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@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+color_features.json filter=lfs diff=lfs merge=lfs -text
+image_subset.json filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,190 @@
+import matplotlib.pyplot as plt
+import seaborn as sns
+plt.rcParams["figure.figsize"] = (15, 10)
+plt.rcParams["figure.dpi"] = 125
+plt.rcParams["font.size"] = 14
+plt.rcParams['font.family'] = ['sans-serif']
+plt.rcParams['font.sans-serif'] = ['DejaVu Sans']
+plt.style.use('ggplot')
+sns.set_style("whitegrid", {'axes.grid': False})
+plt.rcParams['image.cmap'] = 'gray' # grayscale looks better
+from pathlib import Path
+import numpy as np
+import pandas as pd
+import os
+from skimage.io import imread as imread
+from skimage.util import montage
+from PIL import Image
+montage_rgb = lambda x: np.stack([montage(x[:, :, :, i]) for i in range(x.shape[3])], -1)
+from skimage.color import label2rgb
+
+image_dir = Path('D:/vscodeim/food classification/feature_food/')
+
+
+mapping_file = Path('D:/vscodeim/food classification/feature_food/clean_list.json')
+alleg_df = pd.read_json(mapping_file)
+
+alleg_df['image_path'] = alleg_df['image_path'].map(lambda x: image_dir / 'subset' / x) 
+print(alleg_df['image_path'].map(lambda x: x.exists()).value_counts())
+allergens = alleg_df.columns[3:].tolist()
+alleg_df.sample(2)
+
+import os
+from pathlib import Path
+
+
+color_file = Path('D:/vscodeim/food classification/feature_food/color_features.json')
+color_feat_df = pd.read_json(color_file)
+color_feat_df['image_path'] = color_feat_df['image_path'].map(lambda x: image_dir / 'subset' / x) 
+
+color_feat_dict = {c_row['image_path']: c_row['color_features'] for _, c_row in color_feat_df.iterrows()}
+# add a new color feature column
+alleg_df['color_features'] = alleg_df['image_path'].map(color_feat_dict.get)
+alleg_df.sample(2)
+
+
+co_all = np.corrcoef(np.stack(alleg_df[allergens].applymap(lambda x: 1 if x>0 else 0).values, 0).T)
+fig, ax1 = plt.subplots(1, 1, figsize=(10, 10))
+sns.heatmap(co_all, annot=True, fmt='2.1%', ax=ax1, cmap='RdBu', vmin=-1, vmax=1)
+ax1.set_xticklabels(allergens, rotation=90)
+ax1.set_yticklabels(allergens);
+
+
+# package the allergens together
+alleg_df['allergy_vec'] = alleg_df[allergens].applymap(lambda x: 1 if x>0 else 0).values.tolist()
+
+from sklearn.model_selection import train_test_split
+train_df, valid_df = train_test_split(alleg_df.drop(columns='ingredients_list'), 
+                                      test_size=0.1, 
+                                      random_state=2019, 
+                                      stratify=alleg_df['allergy_vec'].map(lambda x: x[0:3]))
+
+train_df.reset_index(inplace=True)
+valid_df.reset_index(inplace=True)
+
+
+print(train_df.shape[0], 'training images')
+print(valid_df.shape[0], 'validation images')
+
+train_x_vec = np.stack(train_df['color_features'].values, 0)
+train_y_vec = np.stack(train_df['allergy_vec'], 0)
+
+
+
+
+
+from sklearn.pipeline import make_pipeline
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.preprocessing import RobustScaler  # 导入 RobustScaler
+
+rf_pipe = make_pipeline(RobustScaler(), RandomForestRegressor(n_estimators=15))
+rf_pipe.fit(train_x_vec, train_y_vec)
+
+
+import streamlit as st
+import pandas as pd
+import numpy as np
+from sklearn.metrics import roc_auc_score, roc_curve, accuracy_score
+import matplotlib.pyplot as plt
+from imageio import imread
+
+@st.cache
+def show_model_results(in_model, picture_number=None):
+    # Your data loading code here
+    # Ensure 'valid_df', 'valid_x_vec', 'valid_y_vec', 'train_df', 'train_x_vec', 'train_y_vec', 'allergens' are defined
+
+    
+    
+    valid_x_vec = np.stack(valid_df['color_features'].values, 0)
