import os import cv2 import matplotlib import numpy as np import argparse import pandas as pd from bil2rgb import process_bil_files from classification_model.Parallel.predict_plastic import load_model, predict_with_model from mask import detect_microplastic_mask_from_array from shape_spectral import process_images from shape_spectral_background import process_images_background from extact_shape import shape_correct_background, extract_features import time ##批量预测文件夹内的bil文件，不进行降采样，使用新采集的数据进行训练 matplotlib.use('TkAgg') # 训练相机波长（168通道） TRAIN_WAVELENGTHS = [ 898.82, 903.64, 908.46, 913.28, 918.1, 922.92, 927.75, 932.57, 937.4, 942.22, 947.05, 951.88, 956.71, 961.54, 966.38, 971.21, 976.05, 980.88, 985.72, 990.56, 995.4, 1000.2, 1005.1, 1009.9, 1014.8, 1019.6, 1024.5, 1029.3, 1034.2, 1039, 1043.9, 1048.7, 1053.6, 1058.4, 1063.3, 1068.2, 1073, 1077.9, 1082.7, 1087.6, 1092.5, 1097.3, 1102.2, 1107.1, 1111.9, 1116.8, 1121.7, 1126.6, 1131.4, 1136.3, 1141.2, 1146.1, 1150.9, 1155.8, 1160.7, 1165.6, 1170.5, 1175.4, 1180.2, 1185.1, 1190, 1194.9, 1199.8, 1204.7, 1209.6, 1214.5, 1219.4, 1224.3, 1229.2, 1234.1, 1239, 1243.9, 1248.8, 1253.7, 1258.6, 1263.5, 1268.4, 1273.3, 1278.2, 1283.1, 1288.1, 1293, 1297.9, 1302.8, 1307.7, 1312.6, 1317.6, 1322.5, 1327.4, 1332.3, 1337.3, 1342.2, 1347.1, 1352, 1357, 1361.9, 1366.8, 1371.8, 1376.7, 1381.6, 1386.6, 1391.5, 1396.5, 1401.4, 1406.3, 1411.3, 1416.2, 1421.2, 1426.1, 1431.1, 1436, 1441, 1445.9, 1450.9, 1455.8, 1460.8, 1465.8, 1470.7, 1475.7, 1480.6, 1485.6, 1490.6, 1495.5, 1500.5, 1505.5, 1510.4, 1515.4, 1520.4, 1525.3, 1530.3, 1535.3, 1540.3, 1545.2, 1550.2, 1555.2, 1560.2, 1565.2, 1570.1, 1575.1, 1580.1, 1585.1, 1590.1, 1595.1, 1600.1, 1605.1, 1610, 1615, 1620, 1625, 1630, 1635, 1640, 1645, 1650, 1655, 1660, 1665, 1670.1, 1675.1, 1680.1, 1685.1, 1690.1, 1695.1, 1700.1, 1705.1, 1710.2, 1715.2, 1720.2 ] def read_wavelengths_from_hdr(bil_path): hdr_path = os.path.splitext(bil_path)[0] + '.hdr' if not os.path.exists(hdr_path): return np.array([], dtype=np.float64) with open(hdr_path, 'r') as f: txt = f.read() if 'wavelength' not in txt: return np.array([], dtype=np.float64) seg = txt.split('wavelength', 1)[1] seg = seg[seg.find('{')+1: seg.find('}')] vals = [v.strip() for v in seg.split(',') if v.strip()] try: waves = np.array([float(v) for v in vals], dtype=np.float64) except Exception: waves = np.array([], dtype=np.float64) return waves def resample_spectra_matrix(X, src_waves, dst_waves): src = np.asarray(src_waves, dtype=np.float64) dst = np.asarray(dst_waves, dtype=np.float64) X = np.asarray(X, dtype=np.float64) if src.size == 0 or dst.size == 0: return X # 线性插值，越界用端点外推，避免维度缺失 out = np.empty((X.shape[0], dst.size), dtype=np.float64) for i in range(X.shape[0]): row = X[i] out[i] = np.interp(dst, src, row, left=row[0], right=row[-1]) return out def apply_background_no_resample(df, bg_spectrum): """ 仅做背景校正，不做任何重采样。 - 自动选择以 wavelength_ 或 band_ 开头的光谱列 - 若背景长度与光谱列数不一致，按尾部对齐取最小长度进行校正 """ # 识别光谱列 spec_cols = [c for c in df.columns if isinstance(c, str) and (c.startswith('wavelength_') or c.startswith('band_'))] if not spec_cols: raise ValueError("未找到光谱列（以 wavelength_ 或 band_ 开头）") bg = np.asarray(bg_spectrum, dtype=np.float64).ravel() if bg.size == 0: raise ValueError("背景光谱长度为0，无法进行背景校正") # 尾部对齐，取最小长度，避免维度不一致 n = min(len(spec_cols), bg.shape[0]) use_cols = spec_cols[-n:] df.loc[:, use_cols] = df.loc[:, use_cols].div(bg[-n:], axis=1) return df def parse_arguments(): """解析命令行参数""" parser = argparse.ArgumentParser(description='Microplastic spectral shape classification - Batch processing') # 必需参数 parser.add_argument('--input_dir', required=True, help='Path to input directory containing BIL files') parser.add_argument('--output_dir', required=True, help='Path to output directory for classification results') parser.add_argument('--model_path', required=True, help='Path to primary classification model') # 可选参数 # parser.add_argument('--primary_model_type', default='SVM', help='Type of primary model (default: SVM)') # parser.add_argument('--primary_process_methods1', default='SS', help='Primary process method 1 (default: SS)') # parser.add_argument('--primary_process_methods2', default='None', help='Primary process method 2 (default: None)') # parser.add_argument('--secondary_model', default="D:\plastic\plastic\modelsave\HDPELDPE_model\svm.m", help='Path to secondary classification model') # parser.add_argument('--secondary_model_type', default='SVM', help='Type of secondary model (default: SVM)') # parser.add_argument('--secondary_process_methods1', default='None', # help='Secondary process method 1 (default: None)') # parser.add_argument('--secondary_process_methods2', default='None', # help='Secondary process method 2 (default: None)') # parser.add_argument('--secondary_target_classes', nargs='+', type=int, default=[1,2], # help='Target classes for secondary classification (space separated)') return parser.parse_args() # ---------------------------- # 配置参数：直接在此修改 # ---------------------------- # BIL_PATH = r"D:/Data/Test/PET_bottle2.bil" # OUTPUT_PATH = r'D:/Data/PET_bottle2_class.bil' # # PRIMARY_MODEL_PATH = r"D:\plastic\plastic\modelsave\svm.m" # PRIMARY_MODEL_TYPE = 'SVM' # PRIMARY_PROCESS_METHODS1 = 'SS' # PRIMARY_PROCESS_METHODS2 = 'None' # # SECONDARY_MODEL_PATH = "D:\plastic\plastic\modelsave\HDPELDPE_model\svm.m" # 若不需要二次分类，则保持为 None # SECONDARY_MODEL_TYPE = 'SVM' # SECONDARY_PROCESS_METHODS1 = 'None' # SECONDARY_PROCESS_METHODS2 = 'None' # SECONDARY_TARGET_CLASSES = [1, 2] def read_hdr_file(bil_path): hdr_path = bil_path.replace('.bil', '.hdr') with open(hdr_path, 'r') as f: header = f.readlines() samples, lines = None, None for line in header: if line.startswith('samples'): samples = int(line.split('=')[-1].strip()) if line.startswith('lines'): lines = int(line.split('=')[-1].strip()) return samples, lines def shrink_contours(bil_path, df, shrink_pixels=1): """ 对DataFrame中的所有轮廓进行收缩操作，避免塑料之间的相连 Args: bil_path: BIL文件路径，用于获取图像尺寸 df: 包含contour列的DataFrame shrink_pixels: 收缩的像素数，默认1像素 Returns: 更新后的DataFrame，contour列已被收缩 """ samples, lines = read_hdr_file(bil_path) # 创建腐蚀核 kernel = np.ones((2 * shrink_pixels + 1, 2 * shrink_pixels + 1), np.uint8) # 创建临时掩膜用于处理 temp_mask = np.zeros((lines, samples), dtype=np.uint8) # 创建DataFrame副本 df = df.copy() # 遍历每一行，更新轮廓 for idx, row in df.iterrows(): contour = row['contour'] if not isinstance(contour, (list, np.ndarray)) or len(contour) < 3: continue try: contour_array = np.array(contour, dtype=np.int32) if len(contour_array.shape) == 1: continue # 清空临时掩膜 temp_mask.fill(0) # 填充轮廓 cv2.fillPoly(temp_mask, [contour_array], 255) # 对掩膜进行腐蚀操作 eroded_mask = cv2.erode(temp_mask, kernel, iterations=1) # 重新提取轮廓 contours, _ = cv2.findContours(eroded_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) if len(contours) > 0: # 选择最大的轮廓（如果有多个） largest_contour = max(contours, key=cv2.contourArea) # 转换为列表格式，保持与原始格式一致 if len(largest_contour) >= 3: updated_contour = largest_contour.reshape(-1, 2).tolist() df.at[idx, 'contour'] = updated_contour except Exception as e: # 如果处理失败，保留原始轮廓 continue return df def save_envi_classification(bil_path, df, savepath): samples, lines = read_hdr_file(bil_path) classification_result = np.zeros((lines, samples), dtype=np.uint16) # 预处理：清除可能存在的类别10和11（移到循环外以提高效率） classification_result[(classification_result == 10)] = 0 classification_result[(classification_result == 11)] = 0 for _, row in df.iterrows(): contour = row['contour'] prediction = int(row['Predictions']) + 1 contour = np.array(contour, dtype=np.int32) cv2.fillPoly(classification_result, [contour], prediction) output_path = savepath with open(output_path, 'wb') as f: classification_result.tofile(f) header_content = f"""ENVI description = {{ Classification Result.}} samples = {samples} lines = {lines} bands = 1 header offset = 0 file type = ENVI Standard data type = 2 interleave = bil classes = 10 class = {{ background, ABS, HDPE, LDPE, PA6, PET, PP, PS, PTFE, PVC }} single pixel area = 0.000036 unit = mm2 byte order = 0 wavelength units = nm """ filename, ext = os.path.splitext(savepath) # 替换扩展名为 '.hdr' header_filename = filename + '.hdr' with open(header_filename, 'w') as header_file: header_file.write(header_content) def change_hdr_file(bil_path, wavelengths=None): # wavelengths=None 时仅在HDR缺失wavelength字段才写入；若提供则按提供内容写入 hdr_path = os.path.splitext(bil_path)[0] + '.hdr' if not os.path.exists(hdr_path): print(f"错误: 找不到对应的HDR文件: {hdr_path}") return with open(hdr_path, 'r') as file: content = file.read() if 'wavelength' in content and wavelengths is None: print(f"File {os.path.basename(hdr_path)} already contains wavelength information; no changes needed.") return if wavelengths is None: print(f"No wavelengths provided and HDR lacks wavelength; skipping write to avoid wrong bands.") return needs_newline = not content.endswith('\n') wavelength_info = "wavelength = {" + ", ".join(str(float(w)) for w in wavelengths) + "}\n" with open(hdr_path, 'a') as file: if needs_newline: file.write('\n') file.write(wavelength_info) print(f"Successfully ensured wavelength information in file: {os.path.basename(hdr_path)}") def