added FAISS Searcher
This commit is contained in:
parent
647a8778ba
commit
2761ccbe95
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@ -7,7 +7,10 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
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git \
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wget \
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nano \
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unzip
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unzip \
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sqlite3 \
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libsqlite3-dev
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RUN pip install numpy tornado flask-restful pillow numpy matplotlib tqdm scikit-learn h5py requests faiss-cpu==1.7.2
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ADD . /workspace
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2
run.sh
2
run.sh
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@ -1 +1 @@
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docker run --net=host -p 8190:8190 -v /media/data2/data/swoads/data:/workspace/data -it image-recognition:swoads python3 /workspace/src/beniculturali.py /workspace/data/conf/img_rec_conf.json
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docker run --net=host -p 8190:8190 -v /media/data2/data/swoads/data:/workspace/data -it image-recognition:swoads python3 /workspace/src/ImageRecognitionService.py /workspace/data/conf/img_rec_conf.json
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@ -8,7 +8,7 @@ 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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descriptor = cv2.xfeatures2d.BEBLID_create(0.75, 101)
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def extract(img_path):
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@ -1,5 +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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MIN_GOOD_MATCHES = 22
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MIN_INLIERS = 15
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KEYPOINTS = 800
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IMG_SIZE = 500
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@ -3,7 +3,7 @@ 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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import ImageRecognitionSettings as settings
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class BEBLIDRescorer:
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@ -15,38 +15,44 @@ class BEBLIDRescorer:
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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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#query_idx = self.ids.index(query_id)
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query = LFUtilities.load_img_lf(settings.DATASET_LF_FOLDER, 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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if len(query[0]) > 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(data_el[1]) > 0:
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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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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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M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 3.0)
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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(data_id)
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print(f'candidate n. {counter}')
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#to get just the first candidate
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break
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except Exception as e:
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print('rescore error evaluating ' + data_id)
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print(e)
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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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@ -0,0 +1,81 @@
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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 ImageRecognitionSettings as settings
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from LFDB import LFDB
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class BEBLIDRescorerDB:
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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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self.lf_db = LFDB(settings.DB_LF)
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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_LF_FOLDER, 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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if len(query[0]) > 0:
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for data_id, _ in resultset:
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try:
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blob = self.lf_db.get(data_id)
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serialized_obj = LFUtilities.deserialize_object(blob)
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data_el = LFUtilities.unpickle_keypoints(serialized_obj)
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if len(data_el[1]) > 0:
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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, 3.0)
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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(data_id)
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print(f'candidate n. {counter}')
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#to get just the first candidate
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break
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except Exception as e:
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print('rescore error evaluating ' + data_id)
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print(e)
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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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@ -0,0 +1,75 @@
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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 ImageRecognitionSettings as settings
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class BEBLIDRescorerGPU:
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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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self.bf = cv2.cuda.DescriptorMatcher_createBFMatcher(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_GPU(settings.DATASET_LF_FOLDER, 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_GPU(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(data_id)
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print(f'candidate n. {counter}')
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#to get just the first candidate
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break
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except Exception as e:
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print('rescore error evaluating ' + data_id)
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print(e)
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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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@ -0,0 +1,76 @@
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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 ImageRecognitionSettings 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_LF_FOLDER, 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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if len(query[0]) > 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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if len(data_el[1]) > 0:
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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, 5.0)
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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(data_id)
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print(f'candidate n. {counter}')
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#to get just the first candidate
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break
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except Exception as e:
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print('rescore error evaluating ' + data_id)
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print(e)
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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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@ -1,66 +0,0 @@
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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 beniculturaliSettings as settings
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class BeniCulturaliRescorer:
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def __init__(self):
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self.lf = LFUtilities.load(settings.DATASET_LF)
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self.ids = np.loadtxt(settings.DATASET_IDS_LF, dtype=str).tolist()
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self.orb = cv2.ORB_create()
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self.bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
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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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return self.rescore_by_img(self.lf[query_idx], 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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for data_id, _ in resultset:
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data_idx = self.ids.index(data_id)
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try:
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data_el = self.lf[data_idx]
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matches = self.bf.match(query[1], data_el[1])
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good = [m for m in matches if m.distance <= LFUtilities.THRESHOLD]
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if len(good) > LFUtilities.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 >= LFUtilities.MIN_INLIERS and inliers > max_inliers):
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max_inliers = inliers
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res.append((data_id, inliers))
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except:
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print('rescore error evaluating ' + data_id)
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pass
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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)
|
||||
np.savetxt(ids_file, self.ids, fmt='%s')
|
||||
|
|
@ -1,60 +0,0 @@
|
|||
import numpy as np
|
||||
import beniculturaliSettings as settings
|
||||
|
||||
|
||||
class BeniCulturaliSearchEngine:
|
||||
|
||||
|
||||
def __init__(self):
|
||||
#self.dataset = h5py.File(settings.dataset_file, 'r')['rmac'][...]
