def image_processing(img,device): #img = cv2.resize(img, (32,32)) #img = np.reshape(img, (32, 32, 3)) # normalize img = img.astype(np.float32) img = (img / 255. - mean_value) / std_value img = img.transpose([2, 0, 1]) img = torch.from_numpy(img) img = img.to(device) img = img.view(1, *img.size()) return img
时间: 2023-06-27 14:03:32 浏览: 43
这段代码是一个用于图像处理的函数,输入参数为原始图像和设备类型(CPU或GPU),返回值为经过预处理后的图像张量。
首先,该函数将输入的图像进行缩放和重构,使其变为32x32x3的三维数组,其中32x32是图像的尺寸,3代表RGB颜色通道。接着,函数对图像进行了标准化处理,即将像素值从[0, 255]范围转换到[-1, 1]范围,并且减去均值(mean_value)并除以标准差(std_value)。这是为了使得图像数据更加稳定,方便神经网络进行训练。然后,函数将图像的维度进行转换,使其变为通道维度在前,高度维度在中间,宽度维度在最后的形式。接着,函数将图像数据转换为PyTorch张量,并将其移动到指定的设备上。最后,函数将图像张量的形状变为1x3x32x32的四维数组,并返回该张量。
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详细解释一下这段代码,每一句给出详细注解:def resize(image, image_size): h, w = image.shape[:2] aspect_ratio = h/w smaller_side_size = int(image_size/max(aspect_ratio, 1/aspect_ratio)) if aspect_ratio > 1: # H > W new_size = (image_size, smaller_side_size) else: # H <= W new_size = (smaller_side_size, image_size) image = cv2.resize(image, new_size[::-1]) return image, new_size
这段代码实现了对输入图像进行等比例缩放的功能,具体解释如下:
```python
def resize(image, image_size):
# 获取输入图像的高度和宽度
h, w = image.shape[:2]
# 计算输入图像的宽高比
aspect_ratio = h / w
# 计算缩放后较小的一边的尺寸
smaller_side_size = int(image_size / max(aspect_ratio, 1 / aspect_ratio))
# 判断输入图像的高宽比,选择新的尺寸
if aspect_ratio > 1: # 高大于宽
new_size = (image_size, smaller_side_size)
else: # 宽大于等于高
new_size = (smaller_side_size, image_size)
# 调用OpenCV的resize函数进行缩放
image = cv2.resize(image, new_size[::-1])
# 返回缩放后的图像和新的尺寸
return image, new_size
```
其中,`image`是输入的原始图像,`image_size`是缩放后的目标尺寸。
首先,通过`shape`方法获取输入图像的高度和宽度,并计算出输入图像的宽高比`aspect_ratio`。
接着,根据宽高比和目标尺寸计算出较小的一边的尺寸`smaller_side_size`。
然后,根据输入图像的高宽比选择新的尺寸`new_size`,如果高大于宽,则将高缩放至目标尺寸,宽按比例缩放;如果宽大于等于高,则将宽缩放至目标尺寸,高按比例缩放。
最后,调用OpenCV的`resize`函数对输入图像进行缩放,并返回缩放后的图像和新的尺寸。
逐行详细解释以下代码并加注释from tensorflow import keras import matplotlib.pyplot as plt base_image_path = keras.utils.get_file( "coast.jpg", origin="https://img-datasets.s3.amazonaws.com/coast.jpg") plt.axis("off") plt.imshow(keras.utils.load_img(base_image_path)) #instantiating a model from tensorflow.keras.applications import inception_v3 model = inception_v3.InceptionV3(weights='imagenet',include_top=False) #配置各层对DeepDream损失的贡献 layer_settings = { "mixed4": 1.0, "mixed5": 1.5, "mixed6": 2.0, "mixed7": 2.5, } outputs_dict = dict( [ (layer.name, layer.output) for layer in [model.get_layer(name) for name in layer_settings.keys()] ] ) feature_extractor = keras.Model(inputs=model.inputs, outputs=outputs_dict) #定义损失函数 import tensorflow as tf def compute_loss(input_image): features = feature_extractor(input_image) loss = tf.zeros(shape=()) for name in features.keys(): coeff = layer_settings[name] activation = features[name] loss += coeff * tf.reduce_mean(tf.square(activation[:, 2:-2, 2:-2, :])) return loss #梯度上升过程 @tf.function def gradient_ascent_step(image, learning_rate): with tf.GradientTape() as tape: tape.watch(image) loss = compute_loss(image) grads = tape.gradient(loss, image) grads = tf.math.l2_normalize(grads) image += learning_rate * grads return loss, image def gradient_ascent_loop(image, iterations, learning_rate, max_loss=None): for i in range(iterations): loss, image = gradient_ascent_step(image, learning_rate) if max_loss is not None and loss > max_loss: break print(f"... Loss value at step {i}: {loss:.2f}") return image #hyperparameters step = 20. num_octave = 3 octave_scale = 1.4 iterations = 30 max_loss = 15. #图像处理方面 import numpy as np def preprocess_image(image_path): img = keras.utils.load_img(image_path) img = keras.utils.img_to_array(img) img = np.expand_dims(img, axis=0) img = keras.applications.inception_v3.preprocess_input(img) return img def deprocess_image(img): img = img.reshape((img.shape[1], img.shape[2], 3)) img /= 2.0 img += 0.5 img *= 255. img = np.clip(img, 0, 255).astype("uint8") return img #在多个连续 上运行梯度上升 original_img = preprocess_image(base_image_path) original_shape = original_img.shape[1:3] successive_shapes = [original_shape] for i in range(1, num_octave): shape = tuple([int(dim / (octave_scale ** i)) for dim in original_shape]) successive_shapes.append(shape) successive_shapes = successive_shapes[::-1] shrunk_original_img = tf.image.resize(original_img, successive_shapes[0]) img = tf.identity(original_img) for i, shape in enumerate(successive_shapes): print(f"Processing octave {i} with shape {shape}") img = tf.image.resize(img, shape) img = gradient_ascent_loop( img, iterations=iterations, learning_rate=step, max_loss=max_loss ) upscaled_shrunk_original_img = tf.image.resize(shrunk_original_img, shape) same_size_original = tf.image.resize(original_img, shape) lost_detail = same_size_original - upscaled_shrunk_original_img img += lost_detail shrunk_original_img = tf.image.resize(original_img, shape) keras.utils.save_img("DeepDream.png", deprocess_image(img.numpy()))
这段代码是一个使用 DeepDream 技术对一张图像进行处理的例子。下面对每一部分进行详细解释:
1. 导入所需的库
```python
from tensorflow import keras
import matplotlib.pyplot as plt
```
导入了 TensorFlow 和 Keras 库,以及用于绘制图像的 Matplotlib 库。
2. 加载图像
```python
base_image_path = keras.utils.get_file(
"coast.jpg", origin="https://img-datasets.s3.amazonaws.com/coast.jpg")
plt.axis("off")
plt.imshow(keras.utils.load_img(base_image_path))
```
使用 `keras.utils.get_file` 函数从亚马逊 S3 存储桶中下载名为 "coast.jpg" 的图像,并使用 `keras.utils.load_img` 函数加载该图像。`plt.axis("off")` 和 `plt.imshow` 函数用于绘制该图像并关闭坐标轴。
3. 实例化模型
```python
from tensorflow.keras.applications import inception_v3
model = inception_v3.InceptionV3(weights='imagenet',include_top=False)
```
使用 Keras 库中的 InceptionV3 模型对图像进行处理。`weights='imagenet'` 表示使用预训练的权重,`include_top=False` 表示去掉模型的顶层(全连接层)。
4. 配置 DeepDream 损失
```python
layer_settings = {
"mixed4": 1.0,
"mixed5": 1.5,
"mixed6": 2.0,
"mixed7": 2.5,
}
outputs_dict = dict(
[(layer.name, layer.output) for layer in [model.get_layer(name) for name in layer_settings.keys()]]
)
feature_extractor = keras.Model(inputs=model.inputs, outputs=outputs_dict)
```
