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167 lines
7.1 KiB
Python
167 lines
7.1 KiB
Python
# -*- codeing = utf-8 -*-
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# Time : 2022/7/18 14:03
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# @Auther : zhouchao
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# @File: models.py
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# @Software:PyCharm、
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import datetime
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import pickle
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import cv2
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import numpy as np
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import scipy.io
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import tqdm
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from sklearn.tree import DecisionTreeClassifier
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from sklearn.metrics import classification_report
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from sklearn.model_selection import train_test_split
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from utils import lab_scatter, read_labeled_img
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from tqdm import tqdm
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from elm import ELM
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class Detector(object):
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def __int__(self, *args, **kwargs):
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raise NotImplementedError
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def predict(self, *args, **kwargs):
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raise NotImplementedError
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def load(self, *args, **kwargs):
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raise NotImplementedError
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def save(self, *args, **kwargs):
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raise NotImplementedError
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def fit(self, *args, **kwargs):
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raise NotImplementedError
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class AnonymousColorDetector(Detector):
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def __init__(self, file_path: str = None):
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self.model = None
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self.model_type = 'None'
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if file_path is not None:
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self.load(file_path)
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def fit(self, x: np.ndarray, world_boundary: np.ndarray = None, threshold: float = None,
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is_generate_negative: bool = True, y: np.ndarray = None, model_selection='elm',
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negative_sample_size: int = 1000, train_size: float = 0.8, is_save_dataset=False, **kwargs):
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"""
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拟合到指定的样本分布情况下，根据x进行分布的变化。
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:param x: ndarray类型的正样本数据，给出的正样本形状为 n x feature_num
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:param world_boundary: 整个世界的边界，边界形状为 feature_num个下限, feature_num个上限
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:param threshold: 与正样本之间的距离阈值大于多少则不认为是指定的样本类别
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:param is_generate_negative: 是否生成负样本
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:param y: 给出x对应的样本y
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:param model_selection: 模型的选择, in ['elm', 'decision tree']
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:param negative_sample_size: 负样本的数量
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:param train_size: 训练集的比例, float
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:param is_save_dataset: 是否保存数据集
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:param kwargs: 与模型相对应的参数
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:return:
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"""
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if model_selection == 'elm':
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node_num = kwargs.get('node_num', 10)
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self.model = ELM(input_size=x.shape[1], node_num=node_num, output_num=2, **kwargs)
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elif model_selection == 'dt':
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self.model = DecisionTreeClassifier(**kwargs)
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else:
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raise ValueError("你看看我要的是啥")
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self.model_type = model_selection
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if is_generate_negative:
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negative_samples = self.generate_negative_samples(x, world_boundary, threshold,
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sample_size=negative_sample_size)
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data_x, data_y = np.concatenate([x, negative_samples], axis=0), \
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np.concatenate([np.ones(x.shape[0], dtype=int),
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np.zeros(negative_samples.shape[0], dtype=int)], axis=0)
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else:
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data_x, data_y = x, y
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if is_save_dataset:
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path = datetime.datetime.now().strftime("dataset_%Y-%m-%d_%H-%M.mat")
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scipy.io.savemat(path, {'x': data_x, 'y': data_y})
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x_train, x_val, y_train, y_val = train_test_split(data_x, data_y, train_size=train_size, shuffle=True,
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stratify=data_y)
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self.model.fit(x_train, y_train)
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y_predict = self.model.predict(x_val)
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print(classification_report(y_true=y_val, y_pred=y_predict))
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def predict(self, x):
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"""
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输入rgb彩色图像
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:param x: rgb彩色图像,np.ndarray
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:return:
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"""
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w, h = x.shape[1], x.shape[0]
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x = cv2.cvtColor(x, cv2.COLOR_RGB2LAB)
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result = self.model.predict(x.reshape(w * h, -1))
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return result.reshape(h, w)
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@staticmethod
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def generate_negative_samples(x: np.ndarray, world_boundary: np.ndarray, threshold: float, sample_size: int):
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"""
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根据正样本和世界边界生成负样本
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:param x: ndarray类型的正样本数据，给出的正样本形状为 n x feature_num
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:param world_boundary: 整个世界的边界，边界形状为 feature_num个下限, feature_num个上限, array like
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:param threshold: 与正样本x之间的距离限制
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:return: 负样本形状为：(sample_size, feature_num)
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"""
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feature_num = x.shape[1]
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negative_samples = np.zeros((sample_size, feature_num), dtype=x.dtype)
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generated_sample_num = 0
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bar = tqdm(total=sample_size, ncols=100)
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while generated_sample_num <= sample_size:
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generated_data = np.random.uniform(world_boundary[:feature_num], world_boundary[feature_num:],
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size=(sample_size, feature_num))
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for sample_idx in range(generated_data.shape[0]):
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sample = generated_data[sample_idx, :]
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in_threshold = np.any(np.sum(np.power(sample - x, 2), axis=1) < threshold)
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if not in_threshold:
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negative_samples[sample_idx, :] = sample
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generated_sample_num += 1
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bar.update()
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if generated_sample_num >= sample_size:
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break
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bar.close()
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return negative_samples
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def save(self):
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path = datetime.datetime.now().strftime(f"{self.model_type}_%Y-%m-%d_%H-%M.model")
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with open(path, 'wb') as f:
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pickle.dump((self.model_type, self.model), f)
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def load(self, file_path):
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with open(file_path, 'rb') as model_file:
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data = pickle.load(model_file)
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self.model_type, self.model = data
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def visualize(self, world_boundary: np.ndarray, sample_size: int, ground_truth=None,
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**kwargs):
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feature_num = world_boundary.shape[0] // 2
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x = np.random.uniform(world_boundary[:feature_num], world_boundary[feature_num:],
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size=(sample_size, feature_num))
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pred_y = self.model.predict(x)
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draw_dataset = {'Inside': x[pred_y == 1, :], 'Outside': x[pred_y == 0, :]}
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if ground_truth is not None:
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draw_dataset.update({'Given': ground_truth})
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lab_scatter(draw_dataset, is_3d=True, is_ps_color_space=False, **kwargs)
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if __name__ == '__main__':
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data_dir = "data/dataset"
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color_dict = {(0, 0, 255): "yangeng"}
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dataset = read_labeled_img(data_dir, color_dict=color_dict, is_ps_color_space=False)
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ground_truth = dataset['yangeng']
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detector = AnonymousColorDetector(file_path='models/dt_2022-07-19_14-38.model')
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# x = np.array([[10, 30, 20], [10, 35, 25], [10, 35, 36]])
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world_boundary = np.array([0, 0, 0, 255, 255, 255])
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# detector.fit(x, world_boundary, threshold=5, negative_sample_size=2000)
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detector.visualize(world_boundary, sample_size=50000, class_max_num=5000, ground_truth=ground_truth)
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data = scipy.io.loadmat('data/dataset_2022-07-19_11-35.mat')
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x, y = data['x'], data['y']
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dataset = {'inside': x[y.ravel() == 1, :], "outside": x[y.ravel() == 0, :]}
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lab_scatter(dataset, class_max_num=5000, is_3d=True, is_ps_color_space=False)
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