根据hsv分量模型，各种颜色范围分布如下：

红色的范围是：[0, 43, 46]~[10,255,255]∪[156, 43, 46]~[180,255,255]。

红色比较特殊，覆盖了多个范围，处理起来增加了不少难度，以下介绍两种方法去获取红色。

方法1：

import cv2
import numpy as np

def extract_red(pic):
    ''''method1：使用inRange方法，拼接mask0,mask1'''
    
    img = cv2.imdecode(np.fromfile(pic, dtype=np.uint8), -1)
    img_hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)
    rows, cols, channels = img.shape
    # 区间1
    lower_red = np.array([0, 43, 46])
    upper_red = np.array([10, 255, 255])
    mask0 = cv2.inRange(img_hsv,lower_red,upper_red)
    # 区间2
    lower_red = np.array([156, 43, 46])
    upper_red = np.array([180, 255, 255])
    mask1 = cv2.inRange(img_hsv,lower_red,upper_red)
    # 拼接两个区间
    mask = mask0 + mask1
    # 保存图片
    cv2.imencode('.png', mask )[1].tofile(pic)

方法2：

from PIL import Image

def rgb2hsv(r, g, b):
    '''rgb转hsv'''
    r, g, b = r/255.0, g/255.0, b/255.0
    mx = max(r, g, b)
    mn = min(r, g, b)
    df = mx-mn
    if mx == mn:
        h = 0
    elif mx == r:
        h = (60 * ((g-b)/df) + 360) % 360
    elif mx == g:
        h = (60 * ((b-r)/df) + 120) % 360
    elif mx == b:
        h = (60 * ((r-g)/df) + 240) % 360
    if mx == 0:
        s = 0
    else:
        s = df/mx
    v = mx
    H = h / 2
    S = s * 255.0
    V = v * 255.0
    return H, S, V


def extract_red(fname):
    '''method2：遍历图片像素，把像素转化成hsv,根据hsv筛选出红色'''
    im = Image.open(fname)
    ## 懒加载模式
    pix = im.load()
    width = im.size[0]
    height = im.size[1]
    for x in range(width):
        for y in range(height):
            r, g, b = pix[x, y]
            h,s,v = rgb2hsv(r,g,b)
            if 0 < h < 10 or 156 < h < 180:
                pix[x, y] = 0,0,0
            else:
                pix[x, y] = 255,255,255
    im.save(fname)