ransac.py

import numpy as np
import cv2 as cv
import os
from get_in4 import *
import _pickle as cPickle
from matplotlib import pyplot as plt
MIN_MATCH_COUNT = 10
path_des = 'static/data_img/des/'
path_kp = 'static/data_img/keypoint/'
def ransac(img1, des1, kp1, path2):
    list_des = os.listdir(path_des)
    list_kp = os.listdir(path_kp)
    #img1 = cv.imread(path1,0)          # queryImage
    img2 = cv.imread(path2,0) # trainImage
    # Initiate SIFT detector
    sift = cv.SIFT_create()
    # find the keypoints and descriptors with SIFT
    #kp1, des1 = sift.detectAndCompute(img1,None)
    index2 = get_position_image(path2)
    des2 = np.load(path_des+list_des[index2])
    
    kp2, des2 = sift.detectAndCompute(img2,None)
    FLANN_INDEX_KDTREE = 1
    index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
    search_params = dict(checks = 50)
    flann = cv.FlannBasedMatcher(index_params, search_params)
    matches = flann.knnMatch(des1,des2,k=2)
    # store all the good matches as per Lowe's ratio test.
    good = []
    for m,n in matches:
        if m.distance < 0.7*n.distance:
            good.append(m)
    if len(good)>MIN_MATCH_COUNT:
        src_pts = np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1,1,2)
        dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good ]).reshape(-1,1,2)
        M, mask = cv.findHomography(src_pts,
                                    dst_pts,
                                    cv.RANSAC,5.0)
        matchesMask = mask.ravel().tolist()
        h,w = img1.shape
        pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2)
        dst = cv.perspectiveTransform(pts,M)
        img2 = cv.polylines(img2,[np.int32(dst)],True,255,3, cv.LINE_AA)
    else:
        return 0

    draw_params = dict(matchColor = (0,255,0), # draw matches in green color
                       singlePointColor = None,
                       matchesMask = matchesMask, # draw only inliers
                       flags = 2)
    return(sum(matchesMask))