def search(Image):
idealBins = (8, 12, 3) # 设定HSV的bins
kps = 100
# 打开图片，由于图名可能为中文，不能用imread
queryImage = cv2.imdecode(np.fromfile(Image, dtype=np.uint8), -1)
# 初始化颜色特征提取器和结构特征提取器
colorDescriptor = color_descriptor.ColorDescriptor(idealBins)
structureDescriptor = structure_descriptor.StructureDescriptor(kps)
colorIndexPath = "colorindex.csv"
structureIndexPath = "structureindex.csv"
#获得目标图片的颜色特征和结构特征
queryFeatures = colorDescriptor.describe(queryImage)
queryStructures = structureDescriptor.describe(queryImage)
# 初始化搜索器并进行搜索
imageSearcher = searcher.Searcher(colorIndexPath, structureIndexPath)
searchResults = imageSearcher.search(queryFeatures, queryStructures)
result = []
for imageName, score in searchResults:
#queryResult = cv2.imread(resultPath + "/" + imageName)
result.append(imageName)
return result
def __init__(self):
self.list_of_img_ids = os.listdir("./dataset")
## Color Histogram
# ColorHist for Train
# self.color_hist_searcher = searcher.Searcher("indexfull.csv")
# ColorHist for Test
self.color_hist_searcher = searcher.Searcher("indextest.csv")
## Visual Keyword
from_vis_keyword_dataset_img_file = open(
"visual_keyword_des_dataset_images.txt", "r")
self.list_surf_des = pickle.load(
from_vis_keyword_dataset_img_file) # initialize
## Visual Concept
# For train set
# self.vconcept_img_vectors = vconcept.load_visual_concept_img_vectors("semanticpickle.txt")
# For test set
self.vconcept_img_vectors = vconcept.load_visual_concept_img_vectors(
"semantictestpickle.txt")
## Text retrieval & Visual Concept (Text)
# self.postings_path = "trainset_text_tags_postings.txt"
self.postings_path = "testset_text_tags_postings.txt"
self.visual_concept_classes = text_engine.load_visual_concept_classes()
def index():
if request.method == 'POST':
file = request.files['query_img']
img = Image.open(file.stream) # PIL image
uploaded_img_path = "static/query/" + file.filename
img.save(uploaded_img_path)
idealBins = (8, 12, 3)
idealDimension = (16, 16)
colorDescriptor = color_descriptor.ColorDescriptor(idealBins)
structureDescriptor = structure_descriptor.StructureDescriptor(
idealDimension)
queryImage = cv2.imread(uploaded_img_path)
colorIndexPath = "static/color_index.csv"
structureIndexPath = "static/structure_index.csv"
resultPath = "static/dataset"
queryFeatures = colorDescriptor.describe(queryImage)
queryStructures = structureDescriptor.describe(queryImage)
imageSearcher = searcher.Searcher(colorIndexPath, structureIndexPath)
searchResults = imageSearcher.search(queryFeatures, queryStructures)
return render_template('index.html',
query_path=uploaded_img_path,
scores=searchResults)
else:
return render_template('index.html')
def __init__(self):
"""Constructor.