|
||||
|
||||
#np.save('/media/Data/data/beni_culturali/deploy/dataset', self.dataset)
|
||||
self.descs = np.load(settings.DATASET)
|
||||
#self.desc1 = np.load(settings.DATASET1)
|
||||
#self.desc2 = np.load(settings.DATASET2)
|
||||
|
||||
#self.descs = (self.desc1 + self.desc2) / 2
|
||||
#self.descs /= np.linalg.norm(self.descs, axis=1, keepdims=True)
|
||||
self.ids = np.loadtxt(settings.DATASET_IDS, dtype=str).tolist()
|
||||
|
||||
|
||||
def get_id(self, idx):
|
||||
return self.ids[idx]
|
||||
|
||||
|
||||
def add(self, desc, id):
|
||||
self.ids.append(id)
|
||||
self.descs = np.vstack((self.descs, desc))
|
||||
self.save()
|
||||
|
||||
|
||||
def remove(self, id):
|
||||
idx = self.ids.index(id)
|
||||
del self.ids[idx]
|
||||
self.descs = np.delete(self.descs, idx, axis=0)
|
||||
|
||||
|
||||
def search_by_id(self, query_id, k=10):
|
||||
query_idx = self.ids.index(query_id)
|
||||
return self.search_by_img(self.descs[query_idx], k)
|
||||
|
||||
def search_by_img(self, query, k=10):
|
||||
print('----------query features-------')
|
||||
print(query)
|
||||
dot_product = np.dot(self.descs, query)
|
||||
idx = dot_product.argsort()[::-1][:k]
|
||||
res = []
|
||||
for i in idx:
|
||||
res.append((self.ids[i], round(float(dot_product[i]), 3)))
|
||||
return res
|
||||
|
||||
def save(self, is_backup=False):
|
||||
descs_file = settings.DATASET
|
||||
ids_file = settings.DATASET_IDS
|
||||
|
||||
if is_backup:
|
||||
descs_file += '.bak'
|
||||
ids_file += '.bak'
|
||||
|
||||
np.save(descs_file, self.descs)
|
||||
np.savetxt(ids_file, self.ids, fmt='%s')
|
||||
|
|
@ -1,68 +0,0 @@
|
|||
import cv2
|
||||
import numpy as np
|
||||
import pickle as pickle
|
||||
|
||||
import LFUtilities
|
||||
import beniculturaliSettings as settings
|
||||
from BeniCulturaliRescorer import BeniCulturaliRescorer
|
||||
from BeniCulturaliSearchEngine import BeniCulturaliSearchEngine
|
||||
import FeatureExtractor as fe
|
||||
#import ORBExtractor as lf
|
||||
|
||||
|
||||
class BeniCulturaliSearcher:
|
||||
K_REORDERING = 15
|
||||
|
||||
def __init__(self):
|
||||
# self.dataset = h5py.File(settings.dataset_file, 'r')['rmac'][...]
|
||||
|
||||
# np.save('/media/Data/data/beni_culturali/deploy/dataset', self.dataset)
|
||||
self.search_engine = BeniCulturaliSearchEngine()
|
||||
#self.rescorer = BeniCulturaliRescorer()
|
||||
|
||||
def get_id(self, idx):
|
||||
return self.search_engine.get_id(idx)
|
||||
|
||||
def add(self, img_file, id):
|
||||
self.save(True)
|
||||
|
||||
desc = fe.extract(img_file)
|
||||
#orb = lf.extract(img_file)
|
||||
self.search_engine.add(desc, id)
|
||||
#self.rescorer.add(orb)
|
||||
|
||||
self.save()
|
||||
print('added ' + id)
|
||||
|
||||
def remove(self, id):
|
||||
self.save(True)
|
||||
self.search_engine.remove(id)
|
||||
#self.rescorer.remove(idx)
|
||||
self.save()
|
||||
print('removed ' + id)
|
||||
|
||||
def search_by_id(self, query_id, k=10, rescorer=False):
|
||||
kq = k
|
||||
if rescorer:
|
||||
kq = self.K_REORDERING
|
||||
res = self.search_engine.search_by_id(query_id, kq)
|
||||
# if rescorer:
|
||||
# res_lf = self.rescorer.rescore_by_id(query_id, res)
|
||||
# res = res_lf if res_lf else res[:k]
|
||||
return res
|
||||
|
||||
def search_by_img(self, query_img, k=10, rescorer=False):
|
||||
kq = k
|
||||
if rescorer:
|
||||
kq = self.K_REORDERING
|
||||
query_desc = fe.extract(query_img)
|
||||
res = self.search_engine.search_by_img(query_desc, kq)
|
||||
#if rescorer:
|
||||
# query_lf = lf.extract(query_img)
|
||||
# res_lf = self.rescorer.rescore_by_img(query_lf, res)
|
||||
# res = res_lf if res_lf else res[:k]
|
||||
return res
|
||||
|
||||
def save(self, is_backup=False):
|
||||
self.search_engine.save(is_backup)
|
||||
#self.rescorer.save(is_backup)
|
||||
|
|
@ -0,0 +1,38 @@
|
|||
import requests
|
||||
|
||||
from pathlib import Path
|
||||
import tqdm
|
||||
|
||||
import argparse
|
||||
import os
|
||||
|
||||
|
||||
IMG_REC_SERVICE = 'http://localhost:8290/bcir/'
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(description='Img Recognition Bulk Analysis')
|
||||
parser.add_argument('src', type=str, help='img src folder path')
|
||||
parser.add_argument('dest', type=str, help='dest file path')
|
||||
|
||||
args = parser.parse_args()
|
||||
src = args.src
|
||||
dest = args.dest
|
||||
|
||||
paths = Path(src).rglob('*.*')
|
||||
paths_list = list(paths)
|
||||
|
||||
print('Analyzing images...')
