353 lines
12 KiB
Python
353 lines
12 KiB
Python
import os
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import time
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import asyncio
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import base64
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import json
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import httpx
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import numpy as np
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from PIL import Image
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import joblib
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.pipeline import make_pipeline
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from sklearn.preprocessing import StandardScaler
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from scipy.stats import entropy
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from concurrent.futures import ProcessPoolExecutor
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import functools
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from .config import settings
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# 模型保存路径 (使用统一配置)
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MODEL_PATH = settings.MODEL_PATH
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def extract_features(image_path: str) -> np.ndarray:
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"""
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从图像中提取用于质量评估的特征向量。
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特征包括：统计特征（均值、方差）、熵、边缘强度等。
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"""
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try:
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if not os.path.exists(image_path):
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return np.zeros(8)
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with Image.open(image_path) as img:
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# 转换为灰度图
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img_gray = img.convert('L')
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# 调整大小以加快处理速度，同时保持特征
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img_small = img_gray.resize((256, 256))
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arr = np.array(img_small)
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# 1. 基础统计特征
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mean = np.mean(arr)
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std = np.std(arr)
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min_val = np.min(arr)
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max_val = np.max(arr)
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# 2. 直方图熵 (反映纹理丰富度)
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# 计算直方图
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hist, _ = np.histogram(arr, bins=256, range=(0, 256), density=True)
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# 计算熵 (使用 base=2)
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img_entropy = entropy(hist, base=2)
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# 3. 简单的边缘/梯度特征 (反映条纹清晰度)
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# 使用简单的差分来近似梯度
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dy, dx = np.gradient(arr)
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gradient_magnitude = np.sqrt(dx**2 + dy**2)
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mean_gradient = np.mean(gradient_magnitude)
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std_gradient = np.std(gradient_magnitude)
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# 4. 对比度 (RMS Contrast)
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contrast = std / (mean + 1e-6) # 避免除以零
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# 组合特征向量
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features = np.array([
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mean, std, min_val, max_val,
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img_entropy,
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mean_gradient, std_gradient,
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contrast
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], dtype=np.float32)
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# 处理可能的 NaN/Inf
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features = np.nan_to_num(features)
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return features
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except Exception as e:
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# 在多进程中，print 可能不会直接显示在主进程终端，但这里保留作为记录
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# print(f"Error extracting features from {image_path}: {e}")
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return np.zeros(8)
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def train_quality_model(labeled_data: list, progress_callback=None):
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"""
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训练质量评估模型。
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Args:
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labeled_data: list of tuples (image_path, label)
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label: 0 (Bad), 1 (Good)
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progress_callback: 可选的回调函数，用于报告进度 (0-100)
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Returns:
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dict: {"accuracy": float, "sample_count": int}
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"""
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X = []
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y = []
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img_paths = [item[0] for item in labeled_data]
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labels = [item[1] for item in labeled_data]
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total = len(img_paths)
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if total == 0:
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raise ValueError("No training data provided.")
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# 使用多进程并行提取特征
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# 注意：在 Windows 上，ProcessPoolExecutor 必须在 if __name__ == "__main__": 保护下或由主进程调用
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# FastAPI 的后台任务环境通常可以正常工作
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max_workers = min(os.cpu_count() or 4, 8)
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features_list = []
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with ProcessPoolExecutor(max_workers=max_workers) as executor:
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# 提交所有任务
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futures = [executor.submit(extract_features, path) for path in img_paths]
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for i, future in enumerate(futures):
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feat = future.result()
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if not np.all(feat == 0):
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X.append(feat)
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y.append(labels[i])
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if progress_callback and i % 5 == 0:
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# 训练阶段特征提取占 80% 的进度
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progress_callback(int((i / total) * 80))
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valid_count = len(X)
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if valid_count < 2:
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raise ValueError("Not enough valid training data. Need at least 2 samples.")
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if len(set(y)) < 2:
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raise ValueError("Training data must contain both 'Good' and 'Bad' examples.")
