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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
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())
models = ["facebook/detr-resnet-50",
"facebook/detr-resnet-101",
'hustvl/yolos-small',
'hustvl/yolos-tiny']
def detect_objects(image_input,threshold):
labels = []
#Extract model and feature extractor
feature_extractor_1 = AutoFeatureExtractor.from_pretrained("facebook/detr-resnet-50")
feature_extractor_2 = AutoFeatureExtractor.from_pretrained("facebook/detr-resnet-101")
feature_extractor_3 = AutoFeatureExtractor.from_pretrained('hustvl/yolos-small')
feature_extractor_4 = AutoFeatureExtractor.from_pretrained('hustvl/yolos-tiny')
model_1 = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
model_2 = YolosForObjectDetection.from_pretrained('hustvl/yolos-small')
model_3 = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-101")
model_4 = YolosForObjectDetection.from_pretrained('hustvl/yolos-tiny')
#Make prediction
processed_outputs_1 = make_prediction(image_input, feature_extractor_1, model_1)
processed_outputs_2 = make_prediction(image_input, feature_extractor_2, model_2)
processed_outputs_3 = make_prediction(image_input, feature_extractor_3, model_3)
processed_outputs_4 = make_prediction(image_input, feature_extractor_4, model_4)
#Visualize prediction
viz_img_1 = visualize_prediction(image_input, processed_outputs_1, threshold, model_1.config.id2label)
viz_img_2 = visualize_prediction(image_input, processed_outputs_2, threshold, model_2.config.id2label)
viz_img_3 = visualize_prediction(image_input, processed_outputs_3, threshold, model_3.config.id2label)
viz_img_4 = visualize_prediction(image_input, processed_outputs_4, threshold, model_4.config.id2label)
return viz_img_1,viz_img_2,viz_img_3,viz_img_4
title = """<h1 id="title">Object Detection App with DETR and YOLOS</h1>"""
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='Select Object Detection Model',show_label=True)
slider_input = gr.Slider(minimum=0.2,maximum=1,value=0.7,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))
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=[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)
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