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.