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X = np.array(X)
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y = np.array(y)
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if progress_callback:
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progress_callback(90)
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# 创建管道：标准化 -> 随机森林
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clf = make_pipeline(StandardScaler(), RandomForestClassifier(n_estimators=100, random_state=42))
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clf.fit(X, y)
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# 保存模型
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joblib.dump(clf, MODEL_PATH)
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if progress_callback:
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progress_callback(100)
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return {
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"accuracy": float(clf.score(X, y)),
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"sample_count": valid_count
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}
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def predict_quality(image_paths: list, progress_callback=None) -> dict:
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"""
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预测一组图像的质量分数。
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使用多进程并行加速特征提取。
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"""
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if not os.path.exists(MODEL_PATH):
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return {}
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try:
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clf = joblib.load(MODEL_PATH)
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except Exception:
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return {}
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results = {}
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total = len(image_paths)
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if total == 0:
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return {}
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valid_paths = []
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features_list = []
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max_workers = min(os.cpu_count() or 4, 8)
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with ProcessPoolExecutor(max_workers=max_workers) as executor:
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futures = [executor.submit(extract_features, path) for path in image_paths]
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for i, future in enumerate(futures):
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feat = future.result()
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path = image_paths[i]
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if not np.all(feat == 0):
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valid_paths.append(path)
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features_list.append(feat)
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else:
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results[path] = 0.0
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if progress_callback and i % 10 == 0:
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# 预测阶段特征提取占 95% 的进度
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progress_callback(int((i / total) * 95))
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if not valid_paths:
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if progress_callback: progress_callback(100)
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return results
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X = np.array(features_list)
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# predict_proba 返回 [[prob_0, prob_1], ...]
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probs = clf.predict_proba(X)[:, 1]
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for path, prob in zip(valid_paths, probs):
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results[path] = float(prob)
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if progress_callback:
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progress_callback(100)
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return results
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def is_model_trained() -> bool:
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return os.path.exists(MODEL_PATH)
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def get_model_info() -> dict:
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"""
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获取模型文件的元数据。
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"""
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if not os.path.exists(MODEL_PATH):
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return None
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try:
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stats = os.stat(MODEL_PATH)
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return {
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"last_modified": time.ctime(stats.st_mtime),
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"size_bytes": stats.st_size
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}
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except Exception:
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return None
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# --- Ollama VLM Integration ---
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OLLAMA_BASE_URL = settings.OLLAMA_BASE_URL
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OLLAMA_API_URL = settings.OLLAMA_API_URL
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DEFAULT_VLM_MODEL = settings.DEFAULT_VLM_MODEL
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async def _get_available_vlm_model() -> str:
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"""自动检测本地可用的 VLM 模型"""
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try:
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async with httpx.AsyncClient(timeout=2.0) as client:
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resp = await client.get(f"{OLLAMA_BASE_URL}/api/tags")
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if resp.status_code == 200:
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models = [m['name'] for m in resp.json().get('models', [])]
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# 优先级：qwen3-vl > qwen2-vl > minicpm-v > 任何包含 vl 的模型
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for target in ["qwen3-vl:8b", "qwen2-vl", "minicpm-v"]:
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for m in models:
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if target in m: return m
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for m in models:
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if "vl" in m.lower(): return m
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except:
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pass
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return DEFAULT_VLM_MODEL
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async def analyze_map_with_vlm(images_base64: list, prompt: str, progress_callback=None) -> str:
