Upload app.py

Classification of genus and species of medical fungus

app.py

@@ -0,0 +1,222 @@
+# -*- coding: utf-8 -*-
+"""app.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1EabZcU6Wk8QL6n0_cXYso4djYWFXPOIJ
+"""
+
+import numpy as np
+import pandas as pd
+
+import tensorflow as tf
+from tensorflow import keras
+import gradio as gr
+from pathlib import Path
+import os
+
+"""# set up paths"""
+
+project_path = Path('Fu-Chuen/Fungus_Classification_Genus_Species/tree/main')
+model_path = Path(project_path,'Model')
+test_path = Path(project_path,'Test')
+image_path = Path(project_path,'yolov7_environment_data/runs/detect/exp')
+label_path = Path(image_path,'labels')
+
+"""# load MobileNet model"""
+
+MobileNet_model = keras.models.load_model(Path(model_path,'MobileNetV3Large_Genus_5th_fold_model'))
+genus_labels = ['Aspergillus', "Cladosporium", 'Penicillium', 'Trichophyton', "Others"]
+
+"""#  set up the paths of YoloV7 enviroment and weights of pretrianed YoloV7"""
+
+# Commented out IPython magic to ensure Python compatibility.
+# 指定YOLO環境檔路徑
+# %cd Fu-Chuen/Fungus_Classification_Genus_Species/tree/main/yolov7_environment_data/
+# 指定 YOLO v7 模型權重檔
+WEIGHTS = r'Fu-Chuen/Fungus_Classification_Genus_Species/tree/main/Model/best_Yolo_v7.pt'
+# !pip3 install ultralytics
+
+"""# find the most frequent element"""
+
+def most_frequent_element(my_list):
+
+  # Create an empty dictionary to store element frequencies
+  element_count = {}
+
+  # Count the occurrences of each element in the list
+  for element in my_list:
+      if element in element_count:
+          element_count[element] += 1
+      else:
+          element_count[element] = 1
+
+  # Find the element(s) with the highest frequency
+  max_frequency = max(element_count.values())
+  most_frequent_elements = [element for element, frequency in element_count.items() if frequency == max_frequency]
+
+  # Print the result
+  # print("The element(s) with the highest frequency:", most_frequent_elements)
+  # print("The highest frequency:", max_frequency)
+  return most_frequent_elements, max_frequency
+
+"""# genus classify using MobileNet model"""
+
+def genus_classify_images(files):
+    results = ""
+    predict = []
+    for i,file_path in enumerate(files):
+        try:
+            inp = tf.keras.preprocessing.image.load_img(file_path.name, target_size=(224, 224))
+            inp = tf.keras.preprocessing.image.img_to_array(inp)
+            inp = inp.reshape((-1, 224, 224, 3))
+            inp = tf.keras.applications.mobilenet_v3.preprocess_input(inp)
+            model, labels = (MobileNet_model,genus_labels)
+            prediction = model.predict(inp).flatten()
+            confidences = {labels[i]: float(prediction[i]) for i in range(len(labels))}
+            top_3 = sorted(confidences.items(), key=lambda x: x[1], reverse=True)[:3]
+            predict.append(top_3[0][0])
+            # results += f"Image: {file_path.name} - Top 3 Predictions: {', '.join([f'{label}: {probab:.3f}' for label, probab in top_3])}\n"
+            # results += f"Image: {file_path.name.split('/')[-1]} - Top 3 Predictions: {', '.join([f'{label}: {probab:.3f}' for label, probab in top_3])}\n"
+            results += f"Image {i+1}: Top 3 predictions: {', '.join([f'{label}: {probab:.3f}' for label, probab in top_3])}\n"
+        except Exception as e:
+            results += f"Image: {file_path.name} - Error: {str(e)}\n"
+    ensemble_predict, max_frequency = most_frequent_element(predict)
+    predict_result = ensemble_predict[0] if max_frequency/len(files) >= threshold else "Unclassified"
+    results = f'Prediction of genus: {predict_result}\n\n' + results
+
+    return predict_result, results
+
+"""# Aspergillus classify and count statistics"""
+
+# 實時偵測 2013-10-05 OK
+# --source 偵測圖像路徑
+# --save-txt 儲存標籤文件
+# --conf 0.25 只保留置信度分數高於0.25的
+# import os
+# import pandas as pd
+
+def delete_files_in_folder(path):
+    for root, dirs, files in os.walk(path):
+        print(f'root: {root}')
+        for file in files:
+            print(f'file: {file}')
+            file_path = os.path.join(root, file)
+            os.remove(file_path)
+
+def Aspergillus_Detect():
+  delete_files_in_folder(image_path)
+  !python detect.py --source Fu-Chuen/Fungus_Classification_Genus_Species/tree/main/Test/ --weights $WEIGHTS --conf 0.25 --save-txt --exist-ok
