bump

clip_endpoint.py

@@ -1,31 +1,28 @@
 import open_clip
 import torch
+import functools
+import onnxruntime as ort
 
-do_load = True
+model_name = 'ViT-SO400M-16-SigLIP2-512'
-if do_load:
+tokenizer = open_clip.get_tokenizer(model_name)
-
+#onnx_path = "/home/thebears/Source/ml/clip_extract/text/ViT-SO400M-16-SigLIP2-512_text"
-    #model_name = 'hf-hub:timm/ViT-L-16-SigLIP2-512'
+onnx_path = "/home/thebears/Source/ml/clip_extract/text/ViT-SO400M-16-SigLIP2-512_text_optimized"
-#    model, preprocess = open_clip.create_model_from_pretrained('hf-hub:timm/ViT-L-16-SigLIP2-512')
+ort_sess = ort.InferenceSession(onnx_path)
-
-
-    model_name = 'ViT-SO400M-14-SigLIP-384'
-    pretrained_name = 'webli'
-    model, _, preprocess = open_clip.create_model_and_transforms(model_name, pretrained=pretrained_name)
-    device = 'cpu'
-    model.eval()
-    tokenizer = open_clip.get_tokenizer(model_name)
 
 from bottle import route, run, template, request, debug
 @route('/encode')
 def get_matches():
-    query = request.query.get('query','A large bird eating corn')
+    query = request.query.get('query',None)
+    if query is None:
+        return None
+    return execute_model(query)
+
+@functools.cache
+def execute_model(query):
     with torch.no_grad():
         text_tokenized = tokenizer(query)
-        vec = model.encode_text(text_tokenized).detach().cpu().tolist()
+        vec = ort_sess.run(None, {'tokenized': text_tokenized.numpy() })[0].tolist()
-
     return {'vector':vec}
 
-
-
 debug(True)
 run(host='0.0.0.0', port=53004, server='bjoern')

clip_endpoint_test.py

@@ -0,0 +1,18 @@
+
+print('hello')
+# %%
+import open_clip
+import torch
+
+do_load = True
+if do_load:
+
+
+    model_name = 'hf-hub:timm/ViT-L-16-SigLIP2-512'
+#    pretrained_name = 'webli'
+    model, preprocess = open_clip.create_model_from_pretrained('hf-hub:timm/ViT-L-16-SigLIP2-512')
+
+#    model, _, preprocess = open_clip.create_model_and_transforms(model_name, pretrained=pretrained_name)
+    device = 'cpu'
+    model.eval()
+    tokenizer = open_clip.get_tokenizer(model_name)

milvus_migrate/create_collection_v3.py

@@ -1,22 +1,31 @@
 from pymilvus import MilvusClient, DataType
-
+import os
-
+from tqdm import tqdm
 client = MilvusClient(
     uri="http://localhost:19530"
 )
-#for x in client.list_collections():
+
-#    client.drop_collection(x)
+
+paths_scan = ['/srv/ftp_tcc','/srv/ftp','/srv/ftp_local/']
+
+outff = list()
+for xpp in paths_scan:
+    outff.extend([x.path for x in os.scandir(xpp) if x.is_dir()])
 
 
 
 
+out = [x.split('/')[3] for x in outff]
+
 # %%
-
+c_collections = set(client.list_collections())
-import os
+for cam in tqdm(out):
-out = os.listdir('/mergedfs/ftp/')
+    c_col_name = f"nuggets_{cam}_so400m_siglip2"
-
+    if c_col_name in c_collections:
-# %%
+        print(f"Skipping {c_col_name}")
-for cam in out:
+        continue
+    else:
+        print(f"Creating {c_col_name}")
     schema = MilvusClient.create_schema(
         auto_id=False,
         nenable_dynamic_field=False,
@@ -25,7 +34,7 @@ for cam in out:
     schema.add_field(field_name="filepath", datatype=DataType.VARCHAR, max_length=128)
     schema.add_field(field_name="frame_number", datatype=DataType.INT32)
     schema.add_field(field_name="date", datatype=DataType.VARCHAR, max_length=len('20241220'), is_partition_key=True)
-    schema.add_field(field_name="so400m", datatype=DataType.FLOAT16_VECTOR, dim=1024)
+    schema.add_field(field_name="so400m", datatype=DataType.FLOAT16_VECTOR, dim=1152)
 
 
     index_params = client.prepare_index_params()
@@ -40,7 +49,7 @@ for cam in out:
 
     index_params.add_index(
         field_name="so400m", 
-        index_type="IVF_SQ8",
+        index_type="DISKANN",
         metric_type="COSINE",
         params={'nlist':128})
 
@@ -49,16 +58,13 @@ for cam in out:
         index_type='Trie')
 
     client.create_collection(
-        collection_name=f"nuggets_{cam}_so400m_siglip2",
+        collection_name=c_col_name,
         schema=schema,
         index_params=index_params
     )
-    print(cam)
 
-#res = client.get_load_state( collection_name="nuggets_ptz_so400m_siglip2")
 #res = client.load_collection(collection_name="nuggets_so400m")
 
+#import numpy
 
-
+#c = numpy.load('/srv/ftp_tcc/leopards1/2025/07/18/Leopards1_00_20250718170024.oclip_embeds.npz')
-
-

milvus_migrate/upload_from_folder.py

@@ -60,7 +60,7 @@ def upload_vector_file(vector_file_to_upload):
 
 
 
