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import sys
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import streamlit as st
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from PIL import Image
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import tensorflow
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import numpy as np
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import base64
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from io import BytesIO
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import joblib
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import os
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from streamlit_drawable_canvas import st_canvas
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BASE_DIR = os.path.dirname(os.path.abspath(__file__))
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# 页面布局
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st.set_page_config(page_title="图像分类平台", page_icon="🔬", layout="wide")
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# 页面布局
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st.title('图像分类平台')
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# 创建两个列，每个列可以放置不同的内容
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col1, col2 = st.columns(2)
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# 在第一个列中放置内容
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with col1:
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st.header('手写数字识别')
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# 创建画布
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canvas = st_canvas(
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fill_color="#FFFFFF", # 画布背景色
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stroke_color="#000000", # 笔触颜色
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height=300, # 画布高度
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width=300, # 画布宽度
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drawing_mode="freedraw", # 绘制模式
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key='canvas'
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)
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# 添加提交按钮
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user_drew = st.button("提交并预测数字")
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# 加载模型
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model_path = os.path.join(BASE_DIR, "model/number_model.h5")
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if os.path.isfile(model_path):
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try:
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num_model = tensorflow.keras.models.load_model(model_path, compile=True)
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except Exception as e:
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st.error(f"加载模型时发生错误: {e}")
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num_model = None
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else:
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st.error(f"模型文件不存在: {model_path}")
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num_flag = 1
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# 执行预测
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if user_drew:
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if canvas is not None and canvas.image_data is not None:
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try:
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# 将 NumPy 数组转换为 PIL 图像
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image = canvas.image_data
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# 检查 canvas.image_data 是否是有效的图像数据
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print("Canvas image data shape:", canvas.image_data.shape)
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print("Canvas image data dtype:", canvas.image_data.dtype)
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if canvas.image_data.shape[-1] == 4:
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my_image = canvas.image_data[..., 3:]
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else:
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my_image = canvas.image_data
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# 显示用户绘制的图像
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st.image(my_image, caption='您绘制的数字')
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print("my_image shape:", my_image.shape)
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# 创建一个新的 PIL 图像，模式设置为 'L'（灰度）
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pil_image = Image.new('L', (my_image.shape[1], my_image.shape[0]))
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# 将 my_image 的数据复制到 PIL 图像中
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pil_image.putdata(my_image.reshape(-1))
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image = pil_image.resize((28, 28), Image.Resampling.LANCZOS) # 调整大小
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st.image(image, caption='调整大小后')
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# 归一化图像数据
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image_array = np.array(image) / 255.0
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image_array = np.expand_dims(image_array, axis=-1) # 添加通道维度
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image_array = np.expand_dims(image_array, axis=0) # 添加批次维度
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# 显示用户绘制的图像
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st.image(image_array[0, :, :, 0], caption='处理后的图像')
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# 打印图像数组的形状和数据类型
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print("Image array shape:", image_array.shape)
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print("Image array dtype:", image_array.dtype)
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# 打印最小和最大像素值
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print("Min pixel value:", image_array.min())
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print("Max pixel value:", image_array.max())
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# 使用模型进行预测
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num_class_labels = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
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predictions = num_model.predict(image_array)[0]
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# st.write(f"predictions：{predictions}")
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predicted_class_index = np.argmax(predictions)
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# st.write(f"predicted_class_index：{np.argmax(predictions)}")
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predicted_class = num_class_labels[predicted_class_index]
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# st.write(f"predicted_class：{num_class_labels[predicted_class_index]}")
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# 获取预测的概率值
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predicted_probabilities = predictions * 100
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st.write(f"对于您绘制的数字的预测结果是：")
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st.write(f"类别：'{predicted_class}' 概率：{predicted_probabilities[predicted_class_index]:.2f}")
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except Exception as e:
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# 显示错误信息
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st.error("图像处理出错")
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st.exception(e)
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num_flag = 0
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else:
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st.warning("没有检测到图像数据。请在画布上绘制数字。")
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num_flag = 0
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# 在第二个列中放置内容
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with col2:
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models = {
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"动物类别判断": tensorflow.keras.models.load_model(os.path.join(BASE_DIR, "model", "animal_model.h5"), compile=True),
