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import io |
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import gradio as gr |
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import matplotlib.pyplot as plt |
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import requests, validators |
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import torch |
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import pathlib |
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from PIL import Image |
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from transformers import AutoFeatureExtractor, YolosForObjectDetection |
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import os |
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os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE" |
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COLORS = [ |
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[0.000, 0.447, 0.741], |
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[0.850, 0.325, 0.098], |
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[0.929, 0.694, 0.125], |
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[0.494, 0.184, 0.556], |
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[0.466, 0.674, 0.188], |
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[0.301, 0.745, 0.933] |
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] |
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def make_prediction(img, feature_extractor, model): |
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inputs = feature_extractor(img, return_tensors="pt") |
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outputs = model(**inputs) |
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img_size = torch.tensor([tuple(reversed(img.size))]) |
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processed_outputs = feature_extractor.post_process(outputs, img_size) |
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return processed_outputs[0] |
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def fig2img(fig): |
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buf = io.BytesIO() |
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fig.savefig(buf) |
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buf.seek(0) |
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pil_img = Image.open(buf) |
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basewidth = 750 |
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wpercent = (basewidth/float(pil_img.size[0])) |
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hsize = int((float(pil_img.size[1])*float(wpercent))) |
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img = pil_img.resize((basewidth,hsize), Image.Resampling.LANCZOS) |
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return img |
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def visualize_prediction(img, output_dict, threshold=0.5, id2label=None): |
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keep = output_dict["scores"] > threshold |
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boxes = output_dict["boxes"][keep].tolist() |
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scores = output_dict["scores"][keep].tolist() |
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labels = output_dict["labels"][keep].tolist() |
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if id2label is not None: |
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labels = [id2label[x] for x in labels] |
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plt.figure(figsize=(50, 50)) |
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plt.imshow(img) |
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ax = plt.gca() |
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colors = COLORS * 100 |
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for score, (xmin, ymin, xmax, ymax), label, color in zip(scores, boxes, labels, colors): |
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ax.add_patch(plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin, fill=False, color=color, linewidth=5)) |
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ax.text(xmin, ymin, f"{label}: {score:0.2f}", fontsize=30, bbox=dict(facecolor="yellow", alpha=0.5)) |
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plt.axis("off") |
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return fig2img(plt.gcf()) |
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def get_original_image(url_input): |
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if validators.url(url_input): |
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image = Image.open(requests.get(url_input, stream=True).raw) |
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return image |
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def detect_objects(model_name,url_input,image_input,webcam_input,threshold): |
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feature_extractor = AutoFeatureExtractor.from_pretrained(model_name) |
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model = YolosForObjectDetection.from_pretrained(model_name) |
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if validators.url(url_input): |
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image = get_original_image(url_input) |
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elif image_input: |
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image = image_input |
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elif webcam_input: |
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image = webcam_input |
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processed_outputs = make_prediction(image, feature_extractor, model) |
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viz_img = visualize_prediction(image, processed_outputs, threshold, model.config.id2label) |
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return viz_img |
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def set_example_image(example: list) -> dict: |
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return gr.Image.update(value=example[0]) |
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def set_example_url(example: list) -> dict: |
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return gr.Textbox.update(value=example[0]), gr.Image.update(value=get_original_image(example[0])) |
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title = """<h1 id="title">Face Mask Detection with YOLOS</h1>""" |
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description = """ |
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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). |
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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). |
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This model was further fine-tuned on the [face mask dataset]("https://www.kaggle.com/datasets/andrewmvd/face-mask-detection") from Kaggle. The dataset consists of 853 images of people with annotations categorised as "with mask","without mask" and "mask not worn correctly". The model was trained for 200 epochs on a single GPU. |
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Links to HuggingFace Models: |
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- [nickmuchi/yolos-small-finetuned-masks](https://huggingface.co/nickmuchi/yolos-small-finetuned-masks) |
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- [hustlv/yolos-small](https://huggingface.co/hustlv/yolos-small) |
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""" |
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models = ["nickmuchi/yolos-small-finetuned-masks","nickmuchi/yolos-base-finetuned-masks"] |
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urls = ["https://api.time.com/wp-content/uploads/2020/03/hong-kong-mask-admiralty.jpg","https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQ7wiGZhgAFuIpwFJzbpv8kUMM_Q3WaAWYf5NpSJduxvHQ7V2WnqZ0wMWS6cK5gvlfPGxc&usqp=CAU"] |
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twitter_link = """ |
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[](https://twitter.com/nickmuchi) |
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""" |
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css = ''' |
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h1#title { |
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text-align: center; |
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} |
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''' |
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demo = gr.Blocks(css=css) |
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with demo: |
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with gr.Box(): |
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gr.Markdown(title) |
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gr.Markdown(description) |
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gr.Markdown(twitter_link) |
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options = gr.Dropdown(choices=models,label='Object Detection Model',show_label=True) |
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slider_input = gr.Slider(minimum=0.2,maximum=1,value=0.5,step=0.1,label='Prediction Threshold') |
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with gr.Tabs(): |
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with gr.TabItem('Image URL'): |
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with gr.Row(): |
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with gr.Column(): |
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url_input = gr.Textbox(lines=2,label='Enter valid image URL here..') |
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original_image = gr.Image(shape=(750,750)) |
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with gr.Column(): |
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img_output_from_url = gr.Image(shape=(750,750)) |
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with gr.Row(): |
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example_url = gr.Dataset(components=[url_input],samples=[[str(url)] for url in urls]) |
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url_but = gr.Button('Detect') |
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with gr.TabItem('Image Upload'): |
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with gr.Row(): |
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img_input = gr.Image(type='pil',shape=(750,750)) |
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img_output_from_upload= gr.Image(shape=(750,750)) |
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with gr.Row(): |
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example_images = gr.Dataset(components=[img_input], |
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samples=[[path.as_posix()] for path in sorted(pathlib.Path('images').rglob('*.j*g'))]) |
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img_but = gr.Button('Detect') |
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with gr.TabItem('WebCam'): |
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with gr.Row(): |
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web_input = gr.Image(source='webcam',type='pil',shape=(750,750),streaming=True) |
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img_output_from_webcam= gr.Image(shape=(750,750)) |
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cam_but = gr.Button('Detect') |
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url_but.click(detect_objects,inputs=[options,url_input,img_input,web_input,slider_input],outputs=[img_output_from_url],queue=True) |
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img_but.click(detect_objects,inputs=[options,url_input,img_input,web_input,slider_input],outputs=[img_output_from_upload],queue=True) |
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cam_but.click(detect_objects,inputs=[options,url_input,img_input,web_input,slider_input],outputs=[img_output_from_webcam],queue=True) |
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example_images.click(fn=set_example_image,inputs=[example_images],outputs=[img_input]) |
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example_url.click(fn=set_example_url,inputs=[example_url],outputs=[url_input,original_image]) |
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gr.Markdown("") |
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demo.launch(debug=True,enable_queue=True) |
