414 lines
19 KiB
Python
414 lines
19 KiB
Python
"""
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病虫害以图搜图
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基于 CLIP 本地模型的图片 Embedding 相似度搜索
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"""
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from __future__ import annotations
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import io
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import os
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from dataclasses import dataclass
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from typing import Literal
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import numpy as np
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import plotly.graph_objects as go
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import requests
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import streamlit as st
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from PIL import Image
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from transformers import CLIPModel, CLIPProcessor
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# ─── Page Config ────────────────────────────────────────────────────────────
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st.set_page_config(
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page_title="病虫害以图搜图",
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page_icon="🌿",
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layout="wide",
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initial_sidebar_state="expanded",
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)
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# ─── Minimal CSS ─────────────────────────────────────────────────────────────
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st.markdown("""
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<style>
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html, body, [class*="css"] {
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font-family: "PingFang SC", "Microsoft YaHei", "Noto Sans SC", sans-serif;
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}
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</style>
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""", unsafe_allow_html=True)
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# ─── Knowledge Base ──────────────────────────────────────────────────────────
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@dataclass(frozen=True)
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class PestItem:
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name: str
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url: str
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symptoms: str
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treatment: str
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crop: str
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category: Literal["病害", "虫害"]
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PEST_KNOWLEDGE: list[PestItem] = [
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PestItem(
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name="水稻稻瘟病",
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url="https://minio.dev.maimaiag.com/crop-prod-bucket/field_photo/20260410_151854_dc9667cf_%E6%B0%B4%E7%A8%BB%E7%A8%BB%E7%98%9F%E7%97%851.jpeg",
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symptoms="叶片出现梭形或纺锤形病斑，中央灰白色，边缘褐色，严重时病斑连片导致叶片枯死",
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treatment="选用抗病品种，合理施肥避免偏施氮肥，发病初期喷施三环唑或稻瘟灵",
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crop="水稻",
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category="病害",
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),
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PestItem(
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name="水稻纹枯病",
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url="https://minio.dev.maimaiag.com/crop-prod-bucket/field_photo/20260410_152022_9f3124ab_%E6%B0%B4%E7%A8%BB%E7%BA%B9%E6%9E%AF%E7%97%851.jpeg",
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symptoms="叶鞘和叶片上出现云纹状灰绿色至灰褐色病斑，后期病斑边缘褐色、中央灰白色",
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treatment="合理密植，科学管水，发病初期喷施井冈霉素或噻呋酰胺",
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crop="水稻",
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category="病害",
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),
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PestItem(
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name="水稻胡麻叶斑病",
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url="https://minio.dev.maimaiag.com/crop-prod-bucket/field_photo/20260410_151936_41fdb1dc_%E6%B0%B4%E7%A8%BB%E8%83%A1%E9%BA%BB%E5%8F%B6%E6%96%91%E7%97%851.jpeg",
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symptoms="叶片上出现暗褐色芝麻粒大小的椭圆形病斑，病斑周围有黄色晕圈",
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treatment="增施硅肥和钾肥提高抗病力，喷施丙环唑或咪鲜胺防治",
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crop="水稻",
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category="病害",
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),
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PestItem(
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name="小麦锈病",
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url="https://minio.dev.maimaiag.com/crop-prod-bucket/field_photo/20260410_153814_3e175ca3_%E5%B0%8F%E9%BA%A6%E9%94%88%E7%97%851.jpeg",
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symptoms="叶片和叶鞘上出现铁锈色粉状疱疹（夏孢子堆），后期变为黑色冬孢子堆",
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treatment="种植抗锈品种，发病初期喷施三唑酮或烯唑醇，注意轮作",
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crop="小麦",
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category="病害",
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),
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PestItem(
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name="小麦赤霉病",
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url="https://minio.dev.maimaiag.com/crop-prod-bucket/field_photo/20260410_152112_2e1f530e_%E5%B0%8F%E9%BA%A6%E8%B5%A4%E9%9C%89%E7%97%851.jpeg",
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symptoms="穗部小穗发病，颖壳上出现水浸状褐色斑，后期产生粉红色霉层",
