import io
import gradio as gr
import matplotlib.pyplot as plt
import requests, validators
import torch
import pathlib
from PIL import Image
from transformers import AutoFeatureExtractor, YolosForObjectDetection
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)
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):
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=55, bbox=dict(facecolor="yellow", alpha=0.5))
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,webcam_input,threshold):
#Extract model and feature extractor
feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
model = YolosForObjectDetection.from_pretrained(model_name)
if validators.url(url_input):
image = get_original_image(url_input)
elif image_input:
image = image_input
elif webcam_input:
image = webcam_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
def set_example_image(example: list) -> dict:
return gr.Image.update(value=example[0])
def set_example_url(example: list) -> dict:
return gr.Textbox.update(value=example[0]), gr.Image.update(value=get_original_image(example[0]))
title = """<h1 id="title">Face Mask Detection with YOLOS</h1>"""
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 [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.
Links to HuggingFace Models:
- [nickmuchi/yolos-small-finetuned-masks](https://huggingface.co/nickmuchi/yolos-small-finetuned-masks)
- [hustlv/yolos-small](https://huggingface.co/hustlv/yolos-small)
"""
models = ["nickmuchi/yolos-small-finetuned-masks","nickmuchi/yolos-base-finetuned-masks"]
urls = ["https://drive.google.com/uc?id=1VwYLbGak5c-2P5qdvfWVOeg7DTDYPbro","https://api.time.com/wp-content/uploads/2020/03/hong-kong-mask-admiralty.jpg"]
twitter_link = """
[](https://twitter.com/nickmuchi)
"""
css = '''
h1#title {
text-align: center;
}
'''
demo = gr.Blocks(css=css)
with demo:
gr.Markdown(title)
gr.Markdown(description)
gr.Markdown(twitter_link)
options = gr.Dropdown(choices=models,label='Object Detection Model',show_label=True,value=models[0])
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(shape=(750,750))
url_input.submit(get_original_image,url_input,original_image)
with gr.Column():
img_output_from_url = gr.Image(shape=(750,750))
with gr.Row():
example_url = gr.Dataset(components=[url_input],samples=[[str(url)] for url in urls])
url_but = gr.Button('Detect')
with gr.TabItem('Image Upload'):
with gr.Row():
img_input = gr.Image(type='pil',shape=(750,750))
img_output_from_upload= gr.Image(shape=(750,750))
with gr.Row():
example_images = gr.Dataset(components=[img_input],
samples=[[path.as_posix()] for path in sorted(pathlib.Path('images').rglob('*.j*g'))])
img_but = gr.Button('Detect')
with gr.TabItem('WebCam'):
with gr.Row():
web_input = gr.Image(source='webcam',type='pil',shape=(750,750),streaming=True)
img_output_from_webcam= gr.Image(shape=(750,750))
#gr.Image(source="webcam",type='pil',shape=(750,750)).stream(detect_objects, inputs=[options,url_input,img_input,slider_input], outputs =[img_output_from_webcam])
cam_but = gr.Button('Detect')
url_but.click(detect_objects,inputs=[options,url_input,img_input,web_input,slider_input],outputs=[img_output_from_url],queue=True)
img_but.click(detect_objects,inputs=[options,url_input,img_input,web_input,slider_input],outputs=[img_output_from_upload],queue=True)
cam_but.click(detect_objects,inputs=[options,url_input,img_input,web_input,slider_input],outputs=[img_output_from_webcam],queue=True)
example_images.click(fn=set_example_image,inputs=[example_images],outputs=[img_input])
example_url.click(fn=set_example_url,inputs=[example_url],outputs=[url_input,original_image])
gr.Markdown("")
demo.launch(debug=True,enable_queue=True)