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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
from keras_cv_attention_models.yolox import *
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, model_name):
if 'yolox' in model_name:
inputs = feature_extractor(img)
outputs = model(**inputs)
processed_outputs = {}
processed_outputs['boxes'], processed_outputs['labels'], processed_outputs['scores'] = model.decode_predictions(outputs)[0]
else:
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)[0]
return processed_outputs
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):
if 'detr' in model_name:
model = DetrForObjectDetection.from_pretrained(model_name)
feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
elif 'yolos' in model_name:
model = YolosForObjectDetection.from_pretrained(model_name)
feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
elif 'yolox' in model_name:
model = YOLOXL(pretrained="coco")
feature_extractor = model.preprocess_input
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, model_name)
#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","yoloxl"]
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()
#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) |