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)
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):
if i != j and i > j:
np_conf_mat[i][j] = np_conf_mat[j][i]
plt.figure(0)
c = plt.imshow(np_conf_mat, interpolation="none")
plt.colorbar(c)
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)
cv2.imshow('frame', grey_scale_image)
k = cv2.waitKey(33) & 0xFF
if k == 27: # Esc key to stop
break
elif k == -1: # normally -1 returned,so don't print it
continue
elif k == ord('c'):
ezsift_matcher.add_reference_image(str(angles_to_capture[current]), grey_scale_image)
print "Reference Added", angles_to_capture[current]
current += 1
if current >= len(angles_to_capture):
cap = False
time.sleep(0.1)
for row in ezsift_matcher.get_reference_image_confusion_matrix():
print row
while True:
gray = vidgrab.grab_frame_return_grey()
grey_scale_image = cv2.cvtColor(gray, cv2.COLOR_BGR2GRAY)
grey_scale_image = np.array(grey_scale_image)
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)
while True:
gray, color = video_grabber.grab_frame_return_grey()
gray = scipy.misc.imresize(gray, 1.0) #grey_scale_image = cv2.cvtColor(gray, cv2.COLOR_BGR2GRAY)
#grey_scale_image = np.array(grey_scale_image)
