514 lines
21 KiB
Python
514 lines
21 KiB
Python
import os
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import cv2
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import matplotlib
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import numpy as np
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import argparse
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import pandas as pd
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from bil2rgb import process_bil_files
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from classification_model.Parallel.predict_plastic import load_model, predict_with_model
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from mask import detect_microplastic_mask_from_array
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from shape_spectral import process_images
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from shape_spectral_background import process_images_background
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from extact_shape import shape_correct_background, extract_features
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import time
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matplotlib.use('TkAgg')
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# 训练相机波长（168通道）
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TRAIN_WAVELENGTHS = [
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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
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]
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def read_wavelengths_from_hdr(bil_path):
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hdr_path = os.path.splitext(bil_path)[0] + '.hdr'
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if not os.path.exists(hdr_path):
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return np.array([], dtype=np.float64)
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with open(hdr_path, 'r') as f:
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txt = f.read()
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if 'wavelength' not in txt:
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return np.array([], dtype=np.float64)
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seg = txt.split('wavelength', 1)[1]
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seg = seg[seg.find('{')+1: seg.find('}')]
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vals = [v.strip() for v in seg.split(',') if v.strip()]
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try:
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waves = np.array([float(v) for v in vals], dtype=np.float64)
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except Exception:
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waves = np.array([], dtype=np.float64)
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return waves
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def resample_spectra_matrix(X, src_waves, dst_waves):
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src = np.asarray(src_waves, dtype=np.float64)
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dst = np.asarray(dst_waves, dtype=np.float64)
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X = np.asarray(X, dtype=np.float64)
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if src.size == 0 or dst.size == 0:
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return X
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# 线性插值，越界用端点外推，避免维度缺失
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out = np.empty((X.shape[0], dst.size), dtype=np.float64)
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for i in range(X.shape[0]):
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row = X[i]
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out[i] = np.interp(dst, src, row, left=row[0], right=row[-1])
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return out
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def parse_arguments():
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"""解析命令行参数"""
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parser = argparse.ArgumentParser(description='Microplastic spectral shape classification')
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# 必需参数
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parser.add_argument('--bil_path', required=True, help='Path to input BIL file')
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parser.add_argument('--output_path', required=True, help='Path to output classification result')
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parser.add_argument('--model_path', required=True, help='Path to primary classification model')
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# 可选参数
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# parser.add_argument('--primary_model_type', default='SVM', help='Type of primary model (default: SVM)')
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# parser.add_argument('--primary_process_methods1', default='SS', help='Primary process method 1 (default: SS)')
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# parser.add_argument('--primary_process_methods2', default='None', help='Primary process method 2 (default: None)')
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# parser.add_argument('--secondary_model', default="D:\plastic\plastic\modelsave\HDPELDPE_model\svm.m", help='Path to secondary classification model')
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# parser.add_argument('--secondary_model_type', default='SVM', help='Type of secondary model (default: SVM)')
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# parser.add_argument('--secondary_process_methods1', default='None',
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# help='Secondary process method 1 (default: None)')
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# parser.add_argument('--secondary_process_methods2', default='None',
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# help='Secondary process method 2 (default: None)')
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# parser.add_argument('--secondary_target_classes', nargs='+', type=int, default=[1,2],
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# help='Target classes for secondary classification (space separated)')
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return parser.parse_args()
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# ----------------------------
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# 配置参数：直接在此修改
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# ----------------------------
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# BIL_PATH = r"D:/Data/Test/PET_bottle2.bil"
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# OUTPUT_PATH = r'D:/Data/PET_bottle2_class.bil'
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#
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# PRIMARY_MODEL_PATH = r"D:\plastic\plastic\modelsave\svm.m"
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# PRIMARY_MODEL_TYPE = 'SVM'
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# PRIMARY_PROCESS_METHODS1 = 'SS'
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# PRIMARY_PROCESS_METHODS2 = 'None'
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#
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# SECONDARY_MODEL_PATH = "D:\plastic\plastic\modelsave\HDPELDPE_model\svm.m" # 若不需要二次分类，则保持为 None
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# SECONDARY_MODEL_TYPE = 'SVM'
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# SECONDARY_PROCESS_METHODS1 = 'None'
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# SECONDARY_PROCESS_METHODS2 = 'None'
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# SECONDARY_TARGET_CLASSES = [1, 2]
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def read_hdr_file(bil_path):
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hdr_path = bil_path.replace('.bil', '.hdr')
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with open(hdr_path, 'r') as f:
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header = f.readlines()
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samples, lines = None, None
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for line in header:
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if line.startswith('samples'):
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samples = int(line.split('=')[-1].strip())
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if line.startswith('lines'):
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lines = int(line.split('=')[-1].strip())
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return samples, lines
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def shrink_contours(bil_path, df, shrink_pixels=1):
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"""
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对DataFrame中的所有轮廓进行收缩操作，避免塑料之间的相连
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Args:
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bil_path: BIL文件路径，用于获取图像尺寸
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df: 包含contour列的DataFrame
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shrink_pixels: 收缩的像素数，默认1像素
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Returns:
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更新后的DataFrame，contour列已被收缩
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"""
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samples, lines = read_hdr_file(bil_path)
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# 创建腐蚀核
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kernel = np.ones((2 * shrink_pixels + 1, 2 * shrink_pixels + 1), np.uint8)
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# 创建临时掩膜用于处理
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temp_mask = np.zeros((lines, samples), dtype=np.uint8)
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# 创建DataFrame副本
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df = df.copy()
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# 遍历每一行，更新轮廓
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for idx, row in df.iterrows():
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contour = row['contour']
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if not isinstance(contour, (list, np.ndarray)) or len(contour) < 3:
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continue
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try:
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contour_array = np.array(contour, dtype=np.int32)
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if len(contour_array.shape) == 1:
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continue
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# 清空临时掩膜
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temp_mask.fill(0)
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# 填充轮廓
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cv2.fillPoly(temp_mask, [contour_array], 255)
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# 对掩膜进行腐蚀操作
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eroded_mask = cv2.erode(temp_mask, kernel, iterations=1)
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# 重新提取轮廓
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contours, _ = cv2.findContours(eroded_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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if len(contours) > 0:
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# 选择最大的轮廓（如果有多个）
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largest_contour = max(contours, key=cv2.contourArea)
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# 转换为列表格式，保持与原始格式一致
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if len(largest_contour) >= 3:
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updated_contour = largest_contour.reshape(-1, 2).tolist()
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df.at[idx, 'contour'] = updated_contour
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except Exception as e:
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# 如果处理失败，保留原始轮廓
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continue
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return df
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def save_envi_classification(bil_path, df, savepath):
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samples, lines = read_hdr_file(bil_path)
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classification_result = np.zeros((lines, samples), dtype=np.uint16)
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for _, row in df.iterrows():
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contour = row['contour']
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prediction = int(row['Predictions']) + 1
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contour = np.array(contour, dtype=np.int32)
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# 先将 classification_result 中的 10 和 11 替换为 0
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classification_result[(classification_result == 10)] = 0
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cv2.fillPoly(classification_result, [contour], prediction)
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output_path = savepath
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with open(output_path, 'wb') as f:
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classification_result.tofile(f)
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header_content = f"""ENVI
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description = {{
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Classification Result.}}
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samples = {samples}
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lines = {lines}
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bands = 1
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header offset = 0
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file type = ENVI Standard
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data type = 2
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interleave = bil
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classes = 10
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class = {{ background, ABS, HDPE, LDPE, PA6, PET, PP, PS, PTFE, PVC }}
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single pixel area = 0.000036
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unit = mm2
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byte order = 0
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wavelength units = nm
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"""
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filename, ext = os.path.splitext(savepath)
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# 替换扩展名为 '.hdr'
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header_filename = filename + '.hdr'
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with open(header_filename, 'w') as header_file:
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header_file.write(header_content)
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def change_hdr_file(bil_path, wavelengths=None):
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# 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
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hdr_path = os.path.splitext(bil_path)[0] + '.hdr'
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if not os.path.exists(hdr_path):
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print(f"错误: 找不到对应的HDR文件: {hdr_path}")
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return
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with open(hdr_path, 'r') as file:
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content = file.read()
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if 'wavelength' in content and wavelengths is None:
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print(f"File {os.path.basename(hdr_path)} already contains wavelength information; no changes needed.")
