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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
sample_images = {
"Sample 1": os.path.join(os.getcwd(), "data/xai/sample1.jpeg"),
"Sample 2": os.path.join(os.getcwd(), "data/xai/sample2.jpg"),
}
def load_sample_image(sample_name):
image_path = sample_images.get(sample_name)
if image_path and os.path.exists(image_path):
return Image.open(image_path)
return None
default_sample_image = load_sample_image("Sample 1")
def load_sample_image(choice):
if choice in sample_images:
image_path = sample_images[choice]
return cv2.imread(image_path)[:, :, ::-1]
else:
raise ValueError("Invalid sample selection.")
def process_image(sample_choice, uploaded_image, yolo_versions=["yolov5"]):
print(sample_choice, upload_image)
if uploaded_image is not None:
image = uploaded_image # Use the uploaded image
else:
# Otherwise, use the selected sample image
image = load_sample_image(sample_choice)
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
with gr.Blocks() as interface:
gr.Markdown("# <span style='color:white'>XAI: Visualize Object Detection of Your Models</span>")
gr.Markdown("<span style='color:white'>Select a sample image to visualize object detection.</span>")
default_sample = "Sample 1"
# Inject CSS with gr.HTML to set the text color to white
gr.HTML("""
<style>
body {
background-color: black;
color: white; /* Set text color to white */
}
.gradio-container {
color: white; /* Ensure Gradio components also have white text */
}
</style>
""")
with gr.Row(elem_classes="orchid-green-bg"):
# Left side: Sample selection and upload image
with gr.Column():
sample_selection = gr.Radio(
choices=list(sample_images.keys()),
label="Select a Sample Image",
type="value",
value=default_sample, # Set default selection
)
# Upload image below sample selection
gr.Markdown("<span style='color:white'>**Or upload your own image:**</span>")
upload_image = gr.Image(
label="Upload an Image",
type="pil", # Correct type for file path compatibility
)
# Right side: Selected sample image display
sample_display = gr.Image(
value=load_sample_image(default_sample),
label="Selected Sample Image",
)
sample_selection.change(
fn=load_sample_image,
inputs=sample_selection,
outputs=sample_display,
)
selected_models = gr.CheckboxGroup(
choices=["yolov5", "yolov8s"],
value=["yolov5"],
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=[sample_selection, upload_image, selected_models], # Include both options
outputs=result_gallery,
)
# Launch the interface without the `css` argument
interface.launch(share=True)
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