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https://github.com/blakeblackshear/frigate.git
synced 2024-11-21 19:07:46 +01:00
allow process clips to output a csv of scores
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1e9eae8d9a
commit
7476bff5fb
@ -98,7 +98,7 @@ class ProcessClip():
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self.detected_objects_queue, process_info,
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objects_to_track, object_filters, mask, stop_event, exit_on_empty=True)
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def objects_found(self, debug_path=None):
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def top_object(self, debug_path=None):
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obj_detected = False
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top_computed_score = 0.0
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def handle_event(name, obj, frame_time):
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@ -117,9 +117,9 @@ class ProcessClip():
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self.save_debug_frame(debug_path, frame_time, current_tracked_objects.values())
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self.camera_state.update(frame_time, current_tracked_objects)
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for obj in self.camera_state.tracked_objects.values():
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obj_data = obj.to_dict()
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print(f"{frame_time}: {obj_data['id']} - {obj_data['label']} - {obj_data['score']} - {obj.score_history}")
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# for obj in self.camera_state.tracked_objects.values():
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# obj_data = obj.to_dict()
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# print(f"{frame_time}: {obj_data['id']} - {obj_data['label']} - {obj_data['score']} - {obj.score_history}")
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self.frame_manager.delete(self.camera_state.previous_frame_id)
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@ -154,8 +154,9 @@ class ProcessClip():
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@click.option("-p", "--path", required=True, help="Path to clip or directory to test.")
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@click.option("-l", "--label", default='person', help="Label name to detect.")
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@click.option("-t", "--threshold", default=0.85, help="Threshold value for objects.")
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@click.option("-s", "--scores", default=None, help="File to save csv of top scores")
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@click.option("--debug-path", default=None, help="Path to output frames for debugging.")
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def process(path, label, threshold, debug_path):
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def process(path, label, threshold, scores, debug_path):
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clips = []
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if os.path.isdir(path):
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files = os.listdir(path)
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@ -196,10 +197,12 @@ def process(path, label, threshold, debug_path):
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process_clip.load_frames()
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process_clip.process_frames(objects_to_track=[label])
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results.append((c, process_clip.objects_found(debug_path)))
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results.append((c, process_clip.top_object(debug_path)))
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for result in results:
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print(f"{result[0]}: {result[1]}")
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if not scores is None:
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with open(scores, 'w') as writer:
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for result in results:
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writer.write(f"{result[0]},{result[1]['top_score']}\n")
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positive_count = sum(1 for result in results if result[1]['object_detected'])
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print(f"Objects were detected in {positive_count}/{len(results)}({positive_count/len(results)*100:.2f}%) clip(s).")
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