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import gradio as gr
from PIL import Image
import numpy as np
import cv2
import os
import random
# Assuming overlay_image function is defined elsewhere in your code as provided previously
def process_image_with_model(image_path):
# Load the Gradio model for closed eyes detection
model = gr.load("models/dima806/closed_eyes_image_detection")
# Load the user input image
image = Image.open(image_path)
image = image.rotate(-90, expand=True)
image_array = np.array(image)
# Predict using the Gradio model (adjust according to the model's input and output format)
predictions = model(image_array)
# Placeholder loop for overlaying images on detected regions (adjust based on actual predictions format)
for bbox in predictions:
x, y, w, h = bbox
random_eye_image_path = os.path.join("result", random.choice(os.listdir("result")))
random_eye_image = cv2.imread(random_eye_image_path, cv2.IMREAD_UNCHANGED) # Load with alpha channel if present
if random_eye_image is None:
print(f"Failed to load image from {random_eye_image_path}")
continue
# Overlay the image and update image_array with the result
image_array = overlay_image(image_array, random_eye_image, x, y, w, h, alpha=0.50)
return Image.fromarray(image_array)
def gr_interface(image):
processed_image = process_image_with_model(image)
return processed_image
# Setup the Gradio interface
demo = gr.Interface(fn=gr_interface,
inputs=gr.Image(type="filepath", label="Upload Image"),
outputs="image",
title="Closed Eyes Image Detection",
description="Upload an image and the model will detect closed eyes.")
if __name__ == "__main__":
demo.launch()
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