python opencv通过4坐标剪裁图片

 crop = img[int(tr[1]):int(bl[1]), int(tl[0]):int(br[0]) ]

import numpy as np
import cv2


def np_list_int(tb):
    tb_2 = tb.tolist() #将np转换为列表
    return tb_2


def shot(img, dt_boxes):#应用于predict_det.py中,通过dt_boxes中获得的四个坐标点,裁剪出图像
    dt_boxes = np_list_int(dt_boxes)
    boxes_len = len(dt_boxes)
    num = 0
    while 1:
        if (num < boxes_len):
            box = dt_boxes[num]
            tl = box[0]
            tr = box[1]
            br = box[2]
            bl = box[3]
            print("打印转换成功数据num =" + str(num))
            print("tl:" + str(tl), "tr:" + str(tr), "br:" + str(br), "bl:" + str(bl))
            print(tr[1],bl[1], tl[0],br[0])


            crop = img[int(tr[1]):int(bl[1]), int(tl[0]):int(br[0]) ]

            
            # crop = img[27:45, 67:119] #测试
            # crop = img[380:395, 368:119]

            cv2.imwrite("K:/paddleOCR/PaddleOCR/screenshot/a/" + str(num) + ".jpg", crop)

            num = num + 1
        else:
            break


def shot1(img_path,tl, tr, br, bl,i):
    tl = np_list_int(tl)
    tr = np_list_int(tr)
    br = np_list_int(br)
    bl = np_list_int(bl)

    print("打印转换成功数据")
    print("tl:"+str(tl),"tr:" + str(tr), "br:" + str(br), "bl:"+ str(bl))

    img = cv2.imread(img_path)
    crop = img[tr[1]:bl[1], tl[0]:br[0]]

    # crop = img[27:45, 67:119]

    cv2.imwrite("K:/paddleOCR/PaddleOCR/screenshot/shot/" + str(i) + ".jpg", crop)

# tl1 = np.array([67,27])
# tl2= np.array([119,27])
# tl3 = np.array([119,45])
# tl4 = np.array([67,45])
# shot("K:\paddleOCR\PaddleOCR\screenshot\zong.jpg",tl1, tl2 ,tl3 , tl4 , 0)

from PIL import Image
import os
import cv2
import time
import matplotlib.pyplot as plt
def label2picture(cropImg,framenum,tracker):
    pathnew ="E:\\img2\\"
    # cv2.imshow("image", cropImg)
    # cv2.waitKey(1)
    if (os.path.exists(pathnew + tracker)):
        cv2.imwrite(pathnew + tracker+'\\'+framenum + '.jpg', cropImg,[int(cv2.IMWRITE_JPEG_QUALITY), 100])
 
    else:
        os.makedirs(pathnew + tracker)
        cv2.imwrite(pathnew + tracker+'\\'+framenum + '.jpg', cropImg,[int(cv2.IMWRITE_JPEG_QUALITY), 100])
 
f = open("E:\\hypotheses.txt","r")
lines = f.readlines()
for line in lines:
    li  = line.split(',')
    print(li[0],li[1],li[2],li[3],li[4],li[5])
    filename = li[0]+'.jpg'
    img = cv2.imread("E:\\DeeCamp\\img1\\" + filename)
    crop_img = img[int(li[3][:-3]):(int(li[3][:-3]) + int(li[5][:-3])),
               int(li[2][:-3]):(int(li[2][:-3]) + int(li[4][:-3]))]
    # print(int(li[2][:-3]),int(li[3][:-3]),int(li[4][:-3]),int(li[5][:-3]))
    label2picture(crop_img, li[0], li[1])
# #
# x,y,w,h = 87,158,109,222
# img = cv2.imread("E:\\DeeCamp\\img1\\1606.jpg")
# # print(img.shape)
# crop = img[y:(h+y),x:(w+x)]
# cv2.imshow("image", crop)
# cv2.waitKey(0)
# img = Image.open("E:\\DeeCamp\\img1\\3217.jpg")
#
# cropImg = img.crop((x,y,x+w,y+h))
# cropImg.show()
    # img = Image.open("E:\\deep_sort-master\\MOT16\\train\\try1\\img1\\"+filename)
    # print(int(li[2][:-3]),(int(li[2][:-3])+int(li[4][:-3])), int(li[3][:-3]),(int(li[3][:-3])+int(li[5][:-3])))
 
    # #裁切图片
    # # cropImg = img.crop(region)
    # # cropImg.show()
    # framenum ,tracker= li[0],li[1]
    # pathnew = 'E:\\DeeCamp\\deecamp项目\\deep_sort-master\\crop_picture\\'
    # if (os.path.exists(pathnew + tracker)):
    #     # 保存裁切后的图片
    #     plt.imshow(cropImg)
    #     plt.savefig(pathnew + tracker+'\\'+framenum + '.jpg')
    # else:
    #     os.makedirs(pathnew + tracker)
    #     plt.imshow(cropImg)
    #     plt.savefig(pathnew + tracker+'\\'+framenum + '.jpg')