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import streamlit as st | |
from transformers import DetrImageProcessor, DetrForObjectDetection | |
import torch | |
from PIL import Image | |
import requests | |
st.set_page_config(page_title="SnapSpot", page_icon="📸", layout="wide", initial_sidebar_state="collapsed") | |
# Function to perform object detection | |
def detect_objects(image): | |
# Load DETR model and processor | |
processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50", revision="no_timm") | |
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50", revision="no_timm") | |
# Preprocess the image | |
inputs = processor(images=image, return_tensors="pt") | |
# Perform object detection | |
outputs = model(**inputs) | |
# Convert outputs to COCO API format | |
target_sizes = torch.tensor([image.size[::-1]]) | |
results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0] | |
return results | |
# Main Streamlit app | |
def main(): | |
st.title("SnapSpot") | |
st.markdown( | |
""" | |
<style> | |
.reportview-container { | |
background: #0e1117; | |
color: #f0f6fc; | |
} | |
.st-bq { | |
background-color: #0e1117; | |
} | |
.st-bm { | |
padding-top: 2rem; | |
} | |
</style> | |
""", | |
unsafe_allow_html=True, | |
) | |
# Upload image | |
uploaded_image = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"]) | |
if uploaded_image is not None: | |
# Display uploaded image | |
image = Image.open(uploaded_image) | |
st.image(image, caption="Uploaded Image", use_column_width=True) | |
# Perform object detection | |
results = detect_objects(image) | |
# Display detection results | |
st.subheader("Detection Results:") | |
for score, label, box in zip(results["scores"], results["labels"], results["boxes"]): | |
box = [round(i, 2) for i in box.tolist()] | |
st.write( | |
f"Detected {model.config.id2label[label.item()]} with confidence " | |
f"{round(score.item(), 3)} at location {box}" | |
) | |
if __name__ == "__main__": | |
main() |