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test_pre_process.py
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test_pre_process.py
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import numpy as np
import cv2, pdb, glob, argparse
MAX_FEATURES = 500
GOOD_MATCH_PERCENT = 0.15
def alignImages(im1, im2,masksDL):
# Convert images to grayscale
# im1Gray = cv2.cvtColor(im1, cv2.COLOR_BGR2GRAY)
# im2Gray = cv2.cvtColor(im2, cv2.COLOR_BGR2GRAY)
# print("im1 type = ",type(im1))
# print("im2 type = ",type(im2))
# print("masksDL type = ",type(masksDL))
#
# print("im1 shape = ",im1.shape)
# print("im2 shape = ",im2.shape)
# print("masksDL shape = ",masksDL.shape)
akaze = cv2.AKAZE_create()
keypoints1, descriptors1 = akaze.detectAndCompute(im1, None)
keypoints2, descriptors2 = akaze.detectAndCompute(im2, None)
# Match features.
matcher = cv2.DescriptorMatcher_create(cv2.DESCRIPTOR_MATCHER_BRUTEFORCE)
matches = matcher.match(descriptors1, descriptors2, None)
# Sort matches by score
matches.sort(key=lambda x: x.distance, reverse=False)
# Remove not so good matches
numGoodMatches = int(len(matches) * GOOD_MATCH_PERCENT)
matches = matches[:numGoodMatches]
# Extract location of good matches
points1 = np.zeros((len(matches), 2), dtype=np.float32)
points2 = np.zeros((len(matches), 2), dtype=np.float32)
for i, match in enumerate(matches):
points1[i, :] = keypoints1[match.queryIdx].pt
points2[i, :] = keypoints2[match.trainIdx].pt
# Find homography
h, mask = cv2.findHomography(points1, points2, cv2.RANSAC)
# Use homography
height, width, channels = im2.shape
im1Reg = cv2.warpPerspective(im1, h, (width, height))
# copy image in the empty region, unless it is a foreground. Then copy background
mask_rep=(np.sum(im1Reg.astype('float32'),axis=2)==0)
im1Reg[mask_rep,0]=im2[mask_rep,0]
im1Reg[mask_rep,1]=im2[mask_rep,1]
im1Reg[mask_rep,2]=im2[mask_rep,2]
mask_rep1=np.logical_and(mask_rep , masksDL[...,0]==255)
im1Reg[mask_rep1,0]=im1[mask_rep1,0]
im1Reg[mask_rep1,1]=im1[mask_rep1,1]
im1Reg[mask_rep1,2]=im1[mask_rep1,2]
return im1Reg
def adjustExposure(img,back,mask):
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
mask = cv2.dilate(mask, kernel, iterations=10)
mask1 = cv2.dilate(mask, kernel, iterations=300)
msk=mask1.astype(np.float32)/255-mask.astype(np.float32)/255; msk=msk.astype(np.bool)
back_tr=back
back_tr[...,0]=bias_gain(img[...,0],back[...,0],msk)
back_tr[...,1]=bias_gain(img[...,1],back[...,1],msk)
back_tr[...,2]=bias_gain(img[...,2],back[...,2],msk)
return back_tr
def bias_gain(orgR,capR,cap_mask):
capR=capR.astype('float32')
orgR=orgR.astype('float32')
xR=capR[cap_mask]
yR=orgR[cap_mask]
gainR=np.nanstd(yR)/np.nanstd(xR);
biasR=np.nanmean(yR)-gainR*np.nanmean(xR);
cap_tran=capR*gainR+biasR;
return cap_tran.astype('float32')
parser = argparse.ArgumentParser(description='Deeplab Segmentation')
parser.add_argument('-i', '--input_dir', type=str, required=True,help='Directory to save the output results. (required)')
args=parser.parse_args()
dir_name=args.input_dir
list_im=glob.glob(dir_name + '/*_img.png'); list_im.sort()
for i in range(0,len(list_im)):
image = cv2.imread(list_im[i],cv2.IMREAD_COLOR)
print('image type = ',type(image))
back = cv2.imread(list_im[i].replace('img','back'),cv2.IMREAD_COLOR)
mask = cv2.imread(list_im[i].replace('img','masksDL'))
#back_new = adjustExposure(image,back,mask[...,0])
back_align = alignImages(back, image,mask)
cv2.imwrite(list_im[i].replace('img','back'),back_align)
#
#str_msg='\nDone: ' + dir_name
#print(str_msg)