def query(image_path):
#"""
indexes = create_bag_words.describe_image(image_path, flann, codebook)
image_scores = create_bag_words.query_images_in_database(indexes, cur)
print image_scores
return image_scores
"""
img = cv2.imread(image_path)
if len(img.shape)>2:
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
keypoints, descriptors = descriptor_extractor.get_key_points_from_img(img)
#indexes = image_encode(descriptors, flann, codebook, 1)
descriptors = np.array(descriptors, dtype=np.int)
indexes, dists = flann.nn_index(descriptors, 1);
#indexes = create_bag_words.describe_image(image_path, flann, codebook)
#image_scores = create_bag_words.query_images_in_database(indexes, cur)
image_scores = {}
image_maps = {}
votes = {}
#print len(keypoints), len(descriptors), len(indexes)
for i in range(len(indexes)):
cur.execute("select image_id,dx,dy from word_record where code_id = %d"%(indexes[i]))
rows = cur.fetchall()
print rows
for r in rows:
votes.setdefault(r[0],[]).append([r[1],r[2]])
for image_id in votes.keys():
if len(votes[image_id]) < 5:
continue
image_map = np.zeros(img.shape)
for vote in votes[image_id]:
scale = keypoints[i].size
gradient = math.radians(keypoints[i].angle)
d_x = vote[0]*scale
d_y = vote[1]*scale
#print d_y, d_x
tx = math.cos(-gradient) * d_x - math.sin(-gradient) * d_y
ty = math.sin(-gradient) * d_x + math.cos(-gradient) * d_y
#print tx, ty
predicted_center_x = int(round(keypoints[i].pt[0]+tx))
predicted_center_y = int(round(keypoints[i].pt[1]+ty))
#print attributes[i][0], attributes[i][1]
#print attributes[i][0], attributes[i][1],
#print predicted_center_x, predicted_center_y
if (predicted_center_x>=0) and (predicted_center_y>=0) and (predicted_center_x<img.shape[1]) and (predicted_center_y<img.shape[0]):
image_map[predicted_center_y][predicted_center_x] += 1.0
#image_scores[image_id] = scipy.ndimage.gaussian_filter(image_map, sigma=10).max()
image_scores[image_id] = scipy.ndimage.uniform_filter(image_map, size=30).max()
print image_scores[image_id]
return image_scores
"""
print image_scores
return image_scores
def query(image_path):
# """
indexes = create_bag_words.describe_image(image_path, flann, codebook)
image_scores = create_bag_words.query_images_in_database(indexes, cur)
print image_scores
return image_scores
"""
img = cv2.imread(image_path)
if len(img.shape)>2:
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
keypoints, descriptors = descriptor_extractor.get_key_points_from_img(img)
#indexes = image_encode(descriptors, flann, codebook, 1)
descriptors = np.array(descriptors, dtype=np.int)
indexes, dists = flann.nn_index(descriptors, 1);
#indexes = create_bag_words.describe_image(image_path, flann, codebook)
#image_scores = create_bag_words.query_images_in_database(indexes, cur)
image_scores = {}
image_maps = {}
votes = {}
#print len(keypoints), len(descriptors), len(indexes)
for i in range(len(indexes)):
cur.execute("select image_id,dx,dy from word_record where code_id = %d"%(indexes[i]))
rows = cur.fetchall()
print rows
for r in rows:
votes.setdefault(r[0],[]).append([r[1],r[2]])
for image_id in votes.keys():
if len(votes[image_id]) < 5:
continue
image_map = np.zeros(img.shape)
for vote in votes[image_id]:
scale = keypoints[i].size
gradient = math.radians(keypoints[i].angle)
d_x = vote[0]*scale
d_y = vote[1]*scale
#print d_y, d_x
tx = math.cos(-gradient) * d_x - math.sin(-gradient) * d_y
ty = math.sin(-gradient) * d_x + math.cos(-gradient) * d_y
#print tx, ty
predicted_center_x = int(round(keypoints[i].pt[0]+tx))
predicted_center_y = int(round(keypoints[i].pt[1]+ty))
#print attributes[i][0], attributes[i][1]
#print attributes[i][0], attributes[i][1],
#print predicted_center_x, predicted_center_y
if (predicted_center_x>=0) and (predicted_center_y>=0) and (predicted_center_x<img.shape[1]) and (predicted_center_y<img.shape[0]):
image_map[predicted_center_y][predicted_center_x] += 1.0
#image_scores[image_id] = scipy.ndimage.gaussian_filter(image_map, sigma=10).max()
image_scores[image_id] = scipy.ndimage.uniform_filter(image_map, size=30).max()
print image_scores[image_id]
return image_scores
"""
print image_scores
return image_scores
image_path = "./oxford02.jpg"
image_path = "./oxford_street_.jpg"
image_path = "./Curso-de-ingles-en-Oxford-Inglaterra__0000000003000000000093002.gif"
image_path = "./Oxford_Balloon_1.jpg"
image_path = "./s9114855.jpg"
codebook = create_bag_words.generate_code_book()
flann = create_bag_words.load_index(codebook)
image_codes = cPickle.load(open('image_codes.txt', 'rb'))
inverted_index = cPickle.load(open('inverted_index.txt', 'rb'))
files = os.listdir("./data/oxford5000/")
print "start query"
start = time.time()
indexes = create_bag_words.describe_image(image_path, flann, codebook)
image_scores = create_bag_words.query_images(indexes, inverted_index,
image_codes)
print indexes.values()
print len(indexes.keys())
"""
image_scores = {}
for key in indexes.keys():
for image_index in inverted_index[key]:
image_scores[image_index] = image_scores.setdefault(image_index, 0) +\
float(min(indexes[key], image_codes[image_index][key])) / max(indexes[key], image_codes[image_index][key])
"""
sorted_image_scores = sorted(image_scores.iteritems(),
key=operator.itemgetter(1))
image_path = "./oxford02.jpg"
image_path = "./oxford_street_.jpg"
image_path = "./Curso-de-ingles-en-Oxford-Inglaterra__0000000003000000000093002.gif"
image_path = "./Oxford_Balloon_1.jpg"
image_path = "./s9114855.jpg"
codebook = create_bag_words.generate_code_book()
flann = create_bag_words.load_index(codebook)
image_codes = cPickle.load(open("image_codes.txt", "rb"))
inverted_index = cPickle.load(open("inverted_index.txt", "rb"))
files = os.listdir("./data/oxford5000/")
print "start query"
start = time.time()
indexes = create_bag_words.describe_image(image_path, flann, codebook)
image_scores = create_bag_words.query_images(indexes, inverted_index, image_codes)
print indexes.values()
print len(indexes.keys())
"""
image_scores = {}
for key in indexes.keys():
for image_index in inverted_index[key]:
image_scores[image_index] = image_scores.setdefault(image_index, 0) +\
float(min(indexes[key], image_codes[image_index][key])) / max(indexes[key], image_codes[image_index][key])
"""
sorted_image_scores = sorted(image_scores.iteritems(), key=operator.itemgetter(1))
print sorted_image_scores
