from bil2rgb import process_bil_files from shape_spectral import process_images import cv2 from classification_model.Parallel.predict_plastic import predict_and_save import numpy as np import os import matplotlib import pandas as pd from mask import detect_microplastic_mask_from_array matplotlib.use('TkAgg') def extract_features(df_correct, mask): # 提取df_correct内mask的特征，包括平均值、50%分位数、90%分位数、标准差、变异系数、径向灰度，返回为pandas的df # 确保mask是正确的数据类型 if mask.dtype != np.uint16: mask = mask.astype(np.uint16) # 获取所有颗粒的标签 unique_labels = np.unique(mask) unique_labels = unique_labels[unique_labels != 0] # 移除背景 features_list = [] for label_id in unique_labels: # 创建当前颗粒的掩膜 particle_mask = (mask == label_id) # 获取颗粒区域的数据 particle_data = df_correct[particle_mask] if len(particle_data) == 0: continue # 基本统计特征 mean_value = np.mean(particle_data) median_value = np.median(particle_data) # 50%分位数 percentile_90 = np.percentile(particle_data, 90) # 90%分位数 std_value = np.std(particle_data) cv_value = std_value / mean_value if mean_value != 0 else 0 # 变异系数 # 计算颗粒重心和轮廓 y_coords, x_coords = np.where(particle_mask) if len(y_coords) == 0: continue # 重心（质心） center_y = np.mean(y_coords) center_x = np.mean(x_coords) # 计算轮廓 contours, _ = cv2.findContours( particle_mask.astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE ) if len(contours) == 0: continue contour = contours[0] # 取最大轮廓 # 面积 area = len(y_coords) # 径向灰度特征 # 计算最大半径R（从重心到轮廓边缘的最远距离） distances = [] for point in contour: px, py = point[0] dist = np.sqrt((px - center_x) ** 2 + (py - center_y) ** 2) distances.append(dist) max_radius = np.max(distances) if len(distances) > 0 else 1.0 # 如果半径太小，跳过径向特征计算 if max_radius < 3: I_inner = mean_value I_mid = mean_value I_outer = mean_value I_center = mean_value I_edge = mean_value R1 = 1.0 R2 = 0.0 else: # 按半径分圈：0-0.3R（inner）、0.3-0.7R（mid）、0.7-R（outer） # 对mask内的每个像素计算距离 inner_values = [] mid_values = [] outer_values = [] center_values = [] edge_values = [] for y, x in zip(y_coords, x_coords): dist = np.sqrt((x - center_x) ** 2 + (y - center_y) ** 2) normalized_dist = dist / max_radius if max_radius > 0 else 0 pixel_value = df_correct[y, x] if normalized_dist <= 0.3: inner_values.append(pixel_value) center_values.append(pixel_value) elif normalized_dist <= 0.7: mid_values.append(pixel_value) else: outer_values.append(pixel_value) edge_values.append(pixel_value) # 计算各圈的平均灰度 I_inner = np.mean(inner_values) if len(inner_values) > 0 else mean_value I_mid = np.mean(mid_values) if len(mid_values) > 0 else mean_value I_outer = np.mean(outer_values) if len(outer_values) > 0 else mean_value I_center = np.mean(center_values) if len(center_values) > 0 else mean_value I_edge = np.mean(edge_values) if len(edge_values) > 0 else mean_value # 构造特征 R1 = I_center / I_edge if I_edge != 0 else 1.0 # R1 = I_center / I_edge R2 = I_edge - I_center # R2 = I_edge - I_center # 将轮廓转换为列表格式（与process_images输出格式一致） # cv2.findContours返回的contour格式是 (n, 1, 2)，需要转换 if len(contour.shape) == 3: contour_list = contour.reshape(-1, 2).tolist() else: contour_list = contour.tolist() # 创建特征字典 feature_dict = { 'ID':label_id, 'mean': mean_value, 'median': median_value, # 50%分位数 'percentile_90': percentile_90, 'std': std_value, 'cv': cv_value, # 变异系数 'I_inner': I_inner, 'I_mid': I_mid, 'I_outer': I_outer, 'I_center': I_center, 'I_edge': I_edge, 'R1': R1, 'R2': R2, 'area': area, 'contour': contour_list, 'center_of_mass': (center_x, center_y) } features_list.append(feature_dict) # 转换为DataFrame df = pd.DataFrame(features_list) return df def shape_correct_background(bil_path, mask, filter_mask_original): # 读取bil文件的第160波段数据 import numpy as np from spectral.io import envi # 读取BIL文件 img = envi.open(bil_path.replace('.bil', '.hdr'), bil_path) band_160 = img.read_band(159) # 第160波段 # filter_mask_original减去mask掩膜得到只包含滤纸的掩膜 paper_mask = filter_mask_original.copy() paper_mask[mask > 0] = 0 # 减去塑料掩膜区域 # 求只包含滤纸掩膜的第160波段数据的平均值 paper_band_160 = band_160[paper_mask > 0] if len(paper_band_160) == 0: print("Warning: 滤纸掩膜区域内无数据，使用全局平均值") paper_mean = np.mean(band_160) else: paper_mean = np.mean(paper_band_160) # 将原第160波段数据除以只包含滤纸掩膜的第160波段数据的平均值 corrected_band_160 = band_160 / paper_mean # 返回背景校正后的第160波段数据 return corrected_band_160 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 save_envi_classification(bil_path, df, savepath): samples, lines = read_hdr_file(bil_path) classification_result = np.zeros((lines, samples), dtype=np.uint16) for _, row in df.iterrows(): contour = row['contour'] prediction = int(row['Predictions']) + 1 contour = np.array(contour, dtype=np.int32) # 先将 classification_result 中的 10 和 11 替换为 0 classification_result[(classification_result == 10)] = 0 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 = 11 class = {{ background, ABS, HDPE, LDPE, PA6, PET, PP, PS, PTFE, PVC,background2 }} 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): # 定义要追加的波长信息 wavelength_info = """wavelength = {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}""" # 将.bil路径转换为.hdr路径 hdr_path = os.path.splitext(bil_path)[0] + '.hdr' # 检查.hdr文件是否存在 if not os.path.exists(hdr_path): print(f"错误: 找不到对应的HDR文件: {hdr_path}") return # 读取文件内容 with open(hdr_path, 'r') as file: content = file.read() # 检查是否已包含波长信息 if 'wavelength' in content: print(f"文件 {os.path.basename(hdr_path)} 已包含波长信息，无需修改。") return # 检查文件是否以换行符结尾 needs_newline = not content.endswith('\n') # 追加波长信息 with open(hdr_path, 'a') as file: if needs_newline: file.write('\n') # 确保新内容从新行开始 file.write(wavelength_info + '\n') print(f"已成功添加波长信息到文件: {os.path.basename(hdr_path)}") def process_single_bil(bil_path): """ 处理单个BIL文件 """ try: print(f"\n{'=' * 60}") print(f"Processing: {os.path.basename(bil_path)}") print(f"{'=' * 60}") # 处理BIL文件生成RGB图像 print("Processing BIL file to generate RGB image...") rgb_img = process_bil_files(bil_path) # 修改hdr change_hdr_file(bil_path) # 生成掩膜，mask为16位的塑料标签掩膜 print("Generating mask...") mask, filter_mask_original = detect_microplastic_mask_from_array( image=rgb_img, filter_method='threshold', diameter=None, flow_threshold=0.4, cellprob_threshold=0.0 ) # 返回背景校正后的第160波段数据 df_correct = shape_correct_background(bil_path, mask, filter_mask_original) # 提取特征 df = extract_features(df_correct, mask) # 数据清理 print("Cleaning data...") df = df.dropna() df = df[df['contour'].apply(lambda x: len(x) > 1 if isinstance(x, list) else True)] df = df[df['area'] >= 400] # 添加文件名列（不含扩展名） filename = os.path.splitext(os.path.basename(bil_path))[0] df.insert(0, 'filename', filename) print(f"Extracted {len(df)} objects from {os.path.basename(bil_path)}") return df except Exception as e: print(f"Error processing {bil_path}: {str(e)}") import traceback traceback.print_exc() return None def main(): # 单个文件或文件夹路径 bil_path_or_folder = r"D:\Data\Traindata-11\LDPE7.bil" output_csv_path = r"D:\Data\Traindata-05\HDPELDPE\LDPE7.csv" # 确保输出目录存在 output_dir = os.path.dirname(output_csv_path) os.makedirs(output_dir, exist_ok=True) # 判断是文件还是文件夹 if os.path.isfile(bil_path_or_folder): bil_files = [bil_path_or_folder] elif os.path.isdir(bil_path_or_folder): # 搜索所有.bil文件 bil_files = [os.path.join(bil_path_or_folder, f) for f in os.listdir(bil_path_or_folder) if f.endswith('.bil')] print(f"Found {len(bil_files)} BIL files to process") else: print(f"Error: {bil_path_or_folder} is not a valid file or directory") return # 初始化CSV文件（写入表头） is_first_row = True total_objects = 0 for i, bil_path in enumerate(bil_files, 1): print(f"\n[{i}/{len(bil_files)}] Processing file...") df = process_single_bil(bil_path) if df is not None and len(df) > 0: # 边处理边写入CSV df.to_csv( output_csv_path, mode='a' if not is_first_row else 'w', # 第一行写入模式为'w'，后续追加'w' index=False, header=is_first_row # 只在第一行写入表头 ) total_objects += len(df) is_first_row = False print(f" -> {len(df)} objects appended to CSV file") # 显示统计信息 if total_objects > 0: print(f"\nSummary:") print(f" Total files processed: {len(bil_files)}") print(f" Total objects detected: {total_objects}") print(f" Output file: {output_csv_path}") else: print("\nNo results to save.") if __name__ == "__main__": main()