Photo embeddings

lib/inat_inferrer.py

@@ -1,5 +1,4 @@
 import time
-import magic
 import tensorflow as tf
 import pandas as pd
 import h3
@@ -8,6 +7,14 @@
 import os
 import tifffile
 import numpy as np
+import urllib
+import hashlib
+import magic
+import aiohttp
+import aiofiles
+import aiofiles.os
+import asyncio
+
 from PIL import Image
 from lib.tf_gp_elev_model import TFGeoPriorModelElev
 from lib.vision_inferrer import VisionInferrer
@@ -610,6 +617,67 @@ def limit_leaf_scores_that_include_humans(self, leaf_scores):
         # otherwise return no results
         return leaf_scores.head(0)
 
+    async def embeddings_for_photos(self, photos):
+        response = {}
+        async with aiohttp.ClientSession() as session:
+            queue = asyncio.Queue()
+            workers = [asyncio.create_task(self.embeddings_worker_task(queue, response, session))
+                       for _ in range(5)]
+            for photo in photos:
+                queue.put_nowait(photo)
+            await queue.join()
+            for worker in workers:
+                worker.cancel()
+        return response
+
+    async def embeddings_worker_task(self, queue, response, session):
+        while not queue.empty():
+            photo = await queue.get()
+            try:
+                embedding = await self.embedding_for_photo(photo["url"], session)
+                response[photo["id"]] = embedding
+            finally:
+                queue.task_done()
+
+    async def embedding_for_photo(self, url, session):
+        if url is None:
+            return
+
+        try:
+            cache_path = await self.download_photo_async(url, session)
+            if cache_path is None:
+                return
+            return self.signature_for_image(cache_path)
+        except urllib.error.HTTPError:
+            return
+
+    def signature_for_image(self, image_path):
+        image = InatInferrer.prepare_image_for_inference(image_path)
+        return self.vision_inferrer.signature_for_image(image).tolist()
+
+    async def download_photo_async(self, url, session):
+        checksum = hashlib.md5(url.encode()).hexdigest()
+        cache_path = os.path.join(self.upload_folder, "download-" + checksum) + ".jpg"
+        if await aiofiles.os.path.exists(cache_path):
+            return cache_path
+        try:
+            async with session.get(url, timeout=10) as resp:
+                if resp.status == 200:
+                    f = await aiofiles.open(cache_path, mode="wb")
+                    await f.write(await resp.read())
+                    await f.close()
+        except asyncio.TimeoutError as e:
+            print("`download_photo_async` timed out")
+            print(e)
+        if not os.path.exists(cache_path):
+            return
+        mime_type = magic.from_file(cache_path, mime=True)
+        if mime_type != "image/jpeg":
+            im = Image.open(cache_path)
+            rgb_im = im.convert("RGB")
+            rgb_im.save(cache_path)
+        return cache_path
+
     @staticmethod
     def prepare_image_for_inference(file_path):
         image = Image.open(file_path)

lib/inat_vision_api.py

@@ -30,6 +30,8 @@ def __init__(self, config):
                               self.h3_04_bounds_route, methods=["GET"])
         self.app.add_url_rule("/geo_scores_for_taxa", "geo_scores_for_taxa",
                               self.geo_scores_for_taxa_route, methods=["POST"])
+        self.app.add_url_rule("/embeddings_for_photos", "embeddings_for_photos",
+                              self.embeddings_for_photos_route, methods=["POST"])
         self.app.add_url_rule("/build_info", "build_info", self.build_info_route, methods=["GET"])
 
     def setup_inferrer(self, config):
@@ -96,6 +98,12 @@ def geo_scores_for_taxa_route(self):
             for obs in request.json["observations"]
         }
 
+    async def embeddings_for_photos_route(self):
+        start_time = time.time()
+        response = await self.inferrer.embeddings_for_photos(request.json["photos"])
+        print("embeddings_for_photos_route Time: %0.2fms" % ((time.time() - start_time) * 1000.))
+        return response
+
     def index_route(self):
         form = ImageForm()
         if "observation_id" in request.args:
@@ -145,16 +153,24 @@ def score_image(self, form, file_path, lat, lng, iconic_taxon_id, geomodel):
                 return InatVisionAPIResponses.aggregated_tree_response(
                     aggregated_scores, self.inferrer
                 )
+            embedding = self.inferrer.signature_for_image(file_path) if \
+                form.return_embedding.data == "true" else None
             return InatVisionAPIResponses.aggregated_object_response(
-                leaf_scores, aggregated_scores, self.inferrer
+                leaf_scores, aggregated_scores, self.inferrer,
+                embedding=embedding
             )
 
         # legacy dict response
         if geomodel != "true":
             return InatVisionAPIResponses.legacy_dictionary_response(leaf_scores, self.inferrer)
 
         if form.format.data == "object":
-            return InatVisionAPIResponses.object_response(leaf_scores, self.inferrer)
+            embedding = self.inferrer.signature_for_image(file_path) if \
+                form.return_embedding.data == "true" else None
+            return InatVisionAPIResponses.object_response(
+                leaf_scores, self.inferrer,
+                embedding=embedding
+            )
 
         return InatVisionAPIResponses.array_response(leaf_scores, self.inferrer)
 

lib/inat_vision_api_responses.py

@@ -20,7 +20,7 @@ def array_response(leaf_scores, inferrer):
         return InatVisionAPIResponses.array_response_columns(leaf_scores).to_dict(orient="records")
 