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treatment="选用抗病品种，齐穗至扬花初期喷施多菌灵或戊唑醇",
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crop="小麦",
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category="病害",
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),
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PestItem(
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name="玉米大斑病",
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url="https://minio.dev.maimaiag.com/crop-prod-bucket/field_photo/20260410_153911_ee5a72be_%E7%8E%89%E7%B1%B3%E5%A4%A7%E6%96%91%E7%97%851.jpeg",
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symptoms="叶片上出现灰绿色水浸状斑点，扩展为长梭形灰褐色大型病斑",
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treatment="种植抗病品种，适时早播，发病初期喷施多菌灵或代森锰锌",
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crop="玉米",
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category="病害",
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),
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PestItem(
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name="玉米小斑病",
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url="https://minio.dev.maimaiag.com/crop-prod-bucket/field_photo/20260410_154001_e31a0103_%E7%8E%89%E7%B1%B3%E5%B0%8F%E6%96%91%E7%97%851.jpeg",
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symptoms="叶片上出现椭圆形黄褐色小病斑，有2-3圈同心轮纹，边缘紫褐色",
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treatment="轮作倒茬，清除病残体，喷施百菌清或甲基托布津",
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crop="玉米",
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category="病害",
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),
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PestItem(
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name="玉米螟",
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url="https://minio.dev.maimaiag.com/crop-prod-bucket/field_photo/20260410_153938_8be05006_%E7%8E%89%E7%B1%B3%E8%9E%9F1.jpeg",
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symptoms="幼虫蛀食茎秆和穗轴，茎秆上有蛀孔，孔口有虫粪，造成茎秆折断",
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treatment="心叶期撒施白僵菌颗粒剂，释放赤眼蜂生物防治，大喇叭口期灌心",
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crop="玉米",
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category="虫害",
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),
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PestItem(
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name="稻飞虱",
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url="https://minio.dev.maimaiag.com/crop-prod-bucket/field_photo/20260410_151643_db5e1d36_%E7%A8%BB%E9%A3%9E%E8%99%AB1.jpeg",
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symptoms="稻株基部聚集大量褐色或白色小型飞虫，受害稻株发黄矮缩，严重时枯死倒伏",
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treatment="合理施肥避免贪青晚熟，选用吡蚜酮或烯啶虫胺防治，保护利用天敌",
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crop="水稻",
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category="虫害",
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),
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PestItem(
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name="大豆蚜虫",
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url="https://minio.dev.maimaiag.com/crop-prod-bucket/field_photo/20260410_151549_d9cf327b_%E5%A4%A7%E8%B1%86%E8%9A%9C%E8%99%AB1.jpeg",
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symptoms="嫩叶和茎尖聚集大量绿色或黄色蚜虫，叶片卷缩变形，植株矮化",
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treatment="保护瓢虫等天敌，百株蚜量达1000头时喷施吡虫啉或啶虫脒",
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crop="大豆",
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category="虫害",
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),
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PestItem(
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name="番茄晚疫病",
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url="https://minio.dev.maimaiag.com/crop-prod-bucket/field_photo/20260410_151705_3dd8baab_%E7%95%AA%E8%8C%84%E6%99%9A%E7%96%AB%E7%97%851.jpeg",
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symptoms="叶片出现水浸状暗绿色不规则病斑，潮湿时叶背面产生白色霉层，果实变褐硬化",
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treatment="控制温湿度，及时通风降湿，发病初期喷施甲霜灵锰锌或霜脲氰",
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crop="番茄",
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category="病害",
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),
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PestItem(
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name="黄瓜霜霉病",
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url="https://minio.dev.maimaiag.com/crop-prod-bucket/field_photo/20260410_151804_7be515fa_%E9%BB%84%E7%93%9C%E9%9C%9C%E9%9C%89%E7%97%851.jpeg",
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symptoms="叶片正面出现黄色多角形病斑，叶背面潮湿时产生灰黑色霉层",
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treatment="选用抗病品种，膜下滴灌降低湿度，喷施百菌清或霜霉威盐酸盐",
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crop="黄瓜",
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category="病害",
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),
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]
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EXAMPLE_IMAGES: list[tuple[str, str]] = [
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(
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"水稻稻瘟病",
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"https://minio.dev.maimaiag.com/crop-prod-bucket/field_photo/20260410_151914_4f5b8fef_%E6%B0%B4%E7%A8%BB%E7%A8%BB%E7%98%9F%E7%97%852.jpeg",
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),
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(
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"番茄晚疫病",
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"https://minio.dev.maimaiag.com/crop-prod-bucket/field_photo/20260410_151726_a8f31320_%E7%95%AA%E8%8C%84%E6%99%9A%E7%96%AB%E7%97%852.jpeg",
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),
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(
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"小麦锈病",
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"https://minio.dev.maimaiag.com/crop-prod-bucket/field_photo/20260410_153837_e8ae9f43_%E5%B0%8F%E9%BA%A6%E9%94%88%E7%97%852.jpeg",
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),