get_bil_files(input_dir): """获取输入目录中的所有BIL文件""" if not os.path.exists(input_dir): raise FileNotFoundError(f"输入目录不存在: {input_dir}") bil_files = [] for file in os.listdir(input_dir): if file.lower().endswith('.bil'): bil_path = os.path.join(input_dir, file) hdr_path = os.path.splitext(bil_path)[0] + '.hdr' if os.path.exists(hdr_path): bil_files.append(bil_path) else: print(f"警告: 找到BIL文件 {file} 但缺少对应的HDR文件，跳过") if not bil_files: raise ValueError(f"在输入目录 {input_dir} 中未找到有效的BIL文件") return sorted(bil_files) def validate_inputs(input_dir, output_dir, model_path): """验证输入参数""" # 检查输入目录存在 if not os.path.exists(input_dir): raise FileNotFoundError(f"输入目录不存在: {input_dir}") # 检查输出目录存在，如果不存在则创建 if not os.path.exists(output_dir): try: os.makedirs(output_dir, exist_ok=True) except Exception as e: raise RuntimeError(f"无法创建输出目录: {output_dir}") from e # 检查模型文件存在 if not os.path.exists(model_path): raise FileNotFoundError(f"主模型文件不存在: {model_path}") def validate_single_bil_file(bil_path): """验证单个BIL文件""" # 检查BIL和HDR文件存在 if not os.path.exists(bil_path): raise FileNotFoundError(f"BIL文件不存在: {bil_path}") hdr_path = os.path.splitext(bil_path)[0] + '.hdr' if not os.path.exists(hdr_path): raise FileNotFoundError(f"HDR文件不存在: {hdr_path}") # 检查BIL文件波段数是否足够 try: from spectral.io import envi img = envi.open(hdr_path, bil_path) n_bands = img.nbands # bil2rgb需要波段索引9, 59, 159 if n_bands <= 159: raise ValueError(f"BIL文件波段数不足: 需要至少160个波段，但只有{n_bands}个") except Exception as e: raise RuntimeError(f"无法读取BIL文件头信息: {bil_path}") from e def generate_rgb(bil_path): """处理BIL文件生成RGB图像""" try: rgb_img = process_bil_files(bil_path) return rgb_img except Exception as e: raise RuntimeError(f"生成RGB图像失败: bil_path={bil_path}") from e def run_segmentation(rgb_img): """运行分割获取掩膜""" try: mask, filter_mask_original = detect_microplastic_mask_from_array( image=rgb_img, filter_method='threshold', diameter=None, flow_threshold=0.4, cellprob_threshold=-1, detect_filter=True ) return mask, filter_mask_original except Exception as e: raise RuntimeError("分割失败: 无法检测微塑料颗粒") from e def extract_primary_features(bil_path, mask): """提取主要特征""" try: df = process_images(bil_path, mask) return df except Exception as e: raise RuntimeError(f"特征提取失败: bil_path={bil_path}") from e def compute_background_spectrum(bil_path, mask): """计算背景光谱""" try: df_correct = process_images_background(bil_path, mask) return df_correct except Exception as e: raise RuntimeError(f"背景光谱计算失败: bil_path={bil_path}") from e def apply_background_and_optional_resample(df, bg_spectrum, bil_path): """应用背景校正和可选的重采样""" # 识别光谱列：所有以wavelength_开头的列 spec_cols = [c for c in df.columns if c.startswith('wavelength_')] if not spec_cols: raise ValueError("未找到光谱列（以wavelength_开头的列）") if len(spec_cols) != len(bg_spectrum): raise ValueError(f"光谱列数量({len(spec_cols)})与背景光谱长度({len(bg_spectrum)})不匹配") # 背景校正：用背景光谱逐列相除 df[spec_cols] = df[spec_cols].div(bg_spectrum, axis=1) # 检查是否需要重采样 src_waves = read_wavelengths_from_hdr(bil_path) need_resample = (src_waves.size > 0 and (src_waves.size != len(TRAIN_WAVELENGTHS) or not