|
||||
with open(dest, 'w', encoding='UTF8') as f:
|
||||
for path in tqdm.tqdm(paths_list):
|
||||
try:
|
||||
img_file = {'image': (
|
||||
'query', open(os.path.join(path.parent, path.name), 'rb'))}
|
||||
r = requests.post(IMG_REC_SERVICE + 'searchByImg', files=img_file)
|
||||
res = r.json()
|
||||
tmp = ';'.join([str(i) for x in res for i in x])
|
||||
row = path.name + ";" + tmp
|
||||
f.write(row + '\n')
|
||||
except Exception as e:
|
||||
print("cannot process '%s'" % path)
|
||||
print(e)
|
||||
pass
|
||||
|
|
@ -1,5 +1,5 @@
|
|||
import numpy as np
|
||||
import beniculturaliSettings as settings
|
||||
import ImageRecognitionSettings as settings
|
||||
import faiss
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
import numpy as np
|
||||
import beniculturaliSettings as settings
|
||||
import ImageRecognitionSettings as settings
|
||||
import requests
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,63 @@
|
|||
import requests
|
||||
|
||||
from pathlib import Path
|
||||
import tqdm
|
||||
|
||||
import argparse
|
||||
import os
|
||||
|
||||
|
||||
IMG_REC_SERVICE = 'http://localhost:8290/bcir/'
|
||||
|
||||
groundtruth_file = '/media/ssd2/data/swoads/workdir/data/groundtruth_no_ext.txt'
|
||||
|
||||
precision_at = [0] * 10
|
||||
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(description='Img Recognition Bulk Analysis')
|
||||
parser.add_argument('src', type=str, help='img src folder path')
|
||||
#parser.add_argument('dest', type=str, help='dest file path')
|
||||
|
||||
args = parser.parse_args()
|
||||
src = args.src
|
||||
#dest = args.dest
|
||||
|
||||
groundtruth = {}
|
||||
with open(groundtruth_file, 'r') as f:
|
||||
for line in f:
|
||||
line = line.rstrip() # removes trailing whitespace and '\n' chars
|
||||
|
||||
if "," not in line: continue # skips blanks and comments w/o =
|
||||
if line.startswith("#"): continue # skips comments which contain =
|
||||
|
||||
k, v = line.split(",", 1)
|
||||
groundtruth[k] = v
|
||||
|
||||
paths = Path(src).rglob('*.*')
|
||||
paths_list = list(paths)
|
||||
|
||||
|
||||
print('Analyzing images...')
|
||||
for path in tqdm.tqdm(paths_list):
|
||||
key = path.name
|
||||
exprected_id = groundtruth[key]
|
||||
print(exprected_id)
|
||||
try:
|
||||
img_file = {'image': (
|
||||
'query', open(os.path.join(path.parent, path.name), 'rb'))}
|
||||
params = {'rescorer':'true'}
|
||||
|
||||
r = requests.post(IMG_REC_SERVICE + 'searchByImg', data=params, files=img_file)
|
||||
res = r.json()
|
||||
|
||||
for i in range (0, len(res)):
|
||||
print(res[i][0])
|
||||
if res[i][0] in exprected_id:
|
||||
precision_at[i] = precision_at[i] + 1
|
||||
except Exception as e:
|
||||
print("cannot process '%s'" % path)
|
||||
print(e)
|
||||
pass
|
||||
print(precision_at)
|
||||
|
|
@ -1,200 +1,210 @@
|
|||
from flask import Flask, request, redirect, url_for, flash, render_template, send_from_directory, abort
|
||||
from random import randint
|
||||
import cv2
|
||||
import io
|
||||
import numpy as np
|
||||
import json
|
||||
|
||||
import urllib
|
||||
|
||||
#from BeniCulturaliSearcher import BeniCulturaliSearcher
|
||||
from Searcher import Searcher
|
||||
from BeniCulturaliSearchEngine import BeniCulturaliSearchEngine
|
||||
import beniculturaliSettings as settings
|
||||
import uuid
|
||||
import os, os.path
|
||||
import tornado.wsgi
|
||||
import tornado.httpserver
|
||||
import argparse
|
||||
|
||||
|
||||
app = Flask(__name__)
|
||||
|
||||
|
||||
@app.route('/bcir/')
|
||||
def api_root():
|
||||
print('index_with_randoms.html')
|
||||
random_ids = []
|
||||
for i in range(0, 15):
|
||||
random_ids.append(searcher.get_id(randint(0, 600)))
|
||||
return render_template('index_with_randoms.html', random_ids=random_ids)
|
||||
|
||||
|
||||
def url_to_file(url):
|
||||
dest_file = uuid.uuid4().hex + ".png"
|
||||
dest_path = settings.logs + "/" + dest_file
|
||||
req = urllib.request.Request(
|
||||
url,
|
||||
data=None,
|
||||
headers={
|
||||
'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/35.0.1916.47 Safari/537.36'
|
||||
}
|
||||
)
|
||||
resp = urllib.request.urlopen(req)
|
||||
image = np.asarray(bytearray(resp.read()), dtype="uint8")
|
||||
decoded = cv2.imdecode(image, cv2.IMREAD_COLOR)
|
||||
