Streamlit 結合 yolo 辨識圖片

uv init

uv init

// pyproject.toml
[project]
name = "week13"
version = "0.1.0"
description = "Add your description here"
readme = "README.md"
requires-python = ">=3.13"
dependencies = [
    "altair==4.0",
    "matplotlib>=3.10.7",
    "pillow>=12.0.0",
    "streamlit>=1.51.0",
    "torch>=2.9.1",
    "torchvision>=0.24.1",
    "ultralytics>=8.3.233",
]

// pyproject.toml
[project]
name = "week13"
version = "0.1.0"
description = "Add your description here"
readme = "README.md"
requires-python = ">=3.13"
dependencies = [
    "altair==4.0",
    "matplotlib>=3.10.7",
    "pillow>=12.0.0",
    "streamlit>=1.51.0",
    "torch>=2.9.1",
    "torchvision>=0.24.1",
    "ultralytics>=8.3.233",
]

uv venv

uv venv

source .venv/bin/activate

source .venv/bin/activate

uv add streamlit
uv add torch torchvision 
uv add matplotlib
uv add ultralytics
uv add pillow
uv add altair==4.0

uv add streamlit
uv add torch torchvision 
uv add matplotlib
uv add ultralytics
uv add pillow
uv add altair==4.0

# 匯入所需的函式庫
import PIL
import streamlit as st
from ultralytics import YOLO

# 替換成您的權重檔案的相對路徑
model_path = 'weights/best.pt'

# 設定頁面佈局
st.set_page_config(
    page_title="物件偵測",  # 設定頁面標題
    page_icon="🤖",      # 設定頁面圖示
    layout="wide",       # 設定佈局為寬版
    initial_sidebar_state="expanded",    # 預設展開側邊欄
)

# 建立側邊欄
with st.sidebar:
    st.header("圖片配置")      # 在側邊欄新增標題
    # 在側邊欄新增檔案上傳器，用於選擇圖片
    source_img = st.file_uploader(
        "上傳圖片...", type=("jpg", "jpeg", "png", 'bmp', 'webp'))

    # 模型選項
    confidence = float(st.slider(
        "選擇模型信心閾值", 25, 100, 40)) / 100

# 建立主頁面標題
st.title("物件偵測")
st.caption('上傳一張帶有這些:blue[手勢訊號]的照片：:+1:, :hand:, :i_love_you_hand_sign:, 和 :spock-hand:.')
st.caption('然後點擊:blue[偵測物件]按鈕並查看結果。')
# 在主頁面建立兩個欄位
col1, col2 = st.columns(2)

# 如果圖片已上傳，則將圖片新增到第一個欄位
with col1:
    if source_img:
        # 開啟上傳的圖片
        uploaded_image = PIL.Image.open(source_img)
        # 將上傳的圖片新增到頁面，並附上標題
        st.image(source_img,
                 caption="上傳的圖片",
                 use_column_width=True
                 )

try:
    model = YOLO(model_path)
except Exception as ex:
    st.error(
        f"無法載入模型。請檢查指定的路徑：{model_path}")
    st.error(ex)

if st.sidebar.button('偵測物件'):
    res = model.predict(uploaded_image,
                        conf=confidence
                        )
    boxes = res[0].boxes
    res_plotted = res[0].plot()[:, :, ::-1]
    with col2:
        st.image(res_plotted,
                 caption='偵測結果圖片',
                 use_column_width=True
                 )
        try:
            with st.expander("偵測結果"):
                for box in boxes:
                    st.write(box.xywh)
        except Exception as ex:
            st.write("尚未上傳圖片！")

# 匯入所需的函式庫
import PIL
import streamlit as st
from ultralytics import YOLO

# 替換成您的權重檔案的相對路徑
model_path = 'weights/best.pt'

# 設定頁面佈局
st.set_page_config(
    page_title="物件偵測",  # 設定頁面標題
    page_icon="🤖",      # 設定頁面圖示
    layout="wide",       # 設定佈局為寬版
    initial_sidebar_state="expanded",    # 預設展開側邊欄
)

# 建立側邊欄
with st.sidebar:
    st.header("圖片配置")      # 在側邊欄新增標題
    # 在側邊欄新增檔案上傳器，用於選擇圖片
    source_img = st.file_uploader(
        "上傳圖片...", type=("jpg", "jpeg", "png", 'bmp', 'webp'))

    # 模型選項
    confidence = float(st.slider(
        "選擇模型信心閾值", 25, 100, 40)) / 100

# 建立主頁面標題
st.title("物件偵測")
st.caption('上傳一張帶有這些:blue[手勢訊號]的照片：:+1:, :hand:, :i_love_you_hand_sign:, 和 :spock-hand:.')
st.caption('然後點擊:blue[偵測物件]按鈕並查看結果。')
# 在主頁面建立兩個欄位
col1, col2 = st.columns(2)

# 如果圖片已上傳，則將圖片新增到第一個欄位
with col1:
    if source_img:
        # 開啟上傳的圖片
        uploaded_image = PIL.Image.open(source_img)
        # 將上傳的圖片新增到頁面，並附上標題
        st.image(source_img,
                 caption="上傳的圖片",
                 use_column_width=True
                 )

try:
    model = YOLO(model_path)
except Exception as ex:
    st.error(
        f"無法載入模型。請檢查指定的路徑：{model_path}")
    st.error(ex)

if st.sidebar.button('偵測物件'):
    res = model.predict(uploaded_image,
                        conf=confidence
                        )
    boxes = res[0].boxes
    res_plotted = res[0].plot()[:, :, ::-1]
    with col2:
        st.image(res_plotted,
                 caption='偵測結果圖片',
                 use_column_width=True
                 )
        try:
            with st.expander("偵測結果"):
                for box in boxes:
                    st.write(box.xywh)
        except Exception as ex:
            st.write("尚未上傳圖片！")