def test_list_flattening(self): ezsift_matcher = EZSiftImageMatcher() logo_1 = "example.png" image = cv2.imread(os.path.abspath(logo_1)) grey_scale_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) grey_scale_image_1 = np.array(grey_scale_image) ezsift_matcher.add_reference_image(logo_1, grey_scale_image_1) logo_2 = "logo2.png" image = cv2.imread(os.path.abspath(logo_2)) grey_scale_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) grey_scale_image_2 = np.array(grey_scale_image) ezsift_matcher.add_reference_image(logo_2, grey_scale_image_2) real_photo = "index.png" image = cv2.imread(os.path.abspath(real_photo)) grey_scale_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) grey_scale_image_3 = np.array(grey_scale_image) print ezsift_matcher.match(grey_scale_image_3)
#!/usr/bin/env python # -*- coding:utf-8 -*- import itertools import cv2 from sklearn import manifold from ezsift_wrapper import EZSiftImageMatcher from embedding_data import StudyImageMDSVisualizer2D import numpy as np import matplotlib.pyplot as plt color_cycle = itertools.cycle([[255, 0, 0], [0, 255, 0], [0, 255, 0]]) ezsift_matcher = EZSiftImageMatcher() num_images = 100 for i in range(0, num_images, 1): path = "./img/image-{}.png".format(i) print path img1 = cv2.imread(path) g1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY) ezsift_matcher.add_reference_image(str(i), g1) conf_matrix = ezsift_matcher.get_reference_image_confusion_matrix() np_conf_mat = np.array(conf_matrix) for i in range(num_images): for j in range(num_images):
logo_1 = "left.png" image = misc.imread(logo_1, flatten=True) #cv2.imread(os.path.abspath(logo_1)) import matplotlib.pyplot as plt #grey_scale_image1 = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) #grey_scale_image1 = np.array(grey_scale_image1) ezsift_matcher.add_reference_image(logo_1, image) logo_2 = "feld.png" image = misc.imread(logo_2, flatten=True) #cv2.imread(os.path.abspath(logo_2)) #grey_scale_image2 = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) #grey_scale_image2 = np.array(grey_scale_image2) ezsift_matcher.add_reference_image(logo_2, image) """ ezsift_matcher = EZSiftImageMatcher() # ML Capture Part: vidgrab = VideoGrabber(1) angles_to_capture = [90, 45, 0, -45, -90] current = 0 cap = True while cap: gray = vidgrab.grab_frame_return_grey() grey_scale_image = cv2.cvtColor(gray, cv2.COLOR_BGR2GRAY) grey_scale_image = np.array(grey_scale_image)
import itertools from scipy import misc __author__ = 'sheepy' from ezsift_wrapper import EZSiftImageMatcher import scipy.misc from fileimagegrabber import ImageFromFileGrabber color_cycle = itertools.cycle([[255,0,0], [0, 255, 0]]) video_grabber = ImageFromFileGrabber(os.path.abspath("data/")) ezsift_matcher = EZSiftImageMatcher() logo_1 = "left.png" image = misc.imread(logo_1, flatten=True) #cv2.imread(os.path.abspath(logo_1)) import matplotlib.pyplot as plt #grey_scale_image1 = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) #grey_scale_image1 = np.array(grey_scale_image1) ezsift_matcher.add_reference_image(logo_1, image) logo_2 = "feld.png" image = misc.imread(logo_2, flatten=True) #cv2.imread(os.path.abspath(logo_2)) #grey_scale_image2 = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) #grey_scale_image2 = np.array(grey_scale_image2) ezsift_matcher.add_reference_image(logo_2, image)