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# -*- coding: utf-8 -*-
"""CGI Classification App.ipynb
Automatically generated by Colab.
Original file is located at
https://colab.research.google.com/drive/1ckzOtXUiFW_NqlIandwoH07lnsLGKTLB
"""
import gradio as gr
from PIL import Image
import numpy as np
from PIL import Image
from scipy.fftpack import fft2
from tensorflow.keras.models import load_model, Model
from xgboost import XGBClassifier
# classifier
xgb_clf = XGBClassifier()
xgb_clf.load_model("xgb_cgi_classifier.json")
# Function to apply Fourier transform
def apply_fourier_transform(image):
image = np.array(image)
fft_image = fft2(image)
return np.abs(fft_image)
def preprocess_image(image):
try:
image = Image.fromarray(image)
image = image.convert("L")
image = image.resize((256, 256))
image = apply_fourier_transform(image)
image = np.expand_dims(
image, axis=-1
) # Expand dimensions to match model input shape
image = np.expand_dims(image, axis=0) # Expand to add batch dimension
return image
except Exception as e:
print(f"Error processing image: {e}")
return None
# Function to load embedding model and calculate embeddings
def calculate_embeddings(image, model_path="embedding_modelv2.keras"):
# Load the trained model
model = load_model(model_path)
# Remove the final classification layer to get embeddings
embedding_model = Model(inputs=model.input, outputs=model.output)
# Preprocess the image
preprocessed_image = preprocess_image(image)
# Calculate embeddings
embeddings = embedding_model.predict(preprocessed_image)
return embeddings
def classify_image(image):
embeddings = calculate_embeddings(image)
# Convert to 2D array for model input
probabilities = xgb_clf.predict_proba(embeddings)[0]
labels = ["Photo", "CGI"]
return {f"{labels[i]}": prob for i, prob in enumerate(probabilities)}
interface = gr.Interface(
fn=classify_image, inputs=["image"], outputs=gr.Label(num_top_classes=2)
)
interface.launch(share=True)
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