less IS more
76 lines
2.7 KiB
Python
76 lines
2.7 KiB
Python
from flask import Flask, render_template, request
|
|
import tensorflow as tf
|
|
import cv2
|
|
import numpy as np
|
|
import os
|
|
import pandas as pd
|
|
|
|
app = Flask(__name__)
|
|
|
|
root_dir = "images/images"
|
|
|
|
# 获取图片数据
|
|
files = os.path.join(root_dir)
|
|
File_names = os.listdir(files)
|
|
|
|
# 属性数据集
|
|
data = pd.read_csv("pokemon.csv")
|
|
data_dict = {}
|
|
for key, val in zip(data["Name"], data["Type1"]): # 将多个可迭代对象按照索引位进行排序
|
|
data_dict[key] = val
|
|
labels = data["Type1"].unique()
|
|
labels_idx = dict(zip(labels, range(len(labels))))
|
|
|
|
# 数据预处理
|
|
final_images = []
|
|
final_labels = []
|
|
for file in File_names:
|
|
img = cv2.imread(os.path.join(root_dir, file), cv2.COLOR_BGR2GRAY)
|
|
label = labels_idx[data_dict[file.split(".")[0]]]
|
|
final_images.append(np.array(img))
|
|
final_labels.append(np.array(label))
|
|
|
|
final_images = np.array(final_images, dtype=np.float32) / 255.0
|
|
final_labels = np.array(final_labels, dtype=np.int8).reshape(len(final_labels), 1)
|
|
|
|
model = tf.keras.Sequential([ # 用于构造序列模型
|
|
tf.keras.layers.Flatten(input_shape=(120, 120, 3)),
|
|
tf.keras.layers.Dense(100, activation='relu'),
|
|
tf.keras.layers.Dense(100, activation='relu'),
|
|
tf.keras.layers.Dense(100, activation='relu'),
|
|
tf.keras.layers.Dense(len(labels))
|
|
])
|
|
model.compile(optimizer='adam',
|
|
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
|
|
metrics=['accuracy'])
|
|
history = model.fit(final_images, final_labels, epochs=20)
|
|
|
|
# 自定义 enumerate 函数
|
|
def custom_enumerate(iterable, start=0): # 用于在迭代过程中同时获得索引和值
|
|
return zip(range(start, len(iterable) + start), iterable)
|
|
|
|
# 注册自定义 enumerate 函数
|
|
app.jinja_env.globals['enumerate'] = custom_enumerate
|
|
|
|
@app.route('/')
|
|
def home():
|
|
random_indices = np.random.choice(len(final_images), 10, replace=False)
|
|
random_images = [File_names[i] for i in random_indices]
|
|
random_labels = [final_labels[i] for i in random_indices]
|
|
return render_template('index.html', images=random_images, labels=random_labels) # 渲染名为'index.html'的模板，并将‘random_images’和'random_labels'作为参数传递给index
|
|
|
|
@app.route('/predict', methods=['POST'])
|
|
def predict():
|
|
index = int(request.form['index']) # 从post请求的表单数据中获得名为'index'的字段
|
|
image = final_images[index]
|
|
prediction = model.predict(np.array([image]))
|
|
predicted_label = np.argmax(prediction)
|
|
predicted_attribute = labels[predicted_label]
|
|
return render_template('result.html', prediction=predicted_attribute)
|
|
|
|
if __name__ == '__main__':
|
|
app.run(debug=True)
|
|
|
|
|
|
|