     @staticmethod
-    def object_response(leaf_scores, inferrer):
+    def object_response(leaf_scores, inferrer, embedding=None):
         leaf_scores = InatVisionAPIResponses.limit_leaf_scores_for_response(leaf_scores)
         leaf_scores = InatVisionAPIResponses.update_leaf_scores_scaling(leaf_scores)
         results = InatVisionAPIResponses.array_response_columns(
@@ -39,10 +39,13 @@ def object_response(leaf_scores, inferrer):
                 common_ancestor_frame
             ).to_dict(orient="records")[0]
 
-        return {
+        response = {
             "common_ancestor": common_ancestor,
-            "results": results
+            "results": results,
         }
+        if embedding is not None:
+            response["embedding"] = embedding
+        return response
 
     @staticmethod
     def aggregated_tree_response(aggregated_scores, inferrer):
@@ -73,7 +76,7 @@ def aggregated_tree_response(aggregated_scores, inferrer):
         return "<pre>" + "<br/>".join(printable_tree) + "</pre>"
 
     @staticmethod
-    def aggregated_object_response(leaf_scores, aggregated_scores, inferrer):
+    def aggregated_object_response(leaf_scores, aggregated_scores, inferrer, embedding=None):
         top_leaf_combined_score = aggregated_scores.query(
             "leaf_class_id.notnull()"
         ).sort_values(
@@ -116,10 +119,13 @@ def aggregated_object_response(leaf_scores, aggregated_scores, inferrer):
                 common_ancestor_frame
             ).to_dict(orient="records")[0]
 
-        return {
+        response = {
             "common_ancestor": common_ancestor,
-            "results": final_results.to_dict(orient="records")
+            "results": final_results.to_dict(orient="records"),
         }
+        if embedding is not None:
+            response["embedding"] = embedding
+        return response
 
     @staticmethod
     def limit_leaf_scores_for_response(leaf_scores):

lib/templates/home.html

@@ -23,9 +23,9 @@ <h2>Slim vs Legacy Model</h2>
     Lng: <input type="test" name="lng" value="-70">
     <br/>
     <select name="format">
+      <option value="object">Object</option>
       <option value="json">JSON</option>
       <option value="tree">Tree</option>
-      <option value="object">Object</option>
     </select>
     <br/>
     <select name="geomodel">
@@ -38,6 +38,11 @@ <h2>Slim vs Legacy Model</h2>
       <option value="true">Aggregated</option>
     </select>
     <br/>
+    <select name="return_embedding">
+      <option value="true">Return embedding</option>
+      <option value="false">Do not return embedding</option>
+    </select>
+    <br/>
     <br/>
     <button type="submit">Submit</button>
   </form>

lib/vision_inferrer.py

@@ -16,8 +16,16 @@ def prepare_tf_model(self):
             assert device.device_type != "GPU"
 
         self.vision_model = tf.keras.models.load_model(self.model_path, compile=False)
+        self.signature_model = tf.keras.Model(
+            inputs=self.vision_model.inputs,
+            outputs=self.vision_model.get_layer("global_average_pooling2d_5").output
+        )
+        self.signature_model.compile()
 
     # given an image object (usually coming from prepare_image_for_inference),
     # calculate vision results for the image
     def process_image(self, image):
         return self.vision_model(tf.convert_to_tensor(image), training=False)[0]
+
+    def signature_for_image(self, image):
+        return self.signature_model(tf.convert_to_tensor(image), training=False)[0].numpy()

lib/web_forms.py

@@ -13,4 +13,5 @@ class ImageForm(FlaskForm):
     taxon_id = StringField("taxon_id")
     geomodel = StringField("geomodel")
     aggregated = StringField("aggregated")
+    return_embedding = StringField("return_embedding")
     format = StringField("format")

requirements.txt

@@ -1,6 +1,9 @@
+aiofiles==24.1.0
+aiohttp==3.11.2;python_version>="3.11"
+aiohttp==3.10.11;python_version=="3.8"
 flake8==7.0.0
 flake8-quotes==3.4.0
-Flask==3.0.2
+Flask[async]==3.0.2
 Flask-WTF==1.2.1
 h3==3.7.7
 h3pandas==0.2.6

tests/conftest.py

@@ -36,4 +36,5 @@ def inatInferrer(request, mocker):
             os.path.realpath(os.path.dirname(__file__) + "/fixtures/synonyms.csv")
     }
     mocker.patch("tensorflow.keras.models.load_model", return_value=MagicMock())
+    mocker.patch("tensorflow.keras.Model", return_value=MagicMock())
     return InatInferrer(config)

tests/test_vision_inferrer.py

@@ -6,6 +6,7 @@
 class TestVisionInferrer:
     def test_initialization(self, mocker):
         mocker.patch("tensorflow.keras.models.load_model", return_value=MagicMock())
+        mocker.patch("tensorflow.keras.Model", return_value=MagicMock())
         model_path = "model_path"
         inferrer = VisionInferrer(model_path)
         assert inferrer.model_path == model_path
@@ -16,6 +17,7 @@ def test_initialization(self, mocker):
 
     def test_process_image(self, mocker):
         mocker.patch("tensorflow.keras.models.load_model", return_value=MagicMock())
+        mocker.patch("tensorflow.keras.Model", return_value=MagicMock())
         model_path = "model_path"
         inferrer = VisionInferrer(model_path)
         theimage = "theimage"