Python使用LUT给图片添加滤镜¶

图片数据在这 1.png是原图 512512的（必须是）然后对这个图进行调色，然后把给定的图片查表替换对应位置的像素就实现了同样的滤镜效果先把所有色素的映射记录下来color_map ，这个过程大概三十多秒，以后用的话可以把color_map先pickle起来以后读取就快了然后直接用numpy批量替换像素就行了，19201080的一张图片大概100ms可以完成

完整代码：如果用ffmpeg的话，这个LUT图需要变成ffmpeg的格式命令是

ffmpeg -i /home/senseport0/Workspace/HighVenueServer/temp/乐活竹山路/1号场地/2020-12-24_12-50-17.mp4 -i /home/senseport0/Workspace/HighVenueServer/static/image/lehuozhushanlu.png -i /home/senseport0/Workspace/HighVenueServer/static/image/logo.png -i /home/senseport0/Workspace/HighVenueServer/temp/乐活竹山路/1 号场地/yujiannan.png -filter_complex '[0][3]haldclut[new_video];[1]scale=w=350:h=94[logo1];[2]scale=w=250:h=64[logo2];[new_video][logo1]overlay=main_w-overlay_w-30:main_h-overlay_h-30[image];[image][logo2]overlay=main_w-overlay_w-30:30' -i /home/senseport0/Workspace/HighVenueServer/static/music/ExtremeSport-极限运动_Version_A-Dr_爱给网_aigei_com.mp3 -vol 40 -af apad  -b 1M -shortest -c:v h264_nvenc -y /home/senseport0/Workspace/HighVenueServer/scripts/output/process_video/2020-12-17_17-06-36_乐活竹山路_1号场地.mp4

import datetime
import os
import pickle

import cv2
import numpy


def contrast_and_brightness(img, alpha, beta):
    """
    调整图片的对比度和亮度
    https://www.codeleading.com/article/5833955990/
    :param img:
    :param alpha: 对比度
    :param beta: 亮度
    :return:
    """
    blank = numpy.zeros(img.shape, img.dtype)
    dst = cv2.addWeighted(img, alpha, blank, 1 - alpha, beta)
    return dst


def getBGR(img, table, i, j):
    # 获取图像颜色
    b, g, r = img[i][j]
    # 计算标准颜色表中颜色的位置坐标
    x = int(g / 4 + int(b / 32) * 63)
    y = int(r / 4 + int((b % 32) / 4) * 63)
    # 返回滤镜颜色表中对应的颜色
    return lj_map[x][y]


class MYLUT:
    def __init__(self, lutpath):
        lut = cv2.imread(lutpath)
        cube64rows = 8
        cube64size = 64
        # cube256rows = 16
        cube256size = 256
        cubescale = int(cube256size / cube64size)  # 4
        reshapelut = numpy.zeros((cube256size, cube256size, cube256size, 3))
        for i in range(cube64size):
            cx = int((i % cube64rows) * cube64size)
            cy = int((i / cube64rows) * cube64size)
            cube64 = lut[cy:cy + cube64size, cx:cx + cube64size]
            _rows, _cols, _ = cube64.shape
            if _rows == 0 or _cols == 0:
                continue
            cube256 = cv2.resize(cube64, (cube256size, cube256size))
            i = i * cubescale
            for k in range(cubescale):
                reshapelut[i + k] = cube256
        self.lut = reshapelut

    def imageInLut(self, src):
        arr = src.copy()
        bs = arr[:, :, 0]
        gs = arr[:, :, 1]
        rs = arr[:, :, 2]
        arr[:, :] = self.lut[bs, gs, rs]  # numpy写的越骚，运行速度越快
        return arr


def getBGR(img, table, i, j):
    # 获取图像颜色
    b, g, r = img[i][j]
    # 计算标准颜色表中颜色的位置坐标
    x = int(g / 4 + int(b / 32) * 64)
    y = int(r / 4 + int((b % 32) / 4) * 64)
    # 返回滤镜颜色表中对应的颜色
    return lj_map[x][y]


if __name__ == '__main__':
    v = cv2.VideoCapture(r"C:\Users\wojia\Desktop\2020-12-24_12-50-17.mp4")
    with open(r"C:\Users\wojia\OneDrive - buaa\1\口红", "rb") as f:
        color_map = pickle.load(f)
    while True:
        ret, frame = v.read()
        if not ret:
            break
        s = datetime.datetime.now()
        dst = numpy.zeros((frame.shape[0], frame.shape[1], 3), dtype=numpy.uint8)

        bs = frame[:, :, 0]
        gs = frame[:, :, 1]
        rs = frame[:, :, 2]


        dst[:,:] = color_map[rs, gs, bs]  # numpy写的越骚，运行速度越快
        print((datetime.datetime.now() - s).total_seconds() * 1000)
        cv2.namedWindow("result", cv2.WINDOW_KEEPRATIO)
        cv2.imshow('result', numpy.hstack((frame, dst)))
        cv2.waitKey(1)
    exit()
    # 读取原始图像
    img = cv2.imread(r"C:\Users\wojia\OneDrive - buaa\1\1.jpg")
    lj_map = cv2.imread(r"C:\Users\wojia\OneDrive - buaa\1\yujiannan.png")
    # color_map = numpy.ones((256, 256, 256, 3))
    # # 获取图像行和列
    rows, cols = img.shape[:2]
    #
    # # 新建目标图像

    with open(r"C:\Users\wojia\OneDrive - buaa\1\口红", "rb") as f:
        color_map = pickle.load(f)
    dst = numpy.zeros((rows, cols, 3), dtype="uint8")
    # for r in range(256):
    #     for g in range(256):
    #         for b in range(256):
    #             x = int(g / 4 + int(b / 32) * 64)
    #             y = int(r / 4 + int((b % 32) / 4) * 64)
    #             color_map[r][g][b] =  lj_map[x][y]
    #     print(r)
    # with open(r"C:\Users\wojia\OneDrive - buaa\1\口红", "wb") as f:
    #     pickle.dump(color_map, f)
    # exit()
    bs = img[:, :, 0]
    gs = img[:, :, 1]
    rs = img[:, :, 2]

    dst = color_map[rs, gs, bs]  # numpy写的越骚，运行速度越快

    # 循环设置滤镜颜色

    # 显示图像

    cv2.namedWindow("result", cv2.WINDOW_KEEPRATIO)
    cv2.imshow('result', numpy.hstack((img, dst)))
    cv2.waitKey(0)
    cv2.destroyAllWindows()

Python使用LUT给图片添加滤镜¶

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