added FAISS Searcher
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parent
ad4a95e000
commit
647a8778ba
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@ -0,0 +1,33 @@
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from pathlib import Path
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import tqdm
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import LFUtilities
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import BEBLIDExtractor as lf
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import argparse
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import os
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='BEBLID bulk extraction')
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parser.add_argument('src', type=str, help='src folder file containing a list of img paths')
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parser.add_argument('dest', type=str, help='BEBLID dest file')
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args = parser.parse_args()
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src = args.src
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dest = args.dest
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with open(src, 'r') as src_file:
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dataset = []
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print('Extracting lf...')
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for line in src_file:
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try:
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kp, des = lf.extract(line.strip())
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dataset.append((kp, des))
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except:
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print("cannot process '%s'" % line)
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pass
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LFUtilities.save(dataset, dest)
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print('lf extracted.')
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@ -0,0 +1,33 @@
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from pathlib import Path
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import tqdm
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import LFUtilities
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import BEBLIDExtractor as lf
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import argparse
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import os
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='BEBLID bulk extraction')
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parser.add_argument('src', type=str, help='text file containing a list of img paths')
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parser.add_argument('dest', type=str, help='BEBLID dest file')
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args = parser.parse_args()
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src = args.src
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dest = args.dest
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with open(src, 'r') as src_file:
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dataset = []
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print('Extracting lf...')
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for line in src_file:
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try:
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kp, des = lf.extract(line.strip())
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dataset.append((kp, des))
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except:
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print("cannot process '%s'" % line)
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pass
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LFUtilities.save(dataset, dest)
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print('lf extracted.')
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@ -0,0 +1,19 @@
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import cv2
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from pathlib import Path
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import tqdm
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import pickle
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import os
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import LFUtilities
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import BEBLIDParameters as params
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detector = cv2.ORB_create(params.KEYPOINTS)
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descriptor = cv2.xfeatures2d.BEBLID_create(0.75)
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def extract(img_path):
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img = LFUtilities.resize(params.IMG_SIZE, cv2.imread(img_path))
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kp = detector.detect(img, None)
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kp, des = descriptor.compute(img, kp)
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return (kp, des)
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@ -0,0 +1,5 @@
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NN_MATCH_RATIO = 0.8
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MIN_GOOD_MATCHES = 12
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MIN_INLIERS = 10
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KEYPOINTS = 500
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IMG_SIZE = 500
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@ -0,0 +1,70 @@
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import cv2
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import numpy as np
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import LFUtilities
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import BEBLIDParameters
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import beniculturaliSettings as settings
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class BEBLIDRescorer:
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def __init__(self):
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#self.lf = LFUtilities.load(settings.DATASET_BEBLID)
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#self.ids = np.loadtxt(settings.DATASET_IDS, dtype=str).tolist()
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#self.bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
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self.bf = cv2.DescriptorMatcher_create(cv2.DescriptorMatcher_BRUTEFORCE_HAMMING)
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def rescore_by_id(self, query_id, resultset):
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query_idx = self.ids.index(query_id)
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query = LFUtilities.load_img_lf(settings.DATASET_BEBLID, query_id)
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return self.rescore_by_img(query, resultset)
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def rescore_by_img(self, query, resultset):
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max_inliers = -1
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res = []
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counter = 0
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for data_id, _ in resultset:
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try:
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data_el = LFUtilities.load_img_lf(settings.DATASET_LF_FOLDER, data_id)
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nn_matches = self.bf.knnMatch(query[1], data_el[1], 2)
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good = [m for m, n in nn_matches if m.distance < BEBLIDParameters.NN_MATCH_RATIO * n.distance]
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if len(good) > BEBLIDParameters.MIN_GOOD_MATCHES:
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src_pts = np.float32([query[0][m.queryIdx].pt for m in good]).reshape(-1, 1, 2)
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dst_pts = np.float32([data_el[0][m.trainIdx].pt for m in good]).reshape(-1, 1, 2)
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M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 1.0)
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matches_mask = mask.ravel().tolist()
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# print(len(good))
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inliers = np.count_nonzero(matches_mask)
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# print(inliers)
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if (inliers >= BEBLIDParameters.MIN_INLIERS and inliers > max_inliers):
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max_inliers = inliers
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res.append((data_id, round(inliers/len(good), 3)))
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print(f'candidate n. {counter}')
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except:
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print('rescore error evaluating ' + data_id)
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pass
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counter += 1
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if res:
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res.sort(key=lambda result: result[1], reverse=True)
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return res
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def add(self, lf):
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self.lf.append(lf)
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def remove(self, idx):
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self.descs = np.delete(self.descs, idx, axis=0)
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def save(self, is_backup=False):
