feat: timeline REST API

src/ambianic/webapp/flaskr.py

@@ -5,6 +5,7 @@
 from flask import Flask, request, jsonify
 from flask_cors import CORS
 from flask.logging import default_handler
+from flask import g
 import flask
 from requests import get
 from werkzeug.serving import make_server
@@ -21,11 +22,14 @@ class FlaskJob(ManagedService):
 
     def __init__(self, config):
         self.config = config
+        data_dir = config.get('data_dir', './data')
         self.srv = None
-        app = create_app()
+        app = create_app(data_dir=data_dir)
         self.srv = make_server('0.0.0.0', 8778, app)
         ctx = app.app_context()
         ctx.push()
+        with app.app_context():
+            flask.current_app.data_dir = data_dir
         self.flask_stopped = True
         log.debug('Flask process created')
 
@@ -90,7 +94,7 @@ def stop(self):
             log.info('Flask server job stopped.')
 
 
-def create_app():
+def create_app(data_dir=None):
     log.debug('Creating Flask app...')
 
     # if Ambianic is in INFO or DEBUG mode, pass that info on to Flask
@@ -130,6 +134,18 @@ def health_check():
     def view_pipelines():
         return flask.render_template('pipelines.html')
 
+    @app.route('/api/timeline', methods=['GET'])
+    def get_timeline():
+        response_object = {'status': 'success'}
+        req_page = request.args.get('page', default=1, type=int)
+        nonlocal data_dir
+        resp = samples.get_timeline(page=req_page, data_dir=data_dir)
+        response_object['timeline'] = resp
+        log.debug('Returning %d timeline events', len(resp))
+        # log.debug('Returning samples: %s ', response_object)
+        resp = jsonify(response_object)
+        return resp
+
     @app.route('/api/samples', methods=['GET', 'POST'])
     def get_samples():
         response_object = {'status': 'success'}

src/ambianic/webapp/server/samples.py

@@ -5,6 +5,7 @@
 from pathlib import Path
 import os
 import json
+import yaml
 
 log = logging.getLogger()
 
@@ -110,6 +111,62 @@ def get_samples(before_datetime=None, page=1):
     return samples
 
 
+def get_timeline(before_datetime=None, page=1, data_dir=None):
+    """Get stored pipeline timeline events.
+
+    :Parameters:
+    ----------
+    before_datetime : date time in ISO 8601 compatible format,
+        YYYY-MM-DDTHH:MM:SS. For example '2002-12-25 00:00:00-06:39'.
+        It uses python's standard function datetime.fromisoformat().
+        If not provided, the function will start with the most recent available
+        sample.
+    page : positive integer
+        Paginates samples in batches of 5. Defaults to page=1.
+
+    :Returns:
+    -------
+    list: json
+        Returns a list of previously saved pipeline events.
+
+    """
+    parsed_datetime = None
+    assert isinstance(page, int)
+    assert page > 0
+    page_size = 5
+    if before_datetime:
+        try:
+            parsed_datetime = datetime.fromisoformat(before_datetime)
+            log.debug('Fetching samples saved before %s',
+                      parsed_datetime)
+        except ValueError as e:
+            log.warning('Unable to parse before_datetime parameter: %s. '
+                        ' Error: %s', before_datetime, str(e))
+    page_start_position = (page-1)*page_size
+    page_end_position = page_start_position + page_size
+    if not parsed_datetime:
+        log.debug('Fetching most recent saved samples')
+    log.debug('Fetching samples page %d. Page size %d. '
+              'Sample index range [%d:%d]. ',
+              page, page_size, page_start_position, page_end_position)
+    p = Path(data_dir) / 'timeline-event-log.yaml'
+    log.debug('Timeline path: %s', p.resolve())
+    with p.open() as pf:
+        timeline_events = yaml.safe_load(pf)
+    log.debug('Fetched timeline file with %d events: ', len(timeline_events))
+    # files = sorted(files, key=os.path.getmtime, reverse=True)
+    # for json_file in files[page_start_position:page_end_position]:
+    # if
+    #    with open(json_file) as f:
+    #        sample = json.load(f)
+    #        sample['id'] = uuid.uuid4().hex
+    #        sample['file'] = str(json_file)
+    #        samples.append(sample)
+    # lines = map(str, files)
+    # log.debug('File names follow:\n %s', "\n".join(lines))
+    return timeline_events
+
+
 def add_sample(new_sample=None):
     assert new_sample
     log.debug('add_sample new_sample 0 %s', new_sample)

