Simple image classifier using Opencv and a Bag of Words model

classifier.py

import cv2
import numpy as np
from tkinter import Tk
from tkinter.filedialog import askopenfilename

descriptors = []

# evaluate the similarity between two point clouds
# enormous robustness against outliers is necessary
def score(a, b):
  result = []
  for i in range(a.shape[1]):
    best, bestd = 0, float("inf")
    for j in range(b.shape[0]):
      d = cv2.norm(a[i,:], b[j,:], cv2.NORM_L1)
      if d < bestd:
        best = j
        bestd = d
    result.append(bestd)
  # return the sum of 100 smallest distances
  result.sort()
  return sum(result[0:100])

def add_score_row(desc):
  index = len(descriptors)
  for other in descriptors:
    sc = round(score(desc, other))
    print(sc, end="\t")
  print()

def draw_keypoints(keypoints, mat):
  color = (255, 128, 0, 25)
  for kp in keypoints:
    pt = (round(kp.pt[0]), round(kp.pt[1]))
    cv2.circle(mat, pt, round(kp.size), color)
  cv2.imshow("canvas", mat)
  cv2.waitKey(10)

def img_onload(filename):
  mat = cv2.imread(filename)
  while mat.shape[1] + mat.shape[0] > 1000:
    mat = cv2.pyrDown(mat)
  detector = cv2.AKAZE.create()
  keypoints, result = detector.compute(mat, detector.detect(mat))
  add_score_row(result)
  draw_keypoints(keypoints, mat)
  descriptors.append(result)

# the GUI part does cannot be ported that directly from HTML
# it just does not work the same
Tk().withdraw()
while True:
  name = askopenfilename()
  if not name:
    break
  img_onload(name)