zsod / app.py
Kaushik Bar
removing yoloxl
2d4f9f1
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, DetrForObjectDetection, YolosForObjectDetection
import os
# 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)
img = Image.open(buf)
return img
def visualize_prediction(pil_img, output_dict, threshold=0.7, 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, 10))
plt.imshow(pil_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=3))
ax.text(xmin, ymin, f"{label}: {score:0.2f}", fontsize=15, bbox=dict(facecolor="yellow", alpha=0.5))
plt.axis("off")
return fig2img(plt.gcf())
def detect_objects(model_name,url_input,image_input,threshold):
#Extract model and feature extractor
feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
if 'detr' in model_name:
model = DetrForObjectDetection.from_pretrained(model_name)
elif 'yolos' in model_name:
model = YolosForObjectDetection.from_pretrained(model_name)
if validators.url(url_input):
image = Image.open(requests.get(url_input, stream=True).raw)
elif image_input:
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
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])
title = """<h1 id="title">Object Detection App with DETR and YOLOS</h1>"""
description = """
Links to HuggingFace Models:
- [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50)
- [facebook/detr-resnet-101](https://huggingface.co/facebook/detr-resnet-101)
- [hustvl/yolos-small](https://huggingface.co/hustvl/yolos-small)
- [hustvl/yolos-tiny](https://huggingface.co/hustvl/yolos-tiny)
"""
models = ["facebook/detr-resnet-50","facebook/detr-resnet-101",'hustvl/yolos-small','hustvl/yolos-tiny']
urls = ["https://c8.alamy.com/comp/J2AB4K/the-new-york-stock-exchange-on-the-wall-street-in-new-york-J2AB4K.jpg"]
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='Select Object Detection Model',show_label=True)
slider_input = gr.Slider(minimum=0.2,maximum=1,value=0.5,label='Prediction Threshold')
with gr.Tabs():
with gr.TabItem('Image URL'):
with gr.Row():
url_input = gr.Textbox(lines=2,label='Enter valid image URL here..')
img_output_from_url = gr.Image(shape=(650,650))
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')
img_output_from_upload= gr.Image(shape=(650,650))
with gr.Row():
example_images = gr.Dataset(components=[img_input],
samples=[[path.as_posix()]
for path in sorted(pathlib.Path('images').rglob('*.JPG'))])
img_but = gr.Button('Detect')
url_but.click(detect_objects,inputs=[options,url_input,img_input,slider_input],outputs=img_output_from_url,queue=True)
img_but.click(detect_objects,inputs=[options,url_input,img_input,slider_input],outputs=img_output_from_upload,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])
demo.launch(enable_queue=True)