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.gitattributes

@@ -33,3 +33,4 @@ 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
+traffic_sign_document.pdf filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,206 @@
+import streamlit as st
+import pandas as pd
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+import os
+from PIL import Image
+from sklearn.preprocessing import StandardScaler, LabelEncoder
+
+st.set_page_config(layout="centered")
+
+# Add custom CSS for background image and styling
+# Add custom CSS for background image and styling
+st.markdown("""
+    <style>
+        .stApp {
+            background-image: url("https://as1.ftcdn.net/jpg/01/82/21/76/1000_F_182217694_DZi3Ytqsb0RpWQb9dwC7NLFwkwqgnh0r.jpg");
+            background-size: cover;
+            background-position: center;
+            background-repeat: no-repeat;
+            height: auto;  /* Allows the page to expand for scrolling */
+            overflow: auto;  /* Enables scrolling if the page content overflows */
+            # position : relative
+        }
+
+        /* Adjust opacity of overlay to make content more visible */
+        .stApp::before {
+            content: "";
+            position: absolute;
+            top: 0;
+            left: 0;
+            width: 100%;
+            height: 100%;
+            background-color: rgba(255, 255, 255, 0.8);  /* Slightly higher opacity */
+            z-index: -1;
+        }
+
+        /* Ensure content appears above the overlay */
+        .stApp > * {
+            position: relative;
+            z-index: 2;
+        }
+
+        /* Ensure the dataframe is visible */
+        .dataframe {
+            background-color: rgba(255, 255, 255, 0.9) !important;
+            z-index: 3;
+        }
+
+        /* Style text elements for better visibility */
+        h1, h3, span, div {
+            text-shadow: 1px 1px 2px rgba(255, 255, 255, 0.2);
+        }
+            
+        /* Custom CSS for select box heading */
+        div.stSelectbox > label {
+            color: #000000 !important;  /* Change to your desired color */
+            # background-color: black !important;  /* Background color of the dropdown */
+            font-size: 24px !important;  /* Change font size */
+            font-weight: bold !important;  /* Make text bold */
+        }
+
+        /* Custom CSS for image caption */
+        .custom-caption {
+            color: #000000 !important;  /* Change to your desired color */
+            font-size: 24px !important;  /* Optional: Change font size */
+            text-align: center;  /* Center-align the caption */
+        }
+            
+        .stMainBlockContainer {
+            background-color: white !important;  /* Background color of the dropdown */
+        }
+            
+    </style>
+""", unsafe_allow_html=True)
+
+
+# Custom title styling functions
+def colored_title(text, color):
+    st.markdown(f"<h1 style='color: {color};'>{text}</h1>", unsafe_allow_html=True)
+
+def colored_subheader(text, color):
+    st.markdown(f"<h3 style='color: {color};'>{text}</h3>", unsafe_allow_html=True)
+
+def colored_text(text, color):
+    st.markdown(f"<span style='color: {color};'>{text}</span>", unsafe_allow_html=True)
+
+class ClassNet(nn.Module):
+    
+    def __init__(self):
+        super(ClassNet, self).__init__()
+
+        self.conv1 = nn.Conv2d(3,6,3)
+        self.conv2 = nn.Conv2d(6,16,5)
+        self.maxpool1 = nn.MaxPool2d(2)
+        self.conv3 = nn.Conv2d(16,32,5)
+        self.maxpool2 = nn.MaxPool2d(2)
+
+        self.fc1 = nn.Linear(512,256)
+        self.dropout1 = nn.Dropout(0.5)
+        self.fc2 = nn.Linear(256,128)
+        self.dropout2 = nn.Dropout(0.5)
+        self.fc3 = nn.Linear(128,43)
+    def forward(self,input):
+
+        x = F.relu(self.conv1(input))
+        x = F.relu(self.conv2(x))
+        x = self.maxpool1(x)
+        x = F.relu(self.conv3(x))
+        x = self.maxpool2(x)
+
+        x = torch.flatten(x,1)
+        x = F.relu(self.fc1(x))
+        x = self.dropout1(x)
+        x = F.relu(self.fc2(x))
+        x = self.dropout2(x)
+        output = self.fc3(x)
+
+        return output
+
+@st.cache_resource
+def load_model():
+
+    model = ClassNet()
+    try:
+        state_dict = torch.load('traffic_light_model_weights.pth', map_location=torch.device('cpu'))
+        model.load_state_dict(state_dict)
+        model.eval()
+        return model
+    except Exception as e:
+        st.error(f"Error loading model: {str(e)}")
+        return None
+
+@st.cache_data
+def load_data():
+
+    y_test = pd.read_csv('traffic_lights/Test.csv')
+
+    imgs = y_test["Path"].values
+    labels = y_test["ClassId"].values
+
+    test_images = []
+    for img in imgs:
+        if isinstance(img,str):
+            image = Image.open('traffic_lights/'+img)
+            image = image.resize([30, 30])
+            test_images.append(np.array(image))
+
+    # Load meta images
+    meta_images = {}
+    meta_folder = 'traffic_lights/Meta'  # Replace with the path to your meta folder
+    for class_id in range(43):
+        meta_image_path = os.path.join(meta_folder, f"{class_id}.png")  # Assuming meta images are named as 0.png, 1.png, etc.
+        if os.path.exists(meta_image_path):
+            meta_images[class_id] = Image.open(meta_image_path)
+
+    return test_images, labels, meta_images
+
+def main():
+    colored_title("Traffic Symbol Prediction", "black")
+
+    # Load data
+    test_images, labels, meta_images = load_data()
+
+    # Display test images for selection
+    colored_subheader("Select an Image for Prediction:", "black")
+    selected_index = st.selectbox("Select an image by index:", options=range(len(test_images)), index=0)
+
+    # Display the selected test image
+    st.image(test_images[selected_index], width=150)
+
+    st.markdown(
+        f'<p class="custom-caption">Selected Test Image (Class: {labels[selected_index]})</p>',
+        unsafe_allow_html=True
+    )
+
+    # Predict button
+    if st.button("Predict"):
+        model = load_model()
+        if model is not None:
+            # Preprocess the selected image
+            image = test_images[selected_index] / 255.0  # Normalize
+            image = torch.tensor(image, dtype=torch.float32).permute(2, 0, 1).unsqueeze(0)  # Convert to tensor
+
+            # Make prediction
+            with torch.no_grad():
+                output = model(image)
+                predicted_class = torch.argmax(output, dim=1).item()
+
+            # Display prediction result
+            colored_subheader("Prediction Results:", "green")
+            colored_text(f"Predicted Class: {predicted_class}", "green")
+
+            # Display the corresponding meta image
+            if predicted_class in meta_images:
+                st.image(meta_images[predicted_class], width=150)
+                st.markdown(
+                    f'<p class="custom-caption">Clear Image for Class: {predicted_class}</p>',
+                    unsafe_allow_html=True
+                )
+            else:
+                st.warning(f"No clear image found for class {predicted_class} in the meta folder.")  
+            
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -0,0 +1,7 @@
+streamlit==1.25.0
+pandas==1.5.3
+numpy==1.24.3
+scikit-learn==1.2.2
+torch==2.0.1
+torchvision==0.15.2
+torchaudio==2.0.2

traffic_light_model_weights.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:20755445924104845831325f77bef167d20929c9e5a1521b9bcf2955e0ef2304
+size 745522

traffic_sign_document.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fe36f16740a86d72283919b6e698c4ffd8b69346b64008039a3474421a21fcf7
+size 109756