tigercosmos · June 7, 2022 12:47
diff --git a/bovw.py b/bovw.py
 #!/usr/bin/env python
 # coding: utf-8

 import numpy as np
 import cv2
 import os
 import math
 from cyvlfeat.kmeans import kmeans
 from scipy import ndimage
 from scipy.spatial import distance
 from tqdm import tqdm
 import pickle
 from cyvlfeat.kmeans import kmeans
 from cyvlfeat.sift.dsift import dsift
 from libsvm.svmutil import *

 """
 Image Hierarchy:

 data
 - train
  - class1
  - class2
  - ...
 - test
  - class1
  - class2
  - ...
 """
 def get_images(path, size):
    total_pic = {}
    labels = []
    for i, doc in enumerate(os.listdir(path)):
        tmp = []
        for file in os.listdir(os.path.join(path, doc)):      
            
            if file.endswith(".jpg"):
                img = cv2.imread(os.path.join(path, doc, file), cv2.IMREAD_GRAYSCALE)
                pic = cv2.resize(img, (size, size))
                tmp.append(pic)
                labels.append(i)
        total_pic[doc] = tmp
    return total_pic, labels



 # get images with resize
 train, train_digit_labels = get_images('./data/train/', 256)
 test, test_digit_labels = get_images('./data/test/', 256)


 # visual_words
 def sift_features(images, size):
    print("feature number", size)

    bag_of_features = []
    print("Extract SIFT features...")
    for key, value in tqdm(images.items()):
        for img in value:
            # orb = cv2.xfeatures2d.SIFT_create(500)
            # orb = cv2.ORB_create()
            # keypoints, descriptors = orb.detectAndCompute(img, None)
            _, descriptors = dsift(img, step=[5,5], fast=True)
            if descriptors is not None:
                for des in descriptors:
                    bag_of_features.append(des)
    
    print("Compute kmeans in dimensions:", size)
    
    km = kmeans(np.array(bag_of_features).astype('float32'), size, initialization="PLUSPLUS")    

    return km


 features = sift_features(train, size=15)


 def image_class(images, features):
    image_feats = []
    print("Construct bags of sifts...")
    
    for key, value in tqdm(images.items()):
        empty = [0 for i in range(0, len(features))]
        
        for img in value:
            # orb = cv2.ORB_create()
            # orb = cv2.xfeatures2d.SIFT_create()
            # keypoints, descriptors = orb.detectAndCompute(img, None)
            _, descriptors = dsift(img, step=[5,5], fast=True)
            if descriptors is not None:
                dist = distance.cdist(features, descriptors, metric='euclidean')
                
                idx = np.argmin(dist, axis=0) 
                hist, bin_edges = np.histogram(idx, bins=len(features))
                hist_norm = [float(i)/sum(hist) for i in hist]
                image_feats.append(hist_norm)
            else:
                print("NONE")
                image_feats.append(empty)
        
    image_feats = np.asarray(image_feats)
    return image_feats


 bovw_train = image_class(train, features) 
 bovw_test = image_class(test, features) 


 def nearest_neighbor_classify(train_image_feats, train_labels, test_image_feats, K=50):
    dist = distance.cdist(test_image_feats, train_image_feats, metric='euclidean')
    
    test_predicts = []
    
    for test in dist:
        label_count = {}
        for key in train.keys():
            label_count[key] = 0

        idx = np.argsort(test)
        for i in range(K):
            cat = train_labels[idx[i]]
            label_count[cat] += 1
        

        final = ""
        max_value = 0
        for key in label_count:
            if label_count[key] > max_value:
                final = key
                max_value = label_count[key]
                
        test_predicts.append(final)
    
    return test_predicts


 # In[112]:


 train_labels = np.array([item for item in train.keys() for i in range(100)])
 test_labels = np.array([item for item in test.keys() for i in range(10)])
 knn = nearest_neighbor_classify(bovw_train, train_labels, bovw_test)


 # In[114]:


 def accuracy(results, test_labels):
    num_correct = 0
    for i, res in enumerate(results):
        if res == test_labels[i]:
            num_correct += 1
    return num_correct / len(results)

 print("Bag of SIFT representation & nearest neighbor classifier \nAccuracy score: {:.1%}".format(accuracy(knn, test_labels)))

