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# Import required libraries
import os
import keras
import gradio as gr
import numpy as np
import pandas as pd
from PIL import Image
# Function to safely load the models
def load_model_safely(path: str):
if not os.path.isfile(path) or not path.endswith('.keras'):
raise FileNotFoundError(f"The file '{path}' does not exist or is not a .keras file.")
return keras.saving.load_model(path)
# Retrieve the current directory and specify model paths
current_dir = os.getcwd() # Ensure correct initial directory
model_paths = {
'CNN': os.path.join(current_dir, 'models', 'cnn_model.keras'),
'VGG19': os.path.join(current_dir, 'models', 'vgg19_model.keras'),
'ResNet50': os.path.join(current_dir, 'models', 'resnet50_model.keras'),
}
# Load models and handle potential exceptions
models = {}
for name, path in model_paths.items():
try:
models[name] = load_model_safely(path)
except Exception as e:
print(f"Error loading model {name} from {path}: {str(e)}")
# Define the class labels
classes = { 0:'Speed limit (20km/h)',
1:'Speed limit (30km/h)',
2:'Speed limit (50km/h)',
3:'Speed limit (60km/h)',
4:'Speed limit (70km/h)',
5:'Speed limit (80km/h)',
6:'End of speed limit (80km/h)',
7:'Speed limit (100km/h)',
8:'Speed limit (120km/h)',
9:'No passing',
10:'No passing veh over 3.5 tons',
11:'Right-of-way at intersection',
12:'Priority road',
13:'Yield',
14:'Stop',
15:'No vehicles',
16:'Veh > 3.5 tons prohibited',
17:'No entry',
18:'General caution',
19:'Dangerous curve left',
20:'Dangerous curve right',
21:'Double curve',
22:'Bumpy road',
23:'Slippery road',
24:'Road narrows on the right',
25:'Road work',
26:'Traffic signals',
27:'Pedestrians',
28:'Children crossing',
29:'Bicycles crossing',
30:'Beware of ice/snow',
31:'Wild animals crossing',
32:'End speed + passing limits',
33:'Turn right ahead',
34:'Turn left ahead',
35:'Ahead only',
36:'Go straight or right',
37:'Go straight or left',
38:'Keep right',
39:'Keep left',
40:'Roundabout mandatory',
41:'End of no passing',
42:'End no passing veh > 3.5 tons' }
# Function to import and resize example images
def get_example_images(images_dir:str, size=(50, 50)) -> list:
# Check if the images directory exists
if not os.path.exists(images_dir):
print(f"The images directory does not exist: {images_dir}")
return []
images = []
image_list = os.listdir(images_dir)
for image in image_list:
if image.lower().endswith('.png'):
image_path = os.path.join(images_dir, image)
img = Image.open(image_path)
img = img.resize(size)
images.append(img)
return images
# Directory for example images
images_dir = os.path.join(current_dir, 'images')
examples = get_example_images(images_dir, (50, 50))
# Function to preprocess the image and predict the class
def preprocess_and_predict(image: Image.Image, size=(50, 50)) -> pd.DataFrame:
img_resized = image.resize(size)
img_array = np.array(img_resized).astype(np.float32) / 255.0
img_array = np.expand_dims(img_array, axis=0) # Shape (1, 50, 50, 3)
predictions = []
for name, model in models.items():
predicted_class_index = np.argmax(model.predict(img_array), axis=-1)[0]
predictions.append({'Model': name, 'Predicted Label': classes[predicted_class_index]})
return pd.DataFrame(predictions)
# Create Gradio interface
iface = gr.Interface(
fn=preprocess_and_predict,
inputs=gr.Image(type='pil'), # Changed to 'pil' for direct use with PIL
outputs="dataframe", # Correct the output type
examples=examples,
title="Traffic Sign Recognition",
description="Upload a traffic sign image or choose an example to get the recognition result."
)
# Launch the Gradio app
iface.launch() |