Raises:
Exception: ROS node does not exist
"""
argvs = sys.argv
argc = len(argvs)
# Initialize
if not (argc != 5 or argc != 7):
raise Exception('Not enough arguments')
self.output_path = argvs[1]
self.log_dir_path = argvs[2]
self.main_func_path = argvs[3]
self.fop = file_operator.FileOperator(self.output_path)
self.convert_file_path, self.data_file_path = self.fop._getWorkFilesPath(
)
self.logger = cfs_logger.CfsLogger(self.log_dir_path)
self.searcher = searcher.Searcher()
self.topic_manager = topic_manager.TopicManager(
self.convert_file_path, self.data_file_path)
self.conv = converter.Converter(self.output_path,
self.convert_file_path,
self.data_file_path, self.fop,
self.logger, self.topic_manager)
self.nh_name = None
def search(self, image):
# cd = colordescriptor.ColorDescriptor((8, 12, 3))
# cd = colordescriptor.ColorDescriptor((8, 4, 4))
cd = graydescriptor.GrayDescriptor((256))
#load the query image and describe it
query = cv2.imread(image)
features = cd.describe(query)
print("query image features extracted")
#perform the search
# s = searcher.Searcher("color_index.csv")
s = searcher.Searcher("gray_index.csv")
results = s.search(features)
print("search finished")
"""for (result, idd) in results:
cv2.imshow("Result", result)
cv2.waitKey(0)"""
return results
def find_name(queryImage):
idealBins = (8, 12, 3)
idealDimension = (16, 16)
colorDescriptor = color_descriptor.ColorDescriptor(idealBins) #掩模处理后的直方图
structureDescriptor = structure_descriptor.StructureDescriptor(
idealDimension) #HSV色彩空间的矩阵
#queryImage = cv2.imread("query.jpg")
colorIndexPath = os.path.abspath("colorindex")
structureIndexPath = os.path.abspath("structureindex")
queryFeatures = colorDescriptor.describe(queryImage) #掩模处理后的直方图
queryStructures = structureDescriptor.describe(queryImage) #HSV色彩空间的矩阵
imageSearcher = searcher.Searcher(colorIndexPath, structureIndexPath)
searchResults = imageSearcher.search(queryFeatures, queryStructures)
result = [] #图片名称所在的列表
for imageName, score in searchResults:
result.append(imageName)
return result
def __init__(self, args):
self.clusters = dict()
self.tracks = []
self.gallery = []
self.vehicle_size = defaultdict(int)
self.track_vehicle = dict()
self.topk = args.topk
self.reid_threshold = args.reid_threshold
self.max_cluster_size = args.max_cluster_size
if args.faiss_search_type == 'dot product':
self.searcher = searcher.Searcher2(feat_len=256)
elif args.faiss_search_type == 'euclid':
self.searcher = searcher.Searcher(feat_len=256)
with open(args.model_config_path, 'r') as f:
args = json.load(fp=f)
self.model = Model(args)
self.model.restore()
def main():
bot = searcher.Searcher((FIRST_EVALUATOR, SECOND_EVALUATOR))
bot.board.display()
bot.expand()
if len(sys.argv) == 1:
sys.argv.append("black")
if sys.argv[1] == "white":
computer_move_timed(bot)
while not bot.board.is_over():
if sys.argv[1] != "bot":
if bot.board.is_over():
break
human_move(bot)
if bot.board.is_over():
break
computer_move_timed(bot)
print "Final score:", " - ".join(map(str, bot.board.score()))
information_label.config(text=information)
evaluation = "Evaluation: {}".format(
round(bot.game_tree.score / 100, 2))
evaluation_label.config(text=evaluation)
if __name__ == "__main__":
# Suppress the useless output... :D
import os
import sys
sys.stdout = open(os.devnull, "w")
bot = searcher.Searcher((EVALUATOR, EVALUATOR))
bot.update_scores()
WINDOW_SIZE = 4
button = Tkinter.Button(root,
font=("Comic Sans MS", 10, "bold"),
foreground="red",
width=2 * WINDOW_SIZE - 1,
text="QUIT",
command=quit)
button.grid(row=0, column=7)
function_list = []
for row in xrange(8):
for column in xrange(8):
ap.add_argument("-i",
"--index",
required=True,
help="Path to storage directory")
ap.add_argument("-q", "--query", required=True, help="Path to query directory")
ap.add_argument("-r",
"--result-path",
required=True,
help="Path to results directory")
args = vars(ap.parse_args())
# initialize colour descriptor
cd = colourdescriptor.ColourDescriptor((8, 12, 3))
# load query and get it's features
query = cv2.imread(args["query"])
features = cd.describe(query)
# perform search
searcher = searcher.Searcher(args["index"])
results = searcher.search(features)
# display query
cv2.imshow("Query", query)
# loop over results
for (score, result_id) in results:
result = cv2.imread(result_id)
cv2.imshow("Result", result)
cv2.waitKey(0)
import searcher as ac
s = ac.Searcher(rulefile="../../test.txt")
#s = ac.Searcher(imgfile="./compiled.gz")
filename = "../../banned.txt"
fp = open(filename, "r")
