from skimage.feature import hog
时间: 2023-09-28 12:08:56 浏览: 46
As an AI language model, I cannot import any libraries or packages. However, I can tell you that 'hog' stands for Histogram of Oriented Gradients, which is a feature descriptor used in computer vision and image processing. It is used to extract features from an image by calculating the gradients of the image and creating a histogram of gradient orientations. The resulting feature vector can then be used in machine learning algorithms for tasks such as object detection and recognition.
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skimage.feature.hog函数用法
skimage.feature.hog函数是用于计算图像的方向梯度直方图(HOG)特征的函数。HOG特征是一种在计算机视觉中广泛使用的特征描述符,它可以用于图像分类、目标检测等任务。
该函数的基本用法如下:
```python
from skimage.feature import hog
# image为输入的图像,orientations为梯度方向的数量,pixels_per_cell为每个细胞的像素数,cells_per_block为每个块的细胞数
features = hog(image, orientations=9, pixels_per_cell=(8, 8), cells_per_block=(3, 3))
```
其中,`orientations`参数指定了梯度方向的数量,通常取9。`pixels_per_cell`参数指定了每个细胞的像素数,通常取(8, 8)。`cells_per_block`参数指定了每个块的细胞数,通常取(3, 3)。
该函数返回的`features`为一个一维的向量,表示输入图像的HOG特征。可以将该特征向量用于机器学习算法进行分类或检测等任务。
请修改这一份代码:import random from sklearn import svm from sklearn.metrics import accuracy_score from skimage.feature import hog # 将X_processed列表按3:2的比例随机划分为"员工"和"陌生人"两个集合 def split_dataset(X_processed): random.shuffle(X_processed) split_index = int(len(X_processed) * 3 / 5) employee_set = X_processed[:split_index] stranger_set = X_processed[split_index:] return employee_set, stranger_set # 使用HOG特征提取进行人脸识别训练 def train_face_recognition(employee_set): X = [] = [] for i, face_images in enumerate(employee_set): for face_image in face_images: feature = hog(face_image, orientations=8, pixels_per_cell=(10, 10), cells_per_block=(1, 1), visualize=False) X.append(feature) y.append(i) # i代表员工的标签 clf = svm.SVC() clf.fit(X, y) return clf # 随机抽取一张图片进行识别 def recognize_random_face(clf, X_processed): random_index = random.randint(0, len(X_processed)-1) random_face_images = X_processed[random_index] random_face_image = random.choice(random_face_images) feature = hog(random_face_image, orientations=8, pixels_per_cell=(10, 10), cells_per_block=(1, 1), visualize=False) prediction = clf.predict([feature]) return prediction[0] == random_index # 示例用法 X_processed = [...] # X_processed列表的具体内容 employee_set, stranger_set = split_dataset(X_processed) clf = train_face_recognition(employee_set) result = recognize_random_face(clf, X_processed) print("识别结果:", result),增加如下功能:如果测试时认为图片不属于员工集中的任何一个员工,prediction应该等于0;“陌生人”集合也应当拥有标签,“陌生人”的标签都是0,代表非员工
以下是根据您的要求修改后的代码:
```python
import random
from sklearn import svm
from sklearn.metrics import accuracy_score
from skimage.feature import hog
# 将X_processed列表按3:2的比例随机划分为"员工"和"陌生人"两个集合
def split_dataset(X_processed):
random.shuffle(X_processed)
split_index = int(len(X_processed) * 3 / 5)
employee_set = X_processed[:split_index]
stranger_set = X_processed[split_index:]
return employee_set, stranger_set
# 使用HOG特征提取进行人脸识别训练
def train_face_recognition(employee_set, stranger_set):
X = []
y = []
for i, face_images in enumerate(employee_set):
for face_image in face_images:
feature = hog(face_image, orientations=8, pixels_per_cell=(10, 10), cells_per_block=(1, 1), visualize=False)
X.append(feature)
y.append(i) # i代表员工的标签
for face_images in stranger_set:
for face_image in face_images:
feature = hog(face_image, orientations=8, pixels_per_cell=(10, 10), cells_per_block=(1, 1), visualize=False)
X.append(feature)
y.append(0) # 陌生人的标签都设为0
clf = svm.SVC()
clf.fit(X, y)
return clf
# 随机抽取一张图片进行识别
def recognize_random_face(clf, X_processed):
random_index = random.randint(0, len(X_processed)-1)
random_face_images = X_processed[random_index]
random_face_image = random.choice(random_face_images)
feature = hog(random_face_image, orientations=8, pixels_per_cell=(10, 10), cells_per_block=(1, 1), visualize=False)
prediction = clf.predict([feature])
return prediction[0] == random_index or prediction[0] == 0
# 示例用法
X_processed = [...] # X_processed列表的具体内容
employee_set, stranger_set = split_dataset(X_processed)
clf = train_face_recognition(employee_set, stranger_set)
result = recognize_random_face(clf, X_processed)
print("识别结果:", result)
```
在上述代码中,我修改了`train_face_recognition`函数,将陌生人集合的人脸图片也作为训练样本,并将标签设为0。这样,如果在识别时判断为陌生人,则预测结果为0。在`recognize_random_face`函数中,如果预测结果为0,则也判定为识别正确。
请注意,上述代码只是示例,您需要根据实际情况进行适当修改。例如,您需要将`X_processed`替换为实际的列表,其中包含50个元素,每个元素是一个含有15张人脸图片的列表。另外,您可能需要根据实际情况调整特征提取的参数。