SIFT_Feature_Match

# coding=utf-8
import cv2
import numpy as np
from matplotlib import pyplot as plt
plt.switch_backend('TkAgg')
import numpy as np
from PIL import Image
plt.switch_backend('TkAgg')
from skimage.measure import label as Label
from skimage.measure import regionprops as Region

img0_path='/home/didia/us_data/npydata_2/1508788_0_clean.npy'
img1_path='/home/didia/us_data/npydata_2/1508788_1_clean.npy'
ann0_path='/home/didia/us_data/npydata_2/1508788_0_seg.npy'
ann1_path='/home/didia/us_data/npydata_2/1508788_1_seg.npy'

# 测试
# img1_gray = cv2.cvtColor(cv2.imread('left_01.png'), cv2.COLOR_BGR2GRAY)
# img2_gray =cv2.cvtColor(cv2.imread('right_01.png'), cv2.COLOR_BGR2GRAY)

save_dir='/home/zrj/lab_project/184_ultrasonic/save_img/'

img0=np.load(img0_path)
img1=np.load(img1_path)
ann0=np.load(ann0_path)
ann1=np.load(ann1_path)

def drawMatchesKnn_cv2(img1_gray, kp1, img2_gray, kp2, goodMatch,i,j,ymin,xmin,ymax,xmax,y_1, x_1, y_2, x_2):
    h1, w1 = img1_gray.shape
    h2, w2 = img2_gray.shape

    vis = np.zeros((max(h1, h2), w1 + w2), np.uint8)
    vis[:h1, :w1] = img1_gray
    vis[:h2, w1:w1 + w2] = img2_gray

    p1 = [kpp.queryIdx for kpp in goodMatch]
    p2 = [kpp.trainIdx for kpp in goodMatch]

    post1 = np.int32([kp1[pp].pt for pp in p1])
    post2 = np.int32([kp2[pp].pt for pp in p2]) + (w1, 0)

    for (x1, y1), (x2, y2) in zip(post1, post2):
         if x1 >xmin  and x1 < xmax  and y1 >ymin  and y1 < ymax:
            # print (x1,y1),(x2-w1,y2)
            cv2.line(vis, (x1, y1), (x2, y2), (0,255,0))

    cv2.rectangle(vis, (xmin, ymin), (xmax, ymax), (0, 255, 0), 4)
    cv2.rectangle(vis, (x_1+w1, y_1), (x_2+w1, y_2), (0, 255, 0), 4)

    cv2.imwrite(save_dir+'img0_'+str(i)+'_'+'img1_'+str(j)+'.png',vis)

def bbox_cor(ann):
    ann_regions = Label(ann)
    ann_proposal = Region(ann_regions)
    for proposal in ann_proposal:
        y1,x1,y2,x2=proposal.bbox
    return y1,x1,y2,x2


for i in range(212,226):
    for j in range(173,212):
        img1_gray = img0[i]
        img2_gray = img1[j]
        y1, x1, y2, x2= bbox_cor(ann0[i])
        y_1, x_1, y_2, x_2= bbox_cor(ann1[j])
        sift =  cv2.xfeatures2d.SIFT_create()  # 建立SIFT生成器
        kp1, des1 = sift.detectAndCompute(img1_gray, None)  # 检测SIFT特征点，并计算描述子
        kp2, des2 = sift.detectAndCompute(img2_gray, None)

        # BFmatcher with default parms
        bf = cv2.BFMatcher(cv2.NORM_L2) # 建立暴力匹配器
        matches = bf.knnMatch(des1, des2, k=2) # 使用KNN检测来自A、B图的SIFT特征匹配对，K=2

        goodMatch = []
        for m, n in matches:
            # 当最近距离跟次近距离的比值小于ratio值时，保留此匹配对
            # if m.distance < 0.75 * n.distance:
            goodMatch.append(m)

        drawMatchesKnn_cv2(img1_gray, kp1, img2_gray, kp2, goodMatch,i,j,y1, x1, y2, x2,y_1, x_1, y_2, x_2)