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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!") | |