Update code

Access_control_system.py

+# -*- coding: utf-8 -*-
+"""Smart_door_lock
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1wmlnG3tDfHSrv9bvrnCxesrN4mApPCgl
+
+#Preprocessing
+#1. Resizing
+#2. flatten
+
+
+import os
+import matplotlib.pyplot as plt
+import numpy as np
+from skimage.io import imread
+from skimage.transform import resize
+from skimage import exposure
+
+target = []
+images = []
+flat_data = []
+DATADIR = '/content/sample_data/images'
+CATEGORIES = ['user', 'other']
+
+# Preprocessing: Image Enhancement
+for category in CATEGORIES:
+    class_num = CATEGORIES.index(category)  # label encoding
+    path = os.path.join(DATADIR, category)  # create path to use all images
+    for img in os.listdir(path):
+        img_array = imread(os.path.join(path, img))
+        # print(img_array.shape)
+        # plt.imshow(img_array)
+        img_resized = resize(img_array, (150, 150, 3))  # Normalizes to 0 to 1
+
+        # Contrast Stretching
+        p2, p98 = np.percentile(img_resized, (2, 98))
+        img_enhanced = exposure.rescale_intensity(img_resized, in_range=(p2, p98))
+
+        # Histogram Equalization
+        img_enhanced = exposure.equalize_hist(img_enhanced)
+
+        flat_data.append(img_enhanced.flatten())
+        images.append(img_enhanced)
+        target.append(class_num)
+
+flat_data = np.array(flat_data)
+target = np.array(target)
+images = np.array(images)
+"""
+
+#Preprocessing
+#1. Resizing
+#2. flatten
+
+
+import os
+import matplotlib.pyplot as plt
+import numpy as np
+from skimage.io import imread
+from skimage.transform import resize
+
+target = []
+images = []
+flat_data = []
+DATADIR ='/content/sample_data/images'
+CATEGORIES = ['user','other']
+
+for category in CATEGORIES:
+  class_num =CATEGORIES.index(category)#label encoding
+  path =os.path.join(DATADIR,category)#create path to use all images
+  for img in os.listdir(path):
+      try:
+        img_array = imread(os.path.join(path,img))
+        #print(img_array.shape)
+        #plt.imshow(img_array)
+        img_resized=resize(img_array,(150,150,3))#Normalizes to 0 to 1
+        flat_data.append(img_resized.flatten())
+        images.append(img_resized)
+        target.append(class_num)
+
+      except:
+            print(f"Unable to read image {img_path}")
+
+
+flat_data =np.array(flat_data)
+target =np.array(target)
+images =np.array(images)
+
+len(flat_data[0])
+
+unique,count=np.unique(target,return_counts=True)
+plt.bar(CATEGORIES,count)
+
+# split data into Training and Testing
+from sklearn.model_selection import train_test_split
+X_train, X_test, y_train, y_test = train_test_split(flat_data, target,
+                                                    test_size=0.3, random_state=109, stratify=target)
+
+unique_train, count_train = np.unique(y_train, return_counts=True)
+print(f"Unique classes in y_train: {unique_train}")
+
+# After the loop for reading and processing images
+print(f"Total images read: {len(images)}")
+print(f"Total target values: {len(target)}")
+
+unique_classes, count_classes = np.unique(target, return_counts=True)
+print(f"Unique classes in target: {unique_classes}")
+print(f"Count of images per class: {count_classes}")
+
+from sklearn.model_selection import GridSearchCV, StratifiedKFold
+from sklearn.svm import SVC
+
+# Define the parameter grid
+param_grid = [
+    {'C': [1, 10, 100, 1000], 'kernel': ['linear']},
+    {'C': [1, 10, 100, 1000], 'gamma': [0.001, 0.0001], 'kernel': ['rbf']},
+]
+
+# Create the SVM model with probability=True
+svc = SVC(probability=True)
+
