YACWC

flycheck_search_me.py

@@ -0,0 +1,136 @@
+do_load = True
+from qdrant_client import QdrantClient
+from qdrant_client.models import Filter, FieldCondition, Range, MatchText
+from datetime import datetime, timedelta
+import numpy as np
+import traceback
+from bottle import route, run, template, request, debug
+import open_clip
+# %%
+if do_load:
+    
+    from lavis.models import load_model_and_preprocess, model_zoo
+    import torch
+    device = 'cpu'
+    model, vis_processors, txt_processors = load_model_and_preprocess("clip_feature_extractor", model_type="ViT-B-16", is_eval=True, device=device)
+    model.eval()
+
+TIMEOUT=2
+# %%
+
+collection_name = "nuggets_so400m"
+client = QdrantClient(host="localhost", grpc_port=6334, prefer_grpc=True, timeout=TIMEOUT)
+# %%
+from bottle import route, run, template
+
+@route('/get_text_match')
+def get_matches():
+    valid_cameras = {'sidefeeder','ptz','railing','hummingbird','pond'}
+    query = request.query.get('query','A large bird eating corn')
+    cameras = request.query.get('cameras','sidefeeder')
+    cams = set(cameras.split(',')).intersection(valid_cameras)
+
+    max_age = int(request.query.get('age',5));
+    print({'Cameras':cams,'Max Age':max_age,'Query':query})
+# %%
+    max_date = datetime.now()
+    min_date = max_date - timedelta(days=(max_age))
+
+    days_step = (max_date- min_date).days
+    date_arrays = list()
+    for i in range(days_step):
+        date_arrays.append(max_date - timedelta(days=i))
+# %%
+    string_filter = list()
+    for cand_date in date_arrays:
+        string_filter.append(cand_date.strftime('%Y/%m/%d'))
+
+
+    should_list = list()
+    if max_age == 0:
+        string_filter=['']
+
+    for str_filt in string_filter:
+        for cam in cams:
+            str_use = cam+'/'+str_filt
+            print(str_use)
+            ccond  = FieldCondition(key='filepath',match=MatchText(text=str_use))
+            should_list.append(ccond)
+
+            
+
+
+    condition_dict = Filter(should = should_list)
+    if len(should_list) == 0:
+        return_this = {'query':query,'num_videos':0,'results':[]}
+        return return_this
+
+  
+    averaged = False
+    if isinstance(query, str):
+        averaged = bool(averaged)
+        
+    num_videos = int(request.query.get('num_videos',5))
+
+    if do_load:
+        with torch.no_grad():
+            text_input = txt_processors['eval'](query)
+            sample = {'text_input':text_input}
+            vec = model.extract_features( sample)
+            vec_search = vec.cpu().numpy().squeeze().tolist()
+    else:
+        sz_vec= client.get_collection(collection_name).config.params.vectors.size
+        vec_search = np.random.random(sz_vec).tolist()
+
+
+
+    if averaged:
+        col_name=collection_name+'_averaged'
+    else:
+        col_name = collection_name
+
+
+    try:
+        error = ''
+        if True:
+            for i in range(num_videos, 100, 10):
+                results = client.search(collection_name = col_name,
+                                        query_vector = vec_search, limit=i, query_filter=condition_dict, timeout=TIMEOUT)
+                num_video_got = len(set([x.payload['filepath'] for x in results]))
+                if num_video_got >= num_videos:
+                    break
+        else:
+            results = client.search(collection_name = col_name,
+                                    query_vector = vec_search, limit=1, query_filter=condition_dict, timeout=TIMEOUT)
+    except Exception as e:
+        print(traceback.format_exc())
+        error = traceback.format_exc()
+        results = [];
+  
+
+    def linux_to_win_path(form):
+        form = form.replace('/srv','file://192.168.1.242/thebears/Videos/merged/')
+        return form
+
+    def normalize_to_merged(path):
+        path = path.replace('/srv/ftp','/mergedfs/ftp')
+        path = path.replace('/mnt/archive2/videos/ftp','/mergedfs/ftp')
+        return path
+
+    resul = list()
+    for x in results:
+        pload =dict( x.payload)
+        pload['filepath'] = normalize_to_merged(pload['filepath'])
+        pload['score'] = x.score
+        pload['winpath'] = linux_to_win_path(pload['filepath'])
+        resul.append(pload)
+        
+# %%
+    return_this = {'query':query,'num_videos':num_videos,'results':resul,'error':error}
+    return return_this
+
+
+
+
+debug(True)
+run(host='0.0.0.0', port=53003, server='bjoern')

search_me.py

@@ -1,39 +1,76 @@
 do_load = True
 from qdrant_client import QdrantClient
+from qdrant_client.models import Filter, FieldCondition, Range, MatchText
+from datetime import datetime, timedelta
 import numpy as np
+import traceback
 from bottle import route, run, template, request, debug
+import open_clip
 # %%
 if do_load:
+    
     from lavis.models import load_model_and_preprocess, model_zoo
     import torch
     device = 'cpu'
     model, vis_processors, txt_processors = load_model_and_preprocess("clip_feature_extractor", model_type="ViT-B-16", is_eval=True, device=device)
     model.eval()
-    collection_name="nuggets_clip"
 