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"花卉类别判断": tensorflow.keras.models.load_model(os.path.join(BASE_DIR, "model", "flower_model.h5"), compile=True),
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"风景地点判断": tensorflow.keras.models.load_model(os.path.join(BASE_DIR, "model", "scenery_model.h5"), compile=True),
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}
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know_advice = ["动物类别判断", "花卉类别判断"]
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def generate_report(selected_model, predicted_class, advice, image_data, predicted_probabilities, class_labels):
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try:
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image = Image.open(BytesIO(image_data)).convert('RGB') # 使用提供的图像数据打开图像
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except Exception as e:
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st.error("处理图片时出现问题，请确认图片格式和数据。")
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st.error(f"错误信息: {e}")
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return
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# 将图片转换为Base64编码
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buffered = BytesIO()
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image.save(buffered, format="PNG")
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img_str = base64.b64encode(buffered.getvalue()).decode('utf-8')
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# 构建HTML代码来显示图片
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img_html = f'<img src="data:image/png;base64,{img_str}" alt="Uploaded Image">'
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# 构建预测结果和概率信息
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predictions_info = ""
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for i, prob in enumerate(predicted_probabilities):
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predictions_info += f"{class_labels[i]}: {prob:.2f}%<br>\n"
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# 构建报告内容
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advice_content = ""
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for item in advice:
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advice_content += f"{item}<br>\n"
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report_content = f"""<html>
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<head>
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<style>
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body {{
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font-family: Arial, sans-serif;
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text-align: center; /* 文本居中显示 */
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background-color: #f0f0f0; /* 背景色 */
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}}
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h1 {{
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color: #333;
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}}
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table {{
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margin: auto; /* 表格居中显示 */
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border-collapse: collapse;
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width: 80%; /* 表格宽度 */
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background-color: #fff; /* 表格背景色 */
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padding: 20px;
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}}
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th, td {{
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border: 1px solid #ccc;
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padding: 10px;
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}}
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</style>
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</head>
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<body>
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<h1>图像分类分析报告</h1>
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<table>
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<tr>
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<th>项目</th>
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<th>内容</th>
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</tr>
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<tr>
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<td>选择的模型分类</td>
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<td>{selected_model}</td>
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</tr>
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<tr>
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<td>预测结果</td>
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<td>{predicted_class}</td>
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</tr>
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<tr>
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<td>预测概率</td>
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<td>
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{predictions_info}
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</td>
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</tr>
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<tr>
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<td>简单介绍</td>
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<td>
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{advice_content}
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</td>
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</tr>
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<tr>
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<td>上传的图片</td>
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<td>
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{img_html}
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</td>
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</tr>
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</table>
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<h3>以上结果仅供参考</h3>
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</body>
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</html>"""
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report_filename = f"{selected_model}_diagnosis_report.html"
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with open(report_filename, "w") as file:
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file.write(report_content)
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st.success("报告生成成功！")
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# 提供下载链接
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with open(report_filename, "rb") as file:
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report_data = file.read()
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b64 = base64.b64encode(report_data).decode()
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href = f'<a href="data:file/html;base64,{b64}" download="{report_filename}">点击这里下载报告</a>'
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st.markdown(href, unsafe_allow_html=True)
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st.header("选择模型分类并提供图片进行判断")
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selected_model = st.selectbox("选择模型分类", list(models.keys()))
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uploaded_image = st.file_uploader("上传一张图片", type=["jpg", "jpeg", "png"])
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if uploaded_image is not None:
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image_data = uploaded_image.read()
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image = Image.open(BytesIO(image_data))
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st.image(image, caption="Uploaded Image", use_column_width=True)
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model = models[selected_model]
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input_shape = model.input_shape[1:3]
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image = image.resize(input_shape)
