import io import gradio as gr import matplotlib.pyplot as plt import requests import validators import torch import pathlib from PIL import Image from transformers import AutoFeatureExtractor, YolosForObjectDetection, DetrForObjectDetection import os os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE" # Colors for visualization COLORS = [ [0.000, 0.447, 0.741], [0.850, 0.325, 0.098], [0.929, 0.694, 0.125], [0.494, 0.184, 0.556], [0.466, 0.674, 0.188], [0.301, 0.745, 0.933] ] def make_prediction(img, feature_extractor, model): inputs = feature_extractor(img, return_tensors="pt") outputs = model(**inputs) img_size = torch.tensor([tuple(reversed(img.size))]) processed_outputs = feature_extractor.post_process(outputs, img_size) return processed_outputs[0] def fig2img(fig): buf = io.BytesIO() fig.savefig(buf, format='png') buf.seek(0) pil_img = Image.open(buf) basewidth = 750 wpercent = (basewidth / float(pil_img.size[0])) hsize = int((float(pil_img.size[1]) * float(wpercent))) img = pil_img.resize((basewidth, hsize), Image.Resampling.LANCZOS) return img def visualize_prediction(img, output_dict, threshold=0.5, id2label=None): keep = output_dict["scores"] > threshold boxes = output_dict["boxes"][keep].tolist() scores = output_dict["scores"][keep].tolist() labels = output_dict["labels"][keep].tolist() if id2label is not None: labels = [id2label[x] for x in labels] plt.figure(figsize=(50, 50)) plt.imshow(img) ax = plt.gca() colors = COLORS * 100 for score, (xmin, ymin, xmax, ymax), label, color in zip(scores, boxes, labels, colors): if label == 'license-plates': ax.add_patch(plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin, fill=False, color=color, linewidth=10)) ax.text(xmin, ymin, f"{label}: {score:0.2f}", fontsize=60, bbox=dict(facecolor="yellow", alpha=0.8)) plt.axis("off") return fig2img(plt.gcf()) def get_original_image(url_input): if validators.url(url_input): image = Image.open(requests.get(url_input, stream=True).raw) return image def detect_objects(model_name, url_input, image_input,threshold): # Extract model and feature extractor feature_extractor = AutoFeatureExtractor.from_pretrained(model_name) if "yolos" in model_name: model = YolosForObjectDetection.from_pretrained(model_name) elif "detr" in model_name: model = DetrForObjectDetection.from_pretrained(model_name) if validators.url(url_input): image = get_original_image(url_input) elif image_input is not None: image = image_input # Make prediction processed_outputs = make_prediction(image, feature_extractor, model) # Visualize prediction viz_img = visualize_prediction(image, processed_outputs, threshold, model.config.id2label) return viz_img title = """

License Plate Detection with YOLOS

""" description = """ YOLOS is a Vision Transformer (ViT) trained using the DETR loss. Despite its simplicity, a base-sized YOLOS model is able to achieve 42 AP on COCO validation 2017 (similar to DETR and more complex frameworks such as Faster R-CNN). The YOLOS model was fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in the paper [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://arxiv.org/abs/2106.00666) by Fang et al. and first released in [this repository](https://github.com/hustvl/YOLOS). This model was further fine-tuned on the [Car license plate dataset](https://www.kaggle.com/datasets/andrewmvd/car-plate-detection) from Kaggle. The dataset consists of 443 images of vehicles with annotations categorized as "Vehicle" and "Rego Plates". The model was trained for 200 epochs on a single GPU. Links to HuggingFace Models: - [nickmuchi/yolos-small-rego-plates-detection](https://huggingface.co/nickmuchi/yolos-small-rego-plates-detection) - [hustlv/yolos-small](https://huggingface.co/hustlv/yolos-small) """ models = ["nickmuchi/yolos-small-finetuned-license-plate-detection", "nickmuchi/detr-resnet50-license-plate-detection"] urls = ["https://drive.google.com/uc?id=1j9VZQ4NDS4gsubFf3m2qQoTMWLk552bQ", "https://drive.google.com/uc?id=1p9wJIqRz3W50e2f_A0D8ftla8hoXz4T5"] images = [[path.as_posix()] for path in sorted(pathlib.Path('images').rglob('*.j*g'))] css = ''' h1#title { text-align: center; } ''' demo = gr.Blocks(css=css) with demo: gr.Markdown(title) gr.Markdown(description) options = gr.Dropdown(choices=models, label='Object Detection Model', value=models[0], show_label=True) slider_input = gr.Slider(minimum=0.2, maximum=1, value=0.5, step=0.1, label='Prediction Threshold') with gr.Tabs(): with gr.TabItem('Image URL'): with gr.Row(): with gr.Column(): url_input = gr.Textbox(lines=2, label='Enter valid image URL here..') original_image = gr.Image() url_input.change(fn=get_original_image, inputs=url_input, outputs=original_image) with gr.Column(): img_output_from_url = gr.Image() with gr.Row(): example_url = gr.Examples(examples=urls, inputs=[url_input]) url_but = gr.Button('Detect') with gr.TabItem('Image Upload'): with gr.Row(): img_input = gr.Image(type='pil') img_output_from_upload = gr.Image() with gr.Row(): example_images = gr.Examples(examples=images, inputs=[img_input]) img_but = gr.Button('Detect') url_but.click(fn=detect_objects, inputs=[options, url_input, img_input, slider_input], outputs=[img_output_from_url], queue=True) img_but.click(fn=detect_objects, inputs=[options, url_input, img_input, slider_input], outputs=[img_output_from_upload], queue=True) #gr.Markdown("![visitor badge](https://visitor-badge.glitch.me/badge?page_id=nickmuchi-license-plate-detection-with-yolos)") demo.launch(debug=True)