cv2.imwrite(dest_path, decoded)
|
||||
#im = Image.fromarray(image)
|
||||
#im.save(dest_path)
|
||||
return dest_path
|
||||
|
||||
|
||||
def post_to_file(image):
|
||||
dest_file = uuid.uuid4().hex + ".png"
|
||||
dest_path = settings.logs + "/" + dest_file
|
||||
image.save(dest_path)
|
||||
return dest_path
|
||||
|
||||
|
||||
def get_res(results, query_url=None):
|
||||
if query_url is not None:
|
||||
return render_template('search.html', results=results, query_url=query_url)
|
||||
json_res = json.dumps(results)
|
||||
return json_res
|
||||
|
||||
|
||||
@app.route('/bcir/searchById')
|
||||
def search_by_id():
|
||||
id = request.args.get('id')
|
||||
rescorer = False
|
||||
if request.args.get("rescorer") == 'true':
|
||||
rescorer = True
|
||||
results = searcher.search_by_id(id, settings.k, rescorer)
|
||||
query_url = None
|
||||
if request.args.get("tohtml") is not None:
|
||||
query_url = id + ".jpg"
|
||||
return get_res(results, query_url)
|
||||
|
||||
|
||||
@app.route('/bcir/searchByImg', methods=['POST'])
|
||||
def search_by_img():
|
||||
if 'image' not in request.files:
|
||||
flash('No file part')
|
||||
return redirect(request.url)
|
||||
|
||||
file = request.files['image']
|
||||
img_file = post_to_file(file)
|
||||
rescorer = False
|
||||
if request.form.get("rescorer") == 'true':
|
||||
rescorer = True
|
||||
#dest_file = uuid.uuid4().hex + ".jpg"
|
||||
#dest_path = settings.logs + "/" + dest_file
|
||||
#file.save(dest_path)
|
||||
#files = {'image': (dest_file, open(dest_path, 'rb'))}
|
||||
#r = requests.post(settings.rmac_service, files=files)
|
||||
#results = search_engine.search_by_img(np.array(r.json()), settings.k)
|
||||
results = searcher.search_by_img(img_file, settings.k, rescorer)
|
||||
query_url = None
|
||||
if request.form.get("tohtml") is not None:
|
||||
query_url = ""
|
||||
return get_res(results, query_url)
|
||||
|
||||
|
||||
@app.route('/bcir/searchByURL')
|
||||
def search_by_url():
|
||||
url = request.args.get('url')
|
||||
rescorer = False
|
||||
if request.args.get("rescorer") == 'true':
|
||||
rescorer = True
|
||||
img_file = url_to_file(url)
|
||||
# query = cv2.imdecode(image, cv2.IMREAD_COLOR)
|
||||
# dest_file = uuid.uuid4().hex + ".jpg"
|
||||
# dest_path = settings.logs + "/" + dest_file
|
||||
# cv2.imwrite(dest_path, query)
|
||||
# files = {'image': open(dest_path, 'rb')}
|
||||
# r = requests.post(settings.rmac_service, files=files)
|
||||
# results = search_engine.search_by_img(np.array(r.json()), settings.k)
|
||||
results = searcher.search_by_img(img_file, settings.k, rescorer)
|
||||
query_url = None
|
||||
if request.args.get("tohtml") is not None:
|
||||
query_url = url
|
||||
return get_res(results, query_url)
|
||||
|
||||
@app.route('/bcir/addImg', methods=['POST'])
|
||||
def add_img():
|
||||
if 'image' not in request.files:
|
||||
flash('No file part')
|
||||
return redirect(request.url)
|
||||
try:
|
||||
file = request.files['image']
|
||||
id = request.files['image'].filename
|
||||
id, _ = os.path.splitext(id)
|
||||
img_file = post_to_file(file)
|
||||
searcher.add(img_file, id)
|
||||
json_res = json.dumps("done")
|
||||
return json_res
|
||||
except:
|
||||
abort(500)
|
||||
|
||||
|
||||
@app.route('/bcir/rmImg')
|
||||
def remove_img():
|
||||
try:
|
||||
id = request.args.get('id')
|
||||
searcher.remove(id)
|
||||
json_res = json.dumps("done")
|
||||
return json_res
|
||||
except:
|
||||
abort(500)
|
||||
|
||||
@app.route('/bcir/<path:filename>')
|
||||
def download_file(filename):
|
||||
print(filename)
|
||||
values = filename.split('/')
|
||||
print(values)
|
||||
|
||||
return send_from_directory(settings.img_folder, filename, as_attachment=False)
|
||||
|
||||
"""
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(description='Reading configuration file')
|
||||
parser.add_argument('conf', type=str, help='Configuration file path')
|
||||
|
||||
args = parser.parse_args()
|
||||
settings.load_setting(args.conf)
|
||||
global searcher
|
||||
searcher = BeniCulturaliSearcher()
|
||||
|
||||
#app.run(host='0.0.0.0', port=8090, ssl_context='adhoc')
|
||||
app.run(host='0.0.0.0', port=settings.port)
|
||||
|
||||
# app.run(host='0.0.0.0', port=settings.port)
|
||||
"""
|
||||
|
||||
