舍弃不够整除的部分，对大尺寸的图像裁剪成m行n列的小图，将小图相对大图的行列位置存储在图像名中
之后对小图进行目标检测标注目标位置
再将小图依次拼接，铺成大图
1 # coding=utf-8 2 from PIL import Image 3 # pil paste可以进行图片拼接 4 import cv2 5 import numpy as np 6 import glob as glob 7 import os 8 """ 9 输入：图片路径(path+filename)，裁剪获得小图片的列数、行数（也即宽、高） 10 输出：无 11 """ 12 def crop_one_picture(path, filename, cols, rows): 13 img = cv2.imread(filename,1) ##读取彩色图像，图像的透明度(alpha通道)被忽略，默认参数;灰度图像;读取原始图像，包括alpha通道;可以用1，0，-1来表示 14 sum_rows = img.shape[0] # 高度 15 sum_cols = img.shape[1] # 宽度 16 save_path = path + "\crop{0}_{1}\".format(cols, rows) # 保存的路径 17 if not os.path.exists(save_path): 18 os.makedirs(save_path) 19 print("裁剪所得{0}列图片，{1}行图片.".format(int(sum_cols / cols), int(sum_rows / rows))) 20 21 for i in range(int(sum_cols / cols)): 22 for j in range(int(sum_rows / rows)): 23 print(save_path+str(os.path.splitext(filename)[0].split("\")[-1]) + '_' + str(j) + '_' + str(i) + '.jpg') 24 cv2.imwrite( 25 save_path + str(os.path.splitext(filename)[0].split("\")[-1]) + '_' + str(j) + '_' + str(i) + '.jpg', img[j * rows:(j + 1) * rows, i * cols:(i + 1) * cols, :]) 26 #cv2.imwrite('.//origin-img//{0}_{1}.jpg'.format(i,j), img[j * rows:(j + 1) * rows, i * cols:(i + 1) * cols]) 27 print("裁剪完成，得到{0}张图片.".format(int(sum_cols / cols) * int(sum_rows / rows))) 28 print("文件保存在{0}".format(save_path)) 29 30 31 """遍历文件夹下某格式图片""" 32 def file_name(root_path,picturetype): 33 filename=[] 34 for root,dirs,files in os.walk(root_path): 35 for file in files: 36 if os.path.splitext(file)[1]==picturetype: 37 filename.append(os.path.join(root,file)) 38 return filename 39 40 root_path='.\origin-img\' 41 filenamelist=file_name(root_path,'.jpg') 42 43 print(filenamelist) 44 each_name_list=[] 45 for each_name in filenamelist: 46 each_name_list.append(each_name.split("\")[-1]) 47 print(each_name_list) # final name 48 w=500 49 h=500 50 for each_img in each_name_list: 51 crop_one_picture(root_path,each_name,w,h)
合并图像:
1 # coding=utf-8 2 from PIL import Image 3 # pil paste可以进行图片拼接 4 import cv2 5 import numpy as np 6 import glob as glob 7 import os 8 9 """ 10 11 输入：图片路径(path+filename)，裁剪所的图片的列的数量、行的数量 12 输出：无 13 """ 14 def merge_picture(merge_path): 15 filename=file_name(merge_path,".jpg") 16 shape=cv2.imread(filename[0],1).shape #三通道的影像需把-1改成1 17 cols=shape[1] 18 rows=shape[0] 19 channels=shape[2] 20 21 22 max_cols_th = 0 23 max_rows_th = 0 24 for i in range(len(filename)): 25 img=cv2.imread(filename[i],1) 26 cols_th=int(filename[i].split("_")[-1].split('.')[0]) 27 if cols_th>max_cols_th: 28 max_cols_th=cols_th 29 rows_th=int(filename[i].split("_")[-2]) 30 if rows_th>max_rows_th: 31 max_rows_th=rows_th 32 print(max_rows_th,max_cols_th) 33 num_of_cols=max_cols_th+1 34 num_of_rows=max_rows_th+1 35 36 37 dst=np.zeros((rows*num_of_rows,cols*num_of_cols,channels),np.uint8) 38 for i in range(len(filename)): 39 img=cv2.imread(filename[i],1) 40 cols_th=int(filename[i].split("_")[-1].split('.')[0]) 41 rows_th=int(filename[i].split("_")[-2]) 42 print(rows_th,cols_th) 43 roi=img[0:rows,0:cols,:] 44 45 dst[rows_th*rows:(rows_th+1)*rows,cols_th*cols:(cols_th+1)*cols,:]=roi 46 cv2.imwrite(merge_path+"merge.jpg",dst) 47 48 """遍历文件夹下某格式图片""" 49 def file_name(root_path,picturetype): 50 filename=[] 51 for root,dirs,files in os.walk(root_path): 52 for file in files: 53 if os.path.splitext(file)[1]==picturetype: 54 filename.append(os.path.join(root,file)) 55 return filename 56 57 58 59 merge_path=".\origin-img\crop500_500\" #要合并的小图片所在的文件夹 60 61 merge_picture(merge_path)