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: # Update CSS to make text white and set dark mode gr.HTML(""" """) # with gr.Blocks() as interface: # # Update CSS to make text light grey # gr.HTML(""" # # """) gr.Markdown("
Select a sample image to visualize object detection.
") default_sample = "Sample 1" 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("