[人工智能]Python根据Json文件切割多光谱图，并实现不同通道融合

不多说辽，最近太忙了代码备注应该够清晰。。。

代码如下

import json
from typing import Tuple
import cv2
# from utils import get_mask_bounding, json_to_dict
import numpy as np
from os.path import join, basename
from os import listdir, makedirs

ori_dir = 'C://Users/27651\Desktop\crops_canopy\FirstStage\data\TIF'
tgt_dir = 'C://Users/27651\Desktop\crops_canopy\FirstStage\data\canopy_result_no_mask'

mask_flag = True

def get_mask_bounding(mask: np.ndarray):
    x_min = np.min(mask[:, 0])
    x_max = np.max(mask[:, 0])
    y_min = np.min(mask[:, 1])
    y_max = np.max(mask[:, 1])
    return np.array([[x_min, y_min, x_max, y_max]])


def json_to_dict(json_path):
    with open(json_path) as o:
        content = o.read()
    d = json.loads(content)
    return d

##通道分离融合函数，输入的路径为data\canopy_result_no_mask\0_20210326，即去掉图片的后缀
def afterCrop(crop_img_path):
    img = cv2.imread(crop_img_path+'.tif', -1)
    b = np.zeros((img.shape[0],img.shape[1]),dtype=img.dtype)
    g = np.zeros((img.shape[0],img.shape[1]),dtype=img.dtype)
    r = np.zeros((img.shape[0],img.shape[1]),dtype=img.dtype)
    nr = np.zeros((img.shape[0],img.shape[1]),dtype=img.dtype)
    
    b[:,:] = img[:,:,0]  # 复制 b 通道的数据
    g[:,:] = img[:,:,1]  # 复制 g 通道的数据
    r[:,:] = img[:,:,2]  # 复制 r 通道的数据
    nr[:,:] = img[:,:,3]  # 复制 nr 通道的数据

    merged_b_g_nr = np.dstack([b,g,nr])
    merged_b_nr_r = np.dstack([b,nr,r])
    merged_nr_g_r = np.dstack([nr,g,r])

    cv2.imwrite(crop_img_path+"merged_b_g_nr.tif" , merged_b_g_nr)
    cv2.imwrite(crop_img_path+"merged_b_nr_r.tif", merged_b_nr_r)
    cv2.imwrite(crop_img_path+"merged_nr_g_r.tif", merged_nr_g_r)

##剪切函数
def crop_img(img_path: str):
    date = img_path.split('_')[0]
    ano_path = img_path.split('.')[0] + '.json'
    img = cv2.imread(join(ori_dir, img_path),-1)
    h, w = img.shape[:2]
    ano = json_to_dict(join(ori_dir, ano_path))
    shapes = ano['shapes']
    for i in range(len(shapes)):
        output_path = '{}_{}.tif'.format(i, date)
        points = np.array(shapes[i]['points'], dtype=int)
        x_min, y_min, x_max, y_max = get_mask_bounding(points)[0]
        if x_min < 0 or y_min < 0 or x_max >= w or y_max >= h:
            ins_img = np.zeros(shape=[1, 1, 4], dtype=np.uint8)
            cv2.imwrite(join(tgt_dir, output_path), ins_img)
            continue

        ins_img = img[y_min:y_max, x_min:x_max]
        if not mask_flag:
            cv2.imwrite(join(tgt_dir, output_path), ins_img)
            continue
        points[:, 0] -= x_min
        points[:, 1] -= y_min

        mask = np.zeros(ins_img.shape[:2], np.uint8)
        points = np.array([points], dtype=int)
        cv2.polylines(mask, points, 1, 255)
        cv2.fillPoly(mask, points, 255)
        dst = cv2.bitwise_and(ins_img, ins_img, mask=mask)
        cv2.imwrite(join(tgt_dir, output_path), dst)
       
        after_output_path = '{}_{}'.format(i, date)
        crop_img = join(tgt_dir, after_output_path)
        afterCrop(crop_img)


if __name__ == '__main__':
    file_ls = [f for f in listdir(ori_dir) if 'tif' in f]
    for f in file_ls:
        print(f)
        crop_img(f)

效果

?tips：图像和Json文件放在同一个文件夹