图像增强-点、直方图

def linear_chg(self, img_data):  # 线性变换
        """
        :param img_data
        :return:new_img_data
        """
        print("变换规则为y=kx+b\n默认k=1,b=0,如需调整，请修改linear_chg函数中k，b对应的值")
        k = 1.05
        b = 0
        new_img_data = img_data
        shape = img_data.shape
        for i in range(shape[0]):
            for j in range(shape[1]):
                new_img_data[i, j] = k * img_data[i, j] + b
                # if new_img_data[i, j] >= 256:
                #     new_img_data[i, j] = 255
                # if new_img_data[i, j] < 0:
                #     new_img_data[i, j] = 0
                # 图像显示的时候会自动处理，下面函数中都不再做处理
        return new_img_data

def pow_chg(self, img_data):  # 幂次变换
        '''
        :param img_data:
        :return: a new im object
        '''
        print("变换规则为s=c(r+e)^y\n，c默认值为1，e默认值为0，y默认值为1，如需修改，请在pow_chg函数中对应参数修改")
        c = 1
        e = 0
        y = 1.05
        new_img_data = img_data
        shape = img_data.shape
        for i in range(shape[0]):
            for j in range(shape[1]):
                new_img_data[i, j] = c * pow((img_data[i, j] + e), y)
        return new_img_data

def lg_chg(self, img_data):  # 对数变换
        '''
        :param img_data:
        :return:
        '''
        new_img_data = img_data
        shape = img_data.shape
        print("变换规则为s=clog(r+1)，c默认值为1")
        c = 1
        for i in range(shape[0]):
            for j in range(shape[1]):
                new_img_data[i, j] = c * math.log(img_data[i, j] + 1)
        return new_img_data

def oppo_chg(self, img_data):  # 反相
        L = 256
        new_img_data = img_data
        shape = img_data.shape
        for i in range(shape[0]):
            for j in range(shape[1]):
                new_img_data[i, j] = L - 1 - img_data[i, j]
        return new_img_data

def thre_proce(self, img_data, T=192):  # 阈值处理
        '''
        :param img_data: img_data array
        :param T:
        :return: a new_img_data array
        '''
        new_img_data = img_data
        shape = img_data.shape
        for i in range(shape[0]):
            for j in range(shape[1]):
                if img_data[i, j] >= T:
                    new_img_data[i, j] = 255
                if img_data[i, j] < T:
                    new_img_data[i, j] = 0
        return new_img_data

def img_data_equal(self, img_data):  # 直方图均衡化
        L = 256
        new_img_data = img_data
        num_count = [0] * L
        num_per = [0] * L
        total_per = [0] * 256
        pixels = []
        new = [0] * L
        im_shape = img_data.shape
        for i in range(im_shape[0]):
            for j in range(im_shape[1]):
                pixels.append(img_data[i, j])
        total = len(pixels)
        result = Counter(pixels)
        for i in range(L):
            num_count[i] = result[i]
            num_per[i] = num_count[i] / total
            total_per[i] = total_per[i - 1] + num_per[i]
        for i in range(L):
            new[i] = int((L - 1) * total_per[i] + 0.5)
        for i in range(im_shape[0]):
            for j in range(im_shape[1]):
                new_img_data[i, j] = new[img_data[i, j]]
        return new_img_data