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import os
from PIL import Image
from transformers import AutoImageProcessor, AutoModelForObjectDetection
import streamlit as st
import torch
import requests
def prettier(results):
for item in results:
score = round(item['score'], 3)
label = item['label'] # Use square brackets to access the 'label' key
location = [round(value, 2) for value in item['box'].values()]
print(f'Detected {label} with confidence {score} at location {location}')
def input_image_setup(uploaded_file):
if uploaded_file is not None:
#read the file into byte
bytes_data = uploaded_file.getvalue()
image_parts=[
{
"mime_type": uploaded_file.type,
"data": bytes_data
}
]
return image_parts
else:
raise FileNotFoundError("No file uploaded")
#Streamlit App
st.set_page_config(page_title="Image Detection")
st.header("Object Detection Application")
#Select your model
models = ["facebook/detr-resnet-50", "ciasimbaya/ObjectDetection", "hustvl/yolos-tiny"] # List of supported models
model_name = st.selectbox("Select model", models)
processor = AutoImageProcessor.from_pretrained(model_name)
model = AutoModelForObjectDetection.from_pretrained(model_name)
#Upload an image
uploaded_file = st.file_uploader("choose an image...", type=["jpg","jpeg","png"])
image=""
if uploaded_file is not None:
image = Image.open(uploaded_file)
st.image(image, caption="Uploaded Image.", use_column_width=True)
submit = st.button("Detect Objects ")
if submit:
image_data=input_image_setup(uploaded_file)
st.subheader("The response is..")
#process with model
inputs = processor(images=image_data, return_tensors="pt")
outputs = model(**inputs)
# model predicts bounding boxes and corresponding COCO classes
logits = outputs.logits
bboxes = outputs.pred_boxes
# print results
target_sizes = torch.tensor([image.size[::-1]])
results = image_processor.post_process_object_detection(outputs, threshold=0.9, target_sizes=target_sizes)[0]
for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
box = [round(i, 2) for i in box.tolist()]
print(
f"Detected {model.config.id2label[label.item()]} with confidence "
f"{round(score.item(), 3)} at location {box}"
)
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