The scenario is, Let's suppose I took random images from my Gallery and put in some folder, and also print these image in hard form. Now I turned on my laptop camera and place an image in front of camera, My code need the tell the name of matching image from the folder. Actually the code I have add in the question works fine but when I increases the number of image in folder then Identification start taking more time. I want to know, is there any method that I can try to improve my speed? Here is the working Demo of my scenario.
import cv2
import os
from pathlib import Path
import time
# Define path
pathImages = 'ImagesQuery'
# Set the Number of detected Features
orb = cv2.ORB.create(nfeatures=1000)
# Set the threshold of minimum Features detected to give a positive, around 20 to 30
thres = 27
# List Images and Print out their Names and how many there are in the Folder
images = []
classNamesImages = []
myListImages = os.listdir(pathImages)
print(myListImages)
print('Total Classes Detected', len(myListImages))
# this will read in the images
for cl in myListImages:
imgCur = cv2.imread(f'{pathImages}/{cl}', 0)
images.append(imgCur)
# delete the file extension
classNamesImages.append(os.path.splitext(cl)[0])
print(classNamesImages)
# this will find the matching points in the images
def findDes(images):
desList = []
for img in images:
kp, des = orb.detectAndCompute(img, None)
desList.append(des)
return desList
# this will compare the matches and find the corresponding image
def findID(img, desList):
image_matching_time = time.time()
kp2, des2 = orb.detectAndCompute(img, None)
bf = cv2.BFMatcher()
matchList = []
finalVal = -1
try:
for des in desList:
matches = bf.knnMatch(des, des2, k=2)
good = []
for m, n in matches:
# the distance of the matches in comparison to each other
if m.distance < 0.75 * n.distance:
good.append([m])
matchList.append(len(good))
except:
pass
# uncomment to see how many positive matches, according to this the thres is set
# print(matchList)
if len(matchList) != 0:
if max(matchList) > thres:
finalVal = matchList.index(max(matchList))
print("matching time --- %s seconds ---" % (time.time() - image_matching_time))
return finalVal
desList = findDes(images)
print(len(desList))
# open Webcam
cap = cv2.VideoCapture(1)
while True:
success, img2 = cap.read()
imgOriginal = img2.copy()
# convert Camera to Grayscale
img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
# if Matching with Image in List, send the respective Name
id = findID(img2, desList)
if id != -1:
# put text for the found Image
cv2.putText(imgOriginal, classNamesImages[id], (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 3)
# show the final Image
cv2.imshow('img2', imgOriginal)
cv2.waitKey(1)
I have tried with deep learning base solution but it did't work. Second I have tried FLANN feature matcher its showing the same time delay 0.4 sec as in upper shown code. I am looking for any fast solution. Here is a sample of original image from folder and query image from the camera.
[Edit]: Number of query images is around 1000 and I want to compare 20 fps with all query images.