+TIMEOUT=2
 # %%
-client = QdrantClient(host="localhost", grpc_port=6334, prefer_grpc=True)
+
+collection_name = "nuggets_so400m"
+client = QdrantClient(host="localhost", grpc_port=6334, prefer_grpc=True, timeout=TIMEOUT)
 # %%
 from bottle import route, run, template
 
 @route('/get_text_match')
 def get_matches():
+    valid_cameras = {'sidefeeder','ptz','railing','hummingbird','pond'}
     query = request.query.get('query','A large bird eating corn')
-#    averaged = request.query.get('averaged',False)
+    cameras = request.query.get('cameras','sidefeeder')
-# %%
+    cams = set(cameras.split(',')).intersection(valid_cameras)
-    max_age = request.query.get('age',5);
-# %%
-    max_age = 5
-
-    
 
+    max_age = int(request.query.get('age',5));
+    print({'Cameras':cams,'Max Age':max_age,'Query':query})
 # %%
+    max_date = datetime.now()
+    min_date = max_date - timedelta(days=(max_age))
+
+    days_step = (max_date- min_date).days
+    date_arrays = list()
+    for i in range(days_step):
+        date_arrays.append(max_date - timedelta(days=i))
+# %%
+    string_filter = list()
+    for cand_date in date_arrays:
+        string_filter.append(cand_date.strftime('%Y/%m/%d'))
+
+
+    should_list = list()
+    if max_age == 0:
+        string_filter=['']
+
+    for str_filt in string_filter:
+        for cam in cams:
+            str_use = cam+'/'+str_filt
+            print(str_use)
+            ccond  = FieldCondition(key='filepath',match=MatchText(text=str_use))
+            should_list.append(ccond)
+
+            
+
+
+    condition_dict = Filter(should = should_list)
+    if len(should_list) == 0:
+        return_this = {'query':query,'num_videos':0,'results':[]}
+        return return_this
+
+  
     averaged = False
     if isinstance(query, str):
         averaged = bool(averaged)
         
     num_videos = int(request.query.get('num_videos',5))
-    print(query, num_videos, averaged)
 
     if do_load:
         with torch.no_grad():
@@ -42,7 +79,8 @@ def get_matches():
             vec = model.extract_features( sample)
             vec_search = vec.cpu().numpy().squeeze().tolist()
     else:
-        pass
+        sz_vec= client.get_collection(collection_name).config.params.vectors.size
+        vec_search = np.random.random(sz_vec).tolist()
 
 
 
@@ -51,17 +89,23 @@ def get_matches():
     else:
         col_name = collection_name
 
-    if True:
+
-        for i in range(num_videos, 100, 10):
+    try:
+        error = ''
+        if True:
+            for i in range(num_videos, 100, 10):
+                results = client.search(collection_name = col_name,
+                                        query_vector = vec_search, limit=i, query_filter=condition_dict, timeout=TIMEOUT)
+                num_video_got = len(set([x.payload['filepath'] for x in results]))
+                if num_video_got >= num_videos:
+                    break
+        else:
             results = client.search(collection_name = col_name,
-                            query_vector = vec_search, limit=i)
+                                    query_vector = vec_search, limit=1, query_filter=condition_dict, timeout=TIMEOUT)
-            num_video_got = len(set([x.payload['filepath'] for x in results]))
+    except Exception as e:
-            if num_video_got >= num_videos:
+        print(traceback.format_exc())
-                break
+        error = traceback.format_exc()
-    else:
+        results = [];
-        results = client.search(collection_name = col_name,
-                                query_vector = vec_search, limit=1)
-                
   
 
     def linux_to_win_path(form):
@@ -82,11 +126,11 @@ def get_matches():
         resul.append(pload)
         
 # %%
-    return_this = {'query':query,'num_videos':num_videos,'results':resul}
+    return_this = {'query':query,'num_videos':num_videos,'results':resul,'error':error}
     return return_this
 
 
 
 
 debug(True)
-run(host='0.0.0.0', port=53003)
+run(host='0.0.0.0', port=53003, server='bjoern')

update_qdrant.py

@@ -0,0 +1,35 @@
+from qdrant_client import QdrantClient
+from qdrant_client.http import models
+from qdrant_client.models import Distance, VectorParams
+
+client = QdrantClient(host="localhost", port=6333)
+
+collection_head = "nuggets_clip"
+collection_head = "nuggets_so400m"
+
+
+
+for collection_name in [collection_head, collection_head +'_averaged']:
+    try:
+        client.create_collection(
+                collection_name=collection_name,
+                vectors_config=VectorParams(size=1152, distance=Distance.COSINE),
+                on_disk_payload = True
+            )
+
+        client.create_payload_index(
+            collection_name=collection_name,
+            field_name="filepath",
+            field_schema=models.TextIndexParams(
+                type="text",
+                tokenizer=models.TokenizerType.WORD,
+                min_token_len=1,
+                max_token_len=15,
+                lowercase=True,
+            ),
+        )
+    except Exception as e:
+        print(e)
+    from prettyprinter import cpprint
+    cpprint(client.get_collection(collection_name).dict()['vectors_count'])
+