百度图片源码流出~按照颜色搜图片~提取图片主体颜色

桃扇骨 2023-09-29 18:08 11阅读 0赞

百度图片中有一个按照颜色搜图片的功能，其核心算法是提取图片主体颜色法，本文将使用python实现提取图片主体颜色算法。

百度将图片主体颜色归类到如下11中颜色：

![9179736e5d054905b9fb58267bc57fcf.png][]

颜色代码：

label_colors = [
            [50, 50, 50],
            [250, 250, 250],
            [215, 68, 186],
            [0, 101, 254],
            [222, 32, 32],
            [254, 191, 0],
            [137, 43, 207],
            [89, 167, 37],
            [6, 183, 200],
            [254, 108, 0],
            [115, 52, 19],
        ]

因为cv2和PIL在读取gif图片时候会出现问题，所以我们使用imageio读取gif：

try:
            tmp = imageio.mimread(img_path)
            if tmp is not None:
                imt = np.array(tmp)
                imt = imt[0]
                img = imt[:, :, 0:3]
                img = Image.fromarray(img)
                imageio_success = True
        except:
            imageio_success = False
        if not imageio_success:
            img = Image.open(img_path)

可以设置成截取图片中心部分：

input_size = 100
    crop_size = 100
    
    img = img[int((input_size - crop_size) / 2):int((input_size + crop_size) / 2), int((input_size - crop_size) / 2):int((input_size + crop_size) / 2), :]

颜色值量化，将256种颜色量化到8：

def quantization(img):
        img = np.right_shift(img, quantization_factor)
        img = np.left_shift(img, quantization_factor)
        return img

提取主体颜色并排序：

def sort_color(img):
        img_str = np.asarray(img, np.str)
        img_str = img_str.reshape((-1, 3))
        r_g_b = np.char.array(img_str[..., 0]) + '-' + np.char.array(img_str[..., 1]) + '-' + np.char.array(img_str[..., 2])
        r_g_b = r_g_b.tolist()
        r_g_b_count = Counter(r_g_b)
        r_g_b_count = sorted(r_g_b_count.items(), key=lambda x: x[1], reverse=True)
        max_count_color = r_g_b_count[0][0].split('-')
        max_count_color = np.asarray(max_count_color, np.int)
        return max_count_color, r_g_b_count[0][1]

从11种颜色中选取图片代表颜色：

def get_nearest_color(max_count_color):
        nearest_index_euc = -1
        nearest_distance_euc = 99999
        nearest_index_abs = -1
        nearest_distance_abs = 99999
        for i, label_color in enumerate(label_colors):
            euc = get_euc_distance(label_color, np.asarray(max_count_color))
            if euc < nearest_distance_euc:
                nearest_distance_euc = euc
                nearest_index_euc = i
            abs = get_abs_distance(label_color, np.asarray(max_count_color))
            if abs < nearest_distance_abs:
                nearest_distance_abs = abs
                nearest_index_abs = i
        return nearest_distance_euc, nearest_index_euc, nearest_distance_abs, nearest_index_abs
    
    def get_euc_distance(color1, color2):
        return np.linalg.norm(color1 - color2)
    
    
    def get_abs_distance(color1, color2):
        return np.sum(np.abs(color1 - color2))

**返回值说明：**

max\_count\_color 图片主体颜色  
max\_count\_color\_ratio 主题颜色占图片面积的比例  
nearest\_distance\_euc 使用欧式距离，计算图片代表颜色，返回具体距离数值  
nearest\_index\_euc 使用欧式距离计算出的图片代表颜色  
nearest\_distance\_abs 使用L1距离，计算图片代表颜色，返回具体距离数值  
nearest\_index\_abs 使用L1距离计算出的图片代表颜色

**全部代码：**

# coding=utf-8
    # ================================================================
    #
    #   File name   : color_service.py
    #   Author      : Faye
    #   E-mail      : xiansheng14@sina.com
    #   Created date: 2022/12/10 22:45 
    #   Description :
    #
    # ================================================================
    import numpy as np
    from collections import Counter
    from PIL import Image
    import imageio
    
    
    label_colors = [
            [50, 50, 50],
            [250, 250, 250],
            [215, 68, 186],
            [0, 101, 254],
            [222, 32, 32],
            [254, 191, 0],
            [137, 43, 207],
            [89, 167, 37],
            [6, 183, 200],
            [254, 108, 0],
            [115, 52, 19],
        ]
    
