import pandas as pd

def process_plantcv_outputs(observations):
    """
    直接处理 PlantCV 内存中的观测数据
    :param observations: pcv.outputs.observations 数据结构
    :param file_name: 当前处理的文件名
    :return: 合并后的光谱和形状特征 DataFrame
    """
    all_samples = []

    # 遍历每个样本的观测数据
    for sample_id, sample_data in observations.items():
        # 初始化样本记录
        sample_record = {
            "Sample ID": sample_id,
        }

        # 处理光谱数据
        if "wavelength_means" in sample_data:
            spectral_data = sample_data["wavelength_means"]

            # 提取波长标签和值
            if "label" in spectral_data and "value" in spectral_data:
                wavelengths = spectral_data["label"]
                reflectance = spectral_data["value"]

                # 确保数据长度一致
                if len(wavelengths) == len(reflectance):
                    # 创建波长列（转换为微米）
                    for i, wl in enumerate(wavelengths):
                        try:
                            # 波长单位转换为微米 (nm/1000 = μm)
                            sample_record[f"wavelength_{wl:.2f}"] = reflectance[i] / 10000
                        except (ValueError, TypeError):
                            continue

        # 处理形状特征
        # 在形状特征提取部分添加轮廓处理
        shape_features = [
            'area', 'convex_hull_area', 'solidity', 'perimeter',
            'width', 'height', 'circularity', 'shape_factor', 'aspect_ratio'
        ]

        # 添加轮廓特征
        contour_features = ['contour']

        for feature in shape_features  + contour_features:
            if feature in sample_data:
                feature_data = sample_data[feature]

                if feature == 'contour':  # 特殊处理轮廓数据
                    if "value" in feature_data and feature_data["value"]:
                        # 轮廓数据通常是一个点列表 [[x1, y1], [x2, y2], ...]
                        contour_points = feature_data["value"]
                        # 如果轮廓数据点数小于等于3，跳过该数据
                        if len(contour_points) <= 3:
                            print(f"警告：轮廓数据点数少于或等于3个，跳过该数据")
                            continue  # 跳过该数据，直接进入下一个特征
                        # 存储为元组列表以便后续处理
                        sample_record['contour'] = [(point[0], point[1]) for point in contour_points]
                else:  # 处理其他形状特征
                    if "value" in feature_data:
                        sample_record[feature] = feature_data["value"]
        all_samples.append(sample_record)

    # 创建 DataFrame
    df = pd.DataFrame(all_samples)

    # 确保所有波长列都是数值类型
    wavelength_cols = [col for col in df.columns if col.startswith("wavelength_")]
    for col in wavelength_cols:
        df[col] = pd.to_numeric(df[col], errors='coerce')

    return df