416 lines
7.0 KiB
Python
416 lines
7.0 KiB
Python
from sklearn import datasets
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from skimage.feature import hog
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from sklearn.svm import LinearSVC
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import numpy as np
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import os, math, cv2, struct
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import matplotlib.pyplot as plt
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plt.rcParams["font.sans-serif"] = ["Times New Roman"]
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plt.rcParams["axes.unicode_minus"] = False
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def load_mnist(path, labelfile, datafile):
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labels_path = os.path.join(path, labelfile)
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images_path = os.path.join(path, datafile)
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with open(labels_path, "rb") as lbpath:
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magic, n = struct.unpack(">II", lbpath.read(8))
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labels = np.fromfile(lbpath, dtype=np.uint8)
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with open(images_path, "rb") as imgpath:
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magic, num, rows, cols = struct.unpack(">IIII", imgpath.read(16))
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images = np.fromfile(imgpath, dtype=np.uint8).reshape(len(labels), 784)
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return images, labels
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features, labels = load_mnist(
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"mnist", "train-labels-idx1-ubyte", "train-images-idx3-ubyte"
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)
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print(features.shape, labels.shape)
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features = features[0:6000, :]
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labels = labels[0:6000]
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print("训练集行数: %d, 列数: %d" % (features.shape[0], features.shape[1]))
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x = np.array(features[0, :])
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x = x.reshape([28, 28])
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plt.figure(figsize=(12, 4))
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plt.imshow(x, cmap="Greys")
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plt.show()
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testfeatures, testlabels = load_mnist(
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"mnist", "t10k-labels-idx1-ubyte", "t10k-images-idx3-ubyte"
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)
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print("测试集行数: %d, 列数: %d" % (testfeatures.shape[0], testfeatures.shape[1]))
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list_hog_fd = []
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for feature in features:
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fd = hog(
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feature.reshape((28, 28)),
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orientations=9,
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pixels_per_cell=(14, 14),
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cells_per_block=(1, 1),
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visualize=False,
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)
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list_hog_fd.append(fd)
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hog_features = np.array(list_hog_fd, "float64")
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print(hog_features.shape)
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list_hog_fd = []
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for feature in testfeatures:
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fd = hog(
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feature.reshape((28, 28)),
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orientations=9,
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pixels_per_cell=(14, 14),
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cells_per_block=(1, 1),
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visualize=False,
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)
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list_hog_fd.append(fd)
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hog_testfeatures = np.array(list_hog_fd, "float64")
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print(hog_testfeatures.shape)
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from sklearn.neighbors import KNeighborsClassifier
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k = 5
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kNN_model = KNeighborsClassifier(n_neighbors=k)
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kNN_model.fit(hog_features, labels)
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testnumber = kNN_model.predict(hog_testfeatures)
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KNNscore = kNN_model.score(hog_testfeatures, testlabels)
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print("测试集第一个图像数字是：", testnumber[0])
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print("分类精度：", KNNscore)
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g_mapping = [
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0,
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1,
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2,
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3,
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4,
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58,
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5,
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]
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def LBP(I, radius=2, count=8):
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dh = np.round([radius * math.sin(i * 2 * math.pi / count) for i in range(count)])
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dw = np.round([radius * math.cos(i * 2 * math.pi / count) for i in range(count)])
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I = I.reshape(28, 28)
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height, width = I.shape
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lbp = np.zeros(I.shape, dtype=int)
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I1 = np.pad(I, radius, "edge")
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for k in range(count):
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h, w = int(radius + dh[k]), int(radius + dw[k])
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lbp += (I > I1[h : h + height, w : w + width]) << k
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return lbp
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def calLbpHistogram(lbp, hCount=7, wCount=5, maxLbpValue=255):
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height, width = lbp.shape
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res = np.zeros((hCount * wCount, max(g_mapping) + 1), dtype=float)
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assert maxLbpValue + 1 == len(g_mapping)
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for h in range(hCount):
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for w in range(wCount):
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blk = lbp[
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height * h // hCount : height * (h + 1) // hCount,
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width * w // wCount : width * (w + 1) // wCount,
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]
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hist1 = np.bincount(blk.ravel(), minlength=maxLbpValue)
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hist = res[h * wCount + w, :]
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for v, k in zip(hist1, g_mapping):
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hist[k] += v
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hist /= hist.sum()
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feature = res.reshape(res.shape[0] * res.shape[1], 1)
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return res
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lbp_features = np.array([calLbpHistogram(LBP(d)).ravel() for d in features])
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lbp_testfeatures = np.array([calLbpHistogram(LBP(d)).ravel() for d in testfeatures])
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from sklearn.svm import LinearSVC
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svm_model = LinearSVC(dual=False)
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svm_model.fit(hog_features, labels)
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test_est = svm_model.predict(hog_testfeatures)
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SVMscore = svm_model.score(hog_testfeatures, testlabels)
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print("测试集第一个图像数字是：", testlabels[0])
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print("HOG+SVM分类精度：", SVMscore)
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import sklearn.metrics as metrics
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print("--------混淆矩阵-----------")
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print(
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metrics.confusion_matrix(
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testlabels, test_est, labels=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
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)
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)
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print("--------分类评估报告-----------")
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print(metrics.classification_report(testlabels, test_est))
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print(testlabels.shape)
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from sklearn.svm import LinearSVC
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from sklearn.ensemble import RandomForestClassifier
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rf_model = RandomForestClassifier()
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rf_model.fit(hog_features, labels)
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testnumber = rf_model.predict(hog_testfeatures)
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rf_score = rf_model.score(hog_testfeatures, testlabels)
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print("第一个图像数字是：", testnumber[0])
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print("分类精度：", rf_score)
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from sklearn.linear_model import LogisticRegression
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lr_model = LogisticRegression(max_iter=1000)
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lr_model.fit(hog_features, labels)
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testnumber = lr_model.predict(hog_testfeatures)
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lr_score = lr_model.score(hog_testfeatures, testlabels)
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print("第一个图像数字是：", testnumber[0])
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print("分类精度：", lr_score)
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