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(
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"水稻纹枯病",
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"https://minio.dev.maimaiag.com/crop-prod-bucket/field_photo/20260410_152050_77d568b1_%E6%B0%B4%E7%A8%BB%E7%BA%B9%E6%9E%AF%E7%97%852.jpeg",
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),
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]
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# ─── CLIP Embedder ───────────────────────────────────────────────────────────
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class CLIPEmbedder:
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MODEL_NAME = "openai/clip-vit-base-patch32"
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def __init__(self) -> None:
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self._processor: CLIPProcessor | None = None
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self._model: CLIPModel | None = None
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def _load(self) -> tuple[CLIPProcessor, CLIPModel]:
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if self._processor is None or self._model is None:
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with st.spinner("首次启动正在加载 CLIP 模型，请稍候..."):
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self._processor = CLIPProcessor.from_pretrained(self.MODEL_NAME)
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self._model = CLIPModel.from_pretrained(self.MODEL_NAME)
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return self._processor, self._model
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def embed(self, image: Image.Image) -> np.ndarray:
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processor, model = self._load()
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inputs = processor(images=image, return_tensors="pt")
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image_features = model.get_image_features(**inputs)
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vec = image_features.detach().cpu().numpy().flatten()
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norm = np.linalg.norm(vec)
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if norm == 0:
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return vec
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return vec / norm
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@st.cache_resource(show_spinner=False)
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def get_embedder() -> CLIPEmbedder:
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return CLIPEmbedder()
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# ─── Utilities ───────────────────────────────────────────────────────────────
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def _load_image_raw(source: str | io.BytesIO) -> Image.Image | None:
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try:
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if isinstance(source, str):
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resp = requests.get(source, timeout=30)
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resp.raise_for_status()
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return Image.open(io.BytesIO(resp.content)).convert("RGB")
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return Image.open(source).convert("RGB")
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except Exception:
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return None
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def load_image(source: str | io.BytesIO) -> Image.Image | None:
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img = _load_image_raw(source)
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if img is None:
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st.error("图片加载失败，请检查链接是否可访问或文件是否损坏")
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return img
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def cosine_similarity(a: np.ndarray, b: np.ndarray) -> float:
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return float(np.dot(a, b))
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@st.cache_data(show_spinner=False)
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def build_index() -> tuple[list[dict], list[str], list[str]]:
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embedder = get_embedder()
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items, succeeded, failed = [], [], []
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for pest in PEST_KNOWLEDGE:
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img = _load_image_raw(pest.url)
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if img is None:
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failed.append(pest.name)
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continue
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try:
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embedding = embedder.embed(img)
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items.append({
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"name": pest.name,
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"url": pest.url,
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"embedding": embedding,
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"symptoms": pest.symptoms,
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"treatment": pest.treatment,
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"crop": pest.crop,
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"category": pest.category,
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})
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succeeded.append(pest.name)
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except Exception:
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failed.append(pest.name)
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return items, succeeded, failed
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# ─── Sidebar ─────────────────────────────────────────────────────────────────
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with st.sidebar:
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st.header("🌿 病虫害以图搜图")
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st.caption("上传图片，智能识别相似病虫害")