通过配置不同层对 DeepDream 损失的贡献来控制图像的风格。该代码块中的 `layer_settings` 字典定义了每层对损失的贡献,`outputs_dict` 变量将每层的输出保存到一个字典中,`feature_extractor` 变量实例化一个新模型来提取特征。
5. 定义损失函数
```python
import tensorflow as tf
def compute_loss(input_image):
features = feature_extractor(input_image)
loss = tf.zeros(shape=())
for name in features.keys():
coeff = layer_settings[name]
activation = features[name]
loss += coeff * tf.reduce_mean(tf.square(activation[:, 2:-2, 2:-2, :]))
return loss
```
定义了一个计算 DeepDream 损失的函数。该函数首先使用 `feature_extractor` 模型提取输入图像的特征,然后计算每层对损失的贡献并相加,最终返回总损失。
6. 梯度上升过程
```python
@tf.function
def gradient_ascent_step(image, learning_rate):
with tf.GradientTape() as tape:
tape.watch(image)
loss = compute_loss(image)
grads = tape.gradient(loss, image)
grads = tf.math.l2_normalize(grads)
image += learning_rate * grads
return loss, image
def gradient_ascent_loop(image, iterations, learning_rate, max_loss=None):
for i in range(iterations):
loss, image = gradient_ascent_step(image, learning_rate)
if max_loss is not None and loss > max_loss:
break
print(f"... Loss value at step {i}: {loss:.2f}")
return image
```
定义了一个用于实现梯度上升过程的函数。`gradient_ascent_step` 函数计算输入图像的损失和梯度,然后对图像进行梯度上升并返回更新后的图像和损失。`gradient_ascent_loop` 函数使用 `gradient_ascent_step` 函数实现多次迭代,每次迭代都会计算损失和梯度,并对输入图像进行更新。
7. 设置超参数
```python
step = 20.
num_octave = 3
octave_scale = 1.4
iterations = 30
max_loss = 15.
```
设置了一些 DeepDream 算法的超参数,例如梯度上升步长、金字塔层数、金字塔缩放比例、迭代次数和损失上限。
8. 图像处理
```python
import numpy as np
def preprocess_image(image_path):
img = keras.utils.load_img(image_path)
img = keras.utils.img_to_array(img)
img = np.expand_dims(img, axis=0)
img = keras.applications.inception_v3.preprocess_input(img)
return img
def deprocess_image(img):
img = img.reshape((img.shape[1], img.shape[2], 3))
img /= 2.0
img += 0.5
img *= 255.
img = np.clip(img, 0, 255).astype("uint8")
return img
```
定义了两个函数,`preprocess_image` 函数将输入图像进行预处理,`deprocess_image` 函数将处理后的图像进行还原。
9. DeepDream 算法过程
```python
original_img = preprocess_image(base_image_path)
original_shape = original_img.shape[1:3]
successive_shapes = [original_shape]
for i in range(1, num_octave):
shape = tuple([int(dim / (octave_scale ** i)) for dim in original_shape])
successive_shapes.append(shape)
successive_shapes = successive_shapes[::-1]
shrunk_original_img = tf.image.resize(original_img, successive_shapes[0])
img = tf.identity(original_img)
for i, shape in enumerate(successive_shapes):
print(f"Processing octave {i} with shape {shape}")
img = tf.image.resize(img, shape)
img = gradient_ascent_loop(
img, iterations=iterations, learning_rate=step, max_loss=max_loss
)
upscaled_shrunk_original_img = tf.image.resize(shrunk_original_img, shape)
same_size_original = tf.image.resize(original_img, shape)
lost_detail = same_size_original - upscaled_shrunk_original_img
img += lost_detail
shrunk_original_img = tf.image.resize(original_img, shape)
keras.utils.save_img("DeepDream.png", deprocess_image(img.numpy()))
```
使用预先定义的函数和变量实现了 DeepDream 算法的过程。首先对原始图像进行预处理,然后根据金字塔层数和缩放比例生成多个连续的图像,对每个图像进行梯度上升处理,最终将所有处理后的图像进行合并,并使用 `keras.utils.save_img` 函数保存最终结果。