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strategy_embedding
Commit df29b9ba authored by 赵威's avatar 赵威
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get result from url

parent ce0e5827
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Showing with 60 additions and 37 deletions
src/diary_cover_similarity.py
View file @ df29b9ba
...@@ -95,6 +95,22 @@ def save_faiss_index(load_file, save_path): ...@@ -95,6 +95,22 @@ def save_faiss_index(load_file, save_path):
faiss.write_index(index2, save_path) faiss.write_index(index2, save_path)
def get_similar_diary_ids_by_url(url, index, face_to_vec_f):
img = url_to_ndarray(url)
if img.any():
faces = face_to_vec_f(img)
for face in faces:
face_feature = np.array(json.loads(face["feature"])).astype("float32")
_scores, _ids = index.search(np.array[face_feature], 10)
ids = _ids.flat
scores = _scores.flat
res = list(zip(ids, scores))
res.sort(key=lambda x: x[1], reverse=True)
return res
else:
return []
def main(): def main():
base_dir = os.getcwd() base_dir = os.getcwd()
print("base_dir: " + base_dir) print("base_dir: " + base_dir)
...@@ -111,45 +127,52 @@ def main(): ...@@ -111,45 +127,52 @@ def main():
face_to_vec_f = lambda img: face_to_vec(img, face_rec, face_detector, shape_predictor) face_to_vec_f = lambda img: face_to_vec(img, face_rec, face_detector, shape_predictor)
# save_diary_image_info(diary_after_cover_vec_file, face_to_vec_f) # save_diary_image_info(diary_after_cover_vec_file, face_to_vec_f)
save_faiss_index(diary_after_cover_vec_file, faiss_index_path) # save_faiss_index(diary_after_cover_vec_file, faiss_index_path)
a = [ faiss_index = faiss.read_index(faiss_index_path)
-0.08361373096704483, 0.06760436296463013, 0.10752949863672256, -0.020746365189552307, -0.07035162299871445,
-0.014547230675816536, -0.043201886117458344, -0.12196271121501923, 0.13929598033428192, -0.1360183209180832, img_url = "https://pic.igengmei.com/2020/07/03/1437/1b9975bb0b81-w"
0.23247791826725006, -0.08867999166250229, -0.24177594482898712, -0.05600903555750847, -0.05371646583080292,
0.22015368938446045, -0.12883149087429047, -0.0822330191731453, -0.0413128100335598, 0.08704500645399094, res = get_similar_diary_ids_by_url(img_url, faiss_index, face_to_vec_f)
0.10081718862056732, -0.03764188289642334, 0.036720920354127884, 0.04766431450843811, -0.0685625970363617,
-0.38336044549942017, -0.10978807508945465, -0.07328074425458908, -0.023904308676719666, -0.007438751868903637,
-0.09545779973268509, 0.027364756911993027, -0.1537190079689026, -0.04008519649505615, -0.03581209108233452,
0.04322449117898941, -0.05686069279909134, -0.11610691249370575, 0.1640746295452118, -0.004643512889742851,
-0.34821364283561707, 0.03711444139480591, -0.0026186704635620117, 0.1917344480752945, 0.14298999309539795,
0.04084448516368866, 0.06119539216160774, -0.12611950933933258, 0.10941470414400101, -0.20786598324775696,
0.03435457497835159, 0.11412393301725388, 0.0602775476872921, 0.054409340023994446, -0.002967053558677435,
-0.12524624168872833, 0.026284342631697655, 0.08236880600452423, -0.10654348134994507, 0.00403654295951128,
0.10716681182384491, -0.08270247280597687, 0.018992319703102112, -0.11595900356769562, 0.18344789743423462,
0.0895184576511383, -0.1307670772075653, -0.15750591456890106, 0.11103398352861404, -0.13521818816661835,
-0.03199139982461929, 0.11129119992256165, -0.17407448589801788, -0.20658859610557556, -0.3114454746246338,
0.01914297416806221, 0.39955294132232666, 0.12365783005952835, -0.14545315504074097, -0.03254598751664162,
-0.10342024266719818, 0.03375910595059395, 0.11272192746400833, 0.21788232028484344, 0.08588762581348419,
0.012640122324228287, -0.07646650820970535, -0.043292030692100525, 0.21306097507476807, -0.12407292425632477,
-0.025112995877861977, 0.2634827196598053, 0.005047444254159927, 0.06562616676092148, -0.07397496700286865,
0.06206338107585907, -0.0634055882692337, 0.05882266163825989, -0.05909111723303795, 0.027562778443098068,
0.043835900723934174, 0.00407575536519289, -0.007656056433916092, 0.1048622876405716, -0.17822585999965668,
