import os import cv2 import matplotlib import numpy as np 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 get_glcm import calcu_glcm, calcu_glcm_variance matplotlib.use('TkAgg') # ---------------------------- # 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 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"File {os.path.basename(hdr_path)} already contains wavelength information; no changes needed.") 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"Successfully added wavelength information to file: {os.path.basename(hdr_path)}") def main(): bil_path = BIL_PATH output_path = OUTPUT_PATH model_path = PRIMARY_MODEL_PATH # 处理BIL文件生成RGB图像 print("Processing BIL file to generate RGB image...\n") 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 ...\n") mask, filter_mask_original = detect_microplastic_mask_from_array( image=rgb_img, # 直接传入cv2.imread的结果 filter_method='threshold', diameter=None, flow_threshold=0.4, cellprob_threshold=-1 ) # 提取特征 print("Extracting features from BIL file...\n") df = process_images(bil_path, mask) # 背景校正 print("Applying background correction...\n") df_correct = process_images_background(bil_path, mask) df.iloc[:, 1:169] = df.iloc[:, 1:169].div(df_correct, axis=1) # 数据清理 print("Cleaning data...\n") df = df.dropna() df = df[df['contour'].apply(lambda x: len(x) > 1 if isinstance(x, list) else True)] df = df[df['area'] >= 500] # 使用pandas列选择：获取要删除的列名（从第 94 列到第 118 列，索引从0开始） cols_to_remove = df.columns[np.r_[87:110, -10:-1]] # cols_to_remove = df.columns[87:110] # 删除指定列，保持DataFrame结构 df = df.drop(columns=cols_to_remove) # 使用pandas列选择：选择从第二列开始的所有列（跳过第一列，通常是'Sample ID'或'filename'） # 保持DataFrame结构，不转换为numpy数组（.values会丢失列名和DataFrame结构） df = df.iloc[:, :] # 保存原始特征数据（在第一次预测之前），供第二次模型使用 df_original = df.copy() # 预测分类 print("Predicting classes...\n") loaded_model = load_model(model_path) df_pre = predict_with_model( df, model_path, model_type=PRIMARY_MODEL_TYPE, ProcessMethods1=PRIMARY_PROCESS_METHODS1, ProcessMethods2=PRIMARY_PROCESS_METHODS2 ) # 二次分类：针对指定类别重新预测（使用原始特征值） if SECONDARY_MODEL_PATH: target_classes = set(SECONDARY_TARGET_CLASSES or []) if target_classes: print(f"Running secondary classification for classes: {sorted(target_classes)}\n") mask_secondary = df_pre['Predictions'].isin(target_classes) if mask_secondary.any(): # 使用原始特征数据，而不是第一次预测后的数据 df_secondary_input = df_original.loc[mask_secondary].copy() df_secondary = predict_with_model( df_secondary_input, SECONDARY_MODEL_PATH, model_type=SECONDARY_MODEL_TYPE, ProcessMethods1=SECONDARY_PROCESS_METHODS1, ProcessMethods2=SECONDARY_PROCESS_METHODS2 ) df_pre.loc[mask_secondary, 'Predictions'] = df_secondary['Predictions'].values else: print("No samples from target classes found; skipping secondary classification.\n") else: print("Secondary target classes not provided; skipping secondary classification.\n") else: print("Secondary model path not provided; skipping secondary classification.\n") # 识别类别7中的背景阴影误判：通过边界清晰度特征 # 真正的类别7边界清晰，背景阴影边界模糊 class_7_mask = df_pre['Predictions'] == 7 if class_7_mask.any(): print(f"Processing {class_7_mask.sum()} samples with class 7 to identify background shadows...