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https://github.com/NanjingForestryUniversity/tobacoo-industry.git
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修改灵敏度
修改灵敏度为32，删去了问题数据
This commit is contained in:
FEIJINTI
parent
b992ec150d
commit
a42fcb3438
@ -42,7 +42,7 @@
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"source": [
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"# 一些参数\n",
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"blk_sz = 8\n",
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"sensitivity = 8\n",
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"sensitivity = 32\n",
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"selected_bands = [127, 201, 202, 294]\n",
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"# [76, 146, 216, 367, 383, 406]\n",
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"file_name, labeled_image_file = r\"F:\\zhouchao\\616\\calibrated1.raw\", \\\n",
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File diff suppressed because one or more lines are too long
@ -26,7 +26,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 49,
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"outputs": [],
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"source": [
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"import os\n",
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@ -48,12 +48,12 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 50,
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"outputs": [],
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"source": [
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"# some parameters\n",
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"new_spectra_file = r\"F:\\zhouchao\\618\\bomo\\calibrated7.raw\"\n",
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"new_label_file = r\"F:\\zhouchao\\618\\bomo\\picture\\label7.bmp\"\n",
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"new_spectra_file = r\"F:\\zhouchao\\615\\calibrated0.raw\"\n",
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"new_label_file = r\"F:\\zhouchao\\615\\label0.bmp\"\n",
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"\n",
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"target_class = 0\n",
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"target_class_left, target_class_right = 5, 4\n",
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@ -63,7 +63,7 @@
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"split_line = 500\n",
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"\n",
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"\n",
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"blk_sz, sensitivity = 8, 8\n",
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"blk_sz, sensitivity = 8, 32\n",
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"selected_bands = [127, 201, 202, 294]\n",
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"tree_num = 185\n",
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"\n",
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@ -72,11 +72,11 @@
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"color_dict = {(0, 0, 255): 1, (255, 255, 255): 0, (0, 255, 0): 2, (255, 0, 0): 1, (0, 255, 255): 4,\n",
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" (255, 255, 0): 5, (255, 0, 255): 6}\n",
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"\n",
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"new_dataset_file = f'./dataset/data_{blk_sz}x{blk_sz}_c{len(selected_bands)}_sen{sensitivity}_7.p'\n",
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"dataset_file = f'./dataset/data_{blk_sz}x{blk_sz}_c{len(selected_bands)}_sen{sensitivity}_6.p'\n",
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"new_dataset_file = f'./dataset/data_{blk_sz}x{blk_sz}_c{len(selected_bands)}_sen{sensitivity}_4.p'\n",
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"dataset_file = f'./dataset/data_{blk_sz}x{blk_sz}_c{len(selected_bands)}_sen{sensitivity}_3.p'\n",
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"\n",
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"model_file = f'./models/rf_{blk_sz}x{blk_sz}_c{len(selected_bands)}_{tree_num}_6.model'\n",
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"selected_bands = None"
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"model_file = f'./models/rf_{blk_sz}x{blk_sz}_c{len(selected_bands)}_{tree_num}_sen{sensitivity}_3.model'\n",
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"# selected_bands = None"
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],
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"metadata": {
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"collapsed": false,
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@ -87,10 +87,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 51,
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"outputs": [],
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"source": [
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"# if len(new_spectra_files) == 1:\n",
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"data = read_raw_file(new_spectra_file, selected_bands)"
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],
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"metadata": {
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@ -115,7 +114,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 52,
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"outputs": [],
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"source": [
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"x_list, y_list = [], []\n",
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@ -144,7 +143,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 53,
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"outputs": [],
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"source": [
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"if (new_label_file is None) and (target_class != 1):\n",
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@ -176,7 +175,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 54,
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"outputs": [
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{
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"name": "stdout",
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@ -201,13 +200,13 @@
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 55,
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"148 148\n"
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"301 301\n"
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]
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}
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],
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@ -235,7 +234,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 56,
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"outputs": [],
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"source": [
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"with open(dataset_file, 'rb') as f:\n",
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@ -266,7 +265,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 56,
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"outputs": [],
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"source": [],
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"metadata": {
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File diff suppressed because one or more lines are too long
213
06_other_models.ipynb
Normal file
213
06_other_models.ipynb
Normal file
File diff suppressed because one or more lines are too long
54
models.py
54
models.py
@ -10,11 +10,6 @@ from sklearn.decomposition import PCA
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from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
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# def feature(x):
