update docs and add back benchmark

benchmark.py

@@ -1,20 +1,18 @@
 import statistics
 import numpy as np
-from edgetpu.detection.engine import DetectionEngine
+import time
+from frigate.edgetpu import ObjectDetector
 
-# Path to frozen detection graph. This is the actual model that is used for the object detection.
-PATH_TO_CKPT = '/frozen_inference_graph.pb'
-
-# Load the edgetpu engine and labels
-engine = DetectionEngine(PATH_TO_CKPT)
+object_detector = ObjectDetector()
 
 frame = np.zeros((300,300,3), np.uint8)
-flattened_frame = np.expand_dims(frame, axis=0).flatten()
+input_frame = np.expand_dims(frame, axis=0)
 
 detection_times = []
 
-for x in range(0, 1000):
-    objects = engine.detect_with_input_tensor(flattened_frame, threshold=0.1, top_k=3)
-    detection_times.append(engine.get_inference_time())
+for x in range(0, 100):
+    start = time.monotonic()
+    object_detector.detect_raw(input_frame)
+    detection_times.append(time.monotonic()-start)
 
-print("Average inference time: " + str(statistics.mean(detection_times)))
+print(f"Average inference time: {statistics.mean(detection_times)*1000:.2f}ms")