0.1303984671831131, -0.021631652489304543, 0.0836174339056015, 0.11956407874822617, 0.007379574701189995,
-0.07777556777000427, -0.08474794030189514, 0.09585978090763092, -0.21120299398899078, 0.1435444951057434,
0.19884724915027618, 0.07154559344053268, 0.06259742379188538, 0.10118959099054337, 0.10188969224691391,
-0.015351934358477592, -0.04335442930459976, -0.26258283853530884, -0.021509556099772453, 0.12185295671224594,
-0.011788002215325832, 0.01337978895753622, -0.008025042712688446
]
feature = np.array(a).astype("float32")
index = faiss.read_index(faiss_index_path)
D, I = index.search(np.array([feature]), 5)
ids = I.flat
scores = D.flat
res = list(zip(ids, scores))
res.sort(key = lambda x: x[1], reverse=True)
print(res) print(res)
# a = [
# -0.08361373096704483, 0.06760436296463013, 0.10752949863672256, -0.020746365189552307, -0.07035162299871445,
# -0.014547230675816536, -0.043201886117458344, -0.12196271121501923, 0.13929598033428192, -0.1360183209180832,
# 0.23247791826725006, -0.08867999166250229, -0.24177594482898712, -0.05600903555750847, -0.05371646583080292,
# 0.22015368938446045, -0.12883149087429047, -0.0822330191731453, -0.0413128100335598, 0.08704500645399094,
# 0.10081718862056732, -0.03764188289642334, 0.036720920354127884, 0.04766431450843811, -0.0685625970363617,
# -0.38336044549942017, -0.10978807508945465, -0.07328074425458908, -0.023904308676719666, -0.007438751868903637,
# -0.09545779973268509, 0.027364756911993027, -0.1537190079689026, -0.04008519649505615, -0.03581209108233452,
# 0.04322449117898941, -0.05686069279909134, -0.11610691249370575, 0.1640746295452118, -0.004643512889742851,
# -0.34821364283561707, 0.03711444139480591, -0.0026186704635620117, 0.1917344480752945, 0.14298999309539795,
# 0.04084448516368866, 0.06119539216160774, -0.12611950933933258, 0.10941470414400101, -0.20786598324775696,
# 0.03435457497835159, 0.11412393301725388, 0.0602775476872921, 0.054409340023994446, -0.002967053558677435,
# -0.12524624168872833, 0.026284342631697655, 0.08236880600452423, -0.10654348134994507, 0.00403654295951128,
# 0.10716681182384491, -0.08270247280597687, 0.018992319703102112, -0.11595900356769562, 0.18344789743423462,
# 0.0895184576511383, -0.1307670772075653, -0.15750591456890106, 0.11103398352861404, -0.13521818816661835,
# -0.03199139982461929, 0.11129119992256165, -0.17407448589801788, -0.20658859610557556, -0.3114454746246338,
# 0.01914297416806221, 0.39955294132232666, 0.12365783005952835, -0.14545315504074097, -0.03254598751664162,
# -0.10342024266719818, 0.03375910595059395, 0.11272192746400833, 0.21788232028484344, 0.08588762581348419,
# 0.012640122324228287, -0.07646650820970535, -0.043292030692100525, 0.21306097507476807, -0.12407292425632477,
# -0.025112995877861977, 0.2634827196598053, 0.005047444254159927, 0.06562616676092148, -0.07397496700286865,
# 0.06206338107585907, -0.0634055882692337, 0.05882266163825989, -0.05909111723303795, 0.027562778443098068,
# 0.043835900723934174, 0.00407575536519289, -0.007656056433916092, 0.1048622876405716, -0.17822585999965668,
# 0.1303984671831131, -0.021631652489304543, 0.0836174339056015, 0.11956407874822617, 0.007379574701189995,
# -0.07777556777000427, -0.08474794030189514, 0.09585978090763092, -0.21120299398899078, 0.1435444951057434,
# 0.19884724915027618, 0.07154559344053268, 0.06259742379188538, 0.10118959099054337, 0.10188969224691391,
# -0.015351934358477592, -0.04335442930459976, -0.26258283853530884, -0.021509556099772453, 0.12185295671224594,
# -0.011788002215325832, 0.01337978895753622, -0.008025042712688446
# ]
# feature = np.array(a).astype("float32")
# index = faiss.read_index(faiss_index_path)
# D, I = index.search(np.array([feature]), 5)
# ids = I.flat
# scores = D.flat
# res = list(zip(ids, scores))
# res.sort(key = lambda x: x[1], reverse=True)
# print(res)
if __name__ == "__main__": if __name__ == "__main__":
begin_time = time.time() begin_time = time.time()
......
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