python tif转jpg

　　在同级目录完成tif和jpg的批量转换

import os
import cv2
import numpy as np
from osgeo import gdal

#数据格式转化
def normalization(data):
    _range = np.max(data) - np.min(data)
    return (data - np.min(data)) / _range
def imgto8bit(img):
    img_nrm = normalization(img)
    img_8 = np.uint8(255 * img_nrm)
    return img_8


def tif_jpg(rasterfile):
    in_ds = gdal.Open(rasterfile)  # 打开样本文件
    xsize = in_ds.RasterXSize  # 获取行列数
    ysize = in_ds.RasterYSize
    bands = in_ds.RasterCount
    B_band = in_ds.GetRasterBand(1)
    B= B_band.ReadAsArray(0, 0, xsize, ysize).astype(np.int16)
    G_band = in_ds.GetRasterBand(2)
    G = G_band.ReadAsArray(0, 0, xsize, ysize).astype(np.int16)
    R_band = in_ds.GetRasterBand(3)
    R = R_band.ReadAsArray(0, 0, xsize, ysize).astype(np.int16)
    R1 = imgto8bit(R)
    G1 = imgto8bit(G)
    B1 = imgto8bit(B)
    data2= cv2.merge([R1,G1,B1])
    return data2
if __name__ == '__main__':
    path=r"F:\algorithm\算法练习\拼接与镶嵌\test2_next"
    classs = os.listdir(path)
    for idx, folder in enumerate(classs):
        if folder.endswith('tif') or folder.endswith('tiff') :
            ori_image = os.path.join(path, folder)
            print(ori_image)
            if folder.endswith('tiff'):
                result_name = os.path.basename(ori_image)[:-5]
            else:
                result_name = os.path.basename(ori_image)[:-4]
            # print(result_name)
            a = os.path.dirname(ori_image)
            out = a + "\\" + result_name + ".jpg"
            img=tif_jpg(ori_image)
            cv2.imencode('.jpg', img)[1].tofile(out)

 

#法二
def tif_jpg(rasterfile):
    in_ds = gdal.Open(rasterfile)  # 打开样本文件
    xsize = in_ds.RasterXSize  # 获取行列数
    ysize = in_ds.RasterYSize
    geotransform = in_ds.GetGeoTransform()
    bands = in_ds.RasterCount

    B_band = in_ds.GetRasterBand(1)
    B = B_band.ReadAsArray(0, 0, xsize, ysize)
    G_band = in_ds.GetRasterBand(2)
    G = G_band.ReadAsArray(0, 0, xsize, ysize)
    R_band = in_ds.GetRasterBand(3)
    R = R_band.ReadAsArray(0, 0, xsize, ysize)

    R1 = ((R - np.min(R)) / (np.max(R)) - np.min(R)) * 256

    R1 = cv2.equalizeHist(R1.astype(np.uint8))
    G1 = ((G - np.min(G)) / (np.max(G)) - np.min(G)) * 256
    G1 = cv2.equalizeHist(G1.astype(np.uint8))
    B1 = ((B - np.min(B)) / (np.max(B)) - np.min(B)) * 256
    B1 = cv2.equalizeHist(B1.astype(np.uint8))
    data2 = cv2.merge([R1, G1, B1])

    return data2, geotransform