查找与 +10.000 个其他具有相似特征的图像最接近的匹配

Question

我试图找到与大量其他图像（+10.000）最接近的图像匹配。背景颜色全白色，相同的拍摄角度，图像内容形状彼此接近（见下图）。我尝试使用opencvandORB和BFMatcherwithknnMatch来找到最接近的匹配。但我还没有找到我想要的比赛。

据我理解，图像需要是灰度的，但就我而言，我认为颜色是一个非常重要的描述符？

我对 opencv 和图像匹配都很陌生，所以如果我需要使用其他方法，你能帮助我吗？

import cv2
import os
orb = cv2.ORB_create(nfeatures=1000) # Find 1000 features to match from 
bf = cv2.BFMatcher()

# Image to match
findImg = 'captainA.png'
imgCur = cv2.imread(f'{"Images"}/{findImg}', 0)
kp1,des1 = orb.detectAndCompute(imgCur,None)

# Loop through all superheroe images and find closest match
images = ["img1.png","img2.png","img3.png","img4.png","img5.png","img6.png","img7.png","img8.png","img9.png","img10.png","img11.png","img12.png"]

matchList = []
names = []
for img in images:
    imgCur = cv2.imread(f'{Superheroes}/{img}', 0)
    kp2,des2 = orb.detectAndCompute(imgCur,None)
 
    matches = bf.knnMatch(des1,des2,k=2)
    goodMatches = []
    for m, n in matches:
        if m.distance < 0.75 * n.distance: # Use 75 as a threshold defining a good match
            goodMatches.append([m])
    matchList.append(len(goodMatches))
    names.append(img)

matchIdx = matchList.index(max(matchList))
    
# Name of matched image
print(names[matchIdx])

我想找到什么：

Answer 1

这是一个小代码，应该可以完成这项工作。

from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.vgg16 import VGG16, preprocess_input
from tensorflow.keras.models import Model
import numpy as np
from PIL import Image

base_model = VGG16(weights='imagenet')
model = Model(inputs=base_model.input, outputs=base_model.get_layer('fc1').output)

def extract(img):
    img = img.resize((224, 224)) # Resize the image
    img = img.convert('RGB') # Convert the image color space
    x = image.img_to_array(img) # Reformat the image
    x = np.expand_dims(x, axis=0)
    x = preprocess_input(x)
    feature = model.predict(x)[0] # Extract Features
    return feature / np.linalg.norm(feature)

# Iterate through images and extract Features
images = ["img1.png","img2.png","img3.png","img4.png","img5.png"...+2000 more]
all_features = np.zeros(shape=(len(images),4096))

for i in range(len(images)):
    feature = extract(img=Image.open(images[i]))
    all_features[i] = np.array(feature)

# Match image
query = extract(img=Image.open("image_to_match.png")) # Extract its features
dists = np.linalg.norm(all_features - query, axis=1) # Calculate the similarity (distance) between images
ids = np.argsort(dists)[:5] # Extract 5 images that have lowest distance