content = fp.read()
def callback(s, start, end, level):
#v = from_buffer(s)
print(s[start:end + 1], level)
pass
s.search("abcdefgh", 2, callback)
searchArgParser.add_argument("-r",
"--resultpath",
required=True,
help="Path to the result path")
searchArguments = vars(searchArgParser.parse_args())
idealBins = (8, 12, 3)
idealDimension = (16, 16)
colorDescriptor = color_descriptor.ColorDescriptor(idealBins)
structureDescriptor = structure_descriptor.StructureDescriptor(idealDimension)
queryImage = cv2.imread(searchArguments["query"])
colorIndexPath = searchArguments["colorindex"]
structureIndexPath = searchArguments["structureindex"]
resultPath = searchArguments["resultpath"]
queryFeatures = colorDescriptor.describe(queryImage)
queryStructures = structureDescriptor.describe(queryImage)
imageSearcher = searcher.Searcher(colorIndexPath, structureIndexPath)
searchResults = imageSearcher.search(queryFeatures, queryStructures)
for imageName, score in searchResults:
queryResult = cv2.imread(resultPath + "/" + imageName)
cv2.imshow("Result Score: " + str(int(score)) + " (lower is better)",
queryResult)
cv2.waitKey(0)
cv2.imshow("Query", queryImage)
cv2.waitKey(0)
gr = grabber.Grabber()
gr.start()
gr.join()
thread_list = []
each = len(gr.survey_number_list) / settings.THREAD_COUNT
id_number = sys.argv[1]
for thread_index in xrange(settings.THREAD_COUNT):
start_index = each * thread_index
end_index = start_index + each - 1
survey_numbers = gr.survey_number_list[start_index:end_index]
sr = searcher.Searcher(id_number, survey_numbers)
thread_list.append(sr)
sr.start()
last_survey_numbers = gr.survey_number_list[each * settings.THREAD_COUNT:]
last_sr = searcher.Searcher(id_number, last_survey_numbers)
thread_list.append(last_sr)
last_sr.start()
failed_numbers = []
for thread in thread_list:
thread.join()
for failed_number in thread.failed_numbers:
failed_numbers.append(failed_number)
while len(failed_numbers) != 0:
while not bot.board.is_over():
if sys.argv[1] != "bot":
if bot.board.is_over():
break
human_move(bot)
if bot.board.is_over():
break
computer_move_timed(bot)
print "Final score:", " - ".join(map(str, bot.board.score()))
if __name__ == "__main__":
turn = 0
if STATS:
import cProfile
import pstats
bot = searcher.Searcher(evaluator_ab.evaluate)
cProfile.run('main()', 'main.profile')
stats = pstats.Stats('main.profile')
stats.strip_dirs().sort_stats('time').print_stats()
else:
main()
if GRAPH:
pylab.show()
def main():
DIR_PATH_NOTES = os.path.expanduser("~/Dropbox/tbrush_notes")
PORT = 38906
dir_path = os.path.expanduser(DIR_PATH_NOTES)
if not os.path.exists(dir_path):
print("created directory:", dir_path)
os.makedirs(dir_path)
search_inst = searcher.Searcher(dir_path)
app = flask.Flask(__name__)
port = int(sys.argv[1]) if len(sys.argv) == 2 else PORT
print('Starting httpserver on port', PORT)
@app.route('/search', methods=['POST'])
def search():
post_dict = request.get_json()
query_string = post_dict['query']
selected_index = post_dict.get('selected_index')
if selected_index is not None:
selected_index = int(selected_index)
print('searching for:', query_string)
result_dict = search_inst.search(query_string, selected_index)
print('results:', result_dict)
return json.dumps(result_dict, indent=2)
def monitor_filesystem():
class MyHandler(events.PatternMatchingEventHandler):
patterns = ["*.txt"]
def on_any_event(self, event):
if event.event_type == 'moved' or event.event_type == 'deleted':
search_inst.delete_path(event.src_path)
if event.event_type == 'moved':
search_inst.load_path(event.dest_path)
if event.event_type == 'created' or event.event_type == 'modified':
print('created:', event.src_path)
search_inst.load_path(event.src_path)
event_handler = MyHandler()
observer = observers.Observer()
observer.schedule(event_handler, path=DIR_PATH_NOTES, recursive=False)
observer.start()
print('monitoring:', DIR_PATH_NOTES)
try:
while True:
time.sleep(1)
except KeyboardInterrupt:
observer.stop()
observer.join()
t = threading.Thread(target=monitor_filesystem)
t.daemon = True # allow parent process to kill it
t.start()
app.jinja_env.auto_reload = True
app.config['TEMPLATES_AUTO_RELOAD'] = True
app.run(host='0.0.0.0', port=port, debug=(port != 80))
def __init__(self, parent=None):
super(Interactive, self).__init__(parent)
# work_directory = os.path.abspath(os.path.dirname(__file__))
work_directory = os.path.dirname(os.path.realpath(sys.argv[0]))
self.data_directory = work_directory + '/data/'
self.search = searcher.Searcher(self.data_directory)
