contours[:, :, 0] = contours[:, :, 0] * factor TypeError: tuple indices must be integers or slices, not tuple

这个错误是由于在对`contours`进行索引时，使用了元组`( :, :, 0)`作为索引。然而，元组索引在NumPy中是不被允许的，因为它期望整数或切片作为索引。

如果你想要将`contours`中的数据的第一个维度乘以`factor`，你可以使用以下方式来实现：

contours[:, :, 0] *= factor

这样会将`contours`中的第一维度的所有元素乘以`factor`。请确保`factor`是一个数值类型的变量，并且与`contours[:, :, 0]`的形状相匹配。

相关问题

contours[:, :, 0] *= factor TypeError: tuple indices must be integers or slices, not tuple

抱歉，我之前给出的解决方案有误。这个错误是由于`contours`是一个元组而不是NumPy数组，元组不能被修改。要解决这个问题，你需要将`contours`转换为NumPy数组，然后再进行操作。

可以使用`np.array()`函数将元组转换为NumPy数组，如下所示：

import numpy as np

contours = np.array(contours)
contours[:, :, 0] *= factor

这样，你就可以通过`contours[:, :, 0]`来访问并修改第一个维度的数据，并将其乘以`factor`。请确保`factor`是一个数值类型的变量，并且与`contours[:, :, 0]`的形状相匹配。

def get_postion(image): img = cv2.imread(image) blurred = cv2.GaussianBlur(image,(5,5),0,0) canny = cv2.Canny(blurred,0,100) contours,hierarchy=cv2.findContours(canny,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) print(len(contours)) for contour in contours: x,y,w,h = cv2.boundingRect(contour) area = cv2.contourArea(contour) loop = cv2.arcLength(contour,True) if 152<loop<162 and 1444<area<1849: x,y,w,h = cv2.boundingRect(contour) cv2.rectangle(image,(x,y),(x+w,y+h),(0,0,255),2) cv2.imwrite("111.jpg",image) return x return 0帮我优化一下代码

Sure, here are some optimizations that can be made to the code:

1. Instead of reading the image using `cv2.imread()` inside the function, it would be better to pass in the image as an argument to the function. This way, the image can be read outside the function and the same image can be passed to multiple function calls, saving time and resources.

2. The Gaussian blur operation can be skipped as it is not required for detecting contours. This would save time and also reduce the noise in the image.

3. Instead of using a fixed threshold of 100 for the Canny edge detection, it would be better to use a variable threshold based on the median intensity of the image. This would ensure that the edges are detected appropriately for different images.

4. The loop over all the contours can be replaced with a loop over only the external contours, as the internal contours are not required for the given problem.

5. Instead of using a fixed range for the contour loop and area threshold, it would be better to use variable thresholds based on the size of the image. This would ensure that the contours are detected appropriately for different image sizes.

Here is the optimized code:

import cv2

def get_position(image, min_loop, max_loop, min_area, max_area):
    blurred = cv2.GaussianBlur(image, (5,5), 0)
    median = int(cv2.medianBlur(image, 5).mean())
    canny = cv2.Canny(blurred, median/2, median)
    contours, hierarchy = cv2.findContours(canny, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    print(len(contours))
    for contour in contours:
        area = cv2.contourArea(contour)
        loop = cv2.arcLength(contour, True)
        if min_loop < loop < max_loop and min_area < area < max_area:
            x,y,w,h = cv2.boundingRect(contour)
            cv2.rectangle(image, (x,y), (x+w,y+h), (0,0,255), 2)
            cv2.imwrite("111.jpg", image)
            return x
    return 0

You can adjust the values of `min_loop`, `max_loop`, `min_area`, and `max_area` based on the size and characteristics of your images.