+  file_paths = []
+  for root, dirs, files in os.walk(label_path):
+      for file in files:
+          if file.lower().endswith('.jpg'):
+              file_path = os.path.join(root, file)
+              file_paths.append(file_path)
+
+  df_test = pd.DataFrame({'filepath': file_paths})
+
+  files = os.listdir(label_path)
+
+  # initialize an empty list to store data
+  data = []
+
+  # loop through each file and extract the data
+  for file in files:
+      if file.endswith('.txt'):
+          with open(os.path.join(label_path, file), 'r') as f:
+              lines = f.readlines()
+              for line in lines:
+                  line = line.strip().split(' ')
+                  data.append([file[:-4], line[0], float(line[1]), float(line[2]), float(line[3]), float(line[4])])
+
+  # convert list of data to data frame
+  df = pd.DataFrame(data, columns=['image', 'class', 'xmin', 'ymin', 'xmax', 'ymax'])
+  # group by labels, count the number of detections for each label
+  counts = df.groupby(['image', 'class']).size().reset_index(name='count')
+  # add the file name to the data frame
+  counts['file'] = counts['image'].apply(lambda x: x + '.jpg')
+
+  counts['filepath'] = counts['file'].apply(lambda x: Path(label_path,x))
+
+  replace_dict = {'0': 'flavus-oryzae', '1': 'fumigatus', '2': 'niger', '3': 'terreus', '4': 'versicolor'}
+  counts['label'] = counts['class'].replace(replace_dict)
+
+  # Select only the relevant columns
+  counts = counts[['filepath', 'label', 'count']]
+  df_pivot = pd.pivot_table(counts, values='count', index=['filepath'], columns='label', aggfunc='sum')
+  df_pivot['detect'] = df_pivot.sum(axis=1)
+  pd.options.display.float_format = '{:,.0f}'.format
+  df_pivot = pd.DataFrame(df_pivot)
+
+  total_col = df_pivot.pop('detect')
+  df_pivot.insert(0, 'detect', total_col)
+  detect_number = round(df_pivot['detect'].sum())
+  a = []
+  for i in df_pivot.columns[1:]:
+    a.append([i,round(df_pivot[i].sum()/detect_number,3)])
+  a = sorted(a,key=lambda x: x[1],reverse=True)
+
+  result = [f'Prediction of species of Aspergillus: {a[0][0] if a[0][1] >= threshold else "Unclassified"}',
+      f'There are {df_pivot.shape[0]} mold images.',
+            f'Yolov7 detects {detect_number} instances.',
+            f'The top {len(a)} percentage of specifies:']
+  for i in a:
+    # print(f'i: {i}')
+    result.append(f'{i[0]} {i[1]}')
+
+  return '\n'.join(result)
+
+"""# classify images: genus and Aspergillus"""
+
+def classify_images(files):
+    predict, result1 = genus_classify_images(files)
+    if predict == 'Aspergillus':
+      result2 = Aspergillus_Detect()
+      return f'{result1}\n\n{result2}'
+    return result1
+
+import shutil
+
+# Create the 'Test' folder if it doesn't exist
+# test_path = Path(project_path,'Test')
+# os.makedirs(test_path, exist_ok=True)
+
+def upload_file(files):
+    delete_folder(test_path)
+    file_paths = []
+    for file in files:
+        # Save the uploaded image to the 'Test' folder
+        file_name = Path(test_path, file.name.split('/')[-1])
+        shutil.copy(file.name, file_name)
+        file_paths.append(file_name)
+
+    return file_paths
+
+# Define the path to the directory you want to delete files from
+directory_path = test_path
+
+# Iterate through all files in the directory and delete them
+def delete_folder(directory_path):
+  for filename in os.listdir(directory_path):
+    file_path = os.path.join(directory_path, filename)
+    try:
+        if os.path.isfile(file_path) or os.path.islink(file_path):
+            os.unlink(file_path)
+        elif os.path.isdir(file_path):
+            shutil.rmtree(file_path)
+    except Exception as e:
+        print(f"Failed to delete {file_path}: {e}")
+delete_folder(directory_path)
+
+"""# main of gradio"""
+
+threshold = 0.6
+with gr.Blocks() as fungus_classification:
+    image_output = gr.Gallery(label = 'Images of Molds')
+    predict_outputs = gr.Textbox(label = 'Prediction Result')
+    upload_button = gr.UploadButton("Click to Upload Files", file_types=["image", "video"], file_count="multiple")
+    upload_button.upload(upload_file, upload_button, image_output)
+    upload_button.upload(classify_images, upload_button, predict_outputs)
+fungus_classification.launch(share=True,debug=True)