def compute_feature():
features_path = "/home/ashish/dip/chap7/features_test/"
images_path = "/home/ashish/dip/chap7/images_test/"
images = imlist.get_imlist(images_path) #Get the list of images
nbr_images = len(images)
kplist = []
deslist = []
for img in images: #Create KP and desc files for each image
img_txt = os.path.splitext(img)[0]
kp_file = features_path + img_txt + "kp" + ".txt"
des_file = features_path + img_txt + "des" + ".txt"
kplist.append(kp_file)
deslist.append(des_file)
kp, des = SIFT.SIFT(images_path + img)
np.savetxt(kp_file, kp, fmt="%s")
np.savetxt(des_file, des, fmt="%s")
return deslist
def __init__(self, model_name, folder_path=None):
self.model_name = model_name
self.folder_path = folder_path
self.split_windows = False
self.model = None
if self.model_name == 'LBP':
self.model = LBP.LocalBinaryPatterns(8, 1)
self.split_windows = True
elif self.model_name == 'HOG':
self.model = HOG.Hog(orientations=9, pixels_per_cell=(8, 8), cells_per_block=(2, 2))
elif self.model_name == 'CM':
self.model = ColorMoments.ColorMoments()
self.split_windows = True
elif self.model_name == 'SIFT':
self.model = SIFT.SIFT()
def compareEfficiency():
folder = "DibujosMuestra/dataset2_reyCopia/reyCopia"
imgs = load_imgs(folder)
t_0 = time()
for img in imgs:
sift = SIFT.SIFT({
"s": 3, #Pg 9 of Lowe's paper
"sigma": 1.6, #Pg 10 of Lowe's paper
"visual_debug": True,
"img_name":
"/home/alberto/Documents/CV/M0_SIFT/fotonoticia_20200402133510_420.jpg", #Only works if img is not defined
"img": img,
"assumed_blur": 0.5, #Pg 10 of Lowe's paper
"detection_threshold": 10, #???
"contrast_threshold": 0.04,
"eigenvalue_ratio": 10,
"convergence_attempts": 5,
"image_border_width": 5,
"radius_factor": 3,
"num_bins": 36,
"peak_ratio": 0.8,
"scale_factor": 1.5,
"float_tol": 1e-7,
"window_width": 4,
"num_bins_descriptor": 8,
"scale_multiplier": 3,
"descriptor_max_val": 0.2
})
sift.calculateKeyPoints()
print(f"The custom implementation needed {(time()-t_0)*1000} miliseconds")
t_0 = time()
for img in imgs:
sift = cv2.SIFT_create()
kp = sift.detect(img, None)
print(f"The OpenCV implementation needed {(time()-t_0)*1000} miliseconds")
# Calculando tempo em milisegundos
lr2m_times.append(round((end - start) * 1000, 2))
# Executando algoritmo com RGB
start = t.time()
image = lr2m.lr2MatchingRGB(A, B)
end = t.time()
lr2m.saveImage(testsPath + "LR2M_RGB_" + i[0], image)
# Calculando tempo em milisegundos
lr2mRGB_times.append(round((end - start) * 1000, 2))
# Executando SIFT
start = t.time()
image = sift.SIFT(A_SIFT, B_SIFT)
end = t.time()
sift.saveImage(testsPath + "SIFT_" + i[0], image)
# Calculando tempo em milisegundos
SIFT_times.append(round((end - start) * 1000, 2))
# Definindo valores para gráfico
testCount += 1
values.append("T" + str(testCount))
# print(values)
# print(lr2m_times)
# print(lr2mRGB_times)
# print(SIFT_times)
if matches_21[int(matches_12[n])] != n:
matches_12[n] = 0
return matches_12
for img in images:
img_txt = os.path.splitext(img)[0]
featlist.append(img_txt + "feat" + ".txt")
deslist.append(img_txt + "des" + ".txt")
feature_file = img_txt + "feat" + ".txt"
des_file = img_txt + "des" + ".txt"
f_kp = open(img_txt + "feat" + ".txt", 'w+')
f_des = open(img_txt + "des" + ".txt", 'w+')
kp, des = SIFT.SIFT(path + img)
np.savetxt(feature_file, kp, fmt="%s")
np.savetxt(des_file, des, fmt="%s")
nbr_images = len(images)
matchscores = np.zeros((nbr_images, nbr_images))
for i in range(nbr_images):
for j in range(i, nbr_images):
#print 'comparing', imlist[i], imlist[j]
l1 = read_features_from_file(featlist[i])
l2 = read_features_from_file(featlist[j])
d1 = read_features_from_file(deslist[i])
d2 = read_features_from_file(deslist[j])
des1 = d1.astype(np.float)
"contrast_threshold":0.04,
"eigenvalue_ratio":10,
"img":None,
"convergence_attempts":5,
"image_border_width":5,
"radius_factor":3,
"num_bins":36,
"peak_ratio":0.8,
"scale_factor":1.5,
"float_tol":1e-7,
"window_width":4,
"num_bins_descriptor":8,
"scale_multiplier":3,
"descriptor_max_val":0.2
}
if __name__=="__main__":
sift=SIFT.SIFT(params)
sift.calculateKeyPoints()
#print(sift.DoG)
for i,octave in enumerate(sift.octaves):
for j,img in enumerate(octave):
im = Image.fromarray(img).convert('RGB')
im.save(f'imagesTest/Octave{i}img{j}.jpg')
for i,octave in enumerate(sift.DoG):
for j,img in enumerate(octave):
im = Image.fromarray(img*25).convert('RGB')
im.save(f'imagesTest/DoGOctave{i}img{j}.jpg')
# -*- coding: utf-8 -*-
"""
Created on Sun Apr 21 17:12:04 2019
@author: VÕQUỐCHUY
"""
import numpy as np
import cv2
import sys
import convolution
import Harris
import SIFT
img = cv2.imread("./Input/flowers.jpeg", 0)
siftDetector = SIFT.SIFT(img, 3, 0.707, np.sqrt(2), 5, 4)
listOfOctaves = siftDetector.createImgs_AtMultipleOctaves()
#listOfDoG = siftDetector.calDOG_AtMultipleOctaves()
#listOfKeypoints = siftDetector.findApproxKeypoints()
listOfKeypoints = siftDetector.orientationAssignmentAndKeypointDescription()
#cv2.namedWindow('output',cv2.WINDOW_AUTOSIZE)
#cv2.imshow('output',img)
'''for i in range(len(listOfOctaves)):
for j in range(len(listOfOctaves[i])):
cv2.namedWindow('Output at octave'+str(i)+' scale '+str(j),cv2.WINDOW_AUTOSIZE)
cv2.imshow('Output at octave'+str(i)+' scale '+str(j),listOfOctaves[i][j][0])'''
'''for i in range(len(listOfDoG)):
for j in range(len(listOfDoG[i])):
cv2.namedWindow('Output at octave'+str(i)+' scale '+str(j),cv2.WINDOW_AUTOSIZE)