 # -e: tolerance of termination criterion
 # -t 0: linear kernel
 # -c: parameter C of C-SVC
 m = svm_train(train_digit_labels, bovw_train, '-c 700 -e 0.0001 -t 0')
 p_label, p_acc, p_val = svm_predict(test_digit_labels, bovw_test, m)

 print("Bag of SIFT representation and linear SVM classifier\nAccuracy score: {:.1%}".format(p_acc))
	#!/usr/bin/env python
	# coding: utf-8

	import numpy as np
	import cv2
	import os
	import math
	from cyvlfeat.kmeans import kmeans
	from scipy import ndimage
	from scipy.spatial import distance
	from tqdm import tqdm
	import pickle
	from cyvlfeat.kmeans import kmeans
	from cyvlfeat.sift.dsift import dsift
	from libsvm.svmutil import *

	"""
	Image Hierarchy:

	data
	- train
	- class1
	- class2
	- ...
	- test
	- class1
	- class2
	- ...
	"""
	def get_images(path, size):
	total_pic = {}
	labels = []
	for i, doc in enumerate(os.listdir(path)):
	tmp = []
	for file in os.listdir(os.path.join(path, doc)):

	if file.endswith(".jpg"):
	img = cv2.imread(os.path.join(path, doc, file), cv2.IMREAD_GRAYSCALE)
	pic = cv2.resize(img, (size, size))
	tmp.append(pic)
	labels.append(i)
	total_pic[doc] = tmp
	return total_pic, labels



	# get images with resize
	train, train_digit_labels = get_images('./data/train/', 256)
	test, test_digit_labels = get_images('./data/test/', 256)


	# visual_words
	def sift_features(images, size):
	print("feature number", size)

	bag_of_features = []
	print("Extract SIFT features...")
	for key, value in tqdm(images.items()):
	for img in value:
	# orb = cv2.xfeatures2d.SIFT_create(500)
	# orb = cv2.ORB_create()
	# keypoints, descriptors = orb.detectAndCompute(img, None)
	_, descriptors = dsift(img, step=[5,5], fast=True)
	if descriptors is not None:
	for des in descriptors:
	bag_of_features.append(des)

	print("Compute kmeans in dimensions:", size)

	km = kmeans(np.array(bag_of_features).astype('float32'), size, initialization="PLUSPLUS")

	return km


	features = sift_features(train, size=15)


	def image_class(images, features):
	image_feats = []
	print("Construct bags of sifts...")

	for key, value in tqdm(images.items()):
	empty = [0 for i in range(0, len(features))]

	for img in value:
	# orb = cv2.ORB_create()
	# orb = cv2.xfeatures2d.SIFT_create()
	# keypoints, descriptors = orb.detectAndCompute(img, None)
	_, descriptors = dsift(img, step=[5,5], fast=True)
	if descriptors is not None:
	dist = distance.cdist(features, descriptors, metric='euclidean')

	idx = np.argmin(dist, axis=0)
	hist, bin_edges = np.histogram(idx, bins=len(features))
	hist_norm = [float(i)/sum(hist) for i in hist]
	image_feats.append(hist_norm)
	else:
	print("NONE")
	image_feats.append(empty)

	image_feats = np.asarray(image_feats)
	return image_feats


	bovw_train = image_class(train, features)
	bovw_test = image_class(test, features)


	def nearest_neighbor_classify(train_image_feats, train_labels, test_image_feats, K=50):
	dist = distance.cdist(test_image_feats, train_image_feats, metric='euclidean')

	test_predicts = []

	for test in dist:
	label_count = {}
	for key in train.keys():
	label_count[key] = 0

	idx = np.argsort(test)
	for i in range(K):
	cat = train_labels[idx[i]]
	label_count[cat] += 1


	final = ""
	max_value = 0
	for key in label_count:
	if label_count[key] > max_value:
	final = key
	max_value = label_count[key]

	test_predicts.append(final)

	return test_predicts


	# In[112]:


	train_labels = np.array([item for item in train.keys() for i in range(100)])
	test_labels = np.array([item for item in test.keys() for i in range(10)])
	knn = nearest_neighbor_classify(bovw_train, train_labels, bovw_test)


	# In[114]:


	def accuracy(results, test_labels):
	num_correct = 0
	for i, res in enumerate(results):
	if res == test_labels[i]:
	num_correct += 1
	return num_correct / len(results)

	print("Bag of SIFT representation & nearest neighbor classifier \nAccuracy score: {:.1%}".format(accuracy(knn, test_labels)))

	# -e: tolerance of termination criterion
	# -t 0: linear kernel
	# -c: parameter C of C-SVC
	m = svm_train(train_digit_labels, bovw_train, '-c 700 -e 0.0001 -t 0')
	p_label, p_acc, p_val = svm_predict(test_digit_labels, bovw_test, m)

	print("Bag of SIFT representation and linear SVM classifier\nAccuracy score: {:.1%}".format(p_acc))