    input_size = 100
    crop_size = 100
    quantization_factor = 5
    
    def get_img_main_color(img):
        """
        提取图片主体颜色
        :param img: PIL格式的图片
        :return:
            max_count_color         图片主体颜色
            max_count_color_ratio   主题颜色占图片面积的比例
            nearest_distance_euc    使用欧式距离，计算图片代表颜色，返回具体距离数值
            nearest_index_euc       使用欧式距离计算出的图片代表颜色
            nearest_distance_abs    使用L1距离，计算图片代表颜色，返回具体距离数值
            nearest_index_abs       使用L1距离计算出的图片代表颜色
        """
        img = img.resize((input_size, input_size), Image.ANTIALIAS)
        img = np.asarray(img)
        if len(img.shape) == 2:
            img = np.repeat(img[..., np.newaxis], 3, axis=-1)
        if img.shape[-1] == 4:
            img = img[..., 0:3]
        img = img[int((input_size - crop_size) / 2):int((input_size + crop_size) / 2), int((input_size - crop_size) / 2):int((input_size + crop_size) / 2), :]
        # is_gray = check_gray(img)
    
        h, w, _ = img.shape
        img = quantization(img)
        max_count_color, max_count_color_ratio = sort_color(img)
        nearest_color = get_nearest_color(max_count_color)
        res = []
        res.append(max_count_color)
        res.append(max_count_color_ratio)
        for n in nearest_color:
            res.append(n)
        return res
    
    
    def quantization(img):
        """
        颜色值量化
        :param img:
        :return:
        """
        img = np.right_shift(img, quantization_factor)
        img = np.left_shift(img, quantization_factor)
        return img
    
    
    def sort_color(img):
        """
        提取主体颜色并排序
        :param img:
        :return:
        """
        img_str = np.asarray(img, np.str)
        img_str = img_str.reshape((-1, 3))
        r_g_b = np.char.array(img_str[..., 0]) + '-' + np.char.array(img_str[..., 1]) + '-' + np.char.array(img_str[..., 2])
        r_g_b = r_g_b.tolist()
        r_g_b_count = Counter(r_g_b)
        r_g_b_count = sorted(r_g_b_count.items(), key=lambda x: x[1], reverse=True)
        max_count_color = r_g_b_count[0][0].split('-')
        max_count_color = np.asarray(max_count_color, np.int)
        return max_count_color, r_g_b_count[0][1]
    
    
    def get_nearest_color(max_count_color):
        """
        从11种颜色中选取图片代表颜色
        :param max_count_color:
        :return:
        """
        nearest_index_euc = -1
        nearest_distance_euc = 99999
        nearest_index_abs = -1
        nearest_distance_abs = 99999
        for i, label_color in enumerate(label_colors):
            euc = get_euc_distance(label_color, np.asarray(max_count_color))
            if euc < nearest_distance_euc:
                nearest_distance_euc = euc
                nearest_index_euc = i
            abs = get_abs_distance(label_color, np.asarray(max_count_color))
            if abs < nearest_distance_abs:
                nearest_distance_abs = abs
                nearest_index_abs = i
        return nearest_distance_euc, nearest_index_euc, nearest_distance_abs, nearest_index_abs
    
    
    def get_euc_distance(color1, color2):
        return np.linalg.norm(color1 - color2)
    
    
    def get_abs_distance(color1, color2):
        return np.sum(np.abs(color1 - color2))
    
    
    def check_gray(img):
        if len(img.shape) == 2:
            return True
        if img.shape[-1] == 4:
            img = img[..., 0:3]
        avg = np.mean(img, axis=-1)
        r_diff = np.abs(img[..., 0] - avg)
        g_diff = np.abs(img[..., 1] - avg)
        b_diff = np.abs(img[..., 2] - avg)
        diff_count = r_diff + g_diff + b_diff
        diff_count_gt = np.where(diff_count > 10)[0]
        if len(diff_count_gt) == 0:
            return True
        gray_per = len(diff_count_gt) / input_size**2
        return gray_per > 0.03
    
    
    if __name__ == '__main__':
        img_path = r'1.jpg'
        imageio_success = False
        try:
            tmp = imageio.mimread(img_path)
            if tmp is not None:
                imt = np.array(tmp)
                imt = imt[0]
                img = imt[:, :, 0:3]
                img = Image.fromarray(img)
                imageio_success = True
        except:
            imageio_success = False
        if not imageio_success:
            img = Image.open(img_path)
    
        max_count_color, max_count_color_ratio, nearest_distance_euc, nearest_index_euc, nearest_distance_abs, nearest_index_abs = get_img_main_color(img)
        print(max_count_color, max_count_color_ratio, nearest_distance_euc, nearest_index_euc, nearest_distance_abs, nearest_index_abs)

[9179736e5d054905b9fb58267bc57fcf.png]: https://img-blog.csdnimg.cn/9179736e5d054905b9fb58267bc57fcf.png