app.py

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 pil_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=(16, 16))
    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='large', 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">Plant Disease 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 [Plant Doc Image Dataset]("https://universe.roboflow.com/artificial-intelligence-82oex/detecting-diseases") from Roboflow. The dataset consists of 4700 images of plants with 30 annotations of various diiseases such as "Apple rust leaf","Potato leaf late blight" and "Tomato leaf bacterial spot". The model was trained for 60 epochs on a single GPU.
Links to HuggingFace Models:
- [nickmuchi/yolos-small-plant-disease-detection](https://huggingface.co/nickmuchi/yolos-small-plant-disease-detection)
- [hustlv/yolos-small](https://huggingface.co/hustlv/yolos-small)  
"""

models = ["nickmuchi/yolos-small-plant-disease-detection"]
urls = ["https://www.gardeningknowhow.com/wp-content/uploads/2013/07/early-blight-potatoes.jpg","https://ag.umass.edu/sites/ag.umass.edu/files/fact-sheets/images/cedar-apple_fig2.jpg"]

twitter_link = """
[![](https://img.shields.io/twitter/follow/nickmuchi?label=@nickmuchi&style=social)](https://twitter.com/nickmuchi)
"""

css = '''
h1#title {
  text-align: center;
}
'''
demo = gr.Blocks(css=css)

with demo:
    with gr.Box():
    
        gr.Markdown(title)
        gr.Markdown(description)
        gr.Markdown(twitter_link)
        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(shape=(750,750))
                        url_input.change(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))
    
                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("![visitor badge](https://visitor-badge.glitch.me/badge?page_id=nickmuchi-plant-disease-detection-with-yolos)")

    
demo.launch(debug=True,enable_queue=True)