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newapp.py

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+import sys
+import streamlit as st
+from PIL import Image, ImageOps
+import tensorflow
+import numpy as np
+import base64
+from io import BytesIO
+import joblib
+import os
+from streamlit_drawable_canvas import st_canvas
+
+
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+
+# 页面布局
+st.set_page_config(page_title="图像分类平台", page_icon="🔬", layout="wide")
+
+# 页面布局
+st.title('图像分类平台')
+st.header('手写数字识别')
+# 创建画布
+canvas = st_canvas(
+    fill_color="#FFFFFF",  # 画布背景色
+    stroke_color="#000000",  # 笔触颜色
+    height=300,  # 画布高度
+    width=300,  # 画布宽度
+    drawing_mode="freedraw",  # 绘制模式
+    key='canvas'
+)
+
+# 添加提交按钮
+user_drew = st.button("提交并预测数字")
+
+# 加载模型
+model_path = os.path.join(BASE_DIR, "model/number_model.h5")
+if os.path.isfile(model_path):
+    try:
+        num_model = tensorflow.keras.models.load_model(model_path, compile=True)
+    except Exception as e:
+        st.error(f"加载模型时发生错误: {e}")
+        num_model = None
+else:
+    st.error(f"模型文件不存在: {model_path}")
+
+num_flag = 1
+
+# 执行预测
+if user_drew:
+    if canvas is not None and canvas.image_data is not None:
+        try:
+            # 将 NumPy 数组转换为 PIL 图像
+            image = canvas.image_data
+            # 检查 canvas.image_data 是否是有效的图像数据
+            print("Canvas image data shape:", canvas.image_data.shape)
+            print("Canvas image data dtype:", canvas.image_data.dtype)
+
+            if canvas.image_data.shape[-1] == 4:
+                my_image = canvas.image_data[..., 3:]
+            else:
+                my_image = canvas.image_data
+
+            # 显示用户绘制的图像
+            st.image(my_image, caption='您绘制的数字')
+
+            print("my_image shape:", my_image.shape)
+
+            # 创建一个新的 PIL 图像，模式设置为 'L'（灰度）
+            pil_image = Image.new('L', (my_image.shape[1], my_image.shape[0]))
+
+            # 将 my_image 的数据复制到 PIL 图像中
+            pil_image.putdata(my_image.reshape(-1))
+
+            image = pil_image.resize((28, 28), Image.Resampling.LANCZOS)  # 调整大小
+
+            st.image(image, caption='调整大小后')
+
+            # 归一化图像数据
+            image_array = np.array(image) / 255.0
+            image_array = np.expand_dims(image_array, axis=-1)  # 添加通道维度
+            image_array = np.expand_dims(image_array, axis=0)  # 添加批次维度
+
+            # 显示用户绘制的图像
+            st.image(image_array[0, :, :, 0], caption='处理后的图像')
+
+            # 打印图像数组的形状和数据类型
+            print("Image array shape:", image_array.shape)
+            print("Image array dtype:", image_array.dtype)
+
+            # 打印最小和最大像素值
+            print("Min pixel value:", image_array.min())
+            print("Max pixel value:", image_array.max())
+
+            # 使用模型进行预测
+            num_class_labels = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+            predictions = num_model.predict(image_array)[0]
+            st.write(f"predictions：{predictions}")
+            predicted_class_index = np.argmax(predictions)
+            st.write(f"predicted_class_index：{np.argmax(predictions)}")
+            predicted_class = num_class_labels[predicted_class_index]
+            st.write(f"predicted_class：{num_class_labels[predicted_class_index]}")
+
+            # 获取预测的概率值
+            predicted_probabilities = predictions * 100
+
+            st.write(f"对于您绘制的数字的预测结果是：")
+            st.write(f"类别：'{predicted_class}' 概率：{predicted_probabilities[predicted_class_index]:.2f}")
+
+        except Exception as e:
+            # 显示错误信息
+            st.error("图像处理出错")
+            st.exception(e)
+            num_flag = 0
+    else:
+        st.warning("没有检测到图像数据。请在画布上绘制数字。")
+        num_flag = 0