pred[i] = np.argmax(np.bincount(y_kclose.tolist())) return y_pred注释每一行代码
这是一个K最近邻分类器的Python实现,以下是每一行代码的注释:
```
class KNearestNeighbor(object):
def __init__(self):
pass
# 训练函数,输入训练数据X和对应的标签y
def train(self, X, y):
self.X_train = X
self.y_train = y
# 预测函数,输入测试数据X和参数k表示选择的最近邻数,默认为1
def predict(self, X, k=1):
num_test = X.shape[0] # 测试数据集的大小
num_train = self.X_train.shape[0] # 训练数据集的大小
dists = np.zeros((num_test, num_train)) # 创建一个距离矩阵,大小为(num_test, num_train)
# 计算测试数据集和训练数据集之间的距离
d1 = -2 * np.dot(X, self.X_train.T) # 先计算两个矩阵的乘积,再将结果乘以-2
d2 = np.sum(np.square(X), axis=1, keepdims=True) # 计算测试数据集中每个数据的平方和
d3 = np.sum(np.square(self.X_train), axis=1) # 计算训练数据集中每个数据的平方和
dist = np.sqrt(d1 + d2 + d3) # 计算欧几里得距离
y_pred = np.zeros(num_test) # 创建一个长度为num_test的数组,用于存储预测结果
# 针对每个测试数据,选出距离最近的k个训练数据,并进行分类
for i in range(num_test):
dist_k_min = np.argsort(dist[i])[:k] # 选出距离最近的k个训练数据
y_kclose = self.y_train[dist_k_min] # 获取这k个训练数据的标签
y_pred[i] = np.argmax(np.bincount(y_kclose.tolist())) # 对这k个训练数据的标签进行投票,选出最多的标签作为预测结果
return y_pred
```
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