114 lines
3.7 KiB
Python
114 lines
3.7 KiB
Python
import logging
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# from re import S
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# from oneOrthoAuxData import OrthoAuxData
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# from OrthoImage import ImageHandler
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from tool.algorithm.image.ImageHandle import ImageHandler
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import tarfile
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# from OrthoDB import ManageAlgXML, CheckSource
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from tool.algorithm.xml.AlgXmlHandle import ManageAlgXML, CheckSource # 导入xml文件读取与检查文件
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from OrthoAlg import IndirectOrthorectification, DEMProcess,ImageMatchClass
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# from logHandler import LogHandler
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from tool.algorithm.algtools.logHandler import LogHandler
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from tool.algorithm.xml.CreatMetafile import CreateMetafile
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from OrthoXmlInfo import CreateDict, CreateStadardXmlFile
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from osgeo import gdal, osr
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import os
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import glob
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import gc
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import datetime
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import shutil
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import sys
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import cv2
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ori_sar_path="D:\MicroWorkspace\C-SAR\Ortho\Temporary\TestSAR\GF3_SAY_QPSI_013952_E118.9_N31.5_20190404_L1A_HH_L10003923848.jpg"
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sim_sar_path="D:\MicroWorkspace\C-SAR\Ortho\Temporary\TestSim\sim_img_sum.jpg"
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work_sapce_path="D:\MicroWorkspace\C-SAR\Ortho\Temporary"
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'''
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import matplotlib.pyplot as plt
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img1 = cv2.imread(ori_sar_path, 0)
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img2 = cv2.imread(sim_sar_path, 0)
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def cv_show(name,img):
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cv2.imshow(name, img)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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sift = cv2.SIFT_create()
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kp1, des1 = sift.detectAndCompute(img1, None)
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kp2, des2 = sift.detectAndCompute(img2, None)
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bf = cv2.BFMatcher(crossCheck=True)
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matches = bf.match(des1, des2)
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matches = sorted(matches, key=lambda x: x.distance)
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img3 = cv2.drawMatches(img1, kp1, img2, kp2, matches[:10], None,flags=2)
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cv2.imwrite(work_sapce_path,img3)
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'''
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# 匹配影像
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def ImageMatch(ori_sar_path,sim_sar_path,work_sapce_path):
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ori_sar=gdal.Open(ori_sar_path)
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sim_sar=gdal.Open(sim_sar_path)
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# 影像尺寸
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ori_height=ori_sar.RasterYSize
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ori_width=ori_sar.RasterXSize
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sim_height=sim_sar.RasterYSize
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sim_width=sim_sar.RasterXSize
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# 分块匹配
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ori_sar_arr=ori_sar.GetRasterBand(1).ReadAsArray(0,0,ori_width,ori_height) # 原始影像
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ori_img=(255*ori_sar_arr/np.max(ori_sar_arr)).astype(np.uint8)
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sim_sar_arr=np.log(sim_sar.GetRasterBand(1).ReadAsArray(0,0,sim_width,sim_height)+1) # 模拟影像
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sim_img=(1+253*sim_sar_arr/np.max(sim_sar_arr)).astype(np.uint8)
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res = cv.matchTemplate(img,template,method)
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min_val, max_val, min_loc, max_loc = cv.minMaxLoc(res)
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top_left = max_loc
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# 范围
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min_w=top_left[0] if top_left[0]>0 else 0
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min_h=top_left[1] if top_left[1]>0 else 0
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max_w=top_left[0]+ori_width if top_left[0]+ori_width<sim_width else sim_width
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max_h=top_left[1]+ori_height if top_left[0]+ori_height<sim_height else sim_height
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# 裁剪
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sim_clip=sim_img[min_h:max_h,min_w:max_w]
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for i in range(0,ori_img.shape[0],10):
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for j in range(0,ori_img.shape[1],10):
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pass
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import cv2 as cv
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import numpy as np
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from matplotlib import pyplot as plt
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img = cv.imread(sim_sar_path,0)
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img2 = img.copy()
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template = cv.imread(ori_sar_path,0)
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w, h = template.shape[::-1]
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# 列表中所有的6种比较方法
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methods = ['cv.TM_CCOEFF', 'cv.TM_CCOEFF_NORMED', 'cv.TM_CCORR',
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'cv.TM_CCORR_NORMED', 'cv.TM_SQDIFF', 'cv.TM_SQDIFF_NORMED']
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i=0
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for meth in methods:
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img = img2.copy()
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method = eval(meth)
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# 应用模板匹配
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res = cv.matchTemplate(img,template,method)
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min_val, max_val, min_loc, max_loc = cv.minMaxLoc(res)
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# 如果方法是TM_SQDIFF或TM_SQDIFF_NORMED，则取最小值
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if method in [cv.TM_SQDIFF, cv.TM_SQDIFF_NORMED]:
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top_left = min_loc
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else:
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top_left = max_loc
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bottom_right = (top_left[0] + w, top_left[1] + h)
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cv.rectangle(img,top_left, bottom_right, 255, 2)
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cv.imwrite(os.path.join(work_sapce_path,"matchresult_{}.jpg".format(i)),img)
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i=i+1
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