File size: 1,999 Bytes
bdfa5cf |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 |
import cv2
import gradio as gr
import numpy as np
from tensorflow.keras.models import load_model
import json
# Load the saved models
cnn_model = load_model('cnn_image_classifier.h5')
resnet_model = load_model('resnet_image_classifier.h5')
# Load label map (assuming you have it defined or loaded elsewhere)
# Replace with your actual loading method
with open('EuroSAT/label_map.json', 'r') as f:
label_map = json.load(f)
label_map_inv = {v: k for k, v in label_map.items()}
def predict_image(image):
# Preprocess the image (resize, normalize, etc.)
image = cv2.resize(image, (64, 64))
image = image / 255.0
image = np.expand_dims(image, axis=0)
# Make predictions with both models
cnn_pred = cnn_model.predict(image)[0]
resnet_pred = resnet_model.predict(image)[0]
# Get top 5 predictions for CNN
cnn_top5_indices = np.argsort(cnn_pred)[::-1][:5]
cnn_top5 = {
label_map_inv[idx]: float(cnn_pred[idx]) for idx in cnn_top5_indices
}
# Get top 5 predictions for ResNet
resnet_top5_indices = np.argsort(resnet_pred)[::-1][:5]
resnet_top5 = {
label_map_inv[idx]: float(resnet_pred[idx]) for idx in resnet_top5_indices
}
# Final predictions
cnn_final_prediction = label_map_inv[np.argmax(cnn_pred)]
resnet_final_prediction = label_map_inv[np.argmax(resnet_pred)]
return cnn_top5, cnn_final_prediction, resnet_top5, resnet_final_prediction
iface = gr.Interface(
fn=predict_image,
inputs=gr.Image(type="numpy"),
outputs=[
gr.Label(num_top_classes=5, label="CNN Top 5 Predictions"),
gr.Textbox(label="CNN Final Prediction"),
gr.Label(num_top_classes=5, label="ResNet Top 5 Predictions"),
gr.Textbox(label="ResNet Final Prediction"),
],
title="Image Classification with CNN and ResNet",
description="Upload an image to classify using two different models.",
)
iface.launch(debug=True)
|