NanjingForestryUniversity/supermachine-wood
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调了一个端木说gohome的还不错的参数

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duanmu 2023-03-18 15:19:40 +08:00
parent 99a72c86ac
commit f79393100b
2 changed files with 11 additions and 11 deletions
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20
classifer.py
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@ -31,7 +31,7 @@ sys.path.append(os.getcwd())
from root_dir import ROOT_DIR from root_dir import ROOT_DIR
import utils import utils
FEATURE_INDEX = [0,1,2,4] FEATURE_INDEX = [0,1,2,3,4,5]
delete_columns = 10 # 已弃用 delete_columns = 10 # 已弃用
num_bins = 10 num_bins = 10
@ -60,10 +60,10 @@ class WoodClass(object):
self._single_pick = single_pick_mode self._single_pick = single_pick_mode
self.set_purity(self.pur) self.set_purity(self.pur)
self.change_pick_mode(single_pick_mode) self.change_pick_mode(single_pick_mode)
# self.model = LogisticRegression(C=1e5) self.model = LogisticRegression(C=1e5)
self.left_correct = left_correct self.left_correct = left_correct
# self.model = KNeighborsClassifier() # self.model = KNeighborsClassifier()
self.model = DecisionTreeClassifier() # self.model = DecisionTreeClassifier()
else: else:
self.load(load_from) self.load(load_from)
self.isCorrect = False self.isCorrect = False
@ -113,7 +113,7 @@ class WoodClass(object):
:return: :return:
""" """
# 训练数据文件位置 # 训练数据文件位置
result = self.get_train_data(data_path, plot_2d=False) result = self.get_train_data(data_path, plot_2d=True)
if result is False: if result is False:
return 0 return 0
x, y = result x, y = result
@ -122,7 +122,7 @@ class WoodClass(object):
model_name = self.save(file_name) model_name = self.save(file_name)
return model_name return model_name
def fit(self, x, y, test_size=0.7): def fit(self, x, y, test_size=0.3):
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=test_size, random_state=0) x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=test_size, random_state=0)
self.model.fit(x_train, y_train) self.model.fit(x_train, y_train)
y_pred = self.model.predict(x_test) y_pred = self.model.predict(x_test)
@ -304,14 +304,14 @@ class WoodClass(object):
x = cv2.cvtColor(x, cv2.COLOR_BGR2LAB) x = cv2.cvtColor(x, cv2.COLOR_BGR2LAB)
x = np.concatenate((x, x_hsv), axis=2) x = np.concatenate((x, x_hsv), axis=2)
x = np.reshape(x, (x.shape[0] * x.shape[1], x.shape[2])) x = np.reshape(x, (x.shape[0] * x.shape[1], x.shape[2]))
x = x[x[:, 0] > 30]
# x = x[np.argsort(x[:, 0])] # x = x[np.argsort(x[:, 0])]
# x = x[-self.k:, :] # x = x[-self.k:, :]
hist, bins = np.histogram(x[:, 0], bins=num_bins) hist, bins = np.histogram(x[:, 0], bins=num_bins)
hist = hist[1:] # hist = hist[1:]
bins = bins[1:] # bins = bins[1:]
sorted_indices = np.argsort(hist) sorted_indices = np.argsort(hist)
hist_number = sorted_indices[-1] hist_number = sorted_indices[-1]
second_hist_number = sorted_indices[-2] second_hist_number = sorted_indices[-2]
@ -440,8 +440,8 @@ class WoodClass(object):
:param standard_color: 标准色彩, 默认为白色 :param standard_color: 标准色彩, 默认为白色
:return: 校正后的图片 shape = (n_rows, n_cols - cut_col_num, n_channels) :return: 校正后的图片 shape = (n_rows, n_cols - cut_col_num, n_channels)
""" """
if self.left_correct:
img = img[:, 20:, :] # 图片黑边需要去除 img = img[:, 20:, :] # 图片黑边需要去除
if self.left_correct:
# 按照correct_col_num列数量取出最左侧校正板区域成像结果 # 按照correct_col_num列数量取出最左侧校正板区域成像结果
correct_img = img[:, :correct_col_num, :] correct_img = img[:, :correct_col_num, :]
# 校正区域进行均值化 # 校正区域进行均值化
@ -468,7 +468,7 @@ if __name__ == '__main__':
settings.model_path = str(ROOT_DIR / 'models' / wood.fit_pictures(data_path=data_path)) settings.model_path = str(ROOT_DIR / 'models' / wood.fit_pictures(data_path=data_path))
# 测试单张图片的预测，predict_mode=True表示导入本地的model, False为现场训练的 # 测试单张图片的预测，predict_mode=True表示导入本地的model, False为现场训练的
pic = cv2.imread(r"data/316/dark/rgb70.png") pic = cv2.imread(r"data/318/dark/rgb89.png")
start_time = time.time() start_time = time.time()
# for i in range(100): # for i in range(100):
wood_color = wood.predict(pic) wood_color = wood.predict(pic)

2
hist.py
View File

@ -13,7 +13,7 @@ ratio = np.sqrt(5000 / (w * h))
ww, hh = int(ratio * w), int(ratio * h) ww, hh = int(ratio * w), int(ratio * h)
img = cv2.resize(img, (hh, ww)) img = cv2.resize(img, (hh, ww))
x = img.reshape(img.shape[0]*img.shape[1], img.shape[2]) x = img.reshape(img.shape[0]*img.shape[1], img.shape[2])
x = x[x[:,0]>20]
hist, bins = np.histogram(x[:, 0], bins=10) hist, bins = np.histogram(x[:, 0], bins=10)
hist = hist[1:] hist = hist[1:]
bins = bins[1:] bins = bins[1:]