+    valid_y_vec = np.stack(valid_df['allergy_vec'], 0)
+
+    x_vec = valid_x_vec
+    y_vec = valid_y_vec
+
+    
+    valid_pred = in_model.predict(x_vec)
+    valid_num = picture_number
+
+    fig, m_axs = plt.subplots(2, 2, figsize=(10, 10))
+    all_rows = []
+    ax1 = m_axs[0, 0]
+    for i, c_allergen in enumerate(allergens):
+        tpr, fpr, _ = roc_curve(y_vec[:, i], valid_pred[:, i])
+        auc = roc_auc_score(y_vec[:, i], valid_pred[:, i])
+        acc = accuracy_score(y_vec[:, i], valid_pred[:, i] > 0.5)
+        ax1.plot(tpr, fpr, '.-', label='{}: AUC {:0.2f}, Accuracy: {:2.0%}'.format(c_allergen, auc, acc))
+        all_rows += [{'allegen': c_allergen, 
+                      'prediction': valid_pred[j, i], 
+                      'class': 'Positive' if y_vec[j, i] > 0.5 else 'Negative'} 
+                     for j in range(valid_pred.shape[0])]
+    
+    d_ax = m_axs[0, 1]
+    t_yp = np.mean(valid_pred, 0)
+    t_y = np.mean(y_vec, 0)
+    d_ax.barh(np.arange(len(allergens)) + 0.1, t_yp, alpha=0.5, label='Predicted')
+    d_ax.barh(np.arange(len(allergens)) - 0.1, t_y + 0.001, alpha=0.5, label='Ground Truth')
+    d_ax.set_xlim(0, 1)
+    d_ax.set_yticks(range(len(allergens)))
+    d_ax.set_yticklabels(allergens, rotation=0)
+    d_ax.set_title('Overall')
+    d_ax.legend()
+    
+    ax1.legend()
+    for (_, c_row), (c_ax, d_ax) in zip(
+    valid_df.iloc[valid_num:valid_num+1].iterrows(), 
+    m_axs[1:]):
+        c_ax.imshow(imread(c_row['image_path']))
+        c_ax.set_title(c_row['title'])
+        c_ax.axis('off')
+        t_yp = in_model.predict(np.expand_dims(c_row['color_features'], 0))
+        t_y = np.array(c_row['allergy_vec'])
+        d_ax.barh(np.arange(len(allergens)) + 0.1, t_yp[0], alpha=0.5, label='Predicted')
+        d_ax.barh(np.arange(len(allergens)) - 0.1, t_y + 0.001, alpha=0.5, label='Ground Truth')
+        d_ax.set_yticks(range(len(allergens)))
+        d_ax.set_yticklabels(allergens, rotation=0)
+        d_ax.set_xlim(0, 1)
+        d_ax.legend()
+
+        # 将当前图像添加到 Streamlit 页面
+    st.pyplot(fig)
+    return st.write("Completed")
+
+# Assuming you have already defined 'rf_pipe' and 'valid_df' with image paths
+image_paths = valid_df['image_path'].tolist()
+
+
+# Streamlit app
+# Streamlit app
+def main():
+    st.title('Model Results')
+    st.write(f'<span style="font-size:20px;">This is a prototype, so we use the images from the test set as examples.</span>', unsafe_allow_html=True)
+
+    image_paths = valid_df['image_path'].tolist()
+
+    num_rows = 2
+    num_cols = 5
+    fig, axes = plt.subplots(num_rows, num_cols, figsize=(15, 6))
+
+    for i in range(num_rows):
+        for j in range(num_cols):
+            index = i * num_cols + j + 21  # Starting from index 21
+            image = imread(image_paths[index])
+            axes[i, j].imshow(image)
+            axes[i, j].axis('off')
+            axes[i, j].set_title(f'Image {index-20}')
+
+    st.pyplot(fig)
+
+    num_images = 10 
+
+    # User interaction to select image
+    st.write(f'<span style="font-size:20px;">Enter the image number you want to analyze.</span>', unsafe_allow_html=True)
+
+    choice = st.number_input(f"Range (1-{num_images}): ", min_value=1, max_value=num_images)
+    
+    # Show model results
+    if st.button('Show Results'):
+        show_model_results(rf_pipe, choice+20)
+
+if __name__ == "__main__":
+    main()

color_features.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1c77b1fb4901ec6abd8c19c9bacd2728cedaad6cb3e5a699da6808d5bf4bdb44
+size 18577814

image_subset.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:040c523a6f1d15f1e78ba215766a82b71274bbf128885d840b68582ee40ad9a8
+size 67109125