Fix test

.travis.yml

@@ -6,7 +6,7 @@ services:
 language: python
 
 python:
-    - 3.4
+    - 3.5
 
 cache: pip
 
@@ -16,11 +16,12 @@ before_install:
       -e transport.host=127.0.0.1
       -e xpack.security.enabled=false
       -e xpack.monitoring.enabled=false
-      docker.elastic.co/elasticsearch/elasticsearch:5.4.2
+      docker.elastic.co/elasticsearch/elasticsearch:7.8.0
 
 install:
     - pip install --upgrade pip codecov
     - pip install codecov
+    - pip install --upgrade pytest
     - pip install -e .[test]
 
 script: pytest --cov=image_match && codecov

image_match/elasticsearch_driver.py

@@ -53,24 +53,23 @@ def search_single_record(self, rec, pre_filter=None):
             rec.pop('metadata')
 
         # build the 'should' list
-        should = [{'term': {word: rec[word]}} for word in rec]
+        should = [{'term': {'{}.{}'.format(self.doc_type, word): rec[word]}} for word in rec]
         body = {
             'query': {
                    'bool': {'should': should}
             },
-            '_source': {'excludes': ['simple_word_*']}
+            '_source': {'excludes': ['{}.simple_word_*'.format(self.doc_type)]}
         }
 
         if pre_filter is not None:
             body['query']['bool']['filter'] = pre_filter
 
         res = self.es.search(index=self.index,
-                              doc_type=self.doc_type,
                               body=body,
                               size=self.size,
                               timeout=self.timeout)['hits']['hits']
 
-        sigs = np.array([x['_source']['signature'] for x in res])
+        sigs = np.array([x['_source'][self.doc_type]['signature'] for x in res])
 
         if sigs.size == 0:
             return []
@@ -79,8 +78,8 @@ def search_single_record(self, rec, pre_filter=None):
 
         formatted_res = [{'id': x['_id'],
                           'score': x['_score'],
-                          'metadata': x['_source'].get('metadata'),
-                          'path': x['_source'].get('url', x['_source'].get('path'))}
+                          'metadata': x['_source'][self.doc_type].get('metadata'),
+                          'path': x['_source'][self.doc_type].get('url', x['_source'][self.doc_type].get('path'))}
                          for x in res]
 
         for i, row in enumerate(formatted_res):
@@ -91,7 +90,7 @@ def search_single_record(self, rec, pre_filter=None):
 
     def insert_single_record(self, rec, refresh_after=False):
         rec['timestamp'] = datetime.now()
-        self.es.index(index=self.index, doc_type=self.doc_type, body=rec, refresh=refresh_after)
+        self.es.index(index=self.index, body={ self.doc_type: rec }, refresh=refresh_after)
 
     def delete_duplicates(self, path):
         """Delete all but one entries in elasticsearch whose `path` value is equivalent to that of path.
@@ -101,11 +100,11 @@ def delete_duplicates(self, path):
         matching_paths = [item['_id'] for item in
                           self.es.search(body={'query':
                                                {'match':
-                                                {'path': path}
+                                                {'{}.path'.format(self.doc_type): path}
                                                }
                                               },
                                          index=self.index)['hits']['hits']
-                          if item['_source']['path'] == path]
+                          if item['_source'][self.doc_type]['path'] == path]
         if len(matching_paths) > 0:
             for id_tag in matching_paths[1:]:
-                self.es.delete(index=self.index, doc_type=self.doc_type, id=id_tag)
+                self.es.delete(index=self.index, id=id_tag)

image_match/goldberg.py

@@ -10,6 +10,7 @@
 from io import BytesIO
 import numpy as np
 import xml.etree
+import xml.etree.ElementTree
 
 
 class CorruptImageError(RuntimeError):
@@ -236,14 +237,14 @@ def preprocess_image(image_or_path, bytestream=False, handle_mpo=False):
             return rgb2gray(np.asarray(img, dtype=np.uint8))
         elif type(image_or_path) in string_types or \
              type(image_or_path) is text_type:
-            return imread(image_or_path, as_grey=True)
+            return imread(image_or_path, as_gray=True)
         elif type(image_or_path) is bytes:
             try:
                 img = Image.open(image_or_path)
                 arr = np.array(img.convert('RGB'))
             except IOError:
                 # try again due to PIL weirdness
-                return imread(image_or_path, as_grey=True)
+                return imread(image_or_path, as_gray=True)
             if handle_mpo:
                 # take the first images from the MPO
                 if arr.shape == (2,) and isinstance(arr[1].tolist(), MpoImageFile):

image_match/signature_database_base.py

@@ -52,7 +52,7 @@ def search_single_record(self, rec, pre_filter=None):
                    before applying the matching strategy
 
                 For example:
-                    { "term": {  "metadata.category": "art" } }
+                    { "term": {  "image.metadata.category": "art" } }
 