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"""
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使用本地 Ollama 部署的多模态大模型分析地图截图。
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已改为一次性返回模式，以提高连接稳定性。
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"""
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if not images_base64:
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return "未接收到有效的地图截图。"
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model_name = await _get_available_vlm_model()
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payload = {
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"model": model_name,
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"prompt": prompt,
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"images": [img.split(",")[1] if "," in img else img for img in images_base64],
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"stream": False, # 关闭流式传输，改为一次性返回
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"options": {
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"num_predict": -1, # 彻底放开字数限制，允许生成长报告
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"temperature": 0.6,
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"top_p": 0.9
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},
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"keep_alive": "30m"
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}
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timeout_seconds = 600.0 # 保持长超时，确保复杂诊断不中断
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try:
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async with httpx.AsyncClient(timeout=timeout_seconds) as client:
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response = await client.post(OLLAMA_API_URL, json=payload)
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response.raise_for_status()
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result = response.json()
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full_thinking = result.get("thinking", "")
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full_response = result.get("response", "")
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# 最终组合结果
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final_output = ""
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if full_thinking:
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final_output += f"> [!NOTE] 思考过程\n> {full_thinking}\n\n"
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final_output += full_response
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return final_output.strip() if final_output else f"模型 ({model_name}) 未返回任何内容。"
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except Exception as e:
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return f"AI 分析过程中发生错误: {str(e)}"
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async def generate_dinsar_diagnosis(
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images_base64: list,
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record_name: str,
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date_str: str,
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quality_context: str,
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hazard_info: str,
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progress_callback=None
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) -> str:
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"""
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针对 VLM 优化的 D-InSAR 专家诊断逻辑。
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"""
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model_name = await _get_available_vlm_model()
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prompt = (
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f"你是一位拥有 20 年经验的资深 InSAR 地质灾害解译专家。请根据提供的 D-InSAR 形变图及背景信息，撰写一份专业的诊断报告。\n\n"
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f"### 1. 基础背景\n"
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f"- **任务标识**: `{record_name}`\n"
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f"- **监测周期**: {date_str}\n"
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f"- **数据质量**: {quality_context}\n\n"
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f"### 2. 空间上下文（已知灾害点）\n"
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f"影像覆盖范围内的已知灾害点信息如下：\n"
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f"{hazard_info}\n\n"
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f"### 3. 影像说明\n"
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f"提供的影像采用固定色标（±0.1m），绿色代表稳定，红色代表沉降，蓝色代表抬升。\n\n"
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f"### 4. 诊断任务（请按以下步骤思考）\n"
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f"1. **形态学分析**: 观察影像中是否存在具有空间连续性的色斑或相位条纹？形变区边缘是否清晰？\n"
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f"2. **时空一致性**: 影像中的形变信号是否与已知灾害点位置吻合？如果吻合，评估其当前的活动强度（活跃/趋于稳定）。\n"
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f"3. **风险演化预测**: 在已知点之外，是否发现了新的疑似隐患点？结合地形判断其潜在威胁。\n"
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f"4. **综合风险评级**: 给出“低”、“中”或“高”风险评级，并简述理由。\n\n"
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f"### 5. 输出要求\n"
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f"- 使用 Markdown 格式，语言严谨、专业，严禁幻觉。\n"
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f"- 报告末尾必须包含以下加粗文字：\n"
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f"**--- 免责声明 ---**\n"
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f"**本报告由 AI 自动生成（模型：{model_name}），仅供科研参考，不具备法律效力。**"
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)
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return await analyze_map_with_vlm(images_base64, prompt, progress_callback=progress_callback)
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async def warm_up_vlm() -> bool:
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"""
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预热 VLM 模型，将其加载至显存。
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发送一个轻量级请求以触发模型冷启动。
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返回 True 表示成功，False 表示失败。
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"""
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# 预热时直接使用探测到的模型
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model_name = await _get_available_vlm_model()
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payload = {
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"model": model_name,
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"prompt": "hi",
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"stream": False,
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"keep_alive": "30m"
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}
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try:
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# 预热请求给予 60s 超时，通常模型加载需要 10-30s
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async with httpx.AsyncClient(timeout=60.0) as client:
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response = await client.post(OLLAMA_API_URL, json=payload)
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response.raise_for_status()
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return True
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except Exception as e:
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print(f"模型预热失败: {str(e)}")
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return False
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