Spaces:
Sleeping
Sleeping
File size: 3,374 Bytes
1b7d31e 6180dbd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 |
import streamlit as st
import numpy as np
import cv2 as cv
import supervision as sv
from PIL import Image
class Upload_View:
def __init__(self, app, model):
self.app = app
self.model = model
def show(self):
col1_back, col2_back = st.columns([0.2, 0.8])
with col1_back:
if st.button("Back", key='upload_back', icon = ':material/arrow_back:', type = 'primary'):
self.app.change_page("Main")
st.markdown("<h1 style='text-align: center;'>🧠 Upload MRI or CT scan image</h1>", unsafe_allow_html=True)
st.divider()
col1_img, col2_img = st.columns(2,gap='medium')
with col1_img:
st.header('Input')
uploaded_file = st.file_uploader("Choose an image...", type=["png", "jpg", "jpeg"])
if uploaded_file is not None:
st.divider()
st.image(uploaded_file, caption="Uploaded Image", use_container_width=True)
with col2_img:
st.header('Output')
if uploaded_file is not None:
# Convert uploaded image to OpenCV format
image = Image.open(uploaded_file).convert("RGB")
image_np = np.array(image) # Convert to NumPy array (H, W, C)
image_bgr = cv.cvtColor(image_np, cv.COLOR_RGB2BGR) # Convert RGB to BGR for OpenCV
# Run YOLO inference
with st.spinner("Making the predictions... ⏳"):
results = self.model(image_bgr)[0]
# Convert YOLO results into Supervision Detections format
detections = sv.Detections.from_ultralytics(results)
# Generate labels with confidence scores
labels = [
f"{results.names[class_id]}: {confidence*100:.2f}%"
for class_id, confidence in zip(detections.class_id, detections.confidence)
]
# Annotate image with bounding boxes & labels
box_annotator = sv.BoxAnnotator()
label_annotator = sv.LabelAnnotator()
annotated_image = box_annotator.annotate(image_np, detections)
annotated_image = label_annotator.annotate(annotated_image, detections, labels=labels)
# Add tumor count and confidence description
num_tumors = len(detections) # Total number of detected tumors
# Display the annotated image
st.image(annotated_image, caption="Tumor Detection Result", use_container_width=True)
# Display tumor count and confidence information using Streamlit text element
st.subheader(f"Number of Tumors Detected: {num_tumors}")
# Convert annotated image to bytes for download
annotated_pil = Image.fromarray(annotated_image)
img_bytes = annotated_pil.convert("RGB")
from io import BytesIO
buf = BytesIO()
img_bytes.save(buf, format="PNG")
byte_im = buf.getvalue()
# Add download button
st.download_button(
label="📥 Download Annotated Image",
data=byte_im,
file_name="tumor_prediction.png",
mime="image/png"
)
|