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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/
"""
import time
import PIL
import streamlit as st
import torch
from ultralyticsplus import YOLO, render_result
from convert import convert_to_braille_unicode, parse_xywh_and_class
from test_conver import BrailleConverter
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
convert = BrailleConverter()
# 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
source_img = st.sidebar.file_uploader(
"Choose an image...", type=("jpg", "jpeg", "png", "bmp", "webp")
)
col1, col2 = st.columns(2)
# left column of the page body
with col1:
if source_img is None:
default_image_path = "./images/alpha-numeric.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
with col2:
with st.spinner("Wait for it..."):
start_time = time.time()
try:
with torch.no_grad():
res = model.predict(
image, save=True, save_txt=True, exist_ok=True, conf=conf
)
boxes = res[0].boxes # first image
res_plotted = res[0].plot()[:, :, ::-1]
list_boxes = parse_xywh_and_class(boxes)
st.image(res_plotted, caption="Detected Image", use_column_width=True)
IMAGE_DOWNLOAD_PATH = f"runs/detect/predict/image0.jpg"
except Exception as ex:
st.write("Please upload image with types of JPG, JPEG, PNG ...")
try:
st.success(f"Done! Inference time: {time.time() - start_time:.2f} seconds")
st.subheader("Detected Braille Patterns")
for box_line in list_boxes:
str_left_to_right = ""
box_classes = box_line[:, -1]
for each_class in box_classes:
str_left_to_right += convert.convert_to_braille_unicode(
model.names[int(each_class)]
)
st.write(str_left_to_right)
except Exception as ex:
st.write("Please try again with images with types of JPG, JPEG, PNG ...")
with open(IMAGE_DOWNLOAD_PATH, "rb") as fl:
st.download_button(
"Download object-detected image",
data=fl,
file_name="image0.jpg",
mime="image/jpg",
)
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