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return
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if wavelengths is None:
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print(f"No wavelengths provided and HDR lacks wavelength; skipping write to avoid wrong bands.")
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return
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needs_newline = not content.endswith('\n')
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wavelength_info = "wavelength = {" + ", ".join(str(float(w)) for w in wavelengths) + "}\n"
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with open(hdr_path, 'a') as file:
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if needs_newline:
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file.write('\n')
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file.write(wavelength_info)
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print(f"Successfully ensured wavelength information in file: {os.path.basename(hdr_path)}")
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def main():
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args = parse_arguments()
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bil_path = args.bil_path
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output_path = args.output_path
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primary_model_path = args.model_path
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primary_model_type = 'SVM'
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primary_process_methods1 = 'SS'
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primary_process_methods2 = "None"
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# secondary_model_path = args.secondary_model
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# secondary_model_type = args.secondary_model_type
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# secondary_process_methods1 = args.secondary_process_methods1
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# secondary_process_methods2 = args.secondary_process_methods2
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# secondary_target_classes = args.secondary_target_classes
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secondary_model_path = "E:\code\plastic\plastic20260224\plastic\plastic\modelsave\HDPELDPE_model\svm.m"
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secondary_model_type = 'SVM'
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secondary_process_methods1 = 'None'
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secondary_process_methods2 = 'None'
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secondary_target_classes = [1, 2]
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# 记录总开始时间
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total_start_time = time.time()
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# 处理BIL文件生成RGB图像
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print("Processing BIL file to generate RGB image...\n")
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rgb_img = process_bil_files(bil_path)
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# 修改hdr
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change_hdr_file(bil_path)
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segmentation_start_time = time.time()
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# 生成掩膜，mask为16位的塑料标签掩膜
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print("Generating mask ...\n")
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mask, filter_mask_original = detect_microplastic_mask_from_array(
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image=rgb_img, # 直接传入cv2.imread的结果
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filter_method='threshold',
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diameter=None,
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flow_threshold=0.4,
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cellprob_threshold=-1,
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detect_filter=True
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)
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# 提取特征
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print("Extracting features from BIL file...\n")
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df = process_images(bil_path, mask)
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# 背景校正（保持现有逻辑）
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print("Applying background correction...\n")
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df_correct = process_images_background(bil_path, mask)
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# 自动识别光谱列范围：假设从第2列开始连续为光谱列，长度=背景校正矩阵列数
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spec_start = 1
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spec_len_src = len(df_correct)
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spec_end_src = spec_start + spec_len_src
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# 归一化（按通道逐列相除）
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df.iloc[:, spec_start:spec_end_src] = df.iloc[:, spec_start:spec_end_src].div(df_correct, axis=1)
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# 读取当前BIL的波长，并重采样到训练用波长
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src_waves = read_wavelengths_from_hdr(bil_path)
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need_resample = (src_waves.size > 0) and (src_waves.size != len(TRAIN_WAVELENGTHS) or not np.allclose(src_waves, TRAIN_WAVELENGTHS, atol=1e-2))
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if need_resample:
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print(f"Resampling spectra: src {src_waves.size} bands -> dst {len(TRAIN_WAVELENGTHS)} bands\n")
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X_src = df.iloc[:, spec_start:spec_end_src].to_numpy(dtype=np.float64)
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X_dst = resample_spectra_matrix(X_src, src_waves, TRAIN_WAVELENGTHS)
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# 用训练维度(168)替换原光谱列；其余形状/统计特征保持不变
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spec_col_names = [f"band_{i+1}" for i in range(len(TRAIN_WAVELENGTHS))]
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df = pd.concat(
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[
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df.iloc[:, :spec_start],
