image_v = image.reshape((-1, 3))
时间: 2023-10-21 09:06:03 浏览: 42
好的,我会为您回答这个问题。这段代码的作用是将一个形状为 (height, width, 3) 的图像 reshape 成一个形状为 (height*width, 3) 的数组,其中 -1 表示自动计算维度大小,3 表示颜色通道数。这样做的意义是将图像转换成一个用于机器学习和计算机视觉任务的可展平数组。
相关问题
import numpy as np from sklearn.cluster import KMeans def compress_image(image, n_clusters): # 转换为一维向量 data = image.reshape(-1, 3) # 用 KMeans 算法将像素点聚类到 n_clusters 个簇中 kmeans = KMeans(n_clusters=5, random_state=0).fit(data) # 将每个像素点替换为所属簇的中心像素值 compressed_data = np.array([kmeans.cluster_centers_[label] for label in kmeans.labels_]) # 将压缩后的一维向量转换回原图像的形状 compressed_image = compressed_data.reshape(image.shape) return compressed_imageimport matplotlib.pyplot as plt from PIL import Image # 读取图像 image = np.array(Image.open('0.jpg')) # 将图像数据归一化到 [0, 1] 范围内 image = image.astype('float32') / 255.0 # 压缩图像 compressed_image = compress_image(image_norm, n_clusters=16) # 显示压缩前后的图像 fig, ax = plt.subplots(1, 2, figsize=(10, 5)) ax[0].imshow(image) ax[0].set_title('Original Image') ax[1].imshow(compressed_image) ax[1].set_title('Compressed Image') plt.show()加上计算压缩率的功能
可以在 compress_image 函数中添加计算压缩率的功能,具体实现如下:
```
import numpy as np
from sklearn.cluster import KMeans
def compress_image(image, n_clusters):
# 转换为一维向量
data = image.reshape(-1, 3)
# 用 KMeans 算法将像素点聚类到 n_clusters 个簇中
kmeans = KMeans(n_clusters=n_clusters, random_state=0).fit(data)
# 将每个像素点替换为所属簇的中心像素值
compressed_data = np.array([kmeans.cluster_centers_[label] for label in kmeans.labels_])
# 将压缩后的一维向量转换回原图像的形状
compressed_image = compressed_data.reshape(image.shape)
# 计算压缩率
compressed_size = compressed_data.nbytes
original_size = image.nbytes
compression_ratio = compressed_size / original_size
return compressed_image, compression_ratio
import matplotlib.pyplot as plt
from PIL import Image
# 读取图像
image = np.array(Image.open('0.jpg'))
# 将图像数据归一化到 [0, 1] 范围内
image = image.astype('float32') / 255.0
# 压缩图像
n_clusters = 5
compressed_image, compression_ratio = compress_image(image, n_clusters)
# 显示压缩前后的图像
fig, ax = plt.subplots(1, 2, figsize=(10, 5))
ax[0].imshow(image)
ax[0].set_title('Original Image')
ax[1].imshow(compressed_image)
ax[1].set_title('Compressed Image')
# 显示压缩率
plt.figtext(0.5, 0.9, f'Compression Ratio: {compression_ratio:.2%}', ha='center', fontsize=12)
plt.show()
```
在上述代码中,计算压缩率的代码为:
```
compressed_size = compressed_data.nbytes
original_size = image.nbytes
compression_ratio = compressed_size / original_size
```
其中,`compressed_data.nbytes` 表示压缩后的图像数据的字节数,`image.nbytes` 表示原始图像数据的字节数,压缩率即为两者之比。
以下代码有什么错误,怎么修改: import tensorflow.compat.v1 as tf tf.disable_v2_behavior() from PIL import Image import matplotlib.pyplot as plt import input_data import model import numpy as np import xlsxwriter num_threads = 4 def evaluate_one_image(): workbook = xlsxwriter.Workbook('formatting.xlsx') worksheet = workbook.add_worksheet('My Worksheet') with tf.Graph().as_default(): BATCH_SIZE = 1 N_CLASSES = 4 image = tf.cast(image_array, tf.float32) image = tf.image.per_image_standardization(image) image = tf.reshape(image, [1, 208, 208, 3]) logit = model.cnn_inference(image, BATCH_SIZE, N_CLASSES) logit = tf.nn.softmax(logit) x = tf.placeholder(tf.float32, shape=[208, 208, 3]) logs_train_dir = 'log/' saver = tf.train.Saver() with tf.Session() as sess: print("从指定路径中加载模型...") ckpt = tf.train.get_checkpoint_state(logs_train_dir) if ckpt and ckpt.model_checkpoint_path: global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1] saver.restore(sess, ckpt.model_checkpoint_path) print('模型加载成功, 训练的步数为: %s' % global_step) else: print('模型加载失败,checkpoint文件没找到!') prediction = sess.run(logit, feed_dict={x: image_array}) max_index = np.argmax(prediction) workbook.close() def evaluate_images(test_img): coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(coord=coord) for index,img in enumerate(test_img): image = Image.open(img) image = image.resize([208, 208]) image_array = np.array(image) tf.compat.v1.threading.Thread(target=evaluate_one_image, args=(image_array, index)).start() coord.request_stop() coord.join(threads) if __name__ == '__main__': test_dir = 'data/test/' import glob import xlwt test_img = glob.glob(test_dir + '*.jpg') evaluate_images(test_img)
在代码中,我注意到了几个错误:
1. 在导入`input_data`和`model`模块时,代码中没有找到这两个模块的导入语句。
2. `evaluate_one_image`函数中的`image_array`参数未定义。
3. 在`evaluate_images`函数中,`tf.train.Coordinator()`和`tf.train.start_queue_runners()`函数之间缺少了一个空行。
4. 在调用`evaluate_one_image`函数时,传递的参数应该是`(image_array, index)`而不是`(image_array, index)`。
以下是修改后的代码:
```python
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
from PIL import Image
import matplotlib.pyplot as plt
import input_data
import model
import numpy as np
import xlsxwriter
num_threads = 4
def evaluate_one_image(image_array, index):
workbook = xlsxwriter.Workbook('formatting.xlsx')
worksheet = workbook.add_worksheet('My Worksheet')
with tf.Graph().as_default():
BATCH_SIZE = 1
N_CLASSES = 4
image = tf.cast(image_array, tf.float32)
image = tf.image.per_image_standardization(image)
image = tf.reshape(image, [1, 208, 208, 3])
logit = model.cnn_inference(image, BATCH_SIZE, N_CLASSES)
logit = tf.nn.softmax(logit)
x = tf.placeholder(tf.float32, shape=[208, 208, 3])
logs_train_dir = 'log/'
saver = tf.train.Saver()
with tf.Session() as sess:
print("从指定路径中加载模型...")
ckpt = tf.train.get_checkpoint_state(logs_train_dir)
if ckpt and ckpt.model_checkpoint_path:
global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
saver.restore(sess, ckpt.model_checkpoint_path)
print('模型加载成功, 训练的步数为: %s' % global_step)
else:
print('模型加载失败,checkpoint文件没找到!')
prediction = sess.run(logit, feed_dict={x: image_array})
max_index = np.argmax(prediction)
workbook.close()
def evaluate_images(test_img):
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
for index, img in enumerate(test_img):
image = Image.open(img)
image = image.resize([208, 208])
image_array = np.array(image)
tf.compat.v1.threading.Thread(target=evaluate_one_image, args=(image_array, index)).start()
coord.request_stop()
coord.join(threads)
if __name__ == '__main__':
test_dir = 'data/test/'
import glob
import xlwt
test_img = glob.glob(test_dir + '*.jpg')
evaluate_images(test_img)
```
请注意,我只修复了我在代码中发现的错误,并没有对其他部分进行修改。如果还有其他错误或问题,请提供更多信息,以便我能够更好地帮助你。
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