baseFun.py

import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import fundmental
import math


class PointSet(object):
    def __init__(self, point_set: np.ndarray):
        self.set = point_set

    def fund_point_in_set(self, point: np.ndarray):
        """
        寻找set中距离point最近的点
        :param point: 需要寻找的点
        :return:
        """
        min_dist = 1000
        min_point = [0, 0]
        for i in range(len(self.set)):
            temp_dist = math.sqrt((point[0] - self.set[i][0]) ** 2 + (point[1] - self.set[i][1]) ** 2)
            if temp_dist < min_dist:
                min_dist = temp_dist
                min_point = self.set[i]
        return min_dist, min_point

class Rect(object):
    def __init__(self, x:int, y:int, width:int, height:int):
        self.x = x
        self.y = y
        self.width = width
        self.height = height

class Point(object):
    def __init__(self, x:int=0, y:int=0):
        self.x = x
        self.y = y


def img_hist(img: np.ndarray):
    """
    used to generate a histogram
    :param img: gray image
    :return:
    """
    hist = cv2.calcHist(img, [0], None, [256], [0, 256])
    plt.plot(hist)
    plt.xlim([0, 256])
    plt.show()


# def readname(filePath='/home/lthpc/Documents/Programs/WaveletDetectionChange/mrccut_old/small/'):
def readname(filePath=None):
    if filePath is not None:
        name = os.listdir(filePath)
    else:
        name = None
    return name


def two_value(p, ld):
    """
    图像二值化
    :param p: img needed 2 value
    :param ld: 阈值
    :return: P:阈值化后的image
    """
    p[p < ld] = 0
    p[p >= ld] = 1
    return p


def make_ori_template(r_img: int, ri: int):
    template = np.ones((r_img * 2 + 1, r_img * 2 + 1)).astype(np.float32)
    template = cv2.circle(template, (r_img, r_img), ri, (0., 0., 0.), -1, 8)
    return template


def ncc_value(img1, img2):
    """
    计算两个图片的相似性，两图尺寸相同
    :param img1:
    :param img2:
    :return:
    """
    img1 = img1.reshape(img1.size, order='C')  # 将矩阵转换成向量。按行转换成向量，第一个参数就是矩阵元素的个数
    img2 = img2.reshape(img2.size, order='C')
    # np.corrcoef is return 2*2 shape metrix
    ncc = np.corrcoef(img1, img2)[0, 1]
    return ncc


def normalize(img: np.ndarray):
    '''
    图像归一化函数
    :param img: 输入图像
    :return:
    image:归一化后图像
    Max:归一化前图像的最大值
    Min:归一化前图像的最小值
    '''
    Max = img.max()
    Min = img.min()
    mean, std = cv2.meanStdDev(img)
    Max = Max if mean + 3 * std > Max else mean + 3 * std
    Min = Min if mean - 3 * std < Min else mean - 3 * std
    image = (img - img.min()) / (img.max() - img.min())
    image[image > 1] == 1
    image[image < 0] == 0
    image = image.astype(np.float32)
    return image


def dis_p(point1, point2):
    """
    计算两点间距离
    :param point1:
    :param point2:
    :return:
    """
    dis = np.linalg.norm(point1 - point2, ord=None, axis=None, keepdims=False)
    return dis


def roundness(label_img):
    """
    计算圆度
    :type label_img: np.ndarray
    :return:
    """
    contours, hierarchy = cv2.findContours(np.array(label_img, dtype=np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    a = cv2.contourArea(contours[0]) * 4 * math.pi
    b = math.pow(cv2.arcLength(contours[0], True), 2)
    if b == 0:
        return 0
    return a / b


def find_local_peak(img, m, n, m_w, n_w):
    sub_img = img[m:m + m_w, n:n + n_w]
    min_val, max_val, min_l, max_l = cv2.minMaxLoc(sub_img)
    peak_m = m + max_l[1]
    peak_n = n + max_l[0]
    return peak_m, peak_n


def mkdir(path):
    folder = os.path.exists(path)
    if not folder:
        os.makedirs(path)
        print(f"---new foler {path} ---")
    else:
        print(f"---There is this folder!---")


def fund_point_in_set(point: np.ndarray, point_set: np.ndarray):
    """
    寻找set中距离point最近的点
    :param point: 需要寻找的点
    :param point_set: 寻找的点集
    :return:
    """
    min_dist = 1000
    min_point = [0, 0]
    for i in range(len(point_set)):
        temp_dist = math.sqrt((point[0] - point_set[i][0]) ** 2 + (point[1] - point_set[i][1]) ** 2)
        if temp_dist < min_dist:
            min_dist = temp_dist
            min_point = point_set[i]
    return


if __name__ == '__main__':
    # ori_img = cv2.imread('10111_19.jpg', 0)
    # hist = cv2.calcHist(ori_img, [0], None, [256], [0, 256])
    # plt.plot(hist)
    # plt.xlim([0, 256])
    # plt.show()

    template = make_ori_template(4)
    fundmental.draw(template)