def start_tornado(app, port=8190):
|
||||
http_server = tornado.httpserver.HTTPServer(tornado.wsgi.WSGIContainer(app))
|
||||
http_server.listen(port)
|
||||
app.logger.info("Tornado server starting on port {}".format(port))
|
||||
tornado.ioloop.IOLoop.instance().start()
|
||||
|
||||
|
||||
def start_from_terminal(app):
|
||||
parser = argparse.ArgumentParser(description='Reading configuration file')
|
||||
parser.add_argument('conf', type=str, help='Configuration file path')
|
||||
|
||||
args = parser.parse_args()
|
||||
settings.load_setting(args.conf)
|
||||
global searcher
|
||||
searcher = Searcher()
|
||||
|
||||
#if args.debug:
|
||||
# app.run(debug=True, host='0.0.0.0', port=settings.port)
|
||||
# else:
|
||||
#start_tornado(app, settings.port)
|
||||
|
||||
app.run(debug=False, host='0.0.0.0', port=settings.port)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
start_from_terminal(app)
|
||||
|
||||
from flask import Flask, request, redirect, url_for, flash, render_template, send_from_directory, abort
|
||||
from random import randint
|
||||
import cv2
|
||||
import io
|
||||
import numpy as np
|
||||
import json
|
||||
|
||||
import urllib
|
||||
|
||||
#from BeniCulturaliSearcher import BeniCulturaliSearcher
|
||||
from Searcher import Searcher
|
||||
import ImageRecognitionSettings as settings
|
||||
import uuid
|
||||
import os, os.path
|
||||
import tornado.wsgi
|
||||
import tornado.httpserver
|
||||
import argparse
|
||||
|
||||
|
||||
app = Flask(__name__)
|
||||
|
||||
|
||||
@app.route('/bcir/')
|
||||
def api_root():
|
||||
print('index_with_randoms.html')
|
||||
random_ids = []
|
||||
for i in range(0, 15):
|
||||
random_ids.append(searcher.get_id(randint(0, 600)))
|
||||
return render_template('index_with_randoms.html', random_ids=random_ids)
|
||||
|
||||
|
||||
def url_to_file(url):
|
||||
dest_file = uuid.uuid4().hex + ".png"
|
||||
dest_path = settings.logs + "/" + dest_file
|
||||
req = urllib.request.Request(
|
||||
url,
|
||||
data=None,
|
||||
headers={
|
||||
'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/35.0.1916.47 Safari/537.36'
|
||||
}
|
||||
)
|
||||
resp = urllib.request.urlopen(req)
|
||||
image = np.asarray(bytearray(resp.read()), dtype="uint8")
|
||||
decoded = cv2.imdecode(image, cv2.IMREAD_COLOR)
|
||||
cv2.imwrite(dest_path, decoded)
|
||||
#im = Image.fromarray(image)
|
||||
#im.save(dest_path)
|
||||
return dest_path
|
||||
|
||||
|
||||
def post_to_file(image):
|
||||
dest_file = uuid.uuid4().hex + ".png"
|
||||
dest_path = settings.logs + "/" + dest_file
|
||||
image.save(dest_path)
|
||||
return dest_path
|
||||
|
||||
|
||||
def get_res(results, query_url=None):
|
||||
if query_url is not None:
|
||||
return render_template('search.html', results=results, query_url=query_url)
|
||||
json_res = json.dumps(results)
|
||||
return json_res
|
||||
|
||||
|
||||
@app.route('/bcir/searchById')
|
||||
def search_by_id():
|
||||
id = request.args.get('id')
|
||||
rescorer = True
|
||||
if request.args.get("rescorer") == 'false':
|
||||
rescorer = False
|
||||
results = searcher.search_by_id(id, settings.k, rescorer)
|
||||
query_url = None
|
||||
if request.args.get("tohtml") is not None:
|
||||
query_url = id + ".jpg"
|
||||
return get_res(results, query_url)
|
||||
|
||||
|
||||
@app.route('/bcir/searchByImg', methods=['POST'])
|
||||
def search_by_img():
|
||||
if 'image' not in request.files:
|
||||
flash('No file part')
|
||||
return redirect(request.url)
|
||||
|
||||
file = request.files['image']
|
||||
img_file = post_to_file(file)
|
||||
rescorer = True
|
||||
if request.form.get("rescorer") == 'false':
|
||||
rescorer = False
|
||||
#dest_file = uuid.uuid4().hex + ".jpg"
|
||||
#dest_path = settings.logs + "/" + dest_file
|
||||
#file.save(dest_path)
|
||||
#files = {'image': (dest_file, open(dest_path, 'rb'))}
|
||||
#r = requests.post(settings.rmac_service, files=files)
|
||||
#results = search_engine.search_by_img(np.array(r.json()), settings.k)
|
||||
results = searcher.search_by_img(img_file, settings.k, rescorer)
|
||||
query_url = None
|
||||
if request.form.get("tohtml") is not None:
|
||||
query_url = ""
|
||||
return get_res(results, query_url)
|
||||
|
||||
|
||||
@app.route('/bcir/searchByURL')
|
||||
def search_by_url():
|
||||
