update benchmark script to mirror actual frigate use

benchmark.py

@@ -1,18 +1,79 @@
-import statistics
+import os
+from statistics import mean
+import multiprocessing as mp
 import numpy as np
-import time
-from frigate.edgetpu import ObjectDetector
+import datetime
+from frigate.edgetpu import ObjectDetector, EdgeTPUProcess, RemoteObjectDetector, load_labels
 
-object_detector = ObjectDetector()
+my_frame = np.expand_dims(np.full((300,300,3), 1, np.uint8), axis=0)
+labels = load_labels('/labelmap.txt')
 
-frame = np.zeros((300,300,3), np.uint8)
-input_frame = np.expand_dims(frame, axis=0)
+######
+# Minimal same process runner
+######
+# object_detector = ObjectDetector()
+# tensor_input = np.expand_dims(np.full((300,300,3), 0, np.uint8), axis=0)
 
-detection_times = []
+# start = datetime.datetime.now().timestamp()
 
-for x in range(0, 100):
- start = time.monotonic()
- object_detector.detect_raw(input_frame)
- detection_times.append(time.monotonic()-start)
+# frame_times = []
+# for x in range(0, 1000):
+# start_frame = datetime.datetime.now().timestamp()
 
-print(f"Average inference time: {statistics.mean(detection_times)*1000:.2f}ms")
+# tensor_input[:] = my_frame
+# detections = object_detector.detect_raw(tensor_input)
+# parsed_detections = []
+# for d in detections:
+# if d[1] < 0.4:
+# break
+# parsed_detections.append((
+# labels[int(d[0])],
+# float(d[1]),
+# (d[2], d[3], d[4], d[5])
+# ))
+# frame_times.append(datetime.datetime.now().timestamp()-start_frame)
+
+# duration = datetime.datetime.now().timestamp()-start
+# print(f"Processed for {duration:.2f} seconds.")
+# print(f"Average frame processing time: {mean(frame_times)*1000:.2f}ms")
+
+######
+# Separate process runner
+######
+def start(id, num_detections, detection_queue):
+ object_detector = RemoteObjectDetector(str(id), '/labelmap.txt', detection_queue)
+ start = datetime.datetime.now().timestamp()
+
+ frame_times = []
+ for x in range(0, num_detections):
+ start_frame = datetime.datetime.now().timestamp()
+ detections = object_detector.detect(my_frame)
+ frame_times.append(datetime.datetime.now().timestamp()-start_frame)
+
+ duration = datetime.datetime.now().timestamp()-start
+ print(f"{id} - Processed for {duration:.2f} seconds.")
+ print(f"{id} - Average frame processing time: {mean(frame_times)*1000:.2f}ms")
+
+edgetpu_process = EdgeTPUProcess()
+
+# start(1, 1000, edgetpu_process.detect_lock, edgetpu_process.detect_ready, edgetpu_process.frame_ready)
+
+####
+# Multiple camera processes
+####
+camera_processes = []
+for x in range(0, 10):
+ camera_process = mp.Process(target=start, args=(x, 100, edgetpu_process.detection_queue))
+ camera_process.daemon = True
+ camera_processes.append(camera_process)
+
+start = datetime.datetime.now().timestamp()
+
+for p in camera_processes:
+ p.start()
+
+for p in camera_processes:
+ p.join()
+
+duration = datetime.datetime.now().timestamp()-start
+print(f"Total - Processed for {duration:.2f} seconds.")