import numpy as np
from sklearn.svm import SVC
from sklearn.externals import joblib
from sklearn.metrics import roc_curve, auc
import matplotlib.pyplot as plt
def load_hog_data(hog_txt):
""" load hog features.
Args:
hog_txt: a txt file used to save hog features.
one line data is formated as "img_path \t cls_num \t hog_feature_vector"
Return:
img_names: a list of image names.
labels: numpy array labels (1-dim).
hog_feature: numpy array hog features.
formated as [[hog1], [hog2], ...]
"""
img_names = []
labels = []
hog_features = []
with open(hog_txt, "r") as f:
data = f.readlines()
for row_data in data:
row_data = row_data.rstrip()
img_path, label, hog_str = row_data.split("\t")
img_name = img_path.split("\\")[-1]
hog_feature = hog_str.split(" ")
hog_feature = [float(hog) for hog in hog_feature]
# print "hog feature length = ", len(hog_feature)
img_names.append(img_name)
labels.append(int(label))
hog_features.append(hog_feature)
return img_names, np.array(labels), np.array(hog_features)
def svm_train(hog_features, labels, save_path="\svm_model.pkl"):
""" SVM train
Args:
hog_feature: numpy array hog features.
formated as [[hog1], [hog2], ...]
labels: numpy array labels (1-dim).
save_path: model save path.
Return:
none.
"""
clf = SVC(C=10, tol=1e-3, probability=True)
clf.fit(hog_features, labels)
joblib.dump(clf, save_path)
print("finished.")
def svm_test(svm_model, hog_feature, labels):
"""SVM test
Args:
hog_feature: numpy array hog features.
formated as [[hog1], [hog2], ...]
labels: numpy array labels (1-dim).
Return:
accuracy: test accuracy.
"""
clf = joblib.load(svm_model)
accuracy = clf.score(hog_feature, labels)
return accuracy
def plot_roc_auc(clf, x_test):
y_test = clf.predict(x_test)
y_score = clf.decision_function(x_test)
fpr, tpr, threshold = roc_curve(y_test, y_score)
roc_auc = auc(fpr, tpr)
plt.figure()
plt.plot(fpr, tpr)
plt.plot([0, 1], [1, 0])
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic example')
if __name__ == "__main__":
hog_train_txt = "D:\computer_vision\proposals_train_hog.txt"
hog_test_txt = "D:\computer_vision\proposals_test_hog.txt"
model_path = "\svm_model.pkl"
train = False
if train is True:
img_names, labels, hog_train_features = load_hog_data(hog_train_txt)
svm_train(hog_train_features, labels, model_path)
else:
img_names, labels, hog_test_features = load_hog_data(hog_test_txt)
test_accuracy = svm_test(model_path, hog_test_features, labels)
print
"test accuracy = ", test_accuracy
# clf = joblib.load(model_path)
# plot_roc_auc(clf, hog_test_features)
报错：
Traceback (most recent call last):
File "C:/Users/samyeung/PycharmProjects/pythonProject/svm_train.py", line 94, in <module>
test_accuracy = svm_test(model_path, hog_test_features, labels)
File "C:/Users/samyeung/PycharmProjects/pythonProject/svm_train.py", line 62, in svm_test
clf = joblib.load(svm_model)
File "C:\Users\samyeung\PycharmProjects\pythonProject\venv\lib\site-packages\sklearn\externals\joblib\numpy_pickle.py", line 590, in load
with open(filename, 'rb') as f:
FileNotFoundError: [Errno 2] No such file or directory: '\\svm_model.pkl'
Process finished with exit code 1
No such file or directory: '\\svm_model.pkl'
检查一下你的model是否存在呢
训练集不存在！！！！
https://tensorflow.google.cn/tutorials?hl=zh_cn
自己可以先跟着图像识别、文字情绪识别走一圈。
我可以提供参考文档。。
https://www.scikitlearn.com.cn/
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