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| import cv2
import math
import io
import numpy as np
import zipfile
from tqdm import tqdm
from pwn import *
def Img_Outline(original_img):
gray_img = cv2.cvtColor(original_img, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray_img, (9, 9), 0)
_, RedThresh = cv2.threshold(blurred, 220, 255, cv2.THRESH_BINARY)
h, w = original_img.shape[:2]
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))
marker = np.zeros_like(gray_img)
marker[0, :] = 255
marker[-1, :] = 255
marker[:, 0] = 255
marker[:, -1] = 255
while True:
marker_pre = marker
dilation = cv2.dilate(marker, kernel=kernel)
marker = np.min((dilation, RedThresh), axis=0)
if (marker_pre == marker).all():
break
marker = cv2.morphologyEx(marker, cv2.MORPH_OPEN, kernel)
marker[0:5,:] = 255
marker[-5:,:] = 255
marker[:,0:5] = 255
marker[:,-5:] = 255
return original_img, gray_img, marker
def findContours_img(original_img, marker):
contours, hierarchy = cv2.findContours(marker, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
c = sorted(contours, key=cv2.contourArea, reverse=True)[1]
rect = cv2.minAreaRect(c)
box = np.int0(cv2.boxPoints(rect))
draw_img = cv2.drawContours(original_img.copy(), [box], -1, (0, 0, 255), 3)
return box,draw_img
def Perspective_transform(box,original_img):
orignal_W = math.ceil(np.sqrt((box[3][1] - box[2][1])**2 + (box[3][0] - box[2][0])**2))
orignal_H= math.ceil(np.sqrt((box[3][1] - box[0][1])**2 + (box[3][0] - box[0][0])**2))
pts1 = np.float32([box[0], box[1], box[2], box[3]])
pts2 = np.float32([[int(orignal_W+1),int(orignal_H+1)], [0, int(orignal_H+1)], [0, 0], [int(orignal_W+1), 0]])
M = cv2.getPerspectiveTransform(pts1, pts2)
result_img = cv2.warpPerspective(original_img, M, (int(orignal_W+3),int(orignal_H+1)))
if orignal_H < orignal_W:
result_img = cv2.flip(cv2.transpose(result_img), 1)
return result_img
def img_similarity(img1,img2):
try:
orb = cv2.ORB_create()
kp1, des1 = orb.detectAndCompute(img1, None)
kp2, des2 = orb.detectAndCompute(img2, None)
bf = cv2.BFMatcher(cv2.NORM_HAMMING)
matches = bf.knnMatch(des1, trainDescriptors=des2, k=2)
good = [m for (m, n) in matches if m.distance < 0.75 * n.distance]
similary = len(good) / len(matches)
return similary
except:
print('无法计算两张图片相似度')
exit()
if __name__=="__main__":
if not os.path.exists('./result'):
os.makedirs('result')
final = ''
temp1 = cv2.imread('./temp1.webp', cv2.IMREAD_GRAYSCALE)
temp2 = cv2.imread('./temp2.webp', cv2.IMREAD_GRAYSCALE)
r = remote('week-3.hgame.lwsec.cn', 30802)
r.recvuntil('...')
r.send('\n')
b64 = r.recvline()[:-1]
out = open('row.zip', "wb")
base64.decode(io.BytesIO(b64), out)
out.close()
zipfile_path = './row.zip'
with zipfile.ZipFile(zipfile_path, mode='r') as zfile:
nWaitTime = 1
for name in tqdm(zfile.namelist()):
if '.webp' not in name:
continue
with zfile.open(name,mode='r') as image_file:
content = image_file.read()
image = np.asarray(bytearray(content), dtype='uint8')
image = cv2.imdecode(image, cv2.IMREAD_ANYCOLOR)
original_img, gray_img, RedThresh = Img_Outline(image)
box, draw_img = findContours_img(original_img, RedThresh)
result_img = Perspective_transform(box,original_img)
result_img[50:355,22:280] = 255.0
cv2.imwrite(f"./result/{name}", result_img)
result_img = cv2.cvtColor(result_img, cv2.COLOR_BGR2GRAY)
similarity1 = img_similarity(temp1, result_img)
similarity2 = img_similarity(temp2, result_img)
if (similarity1 + similarity2) > 0.04:
final += '1'
else:
final += '0'
print(final)
r.sendline(final)
r.interactive()
|