tests/pipeline/avsource/test_avsource.py

@@ -148,8 +148,8 @@ def sample_callback(image=None, inference_result=None, **kwargs):
         print('detections: {det}'.format(det=detections))
         print('len(detections): {len}'.format(len=len(detections)))
         if detections:
-            category, confidence, _ = detections[0]
-            if category == 'person' and confidence > 0.9:
+            label, confidence, _ = detections[0]
+            if label == 'person' and confidence > 0.9:
                 # skip video image samples until we reach a person detection
                 # with high level of confidence
                 detection_received.set()
@@ -168,8 +168,8 @@ def sample_callback(image=None, inference_result=None, **kwargs):
     assert sample_image.size[1] == 720
     assert detections
     assert len(detections) == 1
-    category, confidence, (x0, y0, x1, y1) = detections[0]
-    assert category == 'person'
+    label, confidence, (x0, y0, x1, y1) = detections[0]
+    assert label == 'person'
     assert confidence > 0.9
     assert x0 > 0 and x0 < x1
     assert y0 > 0 and y0 < y1
@@ -243,8 +243,8 @@ def sample_callback(image=None, inference_result=None, **kwargs):
         print('detections: {det}'.format(det=detections))
         print('len(detections): {len}'.format(len=len(detections)))
         if detections:
-            category, confidence, _ = detections[0]
-            if category == 'person' and confidence > 0.9:
+            label, confidence, _ = detections[0]
+            if label == 'person' and confidence > 0.9:
                 # skip video image samples until we reach a person detection
                 # with high level of confidence
                 detection_received.set()
@@ -263,8 +263,8 @@ def sample_callback(image=None, inference_result=None, **kwargs):
     assert sample_image.size[1] == 720
     assert detections
     assert len(detections) == 1
-    category, confidence, (x0, y0, x1, y1) = detections[0]
-    assert category == 'person'
+    label, confidence, (x0, y0, x1, y1) = detections[0]
+    assert label == 'person'
     assert confidence > 0.9
     assert x0 > 0 and x0 < x1
     assert y0 > 0 and y0 < y1
@@ -297,8 +297,8 @@ def sample_callback(image=None, inference_result=None, **kwargs):
         print('detections: {det}'.format(det=detections))
         print('len(detections): {len}'.format(len=len(detections)))
         if detections:
-            category, confidence, _ = detections[0]
-            if category == 'person' and confidence > 0.9:
+            label, confidence, _ = detections[0]
+            if label == 'person' and confidence > 0.9:
                 # skip video image samples until we reach a person detection
                 # with high level of confidence
                 detection_received.set()
@@ -317,8 +317,8 @@ def sample_callback(image=None, inference_result=None, **kwargs):
     assert sample_image.size[1] == 720
     assert detections
     assert len(detections) == 1
-    category, confidence, (x0, y0, x1, y1) = detections[0]
-    assert category == 'person'
+    label, confidence, (x0, y0, x1, y1) = detections[0]
+    assert label == 'person'
     assert confidence > 0.9
     assert x0 > 0 and x0 < x1
     assert y0 > 0 and y0 < y1
@@ -417,8 +417,8 @@ def sample_callback(image=None, inference_result=None, **kwargs):
         print('detections: {det}'.format(det=detections))
         print('len(detections): {len}'.format(len=len(detections)))
         if detections:
-            category, confidence, _ = detections[0]
-            if category == 'person' and confidence > 0.9:
+            label, confidence, _ = detections[0]
+            if label == 'person' and confidence > 0.9:
                 # skip video image samples until we reach a person detection
                 # with high level of confidence
                 detection_received.set()
@@ -437,8 +437,8 @@ def sample_callback(image=None, inference_result=None, **kwargs):
     assert sample_image.size[1] == 720
     assert detections
     assert len(detections) == 1
-    category, confidence, (x0, y0, x1, y1) = detections[0]
-    assert category == 'person'
+    label, confidence, (x0, y0, x1, y1) = detections[0]
+    assert label == 'person'
     assert confidence > 0.9
     assert x0 > 0 and x0 < x1
     assert y0 > 0 and y0 < y1
@@ -518,8 +518,8 @@ def sample_callback(image=None, inference_result=None, **kwargs):
         print('detections: {det}'.format(det=detections))
         print('len(detections): {len}'.format(len=len(detections)))
         if detections:
-            category, confidence, _ = detections[0]
-            if category == 'person' and confidence > 0.9:
+            label, confidence, _ = detections[0]
+            if label == 'person' and confidence > 0.9:
                 # skip video image samples until we reach a person detection
                 # with high level of confidence
                 detection_received.set()
@@ -538,8 +538,8 @@ def sample_callback(image=None, inference_result=None, **kwargs):
     assert sample_image.size[1] == 720
     assert detections
     assert len(detections) == 1
-    category, confidence, (x0, y0, x1, y1) = detections[0]
-    assert category == 'person'
+    label, confidence, (x0, y0, x1, y1) = detections[0]
+    assert label == 'person'
     assert confidence > 0.9
     assert x0 > 0 and x0 < x1
     assert y0 > 0 and y0 < y1
@@ -634,8 +634,8 @@ def sample_callback(image=None, inference_result=None, **kwargs):
         print('detections: {det}'.format(det=detections))
         print('len(detections): {len}'.format(len=len(detections)))
         if detections:
-            category, confidence, _ = detections[0]
-            if category == 'person' and confidence > 0.9:
+            label, confidence, _ = detections[0]
+            if label == 'person' and confidence > 0.9:
                 # skip video image samples until we reach a person detection
                 # with high level of confidence
                 detection_received.set()
@@ -654,8 +654,8 @@ def sample_callback(image=None, inference_result=None, **kwargs):
     assert sample_image.size[1] == 720
     assert detections
     assert len(detections) == 1
-    category, confidence, (x0, y0, x1, y1) = detections[0]
-    assert category == 'person'
+    label, confidence, (x0, y0, x1, y1) = detections[0]
+    assert label == 'person'
     assert confidence > 0.9
     assert x0 > 0 and x0 < x1
     assert y0 > 0 and y0 < y1