59 lines
1.7 KiB
Python
59 lines
1.7 KiB
Python
import cv2 as cv
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import joblib
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from matplotlib import pyplot as plt, rcParams
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# 导入模型
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classifier = joblib.load('models/classifier.pkl')
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# 导入测试图片
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img=cv.imread('test.png',cv.IMREAD_GRAYSCALE)
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# 设置中文字体
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rcParams['font.sans-serif'] = ['SimHei']
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rcParams['axes.unicode_minus'] = False
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# 预处理图像
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img=cv.resize(img,(28,28))
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_,img_classifier=cv.threshold(img, 0, 255, cv.THRESH_BINARY + cv.THRESH_OTSU)
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img_classifier=cv.blur(img,(3,3))
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# 特征提取
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def extract_contour_features(img):
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contours, _ = cv.findContours(img, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
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contour = contours[0]
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area = cv.contourArea(contour)
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perimeter = cv.arcLength(contour, True)
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return [area, perimeter]
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def extract_shape_features(contour):
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x, y, w, h = cv.boundingRect(contour)
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aspect_ratio = float(w) / h
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rect_area = w * h
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shape_factor = cv.contourArea(contour) / rect_area
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return [aspect_ratio, shape_factor]
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def extract_hu_moments(contour):
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moments = cv.moments(contour)
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hu_moments = cv.HuMoments(moments)
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return hu_moments.flatten()
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def extract_features(img):
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contours, _ = cv.findContours(img, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
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contour = contours[0]
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contour_features = extract_contour_features(img)
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shape_features = extract_shape_features(contour)
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hu_moments = extract_hu_moments(contour)
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feature_vector = contour_features + shape_features + hu_moments.tolist()
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return feature_vector
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feature_vector = extract_features(img)
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# 预测
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predicted_label=classifier.predict([feature_vector])
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plt.figure()
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plt.imshow(img,cmap='gray')
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plt.title('预测结果：'+str(predicted_label[0]))
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plt.axis('off')
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plt.tight_layout()
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plt.show() |