\n") # 将PIL Image转换为numpy数组 if hasattr(rgb_img, 'mode'): # 检查是否是PIL Image rgb_img_array = np.array(rgb_img) else: rgb_img_array = rgb_img # 转换为灰度图（用于计算梯度） 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（使用Sobel算子） 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) # 先收集所有类别7样本的边缘梯度值，用于确定阈值 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: continue # 基于类别7样本的梯度分布确定阈值 # 使用类别7样本梯度值的中位数作为基准，低于某个分位数（如30%）的认为是背景阴影 if len(all_class7_gradients) > 0: gradient_threshold = np.percentile(all_class7_gradients, 30) # 使用类别7样本梯度值的30%分位数 else: gradient_threshold = np.percentile(gradient_magnitude, 30) # 如果没有有效样本，使用整张图的30%分位数 print(f"Gradient threshold for class 7: {gradient_threshold:.2f}\n") # 处理每个类别7的样本，判断是否为背景阴影 indices_to_update = [] for idx in valid_indices: try: contour = df_pre.loc[idx, 'contour'] contour_array = np.array(contour, dtype=np.int32) # 创建轮廓掩膜（线宽为2像素，用于提取边缘） mask_img = np.zeros(gray_img.shape, dtype=np.uint8) cv2.drawContours(mask_img, [contour_array], -1, 255, thickness=2) # 提取轮廓边缘的梯度值 edge_gradients = gradient_magnitude[mask_img > 0] if len(edge_gradients) == 0: continue # 计算轮廓边缘的平均梯度强度 mean_gradient = np.mean(edge_gradients) # 如果平均梯度强度低于阈值，认为是背景阴影（边界模糊） if mean_gradient < gradient_threshold: indices_to_update.append(idx) print( f"Sample {idx}: mean_gradient={mean_gradient:.2f}, threshold={gradient_threshold:.2f} -> identified as background shadow") except Exception as e: print(f"Error processing sample at index {idx}: {str(e)}") continue # 将背景阴影的类别7改为类别9 if indices_to_update: df_pre.loc[indices_to_update, 'Predictions'] = 9 # 类别9在代码中是索引8（0-based） print(f"Updated {len(indices_to_update)} samples from class 7 to class 9 (background shadows)\n") else: print("No samples needed to be updated from class 7\n") # 区分类别1（HDPE）和类别2（LDPE）：通过亮度和均匀性特征 # 类别2（LDPE）：亮度更亮且不均匀（高亮度 + 高标准差） # 类别1（HDPE）：亮度暗且均匀（低亮度 + 低标准差） class_1_2_mask = df_pre['Predictions'].isin([1, 2]) # 类别1和2在代码中是索引0和1（0-based） if class_1_2_mask.any(): print( f"Processing {class_1_2_mask.sum()} samples with class 1 (HDPE) or class 2 (LDPE) to distinguish them...\n") # 将PIL Image转换为numpy数组（如果还没有转换） if hasattr(rgb_img, 'mode'): # 检查是否是PIL Image rgb_img_array = np.array(rgb_img) else: rgb_img_array = rgb_img # 转换为灰度图（用于计算亮度） if len(rgb_img_array.shape) == 3: gray_img = cv2.cvtColor(rgb_img_array, cv2.COLOR_RGB2GRAY) else: gray_img = rgb_img_array # 收集所有类别1和2样本的亮度和标准差，用于确定阈值 all_brightnesses = [] all_std_devs = [] valid_indices = [] for idx in df_pre[class_1_2_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 # 创建完整轮廓掩膜 full_mask = np.zeros(gray_img.shape, dtype=np.uint8) cv2.fillPoly(full_mask, [contour_array], 255) # 先对轮廓掩膜进行内缩，避免边缘区域包含背景像素 # 使用较小的核进行第一次腐蚀，得到内缩后的掩膜 contour_rect = cv2.boundingRect(contour_array) inner_kernel_size = max(2, min(min(contour_rect[2], contour_rect[3]) // 20, 5)) inner_kernel = np.ones((inner_kernel_size, inner_kernel_size), np.uint8) inner_mask = cv2.erode(full_mask, inner_kernel, iterations=1) # 提取轮廓内部区域的像素值 inner_pixels = gray_img[inner_mask > 0] if len(inner_pixels) > 0: mean_brightness = np.mean(inner_pixels) std_brightness = np.std(inner_pixels) all_brightnesses.append(mean_brightness) all_std_devs.append(std_brightness) valid_indices.append(idx) except Exception as e: print(f"Error processing sample at index {idx} for brightness/std: {str(e)}") continue # 基于类别1和2样本的亮度和标准差分布确定阈值 # LDPE：亮度更亮且不均匀；HDPE：亮度暗且均匀 if len(all_brightnesses) > 0 and len(all_std_devs) > 0: # 使用中位数作为阈值 brightness_threshold = np.median(all_brightnesses) std_threshold = np.median(all_std_devs) else: brightness_threshold = 128 # 默认阈值（0-255范围的中值） std_threshold = 20 # 默认标准差阈值 print(f"Brightness threshold: {brightness_threshold:.3f}") print(f"Standard deviation threshold: {std_threshold:.3f}\n") # 处理每个类别1或2的样本，判断是HDPE还是LDPE indices_to_update_to_ldpe = [] # 需要改为LDPE（类别2，索引1）的样本 indices_to_update_to_hdpe = [] # 需要改为HDPE（类别1，索引0）的样本 for idx in valid_indices: try: contour = df_pre.loc[idx, 'contour'] contour_array = np.array(contour, dtype=np.int32) current_prediction = df_pre.loc[idx, 'Predictions'] # 创建完整轮廓掩膜 full_mask = np.zeros(gray_img.shape, dtype=np.uint8) cv2.fillPoly(full_mask, [contour_array], 255) # 先对轮廓掩膜进行内缩，避免边缘区域包含背景像素 # 使用较小的核进行第一次腐蚀，得到内缩后的掩膜 contour_rect = cv2.boundingRect(contour_array) inner_kernel_size = max(2, min(min(contour_rect[2], contour_rect[3]) // 20, 5)) inner_kernel = np.ones((inner_kernel_size, inner_kernel_size), np.uint8) inner_mask = cv2.erode(full_mask, inner_kernel, iterations=1) # 提取轮廓内部区域的像素值 inner_pixels = gray_img[inner_mask > 0] if len(inner_pixels) > 0: mean_brightness = np.mean(inner_pixels) std_brightness = np.std(inner_pixels) # 判断逻辑： # 类别2（LDPE）：亮度更亮且不均匀（高亮度 + 高标准差） # 类别1（HDPE）：亮度暗且均匀（低亮度 + 低标准差） is_bright = mean_brightness > brightness_threshold is_uneven = std_brightness > std_threshold # 如果亮度高且不均匀，更可能是LDPE（类别2） # 如果亮度暗且均匀，更可能是HDPE（类别1） if is_bright and is_uneven: # 更可能是LDPE（类别2，索引1） if current_prediction == 1: # 如果当前预测是HDPE，改为LDPE indices_to_update_to_ldpe.append(idx) print( f"Sample {idx}: brightness={mean_brightness:.3f} (>{brightness_threshold:.3f}), " f"std={std_brightness:.3f} (>{std_threshold:.3f}) -> changed from HDPE to LDPE") elif not is_bright and not is_uneven: # 更可能是HDPE（类别1，索引0） if current_prediction == 2: # 如果当前预测是LDPE，改为HDPE indices_to_update_to_hdpe.append(idx) print( f"Sample {idx}: brightness={mean_brightness:.3f} (<={brightness_threshold:.3f}), " f"std={std_brightness:.3f} (<={std_threshold:.3f}) -> changed from LDPE to HDPE") except Exception as e: print(f"Error processing sample at index {idx}: {str(e)}") continue # 更新分类结果 if indices_to_update_to_ldpe: df_pre.loc[indices_to_update_to_ldpe, 'Predictions'] = 2 # 改为LDPE（类别2，索引1） print(f"Updated {len(indices_to_update_to_ldpe)} samples from HDPE to LDPE\n") if indices_to_update_to_hdpe: df_pre.loc[indices_to_update_to_hdpe, 'Predictions'] = 1 # 改为HDPE（类别1，索引0） print(f"Updated {len(indices_to_update_to_hdpe)} samples from LDPE to HDPE\n") if not indices_to_update_to_ldpe and not indices_to_update_to_hdpe: print("No samples needed to be updated between HDPE and LDPE\n") # 保存ENVI分类结果 print("Saving ENVI classification results...\n") save_envi_classification(bil_path, df_pre, output_path) print(f"ENVI classification results saved to: {output_path}") if __name__ == "__main__": main()