np.allclose(src_waves, TRAIN_WAVELENGTHS, atol=1e-2))) if need_resample: print(f"重采样光谱: 源波段数 {src_waves.size} -> 目标波段数 {len(TRAIN_WAVELENGTHS)}") # 提取光谱数据 X_src = df[spec_cols].to_numpy(dtype=np.float64) X_dst = resample_spectra_matrix(X_src, src_waves, TRAIN_WAVELENGTHS) # 替换光谱列 spec_col_names = [f"band_{i+1}" for i in range(len(TRAIN_WAVELENGTHS))] df = pd.concat([ df.drop(columns=spec_cols), # 移除原有光谱列 pd.DataFrame(X_dst, columns=spec_col_names, index=df.index) ], axis=1) return df def clean_and_select_columns(df): """数据清理和列选择""" # 移除NaN值 df = df.dropna() # 过滤轮廓点数不足的样本 df = df[df['contour'].apply(lambda x: len(x) > 1 if isinstance(x, list) else True)] # 过滤面积过小的样本 df = df[df['area'] >= 500] # 列筛选：使用原来的硬编码索引删除逻辑 # cols_to_remove = df.columns[np.r_[1:10, 11:15, 97:120, 176:179 ]] cols_to_remove = df.columns[np.r_[-10: -1]] df = df.drop(columns=cols_to_remove) return df def run_primary_classification(df, primary_model_path): """运行主要分类""" try: # 验证特征维度 try: import joblib scaler_path = os.path.join(os.path.dirname(primary_model_path), 'scaler_params.pkl') if os.path.exists(scaler_path): scaler = joblib.load(scaler_path) numeric_cols = [c for c in df.columns[1:] if np.issubdtype(df[c].dtype, np.number) and c != 'contour'] if hasattr(scaler, 'mean_') and len(numeric_cols) != scaler.mean_.shape[0]: raise ValueError(f"特征维度不匹配: 当前{numeric_cols}列 != 训练时{scaler.mean_.shape[0]}维") except Exception as e: print(f"警告: 无法验证特征维度: {e}") df_pre = predict_with_model( df, primary_model_path, model_type='SVM', ProcessMethods1='SS', ProcessMethods2='None' ) return df_pre except Exception as e: raise RuntimeError(f"主要分类失败: model_path={primary_model_path}") from e def run_secondary_classification_if_needed(df_pre, bil_path, mask, filter_mask_original): """根据需要运行二次分类""" # 二次分类配置（保持在代码内） secondary_model_path = os.path.join(os.path.dirname(__file__), 'modelsave', 'HDPELDPE_model', 'svm.m') secondary_target_classes = [1, 2] # HDPE, LDPE target_classes = set(secondary_target_classes or []) mask_secondary = df_pre['Predictions'].isin(target_classes) if not mask_secondary.any(): print("未找到目标类别样本，跳过二次分类") return df_pre print(f"为类别 {sorted(target_classes)} 运行二次分类") # 检查二次模型是否存在 if not os.path.exists(secondary_model_path): print(f"警告: 二次模型不存在: {secondary_model_path}，跳过二次分类") return df_pre try: # 图像信息的背景矫正 df_correct = shape_correct_background(bil_path, mask, filter_mask_original) # 创建只包含目标类别的掩膜 mask_second = np.zeros_like(mask, dtype=np.uint16) for idx in df_pre[mask_secondary].index: contour = df_pre.loc[idx, 'contour'] if isinstance(contour, list) and len(contour) > 0: contour_array = np.array(contour, dtype=np.int32) cv2.fillPoly(mask_second, [contour_array], idx + 1) # 提取特征 df_shape = extract_features(df_correct, mask_second) # 确保使用前13列作为模型输入特征 if len(df_shape.columns) >= 13: df_shape = df_shape.iloc[:, :13] # 二次分类 df_secondary = predict_with_model( df_shape, secondary_model_path, model_type='SVM', ProcessMethods1='None', ProcessMethods2='None' ) # 更新预测结果（类别+1） df_pre.loc[mask_secondary, 'Predictions'] = df_secondary['Predictions'].values + 1 except Exception as e: print(f"警告: 二次分类失败，将继续使用主要分类结果: {e}") return df_pre def postprocess_class7_shadow(df_pre, rgb_img): """后处理类别7中的背景阴影""" class_7_mask = df_pre['Predictions'] == 7 if not class_7_mask.any(): return df_pre print(f"处理 {class_7_mask.sum()} 个类别7样本，识别背景阴影...") # 将PIL Image转换为numpy数组 if hasattr(rgb_img, 'mode'): # PIL Image rgb_img_array = np.array(rgb_img) else: rgb_img_array = rgb_img # 转换为灰度图 if len(rgb_img_array.shape) == 3: gray_img = cv2.cvtColor(rgb_img_array, cv2.COLOR_RGB2GRAY) else: gray_img = rgb_img_array # 计算梯度图 grad_x = cv2.Sobel(gray_img, cv2.CV_64F, 1, 0, ksize=3) grad_y = cv2.Sobel(gray_img, cv2.CV_64F, 0, 1, ksize=3) gradient_magnitude = np.sqrt(grad_x ** 2 + grad_y ** 2) # 收集类别7样本的边缘梯度值 all_class7_gradients = [] valid_indices = [] for idx in df_pre[class_7_mask].index: try: contour = df_pre.loc[idx, 'contour'] if not isinstance(contour, (list, np.ndarray)) or len(contour) < 3: continue contour_array = np.array(contour, dtype=np.int32) if len(contour_array.shape) == 1: continue mask_img = np.zeros(gray_img.shape, dtype=np.uint8) cv2.drawContours(mask_img, [contour_array], -1, 255, thickness=2) edge_gradients = gradient_magnitude[mask_img > 0] if len(edge_gradients) > 0: all_class7_gradients.extend(edge_gradients) valid_indices.append(idx) except Exception: continue # 确定梯度阈值 if len(all_class7_gradients) > 0: gradient_threshold = np.percentile(all_class7_gradients, 30) else: gradient_threshold = np.percentile(gradient_magnitude, 30) print(f"类别7梯度阈值: {gradient_threshold:.2f}") # 处理每个类别7样本 indices_to_update = [] for idx in valid_indices: try: contour = df_pre.loc[idx, 'contour'] contour_array = np.array(contour, dtype=np.int32) mask_img = np.zeros(gray_img.shape, dtype=np.uint8) cv2.drawContours(mask_img, [contour_array], -1, 255, thickness=2) edge_gradients = gradient_magnitude[mask_img > 0] if len(edge_gradients) == 0: continue mean_gradient = np.mean(edge_gradients) if mean_gradient < gradient_threshold: indices_to_update.append(idx) print(f"样本 {idx}: 平均梯度={mean_gradient:.2f}, 阈值={gradient_threshold:.2f} -> 识别为背景阴影") except Exception as e: print(f"处理样本 {idx} 时出错: {e}") continue # 更新分类结果 if indices_to_update: df_pre.loc[indices_to_update, 'Predictions'] = 9 print(f"将 {len(indices_to_update)} 个样本从类别7改为类别9（背景阴影）") else: print("无需更新类别7样本") return df_pre def write_outputs(bil_path, df_pre, output_path): """写入输出结果""" try: # 收缩轮廓 df_pre = shrink_contours(bil_path, df_pre, shrink_pixels=1) # 保存ENVI分类结果 save_envi_classification(bil_path, df_pre, output_path) except Exception as e: raise RuntimeError(f"保存结果失败: output_path={output_path}") from e def process_single_file(bil_path, output_path, primary_model_path): """处理单个BIL文件的完整流程""" try: # 验证输入 validate_single_bil_file(bil_path) # 修改HDR文件 change_hdr_file(bil_path) # 处理BIL文件生成RGB图像 print(f" 处理BIL文件生成RGB图像...") rgb_img = generate_rgb(bil_path) # 分割阶段 segmentation_start_time = time.time() print(f" 生成掩膜...") mask, filter_mask_original = run_segmentation(rgb_img) # 提取特征 print(f" 从BIL文件提取特征...") df = extract_primary_features(bil_path, mask) # 背景校正 print(f" 应用背景校正...") bg_spectrum = compute_background_spectrum(bil_path, mask) # 仅应用背景校正，不进行重采样 df = apply_background_no_resample(df, bg_spectrum) # 数据清理和列选择 print(f" 清理数据...") df = clean_and_select_columns(df) segmentation_time = time.time() - segmentation_start_time # 分类阶段 classification_start_time = time.time() print(f" 预测分类...") df_pre = run_primary_classification(df, primary_model_path) # 二次分类 df_pre = run_secondary_classification_if_needed(df_pre, bil_path, mask, filter_mask_original) # 后处理类别7阴影 df_pre = postprocess_class7_shadow(df_pre, rgb_img) classification_time = time.time() - classification_start_time # 保存结果 print(f" 保存ENVI分类结果...") write_outputs(bil_path, df_pre, output_path) return segmentation_time, classification_time except Exception as e: print(f"处理文件失败 {os.path.basename(bil_path)}: {e}") raise def main(): """主函数 - 批量处理""" args = parse_arguments() input_dir = args.input_dir output_dir = args.output_dir primary_model_path = args.model_path # 记录总开始时间 total_start_time = time.time() try: # 验证输入参数 validate_inputs(input_dir, output_dir, primary_model_path) # 获取所有BIL文件 bil_files = get_bil_files(input_dir) print(f"找到 {len(bil_files)} 个BIL文件待处理") # 统计信息 total_files = len(bil_files) processed_files = 0 failed_files = 0 total_segmentation_time = 0 total_classification_time = 0 # 逐个处理文件 for i, bil_path in enumerate(bil_files, 1): print(f"\n{'='*60}") print(f"处理文件 {i}/{total_files}: {os.path.basename(bil_path)}") print(f"{'='*60}") try: # 生成输出文件名：原文件名 + "_classification.bil" base_name = os.path.splitext(os.path.basename(bil_path))[0] output_filename = f"{base_name}_classification.bil" output_path = os.path.join(output_dir, output_filename) # 处理单个文件 segmentation_time, classification_time = process_single_file( bil_path, output_path, primary_model_path ) # 更新统计信息 total_segmentation_time += segmentation_time total_classification_time += classification_time processed_files += 1 print(f"文件 {os.path.basename(bil_path)} 处理完成") print(f"结果保存至: {output_path}") except Exception as e: print(f"文件 {os.path.basename(bil_path)} 处理失败: {e}") failed_files += 1 continue # 计算平均耗时 if processed_files > 0: avg_segmentation_time = total_segmentation_time / processed_files avg_classification_time = total_classification_time / processed_files avg_total_time = (total_segmentation_time + total_classification_time) / processed_files # 计算总耗时 total_time = time.time() - total_start_time # 打印汇总统计 print(f"\n{'=' * 60}") print("批量处理完成") print(f"{'=' * 60}") print(f"总文件数: {total_files}") print(f"成功处理: {processed_files}") print(f"处理失败: {failed_files}") print(f"成功率: {processed_files/total_files*100:.1f}%" if total_files > 0 else "成功率: 0%") print(f"{'=' * 60}") if processed_files > 0: print(f"平均分割耗时: {avg_segmentation_time:.2f} 秒") print(f"平均分类耗时: {avg_classification_time:.2f} 秒") print(f"平均总耗时: {avg_total_time:.2f} 秒") print(f"实际总耗时: {total_time:.2f} 秒") print(f"{'=' * 60}") print(f"结果保存目录: {output_dir}") except Exception as e: print(f"批量处理失败: {e}") raise if __name__ == "__main__": main()