url = request.args.get('url')
|
||||
rescorer = True
|
||||
if request.args.get("rescorer") == 'false':
|
||||
rescorer = False
|
||||
img_file = url_to_file(url)
|
||||
# query = cv2.imdecode(image, cv2.IMREAD_COLOR)
|
||||
# dest_file = uuid.uuid4().hex + ".jpg"
|
||||
# dest_path = settings.logs + "/" + dest_file
|
||||
# cv2.imwrite(dest_path, query)
|
||||
# files = {'image': open(dest_path, 'rb')}
|
||||
# r = requests.post(settings.rmac_service, files=files)
|
||||
# results = search_engine.search_by_img(np.array(r.json()), settings.k)
|
||||
results = searcher.search_by_img(img_file, settings.k, rescorer)
|
||||
query_url = None
|
||||
if request.args.get("tohtml") is not None:
|
||||
query_url = url
|
||||
return get_res(results, query_url)
|
||||
|
||||
@app.route('/bcir/addImg', methods=['POST'])
|
||||
def add_img():
|
||||
if 'image' not in request.files:
|
||||
flash('No file part')
|
||||
return redirect(request.url)
|
||||
try:
|
||||
file = request.files['image']
|
||||
id = request.files['image'].filename
|
||||
id, _ = os.path.splitext(id)
|
||||
img_file = post_to_file(file)
|
||||
searcher.add(img_file, id)
|
||||
json_res = json.dumps("done")
|
||||
return json_res
|
||||
except:
|
||||
abort(500)
|
||||
|
||||
|
||||
@app.route('/bcir/rmImg')
|
||||
def remove_img():
|
||||
try:
|
||||
id = request.args.get('id')
|
||||
searcher.remove(id)
|
||||
json_res = json.dumps("done")
|
||||
return json_res
|
||||
except:
|
||||
abort(500)
|
||||
|
||||
@app.route('/bcir/<path:filename>')
|
||||
def download_file(filename):
|
||||
print(filename)
|
||||
values = filename.split('/')
|
||||
print(values)
|
||||
|
||||
return send_from_directory(settings.img_folder, filename, as_attachment=False)
|
||||
|
||||
@app.route('/bcir/queries/<path:filename>')
|
||||
def queries(filename):
|
||||
print(filename)
|
||||
values = filename.split('/')
|
||||
folder = values[0]
|
||||
name = values[1]
|
||||
print(folder)
|
||||
print(name)
|
||||
|
||||
return send_from_directory(settings.working_folder + '/' + folder, name, as_attachment=False)
|
||||
|
||||
"""
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(description='Reading configuration file')
|
||||
parser.add_argument('conf', type=str, help='Configuration file path')
|
||||
|
||||
args = parser.parse_args()
|
||||
settings.load_setting(args.conf)
|
||||
global searcher
|
||||
searcher = BeniCulturaliSearcher()
|
||||
|
||||
#app.run(host='0.0.0.0', port=8090, ssl_context='adhoc')
|
||||
app.run(host='0.0.0.0', port=settings.port)
|
||||
|
||||
# app.run(host='0.0.0.0', port=settings.port)
|
||||
"""
|
||||
|
||||
def start_tornado(app, port=8190):
|
||||
http_server = tornado.httpserver.HTTPServer(tornado.wsgi.WSGIContainer(app))
|
||||
http_server.listen(port)
|
||||
app.logger.info("Tornado server starting on port {}".format(port))
|
||||
tornado.ioloop.IOLoop.instance().start()
|
||||
|
||||
|
||||
def start_from_terminal(app):
|
||||
parser = argparse.ArgumentParser(description='Reading configuration file')
|
||||
parser.add_argument('conf', type=str, help='Configuration file path')
|
||||
|
||||
args = parser.parse_args()
|
||||
settings.load_setting(args.conf)
|
||||
global searcher
|
||||
searcher = Searcher()
|
||||
|
||||
#if args.debug:
|
||||
# app.run(debug=True, host='0.0.0.0', port=settings.port)
|
||||
# else:
|
||||
#start_tornado(app, settings.port)
|
||||
|
||||
app.run(debug=False, host='0.0.0.0', port=settings.port)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
start_from_terminal(app)
|
||||
|
||||
|
|
@ -1,35 +1,33 @@
|
|||
import json
|
||||
import os
|
||||
|
||||
def load_setting(conf_file):
|
||||
global port, feature_extractor, k, img_folder, logs, working_folder, data_folder, DATASET, DATASET1, DATASET2, DATASET_LF_FOLDER, DATASET_IDS, DATASET_IDS_LF
|
||||
|
||||
with open(conf_file) as settings_file:
|
||||
|
||||
settings = json.load(settings_file)
|
||||
|
||||
port = settings['port']
|
||||
feature_extractor = settings['fe_service']
|
||||
|
||||
k = settings['k']
|
||||
working_folder = settings['working_folder']
|
||||
|
||||
data_folder = os.path.join(working_folder, settings['data_folder'])
|
||||
|
||||
if not os.path.isdir(data_folder):
|
||||
os.mkdir(data_folder)
|
||||
|
||||
