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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() |