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pd.DataFrame(X_dst, columns=spec_col_names, index=df.index),
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df.iloc[:, spec_end_src:]
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],
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axis=1
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)
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# 更新光谱列的新范围
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spec_end_src = spec_start + len(TRAIN_WAVELENGTHS)
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# 数据清理（保持原有规则）
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print("Cleaning data...\n")
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df = df.dropna()
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df = df[df['contour'].apply(lambda x: len(x) > 1 if isinstance(x, list) else True)]
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df = df[df['area'] >= 500]
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# 列筛选：此时光谱列已与训练对齐为168维，因此区间索引仍可复用
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cols_to_remove = df.columns[np.r_[1:5, 87:110, 166:169, -10:-1]]
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df = df.drop(columns=cols_to_remove)
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# 继续后续流程
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segmentation_time = time.time() - segmentation_start_time
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df = df.iloc[:, :]
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# 预测分类（分类阶段）
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classification_start_time = time.time()
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# 预测分类
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print("Predicting classes...\n")
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loaded_model = load_model(primary_model_path)
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# 断言：数值特征维度应等于训练时的scaler输入维度
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try:
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import joblib
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scaler = joblib.load(os.path.join(os.path.dirname(primary_model_path), 'scaler_params.pkl'))
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numeric_cols = [c for c in df.columns[1:] if np.issubdtype(df[c].dtype, np.number) and c != 'contour']
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if hasattr(scaler, 'mean_') and len(numeric_cols) != scaler.mean_.shape[0]:
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raise ValueError(f"Feature dimension mismatch: current {len(numeric_cols)} != scaler {scaler.mean_.shape[0]}. Check resampling and column selection.")
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except Exception:
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pass
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df_pre = predict_with_model(
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df,
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primary_model_path,
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model_type=primary_model_type,
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ProcessMethods1=primary_process_methods1,
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ProcessMethods2=primary_process_methods2
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)
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# 对HDPE和LDPE进行二次分类
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# 从第一次分类结果中提取SECONDARY_TARGET_CLASSES类别的掩膜轮廓
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target_classes = set(secondary_target_classes or [])
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mask_secondary = df_pre['Predictions'].isin(target_classes)
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if mask_secondary.any():
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# 只有在找到目标类别时才进行背景校正和二次分类
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print(f"Running secondary classification for classes: {sorted(target_classes)}")
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# 图像信息的背景矫正
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df_correct = shape_correct_background(bil_path, mask, filter_mask_original)
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# 创建新的掩膜mask_second，只包含目标类别的轮廓
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mask_second = np.zeros_like(mask, dtype=np.uint16)
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for idx in df_pre[mask_secondary].index:
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contour = df_pre.loc[idx, 'contour']
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if isinstance(contour, list) and len(contour) > 0:
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contour_array = np.array(contour, dtype=np.int32)
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cv2.fillPoly(mask_second, [contour_array], idx + 1) # 使用索引+1作为标签
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# 提取特征
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df_shape = extract_features(df_correct, mask_second)
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# 确保使用第2到13列作为模型输入特征
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if len(df_shape.columns) >= 13:
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df_shape = df_shape.iloc[:, :13]
|
||
|
||
# 二次分类：使用第二个模型预测并更新分类结果
|
||
if secondary_model_path:
|
||
df_secondary = predict_with_model(
|
||
df_shape,
|
||
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 + 1
|
||
else:
|
||
print("Secondary model path not provided; skipping secondary classification.\n")
|
||
else:
|
||
print("No samples from target classes found; 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
|
||
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")
|
||
classification_time = time.time() - classification_start_time
|
||
|
||
df_pre = shrink_contours(bil_path, df_pre, shrink_pixels=1)
|
||
# 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}")
|
||
# 计算总耗时
|
||
total_time = time.time() - total_start_time
|
||
|
||
# 打印耗时统计
|
||
print(f"\n{'=' * 60}")
|
||
print(f"处理完成")
|
||
print(f"{'=' * 60}")
|
||
print(f"分割耗时: {segmentation_time:.2f} 秒")
|
||
print(f"分类耗时: {classification_time:.2f} 秒")
|
||
print(f"总耗时: {total_time:.2f} 秒")
|
||
print(f"{'=' * 60}")
|
||
print(f"结果已保存至: {output_path}")
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main() |