clf = joblib.load(model_path+'model') print("训练之后的模型存放在model文件夹中")保存的svm训练模型如何打开并预测图片

如果你保存的是 SVM 模型，那么可以按照下面的步骤进行加载和预测：

1. 加载模型：

import joblib

model = joblib.load(model_path+'model')

2. 加载测试图片并进行预处理：

from PIL import Image
import numpy as np

# 加载图片
image = Image.open(image_path)

# 将图片缩放为指定大小
image = image.resize((width, height))

# 将图片转换为数组
image_array = np.array(image)

# 将像素值缩放到 [0, 1] 范围内
image_array = image_array / 255.0

# 将图片转换为一维数组，并进行预测
image_vector = image_array.reshape((1, -1))

其中，`image_path` 是测试图片的路径，`width` 和 `height` 是图片的宽度和高度，需要和训练模型时使用的宽度和高度保持一致。

3. 进行预测：

result = model.predict(image_vector)

这样就可以得到测试图片的预测结果 `result`。需要注意的是，对于 SVM 模型，预测结果是样本所属的类别标签，而不是概率值。如果需要得到概率值，可以使用 `model.predict_proba()` 方法。

相关问题

import cv2 import os import numpy as np from sklearn import svm import joblib def read_images(folder): images = [] labels = [] for filename in os.listdir(folder): label = filename.split('.')[0] img = cv2.imread(os.path.join(folder,filename)) if img is not None: images.append(img) labels.append(label) return images, labels # 提取特征向量 def extract_features(images): features = [] for img in images: gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) hist = cv2.calcHist([gray],[0],None,[256],[0,256]) features.append(hist.flatten()) return features # 读取图像和标签 images, labels = read_images('C:/Users/Administrator/Desktop/111') # 提取特征向量 features = extract_features(images) # 训练模型 clf = svm.SVC() clf.fit(features, labels) # 保存模型到文件 joblib.dump(clf, 'model.pkl') clf = joblib.load('E:/xiangmu/measure/model.pkl') print(clf) # 预测新图像 img = cv2.imread('C:/Users/Administrator/Desktop/dc9cd0a3a572635fb27973627f514dfb.jpeg') gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) hist = cv2.calcHist([gray],[0],None,[256],[0,256]) features = np.array(hist.flatten()).reshape(1, -1) print(hist) label = clf.predict(features)[0] print('识别结果：', label) 帮我优化代码，可以打印概率

好的，我们可以使用`predict_proba`方法来获取预测结果的概率。以下是优化后的代码示例：

import cv2
import os
import numpy as np
from sklearn import svm
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from skimage.io import imread
from skimage.transform import resize
import joblib

# 读取图像和标签
def read_images(folder):
    images = []
    labels = []
    for filename in os.listdir(folder):
        label = filename.split('.')[0]
        img = cv2.imread(os.path.join(folder,filename))
        if img is not None:
            images.append(img)
            labels.append(label)
    return images, labels

# 提取特征向量
def extract_features(images):
    features = []
    for img in images:
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        hist = cv2.calcHist([gray],[0],None,[256],[0,256])
        features.append(hist.flatten())
    return features

# 读取图像和标签
images, labels = read_images('C:/Users/Administrator/Desktop/111')

# 提取特征向量
features = extract_features(images)

# 将数据集分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.3)

# 创建SVM分类器
clf = svm.SVC(kernel='linear', probability=True)

# 训练分类器
clf.fit(X_train, y_train)

# 保存模型到文件
joblib.dump(clf, 'model.pkl')

# 加载模型
clf = joblib.load('model.pkl')

# 预测新图像
img = cv2.imread('C:/Users/Administrator/Desktop/dc9cd0a3a572635fb27973627f514dfb.jpeg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
hist = cv2.calcHist([gray],[0],None,[256],[0,256])
features = np.array(hist.flatten()).reshape(1, -1)

# 预测分类和概率
label = clf.predict(features)[0]
prob = clf.predict_proba(features)[0]

print('识别结果：', label)
print('概率分布：', prob)

在这个示例中，我们首先将数据集分为训练集和测试集，然后创建SVM分类器并训练它。我们设置`probability=True`来启用预测结果的概率估计。在预测新图像时，我们使用`predict`方法获取预测分类，使用`predict_proba`方法获取预测结果的概率分布。最后，我们打印预测结果和概率分布。

import cv2 import os import numpy as np from sklearn import svm import joblib def read_images(folder): images = [] labels = [] for filename in os.listdir(folder): label = filename.split('.')[0] img = cv2.imread(os.path.join(folder,filename)) if img is not None: images.append(img) labels.append(label) return images, labels # 提取特征向量 def extract_features(images): features = [] for img in images: gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) hist = cv2.calcHist([gray],[0],None,[256],[0,256]) features.append(hist.flatten()) return features # 读取图像和标签 images, labels = read_images('C:/Users/Administrator/Desktop/111') # 提取特征向量 features = extract_features(images) print(features) # 训练模型 clf = svm.SVC() clf.fit(features, labels) # 保存模型到文件 joblib.dump(clf, 'model.pkl') clf = joblib.load('E:/xiangmu/measure/model.pkl') print(clf) # 预测新图像 img = cv2.imread('C:/Users/Administrator/Downloads/092b08c53b49d92254db7874c0a7b073.jpg') gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) hist = cv2.calcHist([gray],[0],None,[256],[0,256]) features = np.array(hist.flatten()).reshape(1, -1) print(clf.predict(features)) label = clf.predict(features)[0] print('识别结果：', label) 帮我把这写代码，打印识别率

这段代码中没有计算识别率的部分，需要添加一些代码来实现。以下是修改过的代码：

import cv2
import os
import numpy as np
from sklearn import svm
from sklearn.metrics import accuracy_score
import joblib

def read_images(folder):
    images = []
    labels = []
    for filename in os.listdir(folder):
        label = filename.split('.')[0]
        img = cv2.imread(os.path.join(folder,filename))
        if img is not None:
            images.append(img)
            labels.append(label)
    return images, labels

def extract_features(images):
    features = []
    for img in images:
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        hist = cv2.calcHist([gray],[0],None,[256],[0,256])
        features.append(hist.flatten())
    return features

images, labels = read_images('C:/Users/Administrator/Desktop/111')
features = extract_features(images)

clf = svm.SVC()
clf.fit(features, labels)

joblib.dump(clf, 'model.pkl')

clf = joblib.load('model.pkl')

test_images, test_labels = read_images('C:/Users/Administrator/Desktop/222')
test_features = extract_features(test_images)

pred_labels = clf.predict(test_features)
accuracy = accuracy_score(test_labels, pred_labels)

print('识别率：', accuracy)

这段代码会读取一个文件夹中的图片作为训练数据，另一个文件夹中的图片作为测试数据，然后计算识别率并打印出来。注意修改文件夹的路径和文件名的格式。