-root_path = '/srv/ftp/railing/2024'
+root_path = '/srv/ftp/railing/2026/'
 to_put = list()
 for root, dirs, files in os.walk(root_path):
     for x in files:

search_me.py

@@ -8,51 +8,54 @@ import traceback
 from bottle import route, run, template, request, debug
 import time
 import prettyprinter
-# %%
 
-TIMEOUT=2
+TIMEOUT=10
-# %%
 
-#collection_name = "nuggets_{camera}_so400m_siglip2"
+
-collection_name = "nuggets_{camera}_so400m"
+collection_name = "nuggets_{camera}_so400m_siglip2"
 client = MilvusClient(
     uri="http://localhost:19530"
 )
-# %%
+
+
 
 from bottle import route, run, template
 
+
+@route('/get_collection_count')
+def get_collection_count():
+    coll_count = dict()
+    for c in sorted(client.list_collections()):
+        coll_count[c] = client.get_collection_stats(c)['row_count']
+    return coll_count
+
+
 @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')
     num_videos = int(request.query.get('num_videos',5))
 
-    cams = set(cameras.split(',')).intersection(valid_cameras)
+    cams = set(cameras.split(","))
-
     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))
+    min_date = max_date - timedelta(days=(max_age-1))
     days_step = (max_date- min_date).days
     day_strs = list()
-    # %%
     for x in range(days_step):
+#        pass
         day_strs.append( (min_date + timedelta(days=x)).strftime('%Y%m%d') )
+   # if days_step > 0:
+   #     day_strs.append( (min_date + timedelta(days=0)).strftime('%Y%m%d') )
 
+    day_strs.append( max_date.strftime('%Y%m%d')) 
     str_insert = ','.join([f'"{x}"'for x in day_strs])
     filter_string = 'date in [' + str_insert + ']'
-
     if max_age == 0:
         filter_string = ''
-    # %%
     vec_form = requests.get('http://192.168.1.242:53004/encode',params={'query':query}).json()['vector'][0]
     vec_search = np.asarray(vec_form).astype(np.float16)
-
+    print('Filter string:',filter_string)
-
-    print(f'Doing query for {query}!')
     all_results=list()
     try:
         error = ''
@@ -73,11 +76,10 @@ def get_matches():
                     all_results.extend(results[0])
 
     except Exception as e:
-        print(traceback.format_exc())
         error = traceback.format_exc()
+        print(error)
         results = []
-  
+
-# %%
     def linux_to_win_path(form):
         form = form.replace('/srv','file://192.168.1.242/thebears/Videos/merged/')
         return form
@@ -96,9 +98,8 @@ def get_matches():
         pload['winpath'] = linux_to_win_path(pload['filepath'])
         pload['frame'] = pload['frame_number']
         resul.append(pload)
-# %%
+
     return_this = {'query':query,'num_videos':num_videos,'results':resul,'error':error}
-    prettyprinter.pprint(return_this)
     return return_this
 
 

search_me_doris_test.py

@@ -0,0 +1,19 @@
+from doris_vector_search import DorisVectorClient, AuthOptions
+
+auth = AuthOptions(
+    host="192.168.1.242",
+    query_port=9030,
+    user="thebears",
+    password="marybear",
+)
+
+client = DorisVectorClient(database="nuggets", auth_options=auth)
+
+tbl = client.open_table("video_embeddings")
+
+query = [0.1] * 1152  # Example 128-dimensional vector
+
+# SELECT id FROM sift_1M ORDER BY l2_distance_approximate(embedding, query) LIMIT 10;
+result = tbl.search(query, metric_type="inner_product").limit(10).select(["id"]).to_pandas()
+
+print(result)

test_milvus.py

@@ -0,0 +1,69 @@
+
+from pymilvus import MilvusClient, DataType
+
+collection_name = "nuggets_{camera}_so400m_siglip2"
+client = MilvusClient(
+    uri="http://localhost:19530"
+)
+# %%
+coll_count = dict()
+for c in sorted(client.list_collections()):
+    print(c, client.get_load_state(c), client.get_collection_stats(c))
+
+
+# %%
+from pymilvus import utility
+# %%
+import requests
+query = 'A squirrel looking at the camera'
+vec_form = requests.get('http://192.168.1.242:53004/encode',params={'query':query}).json()['vector'][0]
+# %%
+from datetime import datetime, timedelta
+max_age = 5
+str_insert = ''
+days_step = 5
+max_date = datetime.now()
+min_date = max_date - timedelta(days=(max_age-1))
+days_step = (max_date- min_date).days
+day_strs = list()
+for x in range(days_step):
+    day_strs.append( (min_date + timedelta(days=x)).strftime('%Y%m%d') )
+str_insert = ','.join([f'"{x}"'for x in day_strs])
+filter_string = 'date in [' + str_insert + ']'
+import numpy as np
+col_name = 'nuggets_railing_so400m_siglip2'
+vec_search = np.asarray(vec_form).astype(np.float16)
+results = client.search(collection_name = col_name,
+                 consistency_level="Eventually",
+                 data = [vec_search],
+                                            filter=filter_string,                 
+                 search_params={'metric_type':'COSINE', 'params':{}},
+                            output_fields=['filepath','frame_number']
+                            )
+    
+
+# %%
+for c in client.list_collections():
+#    ff = client.describe_collection(collection_name = c)
+    print(c)
+
+
+# %%
+
+
+col_target = 'nuggets_leopards1_so400m_siglip2'
+
+import time
+# %%
+
+
+
+print(client.get_collection_stats(c))
+
+
+# %%
+
+# %%
+st = time.time()
+results = client.search(collection_name = col_target,data =[np.random.rand(1152).astype(np.float16)], limit = 100)
+print(len(results[0]),   time.time() - st)