[UPDATE] hr and conf as parameters to the POST request

cloud_analysis/README.md

@@ -123,9 +123,10 @@ docker push gcr.io/snoskuter-akustikk/snowmobile_cloud:main
 gcloud run deploy \
     model \
     --image gcr.io/snoskuter-akustikk/snowmobile_cloud:main \
-    --memory 4Gi \
+    --memory 8Gi \
     --region europe-north1 \
-    --platform managed
+    --platform managed \
+    --cpus 4
 ```
 
 Be sure to allocate enough memory for the service to be able to process the files and run the model.
@@ -156,7 +157,7 @@ Because of the structure of the `main.py`, the `key-file.json` that has been cre
 Once this is done you can deploy the function using:
 
 ```bash
-gcloud functions deploy trigger_audio_analysis \ 
+gcloud functions deploy trigger_audio_analysis \
     --runtime python310 \
     --trigger-resource snoskuter-detector-test \
     --trigger-event google.storage.object.finalize \

cloud_analysis/key-file.json

@@ -0,0 +1,13 @@
+{
+    "type": "service_account",
+    "project_id": "snoskuter-akustikk",
+    "private_key_id": "74c1e4bcd4b00da4dcaf848c31b7f0e77f077163",
+    "private_key": "-----BEGIN PRIVATE KEY-----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\nFnQZmUYKgliaLaVJBXc3W8E=\n-----END PRIVATE KEY-----\n",
+    "client_email": "snoskuter-akustikk@appspot.gserviceaccount.com",
+    "client_id": "104912621642971586511",
+    "auth_uri": "https://accounts.google.com/o/oauth2/auth",
+    "token_uri": "https://oauth2.googleapis.com/token",
+    "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
+    "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/snoskuter-akustikk%40appspot.gserviceaccount.com",
+    "universe_domain": "googleapis.com"
+}

cloud_analysis/main.py

@@ -110,7 +110,7 @@ def fetch_audio_data(bucket_name, blob_name):
 
 
 def analyseAudioFile(
-        audio_file_object, batch_size=1, num_workers=4, min_hr = 0.1, min_conf = 0.99
+        audio_file_object, min_hr, min_conf, batch_size=1, num_workers=2,
 ):
 
     # Initiate model
@@ -148,14 +148,14 @@ def analyseAudioFile(
 
     return results
 
-def on_process_audio(audio_id: str, audio_rec: dict, bucket_name: str, blob_name: str):
+def on_process_audio(audio_id: str, audio_rec: dict, bucket_name: str, blob_name: str, hr: float, conf:float):
 
     print(f"PROCESSING audioId={audio_id}")
     location = audio_rec["location"]
 
     # A call out to your code here. Optionally we can pass on the recorder coordinates 
     audio_file_object = fetch_audio_data(bucket_name, blob_name)
-    results = analyseAudioFile(audio_file_object)
+    results = analyseAudioFile(audio_file_object, hr, conf)
     # The object results is a list containing detections in the form:
     # [start, end, confidence, harmonic ratio]
 
@@ -165,7 +165,7 @@ def on_process_audio(audio_id: str, audio_rec: dict, bucket_name: str, blob_name
     detections = []
     for r in results: 
         start, end, confidence, harmonic_ratio = r
-        if harmonic_ratio > 0.1 and confidence > 0.99:
+        if harmonic_ratio > hr and confidence > conf:
             count += 1
 
         # create the detections dataset
@@ -189,8 +189,10 @@ def process_audio_endpoint():
     blob_name = data['blob_name']
     audio_id = data['audio_id']
     audio_rec = data['audio_rec']
+    hr = data['hr']
+    conf = data['conf']
 
-    results = on_process_audio(audio_id, audio_rec, bucket_name, blob_name)
+    results = on_process_audio(audio_id, audio_rec, bucket_name, blob_name, hr, conf)
 
     if results > 0:
         send_email("Snowmobile Detection Alert", f"{results} snowmobile detections were made in the audio file!")

cloud_analysis/test_local.sh

@@ -2,5 +2,9 @@
 
 curl -X POST \
      -H "Content-Type: application/json" \
-     -d '{"audio_id": "test-id", "audio_rec": {"location": {"latitude": 0, "longitude": 0}}, "bucket_name": "snoskuter-detector-test", "blob_name": "example_audio.mp3"}' \
+     -d '{"hr": 0.00, "conf": 0.0001, "audio_id": "test-id", "audio_rec": {"location": {"latitude": 0, "longitude": 0}}, "bucket_name": "snoskuter-detector-test", "blob_name": "from_BUGG.mp3"}' \
      http://localhost:8080/process-audio
+
+
+# proj_snowmobile/bugg_RPiID-10000000f8e69c84/conf_6f40914/2023-10-04T11_28_44.579Z.mp3
+# other.mp3

cloud_function_directory/main.py

@@ -56,6 +56,8 @@ def fetch_service_account_key(bucket_name, blob_name):
 def trigger_audio_analysis(data, context):
     file_name = data['name']
     bucket_name = data['bucket']
+    harmonic_ratio = 0.05
+    confidence = 0.80
 
     # URL of your Cloud Run service
     cloud_run_url = "https://model-4uhtnq5xla-lz.a.run.app/process-audio"
@@ -65,7 +67,9 @@ def trigger_audio_analysis(data, context):
         "bucket_name": bucket_name,
         "blob_name": file_name,
         "audio_id": "example-audio-id",
-        "audio_rec": {"location": {"latitude": 0, "longitude": 0}}
+        "audio_rec": {"location": {"latitude": 0, "longitude": 0}},
+        "hr": harmonic_ratio,
+        "conf": confidence
     }
 
     # Specify the GCS bucket and blob name of the service account key file