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lf_save_file = settings.DATASET_LF
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ids_file = settings.DATASET_IDS_LF
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if lf_save_file != "None":
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if is_backup:
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lf_save_file += '.bak'
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ids_file += '.bak'
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LFUtilities.save(lf_save_file, self.lf)
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np.savetxt(ids_file, self.ids, fmt='%s')
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@ -7,4 +7,4 @@ def extract(img_path):
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files = {'image': ('img', open(img_path, 'rb'))}
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data = {'resize': 'true', 'bw': 'true'}
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r = requests.post(settings.feature_extractor, data=data, files=files)
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return np.array(r.json())
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return np.array(r.json(), dtype='f')
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@ -2,7 +2,7 @@ from pathlib import Path
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import tqdm
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import LFUtilities
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import ORBExtractor as lf
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import BEBLIDExtractor as lf
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import argparse
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import os
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@ -0,0 +1,32 @@
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from pathlib import Path
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import tqdm
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import LFUtilities
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import BEBLIDExtractor as lf
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import argparse
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import os
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='LF bulk extraction')
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parser.add_argument('src', type=str, help='img src folder path')
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parser.add_argument('dest', type=str, help='lf dest folder')
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args = parser.parse_args()
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src = args.src
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dest = args.dest
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paths = Path(src).rglob('*.*')
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paths_list = list(paths)
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print('Extracting lf...')
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for path in tqdm.tqdm(paths_list):
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try:
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kp, des = lf.extract(os.path.join(path.parent, path.name))
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filename = os.path.splitext(path.name)[0]
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LFUtilities.save_img_lf(dest, filename, kp, des)
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except:
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print("cannot process '%s'" % path)
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pass
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print('lf extracted.')
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@ -3,14 +3,6 @@ import numpy as np
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import pickle as pickle
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import os
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THRESHOLD = 35
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MIN_GOOD_MATCHES = 12
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MIN_INLIERS = 6
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KEYPOINTS = 128
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IMG_SIZE = 500
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def resize(max_side, img):
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if img.shape[1] > img.shape[0]:
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r = max_side / img.shape[1]
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@ -39,7 +31,7 @@ def unpickle_keypoints(array):
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keypoints = []
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descriptors = []
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for point in array:
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temp_feature = cv2.KeyPoint(x=point[0][0],y=point[0][1],_size=point[1], _angle=point[2], _response=point[3], _octave=point[4], _class_id=point[5])
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temp_feature = cv2.KeyPoint(x=point[0][0],y=point[0][1], size=point[1], angle=point[2], response=point[3], octave=point[4], class_id=point[5])
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temp_descriptor = point[6]
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keypoints.append(temp_feature)
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descriptors.append(temp_descriptor)
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@ -47,7 +39,7 @@ def unpickle_keypoints(array):
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def load(lf_path):
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print('loading LF dataset')
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print('loading LF dataset ' + lf_path)
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ser_dataset = pickle.load(open(lf_path, "rb"))
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lf_dataset = []
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for item in ser_dataset:
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@ -63,5 +55,22 @@ def save(lf_data, lf_path):
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pickle.dump(data, open(lf_path, 'wb'))
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def save_img_lf(dest, id, keypoints, descriptors):
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dest_folder_name = id[0:3]
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filename = id + '.dat'
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dest_folder_path = os.path.join(dest, dest_folder_name)
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if (not os.path.exists(dest_folder_path)):
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os.mkdir(dest_folder_path)
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dest_path = os.path.join(dest_folder_path, filename)
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data = pickle_keypoints(keypoints, descriptors)
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pickle.dump(data, open(dest_path, 'wb'))
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def load_img_lf(lf_path, id):
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dest_folder_name = id[0:3]
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filename = id + '.dat'
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dest_folder_path = os.path.join(lf_path, dest_folder_name)
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dest_path = os.path.join(dest_folder_path, filename)
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data = pickle.load(open(dest_path, "rb"))
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kp, desc = unpickle_keypoints(data)
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return (kp, desc)
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@ -1,16 +0,0 @@
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import cv2
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from pathlib import Path
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import tqdm
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import pickle
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import os
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import LFUtilities as lf
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orb = cv2.ORB.create(lf.KEYPOINTS)
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def extract(img_path):
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img = lf.resize(lf.IMG_SIZE, cv2.imread(img_path))
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kp, des = orb.detectAndCompute(img, mask=None)
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return (kp, des)
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@ -4,21 +4,21 @@ import pickle as pickle
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import LFUtilities
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import beniculturaliSettings as settings
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from BeniCulturaliRescorer import BeniCulturaliRescorer
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from BEBLIDRescorer import BEBLIDRescorer
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from FAISSSearchEngine import FAISSSearchEngine
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import FeatureExtractor as fe
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import ORBExtractor as lf
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import BEBLIDExtractor as lf
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class Searcher:
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K_REORDERING = 15
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K_REORDERING = 1000
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def __init__(self):
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# self.dataset = h5py.File(settings.dataset_file, 'r')['rmac'][...]