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# x = x.reshape((x.shape[0], -1, x.shape[-1]))
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# x = np.mean(x, axis=1)
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# return x
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def feature(x):
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x = x.reshape((x.shape[0], -1))
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return x
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@ -69,10 +64,13 @@ def evaluation_and_report(model, test_x, test_y):
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def train_pca_rf(train_x, train_y, test_x, test_y, n_comp,
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tree_num, save_path=None):
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rfc = RandomForestClassifier(n_estimators=tree_num, random_state=42, class_weight={0: 100, 1: 100})
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pca = PCA(n_components=0.95)
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rfc = rfc.fit(train_x, train_y)
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pca = PCA(n_components=n_comp)
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pca = pca.fit(train_x)
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pca_train_x = pca.transform(train_x)
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rfc = rfc.fit(pca_train_x, train_y)
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t1 = time.time()
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y_pred = rfc.predict(test_x)
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pca_test_x = pca.transform(test_x)
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y_pred = rfc.predict(pca_test_x)
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y_pred_binary = np.ones_like(y_pred)
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y_pred_binary[(y_pred == 0) | (y_pred == 1)] = 0
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y_pred_binary[(y_pred == 2) | (y_pred == 3) | (y_pred == 4)] = 2
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@ -80,8 +78,8 @@ def train_pca_rf(train_x, train_y, test_x, test_y, n_comp,
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test_y_binary[(test_y == 0) | (test_y == 1)] = 0
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test_y_binary[(test_y == 2) | (test_y == 3) | (test_y == 4)] = 2
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print("预测时间：", time.time() - t1)
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print("RFC训练模型评分：" + str(accuracy_score(train_y, rfc.predict(train_x))))
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print("RFC待测模型评分：" + str(accuracy_score(test_y, rfc.predict(test_x))))
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print("RFC训练模型评分：" + str(accuracy_score(train_y, rfc.predict(pca_train_x))))
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print("RFC待测模型评分：" + str(accuracy_score(test_y, rfc.predict(pca_test_x))))
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print('RFC预测结果：' + str(y_pred))
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print('---------------------------------------------------------------------------------------------------')
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print('RFC分类报告：\n' + str(classification_report(test_y, y_pred))) # 生成一个小报告呀
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@ -140,3 +138,39 @@ class SpecDetector(object):
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mask[row, col] = r
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mask = mask.repeat(self.blk_sz, axis=0).repeat(self.blk_sz, axis=1)
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return mask
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class PcaSpecDetector(object):
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def __init__(self, model_path, pca_path, blk_sz=8, channel_num=4):
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self.blk_sz, self.channel_num = blk_sz, channel_num
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if os.path.exists(model_path):
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with open(model_path, "rb") as model_file:
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self.clf = pickle.load(model_file)
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else:
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raise FileNotFoundError("Model File not found")
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if os.path.exists(pca_path):
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with open(pca_path, "rb") as pca_file:
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self.pca = pickle.load(pca_file)
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else:
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raise FileNotFoundError("Pca File not found")
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def predict(self, data):
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blocks = split_x(data, blk_sz=self.blk_sz)
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blocks = np.array(blocks)
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features = feature(np.array(blocks))
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y_pred = self.clf.predict(features)
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y_pred_binary = np.ones_like(y_pred)
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# classes merge
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y_pred_binary[(y_pred == 0) | (y_pred == 1) | (y_pred == 3)] = 0
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# transform to mask
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mask = self.mask_transform(y_pred_binary, (1024, 600))
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return mask
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def mask_transform(self, result, dst_size):
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mask_size = 600 // self.blk_sz, 1024 // self.blk_sz
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mask = np.zeros(mask_size, dtype=np.uint8)
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for idx, r in enumerate(result):
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row, col = idx // mask_size[1], idx % mask_size[1]
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mask[row, col] = r
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mask = mask.repeat(self.blk_sz, axis=0).repeat(self.blk_sz, axis=1)
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return mask
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6
utils.py
6
utils.py
@ -119,9 +119,13 @@ def visualization_evaluation(detector, data_path, selected_bands=None):
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rgb_img = np.asarray(img[..., [372, 241, 169]] * 255, dtype=np.uint8)
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else:
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rgb_img = np.asarray(img[..., [0, 1, 2]] * 255, dtype=np.uint8)
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fig, axs = plt.subplots(1, 2)
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mask_color = np.zeros_like(rgb_img)
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mask_color[mask > 0] = (0, 0 , 255)
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combine = cv2.addWeighted(rgb_img, 1, mask_color, 0.5, 0)
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fig, axs = plt.subplots(1, 3)
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axs[0].imshow(rgb_img)
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axs[1].imshow(mask)
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axs[2].imshow(combine)
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fig.suptitle(f"time spent {time_spent * 1000:.2f} ms" + f"\n{image_path}")
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plt.savefig(f"./dataset/{idx}.png", dpi=300)
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plt.show()
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