Я столкнулся с этим вопросом. Мое исправление заключалось в создании дочерней схемы. См. Ниже пример для ваших моделей.
---- Персональная модель
const mongoose = require('mongoose');
const SingleFriend = require('./SingleFriend');
const Schema = mongoose.Schema;
const productSchema = new Schema({
friends : [SingleFriend.schema]
});
module.exports = mongoose.model('Person', personSchema);
*** Важно: SingleFriend.schema -> обязательно используйте строчные буквы для схемы
--- Схема ребенка
const mongoose = require('mongoose');
const Schema = mongoose.Schema;
const SingleFriendSchema = new Schema({
Name: String
});
module.exports = mongoose.model('SingleFriend', SingleFriendSchema);
это то, что вы хотите?
import numpy as np
import cv2
from matplotlib import pyplot as plt
img1 = cv2.imread( file1,0) # queryImage
img2 = cv2.imread( file2,0) # trainImage
# Initiate SIFT detector
orb = cv2.ORB_create()
# find the keypoints and descriptors with SIFT
kp1, des1 = orb.detectAndCompute(img1,None)
kp2, des2 = orb.detectAndCompute(img2,None)
# FLANN parameters
FLANN_INDEX_KDTREE = 0
index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
search_params = dict(checks=50) # or pass empty dictionary
flann = cv2.FlannBasedMatcher(index_params,search_params)
des1 = np.float32(des1)
des2 = np.float32(des2)
matches = flann.knnMatch(des1,des2,k=2)
# Need to draw only good matches, so create a mask
matchesMask = [[0,0] for i in range(len(matches))]
# ratio test as per Lowe's paper
for i,(m,n) in enumerate(matches):
if m.distance < 0.7*n.distance:
matchesMask[i]=[1,0]
draw_params = dict(matchColor = (0,255,0),
singlePointColor = (255,0,0),
matchesMask = matchesMask,
flags = 0)
img3 = cv2.drawMatchesKnn(img1,kp1,img2,kp2,matches,None,**draw_params)
plt.imshow(img3,),plt.show()