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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"),
}
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")
"""
with gr.Blocks() as interface:
gr.Markdown("# XAI: Upload an image to visualize object detection of your models..")
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=default_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=[uploaded_image, selected_models],
outputs=result_gallery,
)
"""
def load_sample_image(choice):
if choice in sample_images:
image_path = sample_images[choice]
return cv2.imread(image_path)[:, :, ::-1] # Convert BGR to RGB for display
else:
raise ValueError("Invalid sample selection.")
def process_image(choice, yolo_versions=["yolov5"]):
image = load_sample_image(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
import gradio as gr
with gr.Blocks() as interface:
gr.Markdown("# XAI: Visualize Object Detection of Your Models")
gr.Markdown("Select a sample image to visualize object detection.")
sample_selection = gr.Radio(
choices=list(sample_images.keys()),
label="Select a Sample Image",
type="value",
)
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, selected_models],
outputs=result_gallery,
)
interface.launch() |