original_shape = original_img.shape[1:3]什么意思
时间: 2024-05-18 12:12:50 浏览: 15
这段代码获取了经过预处理后的图像的形状信息。其中,"original_img"是经过预处理后的图像数据,".shape"是NumPy数组的一个属性,用于获取数组的形状信息,返回一个元组。在这里,".shape[1:3]"表示获取元组中第2个和第3个元素,即图像的宽度和高度,返回一个形状为(宽度, 高度)的元组。因此,这段代码的作用是获取经过预处理后的图像的宽度和高度信息。
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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。
讲解这段代码:def read_img_and_convert_to_binary(filename): #读取待处理的图片 original_img = cv2.imread(filename) # print(original_img) #将原图分辨率缩小SCALSIZE倍,减少计算复杂度 original_img = cv2.resize(original_img,(np.int(original_img.shape[1]/SCALSIZE),np.int(original_img.shape[0]/SCALSIZE)), interpolation=cv2.INTER_AREA) #降噪 blur = cv2.GaussianBlur(original_img, (5, 5), 0) #将彩色图转化成灰度图 img_gray = cv2.cvtColor(blur,cv2.COLOR_BGR2GRAY) #图片开(opening)处理,用来降噪,使图片中的字符边界更圆滑,没有皱褶 kernel = np.ones((3, 3), np.uint8) opening = cv2.morphologyEx(img_gray, cv2.MORPH_OPEN, kernel) kernel2 = np.ones((3,3), np.uint8) opening = cv2.dilate(opening, kernel2, iterations=1) # Otsu's thresholding after Gaussian filtering # 采用otsu阈值法将灰度图转化成只有0和1的二值图 blur = cv2.GaussianBlur(opening,(13,13),0) #ret, binary_img = cv2.threshold(img_gray, 120, 1, cv2.THRESH_BINARY_INV) ret,binary_img = cv2.threshold(blur,0,1,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU) return original_img,binary_img
这段代码是一个函数,名为`read_img_and_convert_to_binary`,它的功能是读取一张待处理的图片并将其转换为二值图像。下面是对代码的逐行解释:
1. `original_img = cv2.imread(filename)`:使用OpenCV库的`imread`函数读取指定路径的图片,并将其存储在`original_img`变量中。
2. `original_img = cv2.resize(original_img,(np.int(original_img.shape[1]/SCALSIZE),np.int(original_img.shape[0]/SCALSIZE)), interpolation=cv2.INTER_AREA)`:将原图的分辨率缩小`SCALSIZE`倍,通过调整图像大小减少计算复杂度,结果存储在`original_img`变量中。
3. `blur = cv2.GaussianBlur(original_img, (5, 5), 0)`:使用高斯模糊对`original_img`进行降噪处理,减少图像中的噪点,结果存储在`blur`变量中。
4. `img_gray = cv2.cvtColor(blur,cv2.COLOR_BGR2GRAY)`:将降噪后的彩色图像转换为灰度图像,便于后续处理,结果存储在`img_gray`变量中。
5. `kernel = np.ones((3, 3), np.uint8)`:创建一个3x3的矩阵,用于后续图像形态学操作。
6. `opening = cv2.morphologyEx(img_gray, cv2.MORPH_OPEN, kernel)`:对灰度图像进行形态学开运算(opening),通过腐蚀和膨胀操作使字符边界更加平滑,结果存储在`opening`变量中。
7. `kernel2 = np.ones((3,3), np.uint8)`:创建另一个3x3的矩阵,用于后续膨胀操作。
8. `opening = cv2.dilate(opening, kernel2, iterations=1)`:对开运算后的图像进行膨胀操作,进一步平滑字符边界,结果仍存储在`opening`变量中。
9. `blur = cv2.GaussianBlur(opening,(13,13),0)`:再次使用高斯模糊对图像进行降噪处理,参数(13,13)表示高斯核的大小。
10. `ret, binary_img = cv2.threshold(blur,0,1,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)`:使用Otsu阈值法将灰度图像转换为二值图像。该阈值法会自动选择一个合适的阈值,将图像分为黑白两部分,结果存储在`binary_img`变量中。
11. 最后,函数返回原始图像`original_img`和二值图像`binary_img`。
这段代码主要进行了图像预处理的步骤,包括降噪、灰度化、形态学操作和阈值处理,最终得到二值图像以供后续处理使用。
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