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import numpy as np
import cv2
import os
from PIL import Image
import torchvision.transforms as transforms
import gradio as gr
from yolov5 import xai_yolov5
from yolov8 import xai_yolov8s
def process_image(image, yolo_versions=["yolov5"]):
image = np.array(image)
image = cv2.resize(image, (640, 640))
result_images = []
for yolo_version in yolo_versions:
if yolo_version == "yolov5":
result_images.append(xai_yolov5(image))
elif yolo_version == "yolov8s":
result_images.append(xai_yolov8s(image))
else:
result_images.append((Image.fromarray(image), f"{yolo_version} not yet implemented."))
return result_images
sample_images = {
"Sample 1": os.path.join(os.getcwd(), "/data/xai/sample1.jpeg"),
"Sample 2": os.path.join(os.getcwd(), "/data/xai/sample2.jpg")
}
"""
interface = gr.Interface(
fn=process_image,
inputs=[
gr.Image(type="pil", label="Upload an Image"),
gr.CheckboxGroup(
choices=["yolov5", "yolov8s"],
value=["yolov5"], # Set the default value (YOLOv5 checked by default)
label="Select Model(s)",
),
gr.Dropdown(
choices=list(sample_images.keys()),
label="Select a Sample Image",
type="value",
interactive=True,
),
],
outputs=gr.Gallery(label="Results", elem_id="gallery", rows=2, height=500),
title="Visualising the key image features that drive decisions with our explainable AI tool.",
description="XAI: Upload an image or select a sample to visualize object detection of your models.",
)
def main_logic(uploaded_image, selected_models, sample_selection):
# If the user selects a sample image, use that instead of the uploaded one
if sample_selection:
image = load_sample(sample_selection)
else:
image = uploaded_image
# Call the processing function
return process_image(image, selected_models)
interface.launch()
"""
def load_sample_image(sample_name):
if sample_name in sample_images:
try:
return Image.open(sample_images[sample_name]) # Load and return the image
except Exception as e:
print(f"Error loading image: {e}")
return None
return None
# Gradio interface
with gr.Blocks() as interface:
gr.Markdown("# Visualizing Key Features with Explainable AI")
gr.Markdown("Upload an image or select a sample image to visualize object detection.")
with gr.Row():
uploaded_image = gr.Image(type="pil", label="Upload an Image")
sample_selection = gr.Dropdown(
choices=list(sample_images.keys()),
label="Select a Sample Image",
type="value",
)
sample_display = gr.Image(label="Sample Image Preview", value=None)
sample_selection.change(fn=load_sample_image, inputs=sample_selection, outputs=sample_display)
selected_models = gr.CheckboxGroup(
choices=["yolov3", "yolov8s"],
value=["yolov5"], # Default model
label="Select Model(s)",
)
result_gallery = gr.Gallery(label="Results", elem_id="gallery", rows=2, height=500)
gr.Button("Run").click(
fn=process_image,
inputs=[uploaded_image, selected_models],
outputs=result_gallery,
)
interface.launch() |