一、 切片 24*24 和 96*96
import os import random import cv2 def crop_mod(img, scale): h,w = img.shape[0], img.shape[1] a = h%scale b = w%scale if len(img.shape)==2: img = img[0: h-a, 0:w-b] else: img = img[0:h-a, 0:w-b,:] return img nir_path = r'C:Users13141Desktoplow_nir_dataset ir' rgb_path = r'C:Users13141Desktoplow_nir_dataset gb' lr_path = r'C:Users13141Desktoplow_nir_datasetx4 ir_lr' bic_path = r'C:Users13141Desktoplow_nir_datasetx4 ir_bic' image_list = os.listdir(nir_path) image_list.sort() for i in range(len(image_list)): count = 1 nir_img_path = os.path.join(nir_path, image_list[i]) bic_img_path = os.path.join(bic_path, image_list[i]) lr_img_path = os.path.join(lr_path, image_list[i]) rgb_img_path = os.path.join(rgb_path, image_list[i].replace('nir','rgb')) nir_img = cv2.imread(nir_img_path,cv2.IMREAD_GRAYSCALE) bic_img = cv2.imread(bic_img_path,cv2.IMREAD_GRAYSCALE) lr_img = cv2.imread(lr_img_path,cv2.IMREAD_GRAYSCALE) rgb_img = cv2.imread(rgb_img_path) nir_img = crop_mod(nir_img, 4) rgb_img = crop_mod(rgb_img, 4) h,w = nir_img.shape[0], nir_img.shape[1] x_min = list(range(0, (h-96), 48)) y_min = list(range(0, (w-96), 48)) for xx in x_min: for yy in y_min: nir_pat = nir_img[xx:(xx+96), yy:(yy+96)] bic_pat = bic_img[xx:(xx+96), yy:(yy+96)] rgb_pat = rgb_img[xx:(xx+96), yy:(yy+96),:] xx_l = int(xx/4) yy_l = int(yy/4) lr_pat = lr_img[xx_l:(xx_l+24), yy_l:(yy_l+24)] count_0 = 5-len(str(count)) image_name = image_list[i].split('.')[0]+'_'+'0'*count_0+str(count)+'.tiff' print(image_name) cv2.imwrite(os.path.join(r'C:Users13141Desktoplow_nir_datasetx4patches_24 ir', image_name), nir_pat) cv2.imwrite(os.path.join(r'C:Users13141Desktoplow_nir_datasetx4patches_24 ir_bic', image_name), bic_pat) cv2.imwrite(os.path.join(r'C:Users13141Desktoplow_nir_datasetx4patches_24 ir_lr', image_name), lr_pat) cv2.imwrite(os.path.join(r'C:Users13141Desktoplow_nir_datasetx4patches_24 gb', image_name), rgb_pat) count+=1
二、 边缘提取Canny算子
import os import cv2 import numpy as np import matplotlib.pyplot as plt path =r'C:Users13141Desktoplow_nir_datasetx4patches_24 ir' img_list = os.listdir(path) count = 0 img_del = [] for i in range(len(img_list)): img_path = os.path.join(path, img_list[i]) img = cv2.imread(img_path) img = cv2.GaussianBlur(img, (3,3), 0) canny1 = cv2.Canny(img,0, 55) l = canny1.sum() # 记录0边缘图像 if l==0: print(img_list[i]) img_del.append(img_list[i]) count +=1 # plt.imshow(canny1, cmap='gray') # plt.show()
删除0边缘图像
path =r'C:Users13141Desktoplow_nir_datasetx4patches_24 ir' bic_path = r'C:Users13141Desktoplow_nir_datasetx4patches_24 ir_bic' lr_path = r'C:Users13141Desktoplow_nir_datasetx4patches_24 ir_lr' rgb_path=r'C:Users13141Desktoplow_nir_datasetx4patches_24 gb' for i in img_del: os.remove(os.path.join(path, i)) os.remove(os.path.join(bic_path, i)) os.remove(os.path.join(lr_path, i)) os.remove(os.path.join(rgb_path, i))
三、 图像配准
import numpy as np import cv2 def sift_kp(image): if image.shape[2]==3: gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) else: gray_image = image sift = cv2.xfeatures2d.SIFT_create() kp, des = sift.detectAndCompute(image, None) kp_image = cv2.drawKeypoints(gray_image, kp, None) return kp_image, kp, des def get_good_match(des1, des2): bf = cv2.BFMatcher() matches = bf.knnMatch(des1, des2, k=2) good = [] for m, n in matches: if m.distance<0.75*n.distance: good.append(m) return good def siftImageAlignment(img1, img2): _, kp1, des1 = sift_kp(img1) _, kp2, des2 = sift_kp(img2) goodMatch = get_good_match(des1, des2) if len(goodMatch) >4: ptsA = np.float32([kp1[m.queryIdx].pt for m in goodMatch]).reshape(-1, 1, 2) ptsB = np.float32([kp2[m.trainIdx].pt for m in goodMatch]).reshape(-1, 1, 2) ransacReprojThreshold = 4 H, status = cv2.findHomography(ptsA, ptsB, cv2.RANSAC, ransacReprojThreshold) imgOut = cv2.warpPerspective(img2, H, (img1.shape[1], img1.shape[0]), flags=cv2.INTER_LINEAR+cv2.WARP_INVERSE_MAP) return imgOut, H, status img1 = cv2.imread(r'E:datasetsmultispectraldataRGB_NIRstereo4img2.png') img2 = cv2.imread(r'E:datasetsmultispectraldataRGB_NIRstereo4img1.png') while img1.shape[0]>1000 or img1.shape[1]>1000: img1 = cv2.resize(img1, None, fx=0.5, fy=0.5, interpolation=cv2.INTER_AREA) while img2.shape[0]>1000 or img2.shape[1]>1000: img2 = cv2.resize(img2, None, fx=0.5, fy=0.5, interpolation=cv2.INTER_AREA) print(img1.shape, img2.shape) result, _, _ = siftImageAlignment(img1, img2) allImage = np.concatenate((img1, img2, result), axis=1)
import matplotlib.pyplot as plt
plt.imshow(result)
plt.show()
四、 数据增强（Pytorch）
def augment(*args, hflip=True, rot=True): hflip = hflip and random.random() < 0.5 vflip = rot and random.random() < 0.5 rot90 = rot and random.random() < 0.5 def _augment(img): if hflip: img = img[:, ::-1, :].copy() if vflip: img = img[:, :, ::-1].copy() if rot90: img = img.transpose(0, 2, 1).copy() return img return [_augment(a) for a in args]