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"""
Reference
- https://docs.streamlit.io/library/api-reference/layout
- https://github.com/CodingMantras/yolov8-streamlit-detection-tracking/blob/master/app.py
- https://huggingface.co/keremberke/yolov8m-valorant-detection/tree/main
- https://docs.ultralytics.com/usage/python/
"""
from pathlib import Path
import PIL
import streamlit as st
import torch
from ultralyticsplus import YOLO, render_result
def load_model(model_path):
"""load model from path"""
model = YOLO(model_path)
return model
def load_image(image_path):
"""load image from path"""
image = PIL.Image.open(image_path)
return image
# title
st.title("Braille Pattern Detection")
# sidebar
st.sidebar.header("Detection Config")
conf = float(st.sidebar.slider("Class Confidence", 10, 75, 15)) / 100
iou = float(st.sidebar.slider("IoU Threshold", 10, 75, 15)) / 100
model_path = "snoop2head/yolov8m-braille"
try:
model = load_model(model_path)
model.overrides["conf"] = conf # NMS confidence threshold
model.overrides["iou"] = iou # NMS IoU threshold
model.overrides["agnostic_nms"] = False # NMS class-agnostic
model.overrides["max_det"] = 1000 # maximum number of detections per image
except Exception as ex:
print(ex)
st.write(f"Unable to load model. Check the specified path: {model_path}")
source_img = None
st.sidebar.header("Image Config")
source_img = st.sidebar.file_uploader(
"Choose an image...", type=("jpg", "jpeg", "png", "bmp", "webp")
)
c = st.container()
# left column of the page body
if source_img is None:
default_image_path = "./images/example.jpeg"
image = load_image(default_image_path)
st.image(default_image_path, caption="Example Input Image", use_column_width=True)
else:
image = load_image(source_img)
st.image(source_img, caption="Uploaded Image", use_column_width=True)
# right column of the page body
if source_img is None:
default_detected_image_path = "./images/example_detected.jpeg"
image = load_image(default_detected_image_path)
st.image(
default_detected_image_path,
caption="Example Detected Image",
use_column_width=True,
)
else:
if st.sidebar.button("Click for Detection!"):
with torch.no_grad():
res = model.predict(
image, save=True, save_txt=True, exist_ok=True, conf=conf
)
boxes = res[0].boxes
res_plotted = res[0].plot()[:, :, ::-1]
st.image(res_plotted, caption="Detected Image", use_column_width=True)
IMAGE_DOWNLOAD_PATH = f"runs/detect/predict/image0.jpg"
with open(IMAGE_DOWNLOAD_PATH, "rb") as fl:
st.download_button(
"Download object-detected image",
data=fl,
file_name="image0.jpg",
mime="image/jpg",
)
try:
with st.expander("Detection Results"):
for box in boxes:
st.write(box.xywh)
except Exception as ex:
# st.write(ex)
st.write("No image is uploaded yet!")
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