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st.divider()
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st.subheader("🖼️ 输入方式")
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input_mode = st.radio("", ["上传本地图片", "输入图片 URL", "选择示例图片"], label_visibility="collapsed")
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# 初始化 session_state
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if "query_url" not in st.session_state:
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st.session_state.query_url = ""
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if "query_image_bytes" not in st.session_state:
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st.session_state.query_image_bytes = None
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query_source = None
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query_url = ""
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if input_mode == "上传本地图片":
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uploaded = st.file_uploader("选择图片", type=["jpg", "jpeg", "png", "webp"])
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if uploaded is not None:
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st.session_state.query_image_bytes = uploaded.getvalue()
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st.session_state.query_url = ""
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if st.session_state.query_image_bytes is not None:
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query_source = io.BytesIO(st.session_state.query_image_bytes)
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elif input_mode == "输入图片 URL":
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query_url = st.text_input("图片 URL", value=st.session_state.query_url, placeholder="https://example.com/image.jpg")
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st.session_state.query_url = query_url
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st.session_state.query_image_bytes = None
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if query_url.strip():
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query_source = query_url.strip()
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else:
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st.caption("点击选择示例")
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cols = st.columns(2)
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for idx, (name, url) in enumerate(EXAMPLE_IMAGES):
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with cols[idx % 2]:
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if st.button(name, key=f"ex_{name}"):
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st.session_state.query_url = url
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st.session_state.query_image_bytes = None
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st.rerun()
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if st.session_state.query_url:
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query_url = st.session_state.query_url
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query_source = query_url
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st.image(query_url, use_container_width=True)
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st.subheader("⚙️ 搜索设置")
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top_k = st.slider("返回条数", 1, min(12, len(PEST_KNOWLEDGE)), 5)
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search_clicked = st.button("开始搜索", type="primary", use_container_width=True)
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st.divider()
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st.info(
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"**使用说明**\n\n"
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"1. 上传病虫害患处图片\n"
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"2. 系统自动提取图像特征\n"
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"3. 与知识库比对返回相似结果\n"
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"4. 参考症状与防治建议"
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)
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# ─── Build Index ─────────────────────────────────────────────────────────────
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index_items, succeeded, failed = build_index()
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# ─── Main Layout ─────────────────────────────────────────────────────────────
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st.title("🌿 病虫害以图搜图")
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st.caption("基于 CLIP 视觉模型的病虫害相似度检索与防治建议")
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# Status badges
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if succeeded:
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st.badge(f"📚 知识库 {len(succeeded)} 种", color="blue")
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if failed:
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st.badge(f"⚠️ 索引失败 {len(failed)} 种", color="red")
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st.markdown("<br>", unsafe_allow_html=True)
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# ─── Search Logic ────────────────────────────────────────────────────────────
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if search_clicked:
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if query_source is None:
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st.warning("请先上传图片、输入图片 URL 或选择示例图片后再点击搜索")
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elif not index_items:
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st.warning("知识库索引为空，请检查网络连接后刷新页面重试。")
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else:
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query_img = load_image(query_source)
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if query_img is not None:
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col_query, col_preview = st.columns([1, 3])
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with col_query:
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st.subheader("🔍 查询图片")
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st.image(query_img, use_container_width=True)
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with col_preview:
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st.subheader("⏳ 正在分析...")
