Python 使用matplotlib在tfrecord上绘制方框后，如何保存从tfrecord解码的图像？ img=tf.image.decode\u jpeg（摄像机图像） plt.imshow（img，cmap=cmap） 标题=摄像机名称 标题（标题） plt.show（） plt.savefig（'image.png'））

plt.savefig（）

不起作用。保存的图像为空

---

但是，

plt.show（）

清楚地向我显示了一个带有边框的图像

这里是一个示例，我正在保存VGG16模型的第1层块中的特征贴图。稍后，我加载相同的图像并绘制它

我正在使用

plt.savefig（）

保存图像，然后使用

load\u img（）

加载相同的图像并进行打印。您必须在

plt.show（）

之前调用

plt.savefig（）

代码-

# plot feature map of first conv layer for given image
from keras.applications.vgg16 import VGG16
from keras.applications.vgg16 import preprocess_input
from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array
from keras.models import Model
from matplotlib import pyplot
from numpy import expand_dims

# load the model
model = VGG16()

# redefine model to output right after the first hidden layer
model = Model(inputs=model.inputs, outputs=model.layers[1].output)
model.summary()

# load the image with the required shape
img = load_img('bird.jpg', target_size=(224, 224))

# convert the image to an array
img = img_to_array(img)

# expand dimensions so that it represents a single 'sample'
img = expand_dims(img, axis=0)

# prepare the image (e.g. scale pixel values for the vgg)
img = preprocess_input(img)

# get feature map for first hidden layer
feature_maps = model.predict(img)

# plot all 64 maps in an 8x8 squares
square = 8
ix = 1
for _ in range(square):
    for _ in range(square):
        # specify subplot and turn of axis
        ax = pyplot.subplot(square, square, ix)
        ax.set_xticks([])
        ax.set_yticks([])
        # plot filter channel in grayscale
        pyplot.imshow(feature_maps[0, :, :, ix-1], cmap='gray')
        ix += 1

# save the figure
pyplot.savefig("Bird_Layer1")

# load the saved figure
img = load_img('Bird_Layer1.png')

# print the loaded figure
pyplot.show(img)

输出-

Model: "model_17"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_17 (InputLayer)        (None, 224, 224, 3)       0         
_________________________________________________________________
block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      
=================================================================
Total params: 1,792
Trainable params: 1,792
Non-trainable params: 0
_________________________________________________________________

---

希望这能回答你的问题。愉快的学习。

下面是一个示例，我正在保存VGG16模型的第1层块中的特征贴图。稍后，我加载相同的图像并绘制它

我正在使用

plt.savefig（）

保存图像，然后使用

load\u img（）

加载相同的图像并进行打印。您必须在

plt.show（）

之前调用

plt.savefig（）

代码-

# plot feature map of first conv layer for given image
from keras.applications.vgg16 import VGG16
from keras.applications.vgg16 import preprocess_input
from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array
from keras.models import Model
from matplotlib import pyplot
from numpy import expand_dims

# load the model
model = VGG16()

# redefine model to output right after the first hidden layer
model = Model(inputs=model.inputs, outputs=model.layers[1].output)
model.summary()

# load the image with the required shape
img = load_img('bird.jpg', target_size=(224, 224))

# convert the image to an array
img = img_to_array(img)

# expand dimensions so that it represents a single 'sample'
img = expand_dims(img, axis=0)

# prepare the image (e.g. scale pixel values for the vgg)
img = preprocess_input(img)

# get feature map for first hidden layer
feature_maps = model.predict(img)

# plot all 64 maps in an 8x8 squares
square = 8
ix = 1
for _ in range(square):
    for _ in range(square):
        # specify subplot and turn of axis
        ax = pyplot.subplot(square, square, ix)
        ax.set_xticks([])
        ax.set_yticks([])
        # plot filter channel in grayscale
        pyplot.imshow(feature_maps[0, :, :, ix-1], cmap='gray')
        ix += 1

# save the figure
pyplot.savefig("Bird_Layer1")

# load the saved figure
img = load_img('Bird_Layer1.png')

# print the loaded figure
pyplot.show(img)

输出-

Model: "model_17"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_17 (InputLayer)        (None, 224, 224, 3)       0         
_________________________________________________________________
block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      
=================================================================
Total params: 1,792
Trainable params: 1,792
Non-trainable params: 0
_________________________________________________________________

---

希望这能回答你的问题。快乐学习。

尝试将“savefig”放在“show”之前尝试将“savefig”放在“show”之前@Sruthi Kuriakose-如果答案回答了您的问题，请您接受并投赞成票。谢谢。@Sruthi Kuriakose-如果答案回答了您的问题，请您接受并投票表决。非常感谢。