From 8eedaec824137c944d2aec7a133807a0235386bc Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E7=8E=8B=E5=BA=B7=E9=A3=9E?=
 <13191081+shallowdreamqaq@user.noreply.gitee.com>
Date: Sun, 16 Jul 2023 14:42:42 +0000
Subject: [PATCH] knn
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Content-Type: text/plain; charset=UTF-8
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Signed-off-by: 王康飞 <13191081+shallowdreamqaq@user.noreply.gitee.com>
---
 水稻叶片病害分类/大作业/run.py | 84 ++++++++++++++++++++++++++++++++++
 1 file changed, 84 insertions(+)
 create mode 100644 水稻叶片病害分类/大作业/run.py

diff --git a/水稻叶片病害分类/大作业/run.py b/水稻叶片病害分类/大作业/run.py
new file mode 100644
index 0000000..5d6a318
--- /dev/null
+++ b/水稻叶片病害分类/大作业/run.py
@@ -0,0 +1,84 @@
+# 调用第三方库
+import os
+import cv2
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import confusion_matrix, classification_report
+
+# 第一步 切分训练集和测试集
+X = []  # 定义图像名称
+Y = []  # 定义图像分类类标
+Z = []  # 定义图像像素
+for i in range(1, 4):
+    # 遍历文件夹，读取图片，本例中的图像文件可以在上面分享的链接中提取
+    for f in os.listdir("photo\%s" % i):
+        # 获取图像名称
+        X.append("photo//" + str(i) + "//" + str(f))
+        # 获取图像类标即为文件夹名称
+        Y.append(i)
+X = np.array(X)
+Y = np.array(Y)
+# 随机率为100% 选取其中的30%作为测试集
+X_train, X_test, y_train, y_test = train_test_split(X,
+                                                    Y,
+                                                    test_size=0.3,
+                                                    random_state=1)
+print(len(X_train), len(X_test), len(y_train), len(y_test))
+
+# 第二步 图像读取及转换为像素直方图
+# 训练集
+XX_train = []
+for i in X_train:
+    # 读取图像
+    image = cv2.imread(i)
+
+    # 图像像素大小一致
+    img = cv2.resize(image, (256, 256), interpolation=cv2.INTER_CUBIC)
+
+    # 计算图像直方图并存储至X数组
+    hist = cv2.calcHist([img], [0, 1], None, [256, 256],
+                        [0.0, 255.0, 0.0, 255.0])
+
+    XX_train.append(((hist / 255).flatten()))
+
+# 测试集
+XX_test = []
+for i in X_test:
+    # 读取图像
+    # print i
+    image = cv2.imread(i)
+    # 图像像素大小一致
+    img = cv2.resize(image, (256, 256), interpolation=cv2.INTER_CUBIC)
+    # 计算图像直方图并存储至X数组
+    hist = cv2.calcHist([img], [0, 1], None, [256, 256],
+                        [0.0, 255.0, 0.0, 255.0])
+    XX_test.append(((hist / 255).flatten()))
+
+# 第三步 基于KNN的图像分类处理
+from sklearn.neighbors import KNeighborsClassifier  # 调用分类器
+
+clf = KNeighborsClassifier(n_neighbors=10).fit(XX_train, y_train)
+predictions_labels = clf.predict(XX_test)
+
+print('预测结果:')
+print(predictions_labels)
+
+print('算法评价:')
+print((classification_report(y_test, predictions_labels)))
+
+# 输出前10张图片及预测结果
+k = 0
+t=0
+while k < 100:
+    # 读取图像
+    temp = []
+    temp=X_test[k].split('//')
+    image = cv2.imread(X_test[k])
+    if temp[1]==str(predictions_labels[k]):
+        t=t+1
+    
+    # 显示图像
+    # cv2.imshow("img", image)
+    k = k + 1
+
+print(t/100.0)
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