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progress = st.progress(0, text="提取图像特征...")
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embedder = get_embedder()
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query_embedding = embedder.embed(query_img)
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progress.progress(50, text="比对知识库...")
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scores = []
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for item in index_items:
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sim = cosine_similarity(query_embedding, item["embedding"])
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scores.append((sim, item))
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scores.sort(key=lambda x: x[0], reverse=True)
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results = scores[:top_k]
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progress.progress(100, text="搜索完成")
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progress.empty()
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st.subheader(f"🏆 搜索结果（Top-{len(results)}）")
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# Similarity bar chart
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names = [f"{r[1]['name']}" for r in results]
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sims = [r[0] * 100 for r in results]
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||
colors = ["#c45c4a" if r[1]["category"] == "虫害" else "#4a7c59" for r in results]
|
||
|
||
fig_bar = go.Figure()
|
||
fig_bar.add_trace(go.Bar(
|
||
x=sims,
|
||
y=names,
|
||
orientation="h",
|
||
marker=dict(color=colors, opacity=0.85, line=dict(color="rgba(0,0,0,0.08)", width=1)),
|
||
text=[f"{s:.1f}%" for s in sims],
|
||
textposition="outside",
|
||
textfont=dict(color="#5a5a5a", size=10),
|
||
))
|
||
fig_bar.update_layout(
|
||
xaxis=dict(title="相似度 (%)", color="#5a5a5a", gridcolor="rgba(0,0,0,0.06)", range=[0, 105]),
|
||
yaxis=dict(color="#5a5a5a", gridcolor="rgba(0,0,0,0.04)", autorange="reversed"),
|
||
paper_bgcolor="rgba(0,0,0,0)",
|
||
plot_bgcolor="rgba(0,0,0,0)",
|
||
font=dict(color="#2c2c2c", size=11),
|
||
margin=dict(t=10, b=30, l=80, r=50),
|
||
height=160 + len(results) * 34,
|
||
showlegend=False,
|
||
)
|
||
st.plotly_chart(fig_bar, use_container_width=True)
|
||
|
||
# Result cards below
|
||
st.subheader("📋 详细结果")
|
||
for rank, (sim, item) in enumerate(results, 1):
|
||
with st.container(border=True):
|
||
c1, c2 = st.columns([1, 4])
|
||
with c1:
|
||
st.image(item["url"], use_container_width=True)
|
||
with c2:
|
||
header_col, score_col = st.columns([3, 1])
|
||
header_col.markdown(f"**#{rank} {item['name']}**")
|
||
score_col.markdown(f"<div style='text-align:right; font-weight:600;'>相似度 {sim*100:.1f}%</div>", unsafe_allow_html=True)
|
||
|
||
badge_cols = st.columns([1, 1, 4])
|
||
badge_cols[0].caption(f"🌾 {item['crop']}")
|
||
badge_cols[1].caption(f"🐛 {item['category']}" if item["category"] == "虫害" else f"🍃 {item['category']}")
|
||
|
||
st.markdown(f"**症状：** {item['symptoms']}")
|
||
st.markdown(f"**防治：** {item['treatment']}")
|
||
|
||
# Advisory summary
|
||
if results:
|
||
best = results[0][1]
|
||
st.subheader("💡 初步建议")
|
||
st.info(
|
||
f"系统判断该图片与 **{best['name']}**（{best['crop']}{best['category']}）最为相似，"
|
||
f"相似度 **{results[0][0]*100:.1f}%**。\n\n"
|
||
f"建议结合田间实际情况进一步确认，参考防治方案：**{best['treatment']}**"
|
||
)
|
||
|
||
# ─── Footer ───────────────────────────────────────────────────────────────────
|
||
st.divider()
|
||
st.caption("病虫害以图搜图 · 基于 CLIP 视觉模型 · 结果仅供参考，请结合田间实际情况判断")
|