def index():
SIFT = sift_controller.SIFT()
SIFT.dump_onefile()
for img_path in glob.glob("./ResizeData/*.jpg"):
img_name = img_path.split("/")[-1]
SIFT.dump_eachfile(img_name)
# if __name__ == "__main__":
# # index()
def prefetching(query_path):
pkl_file = open("siftdump.pkl", "rb")
sift = sift_controller.SIFT()
query_img = cv2.imread(query_path, 0)
query_des = sift.extract(query_img)
input_list = []
for idx, contents in enumerate(pickleloader(pkl_file)):
id, indexed_des = parse_pkl(contents)
if (indexed_des.all()) == None:
continue
input_list.append([query_des, id, indexed_des])
del sift
pkl_file.close()
return input_list
def test(case):
correct_num = 0
top_1_correct_num = 0
top_5_correct_num = 0
sift = sift_controller.SIFT()
output_file = "noise_{}.txt".format(case)
incorrect_txt = open(output_file, "w")
forder_path = "./{}/*.jpg".format(case)
image_list = glob.glob(forder_path)
total_time = 0.
for img_path in image_list:
start_time = time.time()
# case
result = sift.search(img_path)
end_time = time.time() - start_time
total_time += end_time
filename = img_path.split("/")[2]
gt_label = filename.split("_")[2]
top_1_result = [result[0]]
top_5_result = result
if isCorrect(gt_label, top_1_result):
top_1_correct_num += 1
top_5_correct_num += 1
# print ("Correct: {}".format(gt_label))
elif isCorrect(gt_label, top_5_result):
top_5_correct_num += 1
else:
out_txt = "{} {}\n".format(img_path,
','.join(str(v) for v in result))
# print ("Wrong: {}".format(gt_label))
incorrect_txt.write(out_txt)
# print("(top 1) {} : {} / {}".format(case, top_1_correct_num, len(image_list)))
print("(top 5) {} : {} / {}".format(case, top_5_correct_num,
len(image_list)))
# print ("Total time : {}".format(total_time))
incorrect_txt.close()
def extract_feature_eachfile():
sift = sift_controller.SIFT()
sift.dump_eachfile()
del sift
def Linear_search_prefetching(query_path):
sift = sift_controller.SIFT()
result = sift.inmemory_search(query_path)
del sift
return (result)
def Linear_search_by_FLANN(query_path):
sift = sift_controller.SIFT()
result = sift.fast_search(query_path)
del sift
return (result)
def Linear_search(query_path):
sift = sift_controller.SIFT()
result = sift.search(query_path)
del sift
return (result)
def extract_feature_onedump():
sift = sift_controller.SIFT()
sift.dump_onefile()
del sift
def Linear_search(query_path):
sift = sift_controller.SIFT()
result = sift.search(query_path)
print("{} {}\n".format(img_path, ','.join(str(v) for v in result)))
del sift
def Linear_search(query_path):
sift = sift_controller.SIFT()
sift.search(query_path)
del sift
def Linear_search_by_FLANN(query_path):
sift = sift_controller.SIFT()
sift.fast_search(query_path)
del sift