image = (image - image.min()) / (image.max() - image.min())什么意思

这是一种将图像像素值归一化的方法，通常称为 "min-max 归一化" 或 "区间缩放"。具体来说，对于图像中的每个像素，它减去最小像素值，然后除以最大像素值和最小像素值之差。这样可以将图像像素值缩放到 [0, 1] 区间内。

通过这种方式对图像进行归一化，可以使不同图像的像素值具有可比性，这对于训练机器学习模型或进行图像处理等任务非常有用。

相关问题

优化代码import os image_files=os.listdir('./data/imgs') images=[] gts=[] masks=[] def normalize_image(img): return (img - np.min(img)) / (np.max(img) - np.min(img)) for i in image_files: images.append(os.path.join('./data/imgs',i)) gts.append(os.path.join('./data/gt',i)) for i in range(len(images)): ### YOUR CODE HERE # 10 points img = io.imread(images[i]) #kmeans km_mask = kmeans_color(img, 2) #mean shift ms_mask=(segmIm(img, 20) > 0.5).astype(int) # ms_mask = np.mean(io.imread(gts[i]), axis=2) #gt # gt_mask = np.array(io.imread(gts[i]))[:,:,:3] gt_mask = np.mean(io.imread(gts[i]), axis=2) ### END YOUR CODE #kmeans masks.append([normalize_image(x) for x in [km_mask,ms_mask,gt_mask]]) #output three masks

Here are some suggestions to optimize the code:

1. Instead of using `os.listdir` to get a list of files in a directory and then appending the directory path to each file name, you can use `glob.glob` to directly get a list of file paths that match a certain pattern. For example:

   import glob
   image_files = glob.glob('./data/imgs/*.jpg')

2. Instead of appending each image path and ground truth path to separate lists, you can use a list comprehension to create a list of tuples that contain both paths:

   data = [(os.path.join('./data/imgs', i), os.path.join('./data/gt', i)) for i in image_files]

3. Instead of appending three normalized masks to the `masks` list, you can use a list comprehension to create a list of tuples that contain the three masks:

   masks = [(normalize_image(km_mask), normalize_image(ms_mask), normalize_image(gt_mask)) for km_mask, ms_mask, gt_mask in zip(kmeans_masks, ms_masks, gt_masks)]

4. You can use `skimage.color.rgb2gray` to convert an RGB image to grayscale instead of computing the mean across color channels:

   gt_mask = skimage.color.rgb2gray(io.imread(gt_path))

5. You can use `skimage.io.imread_collection` to read a collection of images instead of using a loop:

   images = skimage.io.imread_collection(image_files)
   gts = skimage.io.imread_collection(gt_files)

Here's the optimized code:

import os
import glob
import numpy as np
import skimage.io
import skimage.color
from sklearn.cluster import KMeans
from skimage.segmentation import mean_shift

def normalize_image(img):
    return (img - np.min(img)) / (np.max(img) - np.min(img))

image_files = glob.glob('./data/imgs/*.jpg')
data = [(os.path.join('./data/imgs', i), os.path.join('./data/gt', i)) for i in image_files]

masks = []
for img_path, gt_path in data:
    # read images
    img = skimage.io.imread(img_path)
    gt = skimage.io.imread(gt_path)

    # k-means segmentation
    kmeans = KMeans(n_clusters=2)
    kmeans_mask = kmeans.fit_predict(img.reshape(-1, 3)).reshape(img.shape[:2])

    # mean shift segmentation
    ms_mask = (mean_shift(img, 20) > 0.5).astype(int)

    # ground truth mask
    gt_mask = skimage.color.rgb2gray(gt)

    # normalize masks
    km_mask_norm = normalize_image(kmeans_mask)
    ms_mask_norm = normalize_image(ms_mask)
    gt_mask_norm = normalize_image(gt_mask)

    # append masks to list
    masks.append((km_mask_norm, ms_mask_norm, gt_mask_norm))

import pygame pygame.init() SCREEN_WIDTH = 640 SCREEN_HEIGHT = 480 screen = pygame.display.set_mode((SCREEN_WIDTH, SCREEN_HEIGHT)) pygame.display.set_caption("Resize Image") image = pygame.image.load("miaojv.jpg") image_rect = image.get_rect() scale = 1.0 min_scale = 0.5 max_scale = 2.0 while True: for event in pygame.event.get(): if event.type == pygame.QUIT: pygame.quit() exit() elif event.type == pygame.MOUSEBUTTONDOWN: if event.button == 1: # left click scale += 0.1 if scale < min_scale: scale = min_scale elif event.button == 3: # right click scale -= 0.1 if scale > max_scale: scale = max_scale screen.fill((255, 255, 255)) scaled_image = pygame.transform.scale(image, (int(image_rect.width * scale), int(image_rect.height * scale))) screen.blit(scaled_image, (SCREEN_WIDTH // 2 - scaled_image.get_width() // 2, SCREEN_HEIGHT // 2 - scaled_image.get_height() // 2)) pygame.display.flip() 改为左击放大，双击缩小

你可以将代码中以下部分：

if event.button == 1: # left click
    scale += 0.1
    if scale < min_scale:
        scale = min_scale
elif event.button == 3: # right click
    scale -= 0.1
    if scale > max_scale:
        scale = max_scale

修改为：

if event.button == 1: # left click
    if event.type == pygame.MOUSEBUTTONDOWN and event.button == 1: # single click
        scale += 0.1
        if scale > max_scale:
            scale = max_scale
    elif event.type == pygame.MOUSEBUTTONDOWN and event.button == 1 and event.pos == last_click_pos: # double click
        scale -= 0.1
        if scale < min_scale:
            scale = min_scale
    last_click_pos = event.pos

这样就可以实现左击放大，双击缩小的效果了。