DATASET = os.path.join(data_folder, 'dataset.npy')
|
||||
#DATASET1 = os.path.join(data_folder, 'dataset_resized.npy')
|
||||
#DATASET2 = os.path.join(data_folder, 'dataset_bw.npy')
|
||||
DATASET_LF_FOLDER = os.path.join(data_folder, 'lf')
|
||||
DATASET_IDS = os.path.join(data_folder, 'dataset.ids')
|
||||
#DATASET_IDS_LF = os.path.join(data_folder, 'dataset_lf.ids')
|
||||
|
||||
img_folder = settings['img_folder']
|
||||
logs = os.path.join(working_folder, settings['log_folder'])
|
||||
if not os.path.isdir(logs):
|
||||
os.mkdir(logs)
|
||||
|
||||
|
||||
|
||||
import json
|
||||
import os
|
||||
|
||||
def load_setting(conf_file):
|
||||
global port, feature_extractor, k, img_folder, logs, working_folder, data_folder, DATASET, DATASET_LF_FOLDER, DATASET_IDS, DB_LF
|
||||
|
||||
with open(conf_file) as settings_file:
|
||||
|
||||
settings = json.load(settings_file)
|
||||
|
||||
port = settings['port']
|
||||
feature_extractor = settings['fe_service']
|
||||
|
||||
k = settings['k']
|
||||
working_folder = settings['working_folder']
|
||||
|
||||
data_folder = os.path.join(working_folder, settings['data_folder'])
|
||||
|
||||
if not os.path.isdir(data_folder):
|
||||
os.mkdir(data_folder)
|
||||
|
||||
DATASET = os.path.join(data_folder, 'dataset.npy')
|
||||
DATASET_LF_FOLDER = os.path.join(data_folder, 'lf')
|
||||
DATASET_IDS = os.path.join(data_folder, 'dataset.ids')
|
||||
DB_LF = os.path.join(data_folder, 'sqlite_lf/lf.db')
|
||||
|
||||
img_folder = settings['img_folder']
|
||||
logs = os.path.join(working_folder, settings['log_folder'])
|
||||
if not os.path.isdir(logs):
|
||||
os.mkdir(logs)
|
||||
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,40 @@
|
|||
from pathlib import Path
|
||||
import tqdm
|
||||
|
||||
import LFUtilities
|
||||
import BEBLIDExtractor as lf
|
||||
import argparse
|
||||
import os
|
||||
from LFDB import LFDB
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(description='LF bulk extraction')
|
||||
parser.add_argument('src', type=str, help='img src folder path')
|
||||
parser.add_argument('dest', type=str, help='LF DB file')
|
||||
|
||||
args = parser.parse_args()
|
||||
src = args.src
|
||||
dest = args.dest
|
||||
|
||||
lf_db = LFDB(dest)
|
||||
|
||||
paths = Path(src).rglob('*.*')
|
||||
paths_list = list(paths)
|
||||
|
||||
print('Extracting lf...')
|
||||
for path in tqdm.tqdm(paths_list):
|
||||
try:
|
||||
kp, des = lf.extract(os.path.join(path.parent, path.name))
|
||||
features = LFUtilities.pickle_keypoints(kp, des)
|
||||
blob = LFUtilities.serialize_object(features)
|
||||
filename = os.path.splitext(path.name)[0]
|
||||
lf_db.put(filename, blob)
|
||||
except Exception as e:
|
||||
print("cannot process '%s'" % path)
|
||||
print(e)
|
||||
pass
|
||||
|
||||
lf_db.commit()
|
||||
lf_db.close()
|
||||
print('lf extracted.')
|
||||
|
|
@ -0,0 +1,55 @@
|
|||
import os
|
||||
import sqlite3
|
||||
from sqlite3 import Error
|
||||
from werkzeug.datastructures import FileStorage
|
||||
|
||||
|
||||
class LFDB:
|
||||
|
||||
def __init__(self, db_path):
|
||||
# self.lf = LFUtilities.load(settings.DATASET_BEBLID)
|
||||
# self.ids = np.loadtxt(settings.DATASET_IDS, dtype=str).tolist()
|
||||
# self.bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
|
||||
self.conn = sqlite3.connect(db_path, check_same_thread=False)
|
||||
|
||||
def close(self):
|
||||
if self.conn:
|
||||
self.conn.close()
|
||||
|
||||
def put(self, docId, features):
|
||||
try:
|
||||
self.conn.text_factory = str
|
||||
#print("[INFO] : Successful connection!")
|
||||
cur = self.conn.cursor()
|
||||
insert_file = '''INSERT INTO lf(docId, features) VALUES(?, ?)'''
|
||||
cur = self.conn.cursor()
|
||||
cur.execute(insert_file, (docId, features,))
|
||||
#print("[INFO] : The blob for ", docId, " is in the database.")
|
||||
except Error as e:
|
||||
print(e)
|
||||
|
||||
def commit(self):
|
||||
try:
|
||||
if self.conn:
|
||||
self.conn.commit()
|
||||
print("committing...")
|
||||
except Error as e:
|
||||
print(e)
|
||||
|
||||
def get(self, docId):
|
||||
try:
|
||||
self.conn.text_factory = str
|
||||
cur = self.conn.cursor()
|
||||
# print("[INFO] : Connected to SQLite to read_blob_data")
|
||||
sql_fetch_blob_query = """SELECT * from lf where docId = ?"""
