补全代码import os image_files=os.listdir('./data/imgs') images=[] gts=[] masks=[] 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 point

import os 
import cv2
image_files=os.listdir('./data/imgs') 
images=[]
gts=[]
masks=[]
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)):
    # 读取图像和掩膜
    img = cv2.imread(images[i])
    gt = cv2.imread(gts[i], cv2.IMREAD_GRAYSCALE)
    
    # 计算掩膜
    mask = (gt > 0).astype(int)
    
    # 将图像和掩膜添加到列表中
    images.append(img)
    masks.append(mask)
    
# 打印图像和掩膜的数量
print("Number of images:", len(images))
print("Number of masks:", len(masks))

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 os from random import shuffle import pickle import librosa import tensorflow.keras as keras import librosa.display import numpy as np from matplotlib import pyplot as plt def load_files(audio_dir): files=os.listdir(audio_dir) wav_files=[] for wav in files: if not wav.endswith('.wav'): continue wav_files.append(wav) if not wav_files: print('未找到数据集') shuffle(wav_files) nfiles=len(wav_files) ntrain=int(nfiles*0.7) return wav_files[:ntrain],wav_files[ntrain:] audio_dir='../data/recordings/' dataset_pickle='../tmp/recordings.pkl' train_files,valid_files=load_files(audio_dir) print('训练集样本数为{}\n验证集样本数为{}'.format(len(train_files),load_files(valid_files)))帮我检查错误

这段代码中有两个问题：

1. 在函数 `load_files()` 中，`wav_files` 列表只会包含以 `.wav` 结尾的文件名，因为 `if not wav.endswith('.wav')` 和 `wav_files.append(wav)` 没有缩进到同一个代码块中。你需要将两行代码缩进到同一级别，才能正确的将所有 `.wav` 文件名添加到 `wav_files` 列表中。

2. 在 `print()` 函数中使用了 `load_files()` 函数调用的返回值，而 `load_files()` 函数返回的是两个列表类型的对象，因此会报错。你需要将 `len(load_files(valid_files))` 改为 `len(valid_files)` 才能正确输出验证集样本数。

下面是已经修改过后的代码：

import os
from random import shuffle
import pickle
import librosa
import tensorflow.keras as keras
import librosa.display
import numpy as np
from matplotlib import pyplot as plt

def load_files(audio_dir):
    files = os.listdir(audio_dir)
    wav_files = []
    for wav in files:
        if not wav.endswith('.wav'):
            continue
        wav_files.append(wav)
    if not wav_files:
        print('未找到数据集')
    shuffle(wav_files)
    n_files = len(wav_files)
    n_train = int(n_files * 0.7)
    return wav_files[:n_train], wav_files[n_train:]

audio_dir = '../data/recordings/'
dataset_pickle = '../tmp/recordings.pkl'

train_files, valid_files = load_files(audio_dir)
print('训练集样本数为{}\n验证集样本数为{}'.format(len(train_files), len(valid_files)))

希望能帮到你！