python opencv通过4坐标剪裁图片_Python

本文主要介绍了python opencv通过4坐标剪裁图片，分享给大家，具体如下：

效果展示,

python opencv通过4坐标剪裁图片

裁剪出的单词图像(如下)

python opencv通过4坐标剪裁图片

这里程序我是用在paddleocr里面,通过识别模型将识别出的图根据程序提供的坐标(即四个顶点的值)进行抠图的程序(上面的our和and就是扣的图),并进行了封装,相同格式的在这个基础上改就是了

[[[368.0, 380.0], [437.0, 380.0], [437.0, 395.0], [368.0, 395.0]], [[496.0, 376.0], [539.0, 378.0], [538.0, 397.0], [495.0, 395.0]], [[466.0, 379.0], [498.0, 379.0], [498.0, 395.0], [466.0, 395.0]], [[438.0, 379
.0], [466.0, 379.0], [466.0, 395.0], [438.0, 395.0]], ]

从程序得到的数据格式大概长上面的样子,由多个四个坐标一组的数据(如下)组成,即下面的[368.0, 380.0]为要裁剪图片左上角坐标,[437.0, 380.0]为要裁剪图片右上角坐标,[437.0, 395.0]为要裁剪图片右下角坐标,[368.0, 395.0]为要裁剪图片左下角坐标.

[[368.0, 380.0], [437.0, 380.0], [437.0, 395.0], [368.0, 395.0]]

而这里剪裁图片使用的是opencv(由于参数的原因没有设置角度的话就只能裁剪出平行的矩形,如果需要裁减出不与矩形图片编译平行的图片的话,参考这个博客进行进一步的改进点击进入)

裁剪部分主要是根据下面这一行代码进行的,这里要记住(我被这里坑了一下午),
参数 tr[1]:左上角或右上角的纵坐标值
参数bl[1]:左下角或右下角的纵坐标值
参数tl[0]:左上角或左下角的横坐标值
参数br[0]:右上角或右下角的横坐标值

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

python opencv通过4坐标剪裁图片

总的程序代码如下

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									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)

特别注意对np类型转换成列表,以及crop = img[tr[1]:bl[1], tl[0]:br[0]]的中参数的位置,

实例

用了两种方法保存图片，opencv和image，实践证明opencv非常快

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									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')