         Returns:
             a formatted list of dicts representing matches.

setup.py

@@ -89,7 +89,7 @@ def find_version(*file_paths):
     ],
     install_requires=[
         'scikit-image>=0.14',
-        'elasticsearch>=5.0.0,<6.0.0',
+        'elasticsearch>=7.0.0,<8.0.0',
         'six>=1.11.0',
     ],
     tests_require=tests_require,

tests/test_elasticsearch_driver.py

@@ -19,20 +19,24 @@
 INDEX_NAME = 'test_environment_{}'.format(hashlib.md5(os.urandom(128)).hexdigest()[:12])
 DOC_TYPE = 'image'
 MAPPINGS = {
-  "mappings": {
-    DOC_TYPE: {
-      "dynamic": True,
-      "properties": {
-        "metadata": {
-            "type": "object",
-            "dynamic": True,
-            "properties": {
-                "tenant_id": { "type": "keyword" }
+    "mappings": {
+        "properties": {
+            DOC_TYPE: {
+                "properties": {
+                    "path": {
+                        "type": "keyword"
+                    },
+                    "metadata": {
+                        "properties": {
+                            "tenant_id": {
+                                "type": "keyword",
+                            }
+                        }
+                    }
+                }
             }
         }
-      }
     }
-  }
 }
 
 
@@ -46,7 +50,7 @@ def setup_index(request, index_name):
     try:
         es.indices.create(index=index_name, body=MAPPINGS)
     except RequestError as e:
-        if e.error == u'index_already_exists_exception':
+        if e.error == u'resource_already_exists_exception':
             es.indices.delete(index_name)
         else:
             raise
@@ -189,15 +193,15 @@ def test_lookup_with_filter_by_metadata(ses):
     )
     ses.add_image('test2.jpg', metadata=metadata2, refresh_after=True)
 
-    r = ses.search_image('test1.jpg', pre_filter={"term": {"metadata.tenant_id": "foo"}})
+    r = ses.search_image('test1.jpg', pre_filter={"term": {'{}.metadata.tenant_id'.format(DOC_TYPE): "foo"}})
     assert len(r) == 1
     assert r[0]['metadata'] == metadata
 
-    r = ses.search_image('test1.jpg', pre_filter={"term": {"metadata.tenant_id": "bar-2"}})
+    r = ses.search_image('test1.jpg', pre_filter={"term": {'{}.metadata.tenant_id'.format(DOC_TYPE): "bar-2"}})
     assert len(r) == 1
     assert r[0]['metadata'] == metadata2
 
-    r = ses.search_image('test1.jpg', pre_filter={"term": {"metadata.tenant_id": "bar-3"}})
+    r = ses.search_image('test1.jpg', pre_filter={"term": {'{}.metadata.tenant_id'.format(DOC_TYPE): "bar-3"}})
     assert len(r) == 0
 
 

tests/test_elasticsearch_driver_metadata_as_nested.py

@@ -19,21 +19,23 @@
 INDEX_NAME = 'test_environment_{}'.format(hashlib.md5(os.urandom(128)).hexdigest()[:12])
 DOC_TYPE = 'image'
 MAPPINGS = {
-  "mappings": {
-    DOC_TYPE: {
-      "dynamic": True,
-      "properties": {
-        "metadata": {
-            "type": "nested",
-            "dynamic": True,
-            "properties": {
-                "tenant_id": { "type": "keyword" },
-                "project_id": { "type": "keyword" }
+    "mappings": {
+        "properties": {
+            DOC_TYPE: {
+                "properties": {
+                    "path": {
+                        "type": "keyword"
+                    },
+                    "metadata": {
+                        "properties": {
+                            "tenant_id": { "type": "keyword" },
+                            "project_id": { "type": "keyword" }
+                        }
+                    }
+                }
             }
         }
-      }
     }
-  }
 }
 
 
@@ -122,16 +124,7 @@ def _metadata(tenant_id, project_id):
     )
 
 def _nested_filter(tenant_id, project_id):
-    return {
-        "nested" : {
-            "path" : "metadata",
-            "query" : {
-                "bool" : {
-                    "must" : [
-                        {"term": {"metadata.tenant_id": tenant_id}},
-                        {"term": {"metadata.project_id": project_id}}
-                    ]
-                }
-             }
-        }
-    }
+    return [
+        {"term": {"image.metadata.tenant_id": tenant_id}},
+        {"term": {"image.metadata.project_id": project_id}}
+    ]

tests/test_goldberg.py

@@ -74,4 +74,4 @@ def test_difference():
     sig1 = gis.generate_signature('test.jpg')
     sig2 = gis.generate_signature(test_diff_img_url)
     dist = gis.normalized_distance(sig1, sig2)
-    assert dist == 0.42263283502672722
+    assert dist == 0.424549547059671