百度图片源码流出~按照颜色搜图片~提取图片主体颜色

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：

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 = Trueexcept:imageio_success = Falseif 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 = 100img = 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 = -1nearest_distance_euc = 99999nearest_index_abs = -1nearest_distance_abs = 99999for 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 = eucnearest_index_euc = iabs = get_abs_distance(label_color, np.asarray(max_count_color))if abs < nearest_distance_abs:nearest_distance_abs = absnearest_index_abs = ireturn nearest_distance_euc, nearest_index_euc, nearest_distance_abs, nearest_index_absdef 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 imageiolabel_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 = 5def 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.shapeimg = 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 resdef 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 imgdef 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 = -1nearest_distance_euc = 99999nearest_index_abs = -1nearest_distance_abs = 99999for 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 = eucnearest_index_euc = iabs = get_abs_distance(label_color, np.asarray(max_count_color))if abs < nearest_distance_abs:nearest_distance_abs = absnearest_index_abs = ireturn nearest_distance_euc, nearest_index_euc, nearest_distance_abs, nearest_index_absdef 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 Trueif 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_diffdiff_count_gt = np.where(diff_count > 10)[0]if len(diff_count_gt) == 0:return Truegray_per = len(diff_count_gt) / input_size**2return gray_per > 0.03if __name__ == '__main__':img_path = r'1.jpg'imageio_success = Falsetry: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 = Trueexcept:imageio_success = Falseif 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)

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