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allow process clips to output a csv of scores

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This commit is contained in:
Blake Blackshear 2020-12-13 10:05:54 -06:00
parent 1e9eae8d9a
commit 7476bff5fb
1 changed files with 11 additions and 8 deletions
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19
frigate/process_clip.py
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@ -98,7 +98,7 @@ class ProcessClip():
self.detected_objects_queue, process_info, self.detected_objects_queue, process_info,
objects_to_track, object_filters, mask, stop_event, exit_on_empty=True) objects_to_track, object_filters, mask, stop_event, exit_on_empty=True)
def objects_found(self, debug_path=None): def top_object(self, debug_path=None):
obj_detected = False obj_detected = False
top_computed_score = 0.0 top_computed_score = 0.0
def handle_event(name, obj, frame_time): def handle_event(name, obj, frame_time):
@ -117,9 +117,9 @@ class ProcessClip():
self.save_debug_frame(debug_path, frame_time, current_tracked_objects.values()) self.save_debug_frame(debug_path, frame_time, current_tracked_objects.values())
self.camera_state.update(frame_time, current_tracked_objects) self.camera_state.update(frame_time, current_tracked_objects)
for obj in self.camera_state.tracked_objects.values(): # for obj in self.camera_state.tracked_objects.values():
obj_data = obj.to_dict() # obj_data = obj.to_dict()
print(f"{frame_time}: {obj_data['id']} - {obj_data['label']} - {obj_data['score']} - {obj.score_history}") # print(f"{frame_time}: {obj_data['id']} - {obj_data['label']} - {obj_data['score']} - {obj.score_history}")
self.frame_manager.delete(self.camera_state.previous_frame_id) self.frame_manager.delete(self.camera_state.previous_frame_id)
@ -154,8 +154,9 @@ class ProcessClip():
@click.option("-p", "--path", required=True, help="Path to clip or directory to test.") @click.option("-p", "--path", required=True, help="Path to clip or directory to test.")
@click.option("-l", "--label", default='person', help="Label name to detect.") @click.option("-l", "--label", default='person', help="Label name to detect.")
@click.option("-t", "--threshold", default=0.85, help="Threshold value for objects.") @click.option("-t", "--threshold", default=0.85, help="Threshold value for objects.")
@click.option("-s", "--scores", default=None, help="File to save csv of top scores")
@click.option("--debug-path", default=None, help="Path to output frames for debugging.") @click.option("--debug-path", default=None, help="Path to output frames for debugging.")
def process(path, label, threshold, debug_path): def process(path, label, threshold, scores, debug_path):
clips = [] clips = []
if os.path.isdir(path): if os.path.isdir(path):
files = os.listdir(path) files = os.listdir(path)
@ -196,10 +197,12 @@ def process(path, label, threshold, debug_path):
process_clip.load_frames() process_clip.load_frames()
process_clip.process_frames(objects_to_track=[label]) process_clip.process_frames(objects_to_track=[label])
results.append((c, process_clip.objects_found(debug_path))) results.append((c, process_clip.top_object(debug_path)))
for result in results: if not scores is None:
print(f"{result[0]}: {result[1]}") with open(scores, 'w') as writer:
for result in results:
writer.write(f"{result[0]},{result[1]['top_score']}\n")
positive_count = sum(1 for result in results if result[1]['object_detected']) positive_count = sum(1 for result in results if result[1]['object_detected'])
print(f"Objects were detected in {positive_count}/{len(results)}({positive_count/len(results)*100:.2f}%) clip(s).") print(f"Objects were detected in {positive_count}/{len(results)}({positive_count/len(results)*100:.2f}%) clip(s).")