本文主要介紹了python opencv通過4坐標剪裁圖片，分享給大家，具體如下：
效果展示,
裁剪出的單詞圖像(如下)
這里程序我是用在paddleOCR里面,通過識別模型將識別出的圖根據程序提供的坐標(即四個頂點的值)進行摳圖的程序(上面的our和and就是扣的圖),并進行了封裝,相同格式的在這個基礎上改就是了
[[[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]], ]
從程序得到的數據格式大概長上面的樣子,由多個四個坐標一組的數據(如下)組成,即下面的[368.0, 380.0]為要裁剪圖片左上角坐標,[437.0, 380.0]為要裁剪圖片右上角坐標,[437.0, 395.0]為要裁剪圖片右下角坐標,[368.0, 395.0]為要裁剪圖片左下角坐標.
[[368.0, 380.0], [437.0, 380.0], [437.0, 395.0], [368.0, 395.0]]
而這里剪裁圖片使用的是opencv(由于參數的原因沒有設置角度的話就只能裁剪出平行的矩形,如果需要裁減出不與矩形圖片編譯平行的圖片的話,參考這個博客進行進一步的改進點擊進入)
裁剪部分主要是根據下面這一行代碼進行的,這里要記住(我被這里坑了一下午),
參數 tr[1]:左上角或右上角的縱坐標值
參數bl[1]:左下角或右下角的縱坐標值
參數tl[0]:左上角或左下角的橫坐標值
參數br[0]:右上角或右下角的橫坐標值
crop = img[int(tr[1]):int(bl[1]), int(tl[0]):int(br[0]) ]
總的程序代碼如下
import numpy as np
import cv2
def np_list_int(tb):
tb_2 = tb.tolist() #將np轉換為列表
return tb_2
def shot(img, dt_boxes):#應用于predict_det.py中,通過dt_boxes中獲得的四個坐標點,裁剪出圖像
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("打印轉換成功數據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] #測試
# 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("打印轉換成功數據")
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)
特別注意對np類型轉換成列表,以及crop = img[tr[1]:bl[1], tl[0]:br[0]]的中參數的位置,
實例
用了兩種方法保存圖片，opencv和Image，實踐證明opencv非常快
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])))
# #裁切圖片
# # cropImg = img.crop(region)
# # cropImg.show()
# framenum ,tracker= li[0],li[1]
# pathnew = 'E:\\DeeCamp\\deecamp項目\\deep_sort-master\\crop_picture\\'
# if (os.path.exists(pathnew + tracker)):
# # 保存裁切后的圖片
# plt.imshow(cropImg)
# plt.savefig(pathnew + tracker+'\\'+framenum + '.jpg')
# else:
# os.makedirs(pathnew + tracker)
# plt.imshow(cropImg)
# plt.savefig(pathnew + tracker+'\\'+framenum + '.jpg')
到此這篇關于python opencv通過4坐標剪裁圖片的文章就介紹到這了,更多相關opencv 剪裁圖片內容請搜索腳本之家以前的文章或繼續瀏覽下面的相關文章希望大家以后多多支持腳本之家！
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