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image_array = np.array(image)
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image_array = image_array / 255.0
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if len(model.input_shape) == 4:
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image_array = np.expand_dims(image_array, axis=0)
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predictions = model.predict(image_array)
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predicted_class_index = np.argmax(predictions[0])
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class_labels = []
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if selected_model == "动物类别判断":
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class_labels = ["蝴蝶", "猫", "鸡", "牛", "狗", "大象", "马", "羊", "蜘蛛", "松鼠"]
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advice_dict = {
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"蝴蝶": ["蝴蝶是昆虫中的一种，属于鳞翅目。",
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"它们的特点是身体细长，有两对薄而有色的翅膀。",
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"蝴蝶通常在花朵周围飞舞，以花蜜为食。",
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"它们在生命周期中经历幼虫、蛹和成虫三个阶段，是自然界中美丽而独特的生物之一。"],
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"猫": ["猫是家猫的通称，是一种家畜动物，属于哺乳动物。",
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"猫有着柔软的毛皮和灵活的身体，以及锋利的爪子。",
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"它们是人类最早驯养的动物之一，广泛分布于世界各地。",
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"猫通常以捕捉小型啮齿动物和鸟类为生，是人类常见的宠物之一"],
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"鸡": ["鸡是一种家禽，常见于全世界各地。",
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"它们是人类最早驯养的动物之一，主要被养殖用于食用和产蛋。",
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"鸡的特征包括具有羽毛的身体、喙和爪子。",
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"除了食用肉和蛋外，鸡的叫声也是农村常见的声音之一。"],
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"牛": ["牛是哺乳动物，属于偶蹄目。",
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"它们被人类驯养用于提供肉、奶、皮革等各种用途。",
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"牛的特征包括强壮的身体、角、四蹄和长长的尾巴。",
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"在许多文化中，牛被视为重要的家畜，承载着农业和经济上的重要角色。"],
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"狗": ["狗是人类最早驯养的动物之一，属于哺乳动物。",
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"它们有着各种不同的品种和体型，从小型犬到大型犬不等。",
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"狗通常被养作宠物，也被用于警戒、搜救、导盲等工作。",
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"它们以其忠诚、友好和忠诚的品质而受到人类的喜爱。"],
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"大象": ["大象是世界上最大的陆地动物之一，属于哺乳动物。",
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"它们有着庞大的身躯、长长的象牙和宽大的耳朵。",
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"大象通常生活在非洲和亚洲的草原、森林和沙漠地带。",
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"它们是社会性动物，以群体为单位生活，拥有复杂的社会结构和交流方式。"],
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"马": ["马是一种家畜动物，属于哺乳动物。",
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"它们有着优雅的体态、强壮的四肢和长长的尾巴。",
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"马被广泛用于运输、农业、体育等各种用途。",
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"它们以其速度、力量和耐力而闻名，是人类历史上重要的伙伴之一。"],
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"羊": ["羊是一种常见的家畜动物，属于哺乳动物。",
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"它们有着蓬松的毛皮和弯曲的角。",
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"羊通常被人类养殖用于提供羊毛、羊肉、羊奶等产品。",
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"它们是社会性动物，以群体为单位生活，常常在草原和山区地带放牧。"],
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"蜘蛛": ["蜘蛛是一种节肢动物，属于蜘蛛纲。",
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"它们有着八只长腿和分节的身体。",
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"蜘蛛通常以捕食昆虫为生，利用自己编织的网来捕捉猎物。",
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"它们生活在各种环境中，从森林到城市都有发现。"],
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"松鼠": ["松鼠是一种啮齿动物，属于松鼠科。",
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"它们有着灵活的身体和长长的尾巴。",
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"松鼠通常生活在树上，以坚果、种子和水果为食。",
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"它们以其活泼好动和敏捷的特点而闻名，是许多人心目中的可爱动物之一。"]
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}
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elif selected_model == "花卉类别判断":
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class_labels = ["洋甘菊", "蒲公英", "玫瑰", "向日葵", "郁金香"]
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advice_dict = {
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"洋甘菊": ["洋甘菊是一种常见的花卉，具有淡蓝色或白色的花瓣，中间是黄色的花蕊。",
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"它们被广泛种植作为园艺植物，并且在医药和美容行业中也很受欢迎。",
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"洋甘菊被用于制作茶和精油，具有舒缓和放松的效果。",
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"在花语中，洋甘菊通常象征着友谊、温和和平静。"],
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"蒲公英": ["蒲公英是一种常见的野生植物，有着带有细小白丝的黄色花朵，成熟后会变成风吹就会飞散的种子。",
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"它们生长在各种环境中，包括草地、道路边缘和田野。",
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"蒲公英在草地上常被认为是杂草，但它们也被一些人视为美丽而坚韧的植物。",
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"在花语中，蒲公英代表着希望、自由和幸福。"],
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"玫瑰": ["玫瑰是最受欢迎和广泛种植的花之一，有成百上千种不同的品种，颜色和形状各异。",
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"玫瑰被视为爱情和美丽的象征，是情人节和其他浪漫场合的常见礼物。",
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"除了作为美丽的花束和花环，玫瑰也被用来提取精油，用于香水和护肤品。",
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"在花语中，不同颜色的玫瑰代表着不同的情感，例如红色代表热情和爱情，白色代表纯洁和无辜。"],
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"向日葵": ["向日葵是一种高大的开花植物，以其大而明亮的黄色花朵和特殊的生长习性而闻名。",
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"它们倾向于朝向太阳，并在一天中跟随太阳的运动而转动，因此得名。",
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"向日葵象征着阳光、活力和希望，在许多文化中被视为吉祥物。",
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"它们也是一种重要的农业作物，提供了食用油和饲料。"],
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"郁金香": ["郁金香是一种多年生草本植物，有着各种各样的颜色和花型，因此在园艺上受到欢迎。",
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"它们在花园、花坛和花瓶中都很常见。",
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"郁金香在荷兰尤其著名，被认为是该国的象征之一。",
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"花语中，郁金香通常代表着爱情、优雅和温柔。"]
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}
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elif selected_model == "风景地点判断":
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class_labels = ["建筑物", "森林", "冰川", "山", "海", "街道"]
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predictions = model.predict(image_array)[0]
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predicted_class_index = np.argmax(predictions)
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predicted_class = class_labels[predicted_class_index]
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# 获取预测的概率值
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predicted_probabilities = predictions * 100
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st.write(f"对于选择的模型分类 '{selected_model}' 的预测结果是：")
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st.write(f"类别：'{predicted_class}' 概率：{predicted_probabilities[predicted_class_index]:.2f}")
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if selected_model in know_advice:
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if predicted_class in advice_dict:
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advice = advice_dict[predicted_class]
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st.write("建议：")
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for item in advice:
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st.write(f"- {item}")
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advice = advice_dict[predicted_class]
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if st.button('生成定制化报告'):
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with st.spinner('正在生成报告...'):
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generate_report(selected_model, predicted_class, advice, image_data, predicted_probabilities,
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class_labels)
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