From 4724c44b19d13882651c823af48f5303209a57cb Mon Sep 17 00:00:00 2001
From: interface_xiongtete <1144722582@qq.com>
Date: Sat, 17 Sep 2022 10:35:13 +0800
Subject: [PATCH] =?UTF-8?q?ResNetRS(=E8=BF=81=E7=A7=BB=E5=AD=A6=E4=B9=A0)?=
 =?UTF-8?q?=E5=AE=9D=E5=8F=AF=E6=A2=A6=E5=9B=BE=E5=83=8F=E8=AF=86=E5=88=AB?=
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---
 .gitignore                                    |  2 +
 .../ResNetRS50-test.py"                       | 77 +++++++++++++++++++
 2 files changed, 79 insertions(+)
 create mode 100644 "\345\233\276\345\203\217\350\257\206\345\210\253/\345\256\235\345\217\257\346\242\246\350\257\206\345\210\253/ResNetRS50-test.py"

diff --git a/.gitignore b/.gitignore
index 0f4a9a6..3ca6777 100644
--- a/.gitignore
+++ b/.gitignore
@@ -45,3 +45,5 @@
 /经典网络/data.zip
 /图像识别/花朵识别/flowers.zip
 /图像识别/花朵识别/model/
+/图像识别/宝可梦识别/data/
+/图像识别/宝可梦识别/model/
diff --git "a/\345\233\276\345\203\217\350\257\206\345\210\253/\345\256\235\345\217\257\346\242\246\350\257\206\345\210\253/ResNetRS50-test.py" "b/\345\233\276\345\203\217\350\257\206\345\210\253/\345\256\235\345\217\257\346\242\246\350\257\206\345\210\253/ResNetRS50-test.py"
new file mode 100644
index 0000000..50da5bb
--- /dev/null
+++ "b/\345\233\276\345\203\217\350\257\206\345\210\253/\345\256\235\345\217\257\346\242\246\350\257\206\345\210\253/ResNetRS50-test.py"
@@ -0,0 +1,77 @@
+import tensorflow as tf
+import numpy as np
+import os
+import matplotlib.pyplot as plt
+from tensorflow.keras import layers
+from tensorflow.keras.models import load_model
+
+# from PIL import Image
+
+model = load_model('model/ResNetRS50-pokeman.h5')
+model.summary()
+
+# 类别总数
+dataset_dir = 'data/train'
+classes = []
+for filename in os.listdir(dataset_dir):
+    classes.append(filename)
+# print('classes:',classes)
+
+# 预测单张图片
+def predict_single_image(img_path):
+    # string类型的tensor
+    img = tf.io.read_file(img_path)
+    # 将jpg格式转换为tensor
+    img = tf.image.decode_jpeg(img, channels=3)
+    # 数据归一化
+    img = tf.image.convert_image_dtype(img, dtype=tf.float32)
+    # resize
+    img = tf.image.resize(img, size=[224, 224])
+    # 扩充一个维度
+    img = np.expand_dims(img, axis=0)
+
+    # 预测：结果是二维的
+    test_result = model.predict(img)
+    # print('test_result：', test_result)
+    # 转化为一维
+    result = np.squeeze(test_result)
+    # print('转化后result：', result)
+
+    # 找到概率值最大的索引
+    predict_class = np.argmax(result)
+    # print('概率值最大的索引：', predict_class)
+
+    # 返回类别和所属类别的概率
+    return classes[int(predict_class)], result[predict_class]
+
+# 对整个文件夹的图片进行预测
+def predict_directory(file_path):
+    classes_pred=[]
+    classes_true=[]
+    probs=[]
+    for file in os.listdir(file_path):
+        # 测试图片完整路径
+        file_dir=os.path.join(file_path,file)
+        # 打印文件路径
+        print(file_dir)
+        # 传入文件路径进行预测
+        preds,prob=predict_single_image(file_dir)
+
+        # 取出图片的真实标签(这里直接将文件夹名称作为真实标签值了)
+        # label_true=file.split('_')[0].title()
+        label_true = file_dir.split('\\')[0].split('/')[-1]
+        # 保存真实值和预测值结果
+        classes_true.append(label_true)
+        classes_pred.append(preds)
+        probs.append(prob)
+    return classes_pred,classes_true,probs
+
+# img_path = 'Gemstones/train/Almandine/almandine_0.jpg'
+# classes, prob = predict_single_image(img_path)
+# print(classes, prob)
+
+file_path= 'data/test/bulbasaur'
+classes_pred,classes_true,probs=predict_directory(file_path)
+print(classes_pred)
+print(classes_true)
+print(probs)
\ No newline at end of file
-- 
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