137 lines
4.2 KiB
Python
137 lines
4.2 KiB
Python
do_load = True
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from qdrant_client import QdrantClient
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from qdrant_client.models import Filter, FieldCondition, Range, MatchText
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from datetime import datetime, timedelta
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import numpy as np
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import traceback
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from bottle import route, run, template, request, debug
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import open_clip
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# %%
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if do_load:
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from lavis.models import load_model_and_preprocess, model_zoo
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import torch
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device = 'cpu'
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model, vis_processors, txt_processors = load_model_and_preprocess("clip_feature_extractor", model_type="ViT-B-16", is_eval=True, device=device)
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model.eval()
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TIMEOUT=2
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# %%
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collection_name = "nuggets_so400m"
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client = QdrantClient(host="localhost", grpc_port=6334, prefer_grpc=True, timeout=TIMEOUT)
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# %%
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from bottle import route, run, template
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@route('/get_text_match')
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def get_matches():
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valid_cameras = {'sidefeeder','ptz','railing','hummingbird','pond'}
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query = request.query.get('query','A large bird eating corn')
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cameras = request.query.get('cameras','sidefeeder')
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cams = set(cameras.split(',')).intersection(valid_cameras)
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max_age = int(request.query.get('age',5));
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print({'Cameras':cams,'Max Age':max_age,'Query':query})
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# %%
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max_date = datetime.now()
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min_date = max_date - timedelta(days=(max_age))
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days_step = (max_date- min_date).days
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date_arrays = list()
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for i in range(days_step):
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date_arrays.append(max_date - timedelta(days=i))
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# %%
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string_filter = list()
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for cand_date in date_arrays:
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string_filter.append(cand_date.strftime('%Y/%m/%d'))
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should_list = list()
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if max_age == 0:
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string_filter=['']
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for str_filt in string_filter:
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for cam in cams:
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str_use = cam+'/'+str_filt
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print(str_use)
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ccond = FieldCondition(key='filepath',match=MatchText(text=str_use))
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should_list.append(ccond)
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condition_dict = Filter(should = should_list)
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if len(should_list) == 0:
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return_this = {'query':query,'num_videos':0,'results':[]}
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return return_this
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averaged = False
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if isinstance(query, str):
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averaged = bool(averaged)
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num_videos = int(request.query.get('num_videos',5))
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if do_load:
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with torch.no_grad():
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text_input = txt_processors['eval'](query)
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sample = {'text_input':text_input}
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vec = model.extract_features( sample)
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vec_search = vec.cpu().numpy().squeeze().tolist()
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else:
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sz_vec= client.get_collection(collection_name).config.params.vectors.size
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vec_search = np.random.random(sz_vec).tolist()
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if averaged:
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col_name=collection_name+'_averaged'
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else:
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col_name = collection_name
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try:
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error = ''
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if True:
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for i in range(num_videos, 100, 10):
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results = client.search(collection_name = col_name,
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query_vector = vec_search, limit=i, query_filter=condition_dict, timeout=TIMEOUT)
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num_video_got = len(set([x.payload['filepath'] for x in results]))
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if num_video_got >= num_videos:
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break
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else:
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results = client.search(collection_name = col_name,
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query_vector = vec_search, limit=1, query_filter=condition_dict, timeout=TIMEOUT)
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except Exception as e:
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print(traceback.format_exc())
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error = traceback.format_exc()
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results = [];
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def linux_to_win_path(form):
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form = form.replace('/srv','file://192.168.1.242/thebears/Videos/merged/')
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return form
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def normalize_to_merged(path):
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path = path.replace('/srv/ftp','/mergedfs/ftp')
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path = path.replace('/mnt/archive2/videos/ftp','/mergedfs/ftp')
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return path
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resul = list()
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for x in results:
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pload =dict( x.payload)
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pload['filepath'] = normalize_to_merged(pload['filepath'])
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pload['score'] = x.score
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pload['winpath'] = linux_to_win_path(pload['filepath'])
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resul.append(pload)
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# %%
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return_this = {'query':query,'num_videos':num_videos,'results':resul,'error':error}
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return return_this
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debug(True)
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run(host='0.0.0.0', port=53003, server='bjoern')
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