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# np.save('/media/Data/data/beni_culturali/deploy/dataset', self.dataset)
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self.search_engine = FAISSSearchEngine()
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#self.rescorer = BeniCulturaliRescorer()
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self.rescorer = BEBLIDRescorer()
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def get_id(self, idx):
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return self.search_engine.get_id(idx)
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@ -46,9 +46,9 @@ class Searcher:
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if rescorer:
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kq = self.K_REORDERING
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res = self.search_engine.search_by_id(query_id, kq)
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# if rescorer:
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# res_lf = self.rescorer.rescore_by_id(query_id, res)
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# res = res_lf if res_lf else res[:k]
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if rescorer:
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res_lf = self.rescorer.rescore_by_id(query_id, res)
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res = res_lf if res_lf else res[:k]
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return res
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def search_by_img(self, query_img, k=10, rescorer=False):
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@ -57,10 +57,10 @@ class Searcher:
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kq = self.K_REORDERING
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query_desc = fe.extract(query_img)
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res = self.search_engine.search_by_img(query_desc, kq)
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#if rescorer:
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# query_lf = lf.extract(query_img)
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# res_lf = self.rescorer.rescore_by_img(query_lf, res)
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# res = res_lf if res_lf else res[:k]
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if rescorer:
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query_lf = lf.extract(query_img)
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res_lf = self.rescorer.rescore_by_img(query_lf, res)
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res = res_lf if res_lf else res[:k]
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return res
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def save(self, is_backup=False):
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@ -26,7 +26,7 @@ def api_root():
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print('index_with_randoms.html')
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random_ids = []
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for i in range(0, 15):
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random_ids.append(searcher.get_id(randint(0, 30)))
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random_ids.append(searcher.get_id(randint(0, 600)))
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return render_template('index_with_randoms.html', random_ids=random_ids)
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@ -2,7 +2,7 @@ import json
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import os
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def load_setting(conf_file):
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global port, feature_extractor, k, img_folder, logs, working_folder, data_folder, DATASET, DATASET1, DATASET2, DATASET_LF, DATASET_IDS, DATASET_IDS_LF
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global port, feature_extractor, k, img_folder, logs, working_folder, data_folder, DATASET, DATASET1, DATASET2, DATASET_LF_FOLDER, DATASET_IDS, DATASET_IDS_LF
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with open(conf_file) as settings_file:
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@ -22,7 +22,7 @@ def load_setting(conf_file):
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DATASET = os.path.join(data_folder, 'dataset.npy')
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#DATASET1 = os.path.join(data_folder, 'dataset_resized.npy')
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#DATASET2 = os.path.join(data_folder, 'dataset_bw.npy')
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DATASET_LF = os.path.join(data_folder, 'dataset_lf.dat')
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DATASET_LF_FOLDER = os.path.join(data_folder, 'lf')
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DATASET_IDS = os.path.join(data_folder, 'dataset.ids')
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#DATASET_IDS_LF = os.path.join(data_folder, 'dataset_lf.ids')
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@ -31,7 +31,7 @@
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<td valign="top">
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<input type="hidden" value="" name="" id="objId">
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<input type="hidden" value="true" name="tohtml">
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<input type="hidden" value="false" name="rescorer">
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<input type="text" value="true" name="rescorer">
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<input style="display: none;" id="urlToUpload" name="url" type="text" size="49" onclick="" onchange="document.getElementById('queryImage').value=''">
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<input id="imageToUpload" name="image" type="file" size="38" onclick="" onchange="document.getElementById('queryImage').value=''">
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