Upload app (1).py

app (1).py

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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
+
+
+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
+
+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_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",
+    )