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import gradio as gr
import numpy as np
import cv2
from PIL import Image
import tensorflow as tf
from tensorflow.keras.applications import MobileNetV2
from tensorflow.keras.applications.mobilenet_v2 import preprocess_input, decode_predictions
import json
# Load MobileNetV2 model
model = MobileNetV2(weights='imagenet')
def add_hsv_noise(image, hue_noise=0, saturation_noise=0, value_noise=0):
"""Add HSV noise to an image"""
if image is None:
return None
# Convert PIL to numpy array
img_array = np.array(image)
# Convert RGB to HSV
hsv = cv2.cvtColor(img_array, cv2.COLOR_RGB2HSV).astype(np.float32)
# Add noise to each channel
hsv[:, :, 0] = np.clip(hsv[:, :, 0] + hue_noise, 0, 179) # Hue: 0-179
hsv[:, :, 1] = np.clip(hsv[:, :, 1] + saturation_noise, 0, 255) # Saturation: 0-255
hsv[:, :, 2] = np.clip(hsv[:, :, 2] + value_noise, 0, 255) # Value: 0-255
# Convert back to RGB
rgb = cv2.cvtColor(hsv.astype(np.uint8), cv2.COLOR_HSV2RGB)
return Image.fromarray(rgb)
def predict_image(image, top_n, hue_noise, saturation_noise, value_noise):
"""Predict image classes with noise applied"""
if image is None:
return None, "Please upload an image first."
# Apply HSV noise
noisy_image = add_hsv_noise(image, hue_noise, saturation_noise, value_noise)
# Preprocess for MobileNet
img_resized = noisy_image.resize((224, 224))
img_array = np.array(img_resized)
img_array = np.expand_dims(img_array, axis=0)
img_array = preprocess_input(img_array)
# Make prediction
predictions = model.predict(img_array)
decoded_predictions = decode_predictions(predictions, top=top_n)[0]
# Format results
results = []
for i, (class_id, class_name, probability) in enumerate(decoded_predictions):
results.append(f"{i+1}. {class_name}: {probability:.4f} ({probability*100:.2f}%)")
results_text = "\n".join(results)
return noisy_image, results_text
# Create Gradio interface
with gr.Blocks(title="MobileNet HSV Noise Analysis") as demo:
gr.Markdown("# MobileNet Classification with HSV Noise")
gr.Markdown("Upload an image and adjust HSV noise sliders to see how it affects MobileNet predictions.")
with gr.Row():
with gr.Column():
# Input controls
input_image = gr.Image(type="pil", label="Upload Image")
top_n = gr.Slider(minimum=1, maximum=10, value=5, step=1, label="Top N Classes")
gr.Markdown("### HSV Noise Controls")
hue_noise = gr.Slider(minimum=-50, maximum=50, value=0, step=1, label="Hue Noise (-50 to 50)")
saturation_noise = gr.Slider(minimum=-100, maximum=100, value=0, step=5, label="Saturation Noise (-100 to 100)")
value_noise = gr.Slider(minimum=-100, maximum=100, value=0, step=5, label="Value/Brightness Noise (-100 to 100)")
with gr.Column():
# Output displays
output_image = gr.Image(label="Image with Noise Applied")
predictions_text = gr.Textbox(label="Top Predictions", lines=10, max_lines=15)
# Set up real-time updates
inputs = [input_image, top_n, hue_noise, saturation_noise, value_noise]
outputs = [output_image, predictions_text]
# Update predictions when any input changes
for input_component in inputs:
input_component.change(
fn=predict_image,
inputs=inputs,
outputs=outputs
)
# Launch the app
if __name__ == "__main__":
demo.launch(share=True, debug=True)
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