+# Create a StratifiedKFold cross-validator
+cv = StratifiedKFold(n_splits=5)
+
+# Set up the GridSearchCV with the SVM model, parameter grid, and stratified cross-validator
+clf = GridSearchCV(svc, param_grid, cv=cv)
+
+# Fit the model to the training data
+clf.fit(X_train, y_train)
+
+y_pred =clf.predict(X_test)
+y_pred
+
+y_test
+
+from sklearn.metrics import accuracy_score, confusion_matrix
+
+accuracy_score(y_pred,y_test)
+
+confusion_matrix(y_pred,y_test)
+
+# save the model using pickle library
+import pickle 
+pickle.dump(clf,open('img_model.p','wb'))
+
+model=pickle.load(open('img_model.p','rb'))
+
+from skimage.io import imread
+from skimage.transform import resize
+import numpy as np
+import urllib.request
+import requests
+from google.colab import files
+import matplotlib.pyplot as plt
+
+# Ask the user to upload an image from their computer or enter a URL
+while True:
+  choice = input("Enter 'u' to upload an image from your computer, or 'b' to enter a URL: ")
+  if choice == 'u':
+    # Upload an image from the computer
+    flat_data=[]
+    uploaded = files.upload()
+    for filename in uploaded.keys():
+      img = imread(filename)
+      img_resized = resize(img, (150,150,3))
+      flat_data.append(img_resized.flatten())
+    flat_data = np.array(flat_data)
+    break
+
+  elif choice == 'b':
+    # Enter a URL to load an image from the internet
+    flat_data=[]
+    url = input("Please enter a valid image URL: ")
+    try:
+        # Check if the URL is valid and the image can be loaded
+        response = requests.get(url)
+        response.raise_for_status()
+        img = imread(urllib.request.urlopen(url), mode='RGB')
+        img_resized = resize(img, (150,150,3))
+        flat_data.append(img_resized.flatten())
+        flat_data = np.array(flat_data)
+        break
+    except:
+        print("Invalid URL or unable to load the image. Please try again.")
+
+  else:
+    print("Invalid choice. Please try again.")
+
+# Show the loaded image and make a prediction using the loaded model
+y_out = model.predict(flat_data)
+y_out = CATEGORIES[y_out[0]]
+print(f'PREDICTED OUTPUT: {y_out}')
+
+# Display the loaded image
+if choice == 'u':
+  img = imread(list(uploaded.keys())[0])
+else:
+  img = imread(urllib.request.urlopen(url), mode='RGB')
+plt.imshow(img)
+
+# Commented out IPython magic to ensure Python compatibility.
+# %%writefile app.py
+# 
+# import streamlit as st
+# import numpy as np
+# from skimage.io import imread
+# from skimage.transform import resize
+# import pickle
+# from PIL import Image
+# st.title('Smart Door Lock Authentication')
+# st.text('Upload the Image')
+# model=pickle.load(open('img_model.p','rb'))
+# uploaded_file =st.file_uploader("choose an image ...",type="jpg")
+# 
+# 
+# 
+#  
+# if uploaded_file is not None:
+# 
+#   img =Image.open(uploaded_file)
+#   st.image(img,caption='uploaded Image')
+#   
+#   if st.button('PREDICT'):
+# 
+# 
+#     CATEGORIES = ['user','other']
+# 
+#     st.write('Result...')
+#     flat_data=[]
+#     img=np.array(img)
+#     img_resized =resize(img,(150,150,3))
+#     flat_data.append(img_resized.flatten())
+#     flat_data=np.array(flat_data)
+#     print(img.shape)
+#   
+#     y_out=model.predict(flat_data)
+#     y_out=CATEGORIES[y_out[0]]
+# 
+#     st.title(f' PREDICTED OUTPUT: {y_out}')
+#     q= model.predict_proba(flat_data)
+#     for index, item in enumerate(CATEGORIES):
+# 	    st.write(f' {item} :{q[0][index]*100}%')
+#     
+# 
+# 
+# 
+# 
+# 
+#
+
+!pip install streamlit
+
+!streamlit run app.py & npx localtunnel --port 8501
+