|
||||
cur.execute(sql_fetch_blob_query, (docId,))
|
||||
record = cur.fetchall()
|
||||
for row in record:
|
||||
converted_file_name = row[1]
|
||||
blob = row[2]
|
||||
# parse out the file name from converted_file_name
|
||||
cur.close()
|
||||
except sqlite3.Error as error:
|
||||
print("[INFO] : Failed to read blob data from sqlite table", error)
|
||||
return blob
|
||||
|
||||
|
|
@ -3,6 +3,7 @@ import numpy as np
|
|||
import pickle as pickle
|
||||
import os
|
||||
|
||||
|
||||
def resize(max_side, img):
|
||||
if img.shape[1] > img.shape[0]:
|
||||
r = max_side / img.shape[1]
|
||||
|
|
@ -27,6 +28,14 @@ def pickle_keypoints(keypoints, descriptors):
|
|||
return temp_array
|
||||
|
||||
|
||||
def serialize_object(obj):
|
||||
return pickle.dumps(obj)
|
||||
|
||||
|
||||
def deserialize_object(serialized_obj):
|
||||
return pickle.loads(serialized_obj)
|
||||
|
||||
|
||||
def unpickle_keypoints(array):
|
||||
keypoints = []
|
||||
descriptors = []
|
||||
|
|
@ -74,3 +83,20 @@ def load_img_lf(lf_path, id):
|
|||
data = pickle.load(open(dest_path, "rb"))
|
||||
kp, desc = unpickle_keypoints(data)
|
||||
return (kp, desc)
|
||||
|
||||
|
||||
def load_img_lf_GPU(lf_path, id):
|
||||
dest_folder_name = id[0:3]
|
||||
filename = id + '.dat'
|
||||
dest_folder_path = os.path.join(lf_path, dest_folder_name)
|
||||
dest_path = os.path.join(dest_folder_path, filename)
|
||||
data = pickle.load(open(dest_path, "rb"))
|
||||
kp, desc = unpickle_keypoints(data)
|
||||
|
||||
data_gpu_mat = cv2.cuda_GpuMat(np.zeros((1500,), dtype=int))
|
||||
if len(desc) > 0:
|
||||
data_gpu_mat = cv2.cuda_GpuMat(desc)
|
||||
desc = data_gpu_mat
|
||||
|
||||
return (kp, desc)
|
||||
|
||||
|
|
|
|||
|
|
@ -3,8 +3,9 @@ import numpy as np
|
|||
import pickle as pickle
|
||||
|
||||
import LFUtilities
|
||||
import beniculturaliSettings as settings
|
||||
from BEBLIDRescorer import BEBLIDRescorer
|
||||
import ImageRecognitionSettings as settings
|
||||
from BEBLIDRescorerDB import BEBLIDRescorerDB
|
||||
#from BEBLIDRescorerGPU import BEBLIDRescorerGPU
|
||||
from FAISSSearchEngine import FAISSSearchEngine
|
||||
import FeatureExtractor as fe
|
||||
import BEBLIDExtractor as lf
|
||||
|
|
@ -18,7 +19,7 @@ class Searcher:
|
|||
|
||||
# np.save('/media/Data/data/beni_culturali/deploy/dataset', self.dataset)
|
||||
self.search_engine = FAISSSearchEngine()
|
||||
self.rescorer = BEBLIDRescorer()
|
||||
self.rescorer = BEBLIDRescorerDB()
|
||||
|
||||
def get_id(self, idx):
|
||||
return self.search_engine.get_id(idx)
|
||||
|
|
|
|||
|
|
@ -9,7 +9,7 @@ import urllib
|
|||
|
||||
|
||||
from BeniCulturaliSearchEngine import BeniCulturaliSearchEngine
|
||||
import beniculturaliSettings as settings
|
||||
import ImageRecognitionSettings as settings
|
||||
import uuid
|
||||
import requests
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,14 @@
|
|||
#!/bin/bash
|
||||
IMG_FOLDER=/workspace/workdir
|
||||
DATA_FOLDER=/workspace/workdir/data/lf
|
||||
|
||||
mkdir $DATA_FOLDER
|
||||
|
||||
#if [[ $2 = '-o' ]]; then
|
||||
# echo "deleting existing features"
|
||||
|
||||
python3 /workspace/src/LFBulkExtraction4File.py $IMG_FOLDER/$1 $DATA_FOLDER
|
||||
|
||||
chmod 777 $DATA_FOLDER/*
|
||||
|
||||
echo "Done"
|
||||
|
|
@ -0,0 +1,12 @@
|
|||
#!/bin/bash
|
||||
IMG_FOLDER=/workspace/workdir
|
||||
DB_PATH=/workspace/workdir/data/sqlite_lf/lf.db
|
||||
|
||||
#if [[ $2 = '-o' ]]; then
|
||||
# echo "deleting existing features"
|
||||
|
||||
python3 /workspace/src/LFBulkExtractionToDB.py $IMG_FOLDER/$1 $DB_PATH
|
||||
|
||||
chmod 777 $DB_PATH/*
|
||||
|
||||
echo "Done"
|
||||
|
|
@ -31,7 +31,7 @@
|
|||
<td valign="top">
|
||||
<input type="hidden" value="" name="" id="objId">
|
||||
<input type="hidden" value="true" name="tohtml">
|
||||
<input type="text" value="true" name="rescorer">
|
||||
<input type="hidden" value="true" name="rescorer">
|
||||
|
||||
<input style="display: none;" id="urlToUpload" name="url" type="text" size="49" onclick="" onchange="document.getElementById('queryImage').value=''">
|
||||
<input id="imageToUpload" name="image" type="file" size="38" onclick="" onchange="document.getElementById('queryImage').value=''">
|
||||
|
|
|
|||
|
|
@ -36,6 +36,8 @@
|
|||
<td valign="top">
|
||||
<input type="hidden" value="" name="" id="objId">
|
||||
<input type="hidden" value="true" name="tohtml">
|
||||
<input type="hidden" value="true" name="rescorer">
|
||||
|
||||
|
||||
<input style="display: none;" id="urlToUpload" name="url" type="text" size="49" onclick="" onchange="document.getElementById('queryImage').value=''">
|
||||
<input id="imageToUpload" name="image" type="file" size="38" onclick="" onchange="document.getElementById('queryImage').value=''">
|
||||
|
|
|
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Loading…
Reference in New Issue