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revamp process clip

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This commit is contained in:
Blake Blackshear 2021-11-07 14:55:09 -06:00
parent 294c79a271
commit 0695bb097d
2 changed files with 79 additions and 47 deletions
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1
.gitignore vendored
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@ -8,6 +8,7 @@ models
*.mp4
*.ts
*.db
*.csv
frigate/version.py
web/build
web/node_modules

123
frigate/process_clip.py → process_clip.py
View File

@ -1,23 +1,26 @@
import datetime
import sys
from typing_extensions import runtime
sys.path.append("/lab/frigate")
import json
import logging
import multiprocessing as mp
import os
import subprocess as sp
import sys
from unittest import TestCase, main
import click
import csv
import cv2
import numpy as np
from frigate.config import FRIGATE_CONFIG_SCHEMA, FrigateConfig
from frigate.config import FrigateConfig
from frigate.edgetpu import LocalObjectDetector
from frigate.motion import MotionDetector
from frigate.object_processing import CameraState
from frigate.objects import ObjectTracker
from frigate.util import (
DictFrameManager,
EventsPerSecond,
SharedMemoryFrameManager,
draw_box_with_label,
@ -96,20 +99,22 @@ class ProcessClip:
ffmpeg_process.wait()
ffmpeg_process.communicate()
def process_frames(self, objects_to_track=["person"], object_filters={}):
def process_frames(
self, object_detector, objects_to_track=["person"], object_filters={}
):
mask = np.zeros((self.frame_shape[0], self.frame_shape[1], 1), np.uint8)
mask[:] = 255
motion_detector = MotionDetector(
self.frame_shape, mask, self.camera_config.motion
)
motion_detector = MotionDetector(self.frame_shape, self.camera_config.motion)
motion_detector.save_images = False
object_detector = LocalObjectDetector(labels="/labelmap.txt")
object_tracker = ObjectTracker(self.camera_config.detect)
process_info = {
"process_fps": mp.Value("d", 0.0),
"detection_fps": mp.Value("d", 0.0),
"detection_frame": mp.Value("d", 0.0),
}
detection_enabled = mp.Value("d", 1)
stop_event = mp.Event()
model_shape = (self.config.model.height, self.config.model.width)
@ -118,6 +123,7 @@ class ProcessClip:
self.frame_queue,
self.frame_shape,
model_shape,
self.camera_config.detect,
self.frame_manager,
motion_detector,
object_detector,
@ -126,25 +132,16 @@ class ProcessClip:
process_info,
objects_to_track,
object_filters,
mask,
detection_enabled,
stop_event,
exit_on_empty=True,
)
def top_object(self, debug_path=None):
obj_detected = False
top_computed_score = 0.0
def handle_event(name, obj, frame_time):
nonlocal obj_detected
nonlocal top_computed_score
if obj.computed_score > top_computed_score:
top_computed_score = obj.computed_score
if not obj.false_positive:
obj_detected = True
self.camera_state.on("new", handle_event)
self.camera_state.on("update", handle_event)
def stats(self, debug_path=None):
total_regions = 0
total_motion_boxes = 0
object_ids = set()
total_frames = 0
while not self.detected_objects_queue.empty():
(
@ -154,7 +151,8 @@ class ProcessClip:
motion_boxes,
regions,
) = self.detected_objects_queue.get()
if not debug_path is None:
if debug_path:
self.save_debug_frame(
debug_path, frame_time, current_tracked_objects.values()
)
@ -162,10 +160,22 @@ class ProcessClip:
self.camera_state.update(
frame_time, current_tracked_objects, motion_boxes, regions
)
total_regions += len(regions)
total_motion_boxes += len(motion_boxes)
for id, obj in self.camera_state.tracked_objects.items():
if not obj.false_positive:
object_ids.add(id)
total_frames += 1
self.frame_manager.delete(self.camera_state.previous_frame_id)
return {"object_detected": obj_detected, "top_score": top_computed_score}
return {
"total_regions": total_regions,
"total_motion_boxes": total_motion_boxes,
"true_positive_objects": len(object_ids),
"total_frames": total_frames,
}
def save_debug_frame(self, debug_path, frame_time, tracked_objects):
current_frame = cv2.cvtColor(
@ -178,7 +188,6 @@ class ProcessClip:
for obj in tracked_objects:
thickness = 2
color = (0, 0, 175)
if obj["frame_time"] != frame_time:
thickness = 1
color = (255, 0, 0)
@ -221,10 +230,9 @@ class ProcessClip:
@click.command()
@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("-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("-o", "--output", default=None, help="File to save csv of data")
@click.option("--debug-path", default=None, help="Path to output frames for debugging.")
def process(path, label, threshold, scores, debug_path):
def process(path, label, output, debug_path):
clips = []
if os.path.isdir(path):
files = os.listdir(path)
@ -235,51 +243,74 @@ def process(path, label, threshold, scores, debug_path):
json_config = {
"mqtt": {"host": "mqtt"},
"detectors": {"coral": {"type": "edgetpu", "device": "usb"}},
"cameras": {
"camera": {
"ffmpeg": {
"inputs": [
{
"path": "path.mp4",
"global_args": "",
"input_args": "",
"global_args": "-hide_banner",
"input_args": "-loglevel info",
"roles": ["detect"],
}
]
},
"height": 1920,
"width": 1080,
"rtmp": {"enabled": False},
"record": {"enabled": False},
}
},
}
object_detector = LocalObjectDetector(labels="/labelmap.txt")
results = []
for c in clips:
logger.info(c)
frame_shape = get_frame_shape(c)
json_config["cameras"]["camera"]["height"] = frame_shape[0]
json_config["cameras"]["camera"]["width"] = frame_shape[1]
json_config["cameras"]["camera"]["ffmpeg"]["inputs"][0]["path"] = c
config = FrigateConfig(config=FRIGATE_CONFIG_SCHEMA(json_config))
frigate_config = FrigateConfig(**json_config)
runtime_config = frigate_config.runtime_config
process_clip = ProcessClip(c, frame_shape, config)
process_clip = ProcessClip(c, frame_shape, runtime_config)
process_clip.load_frames()
process_clip.process_frames(objects_to_track=[label])
process_clip.process_frames(object_detector, objects_to_track=[label])
results.append((c, process_clip.top_object(debug_path)))
results.append((c, process_clip.stats(debug_path)))
if not scores is None:
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]["true_positive_objects"] > 0
)
print(
f"Objects were detected in {positive_count}/{len(results)}({positive_count/len(results)*100:.2f}%) clip(s)."
)
if output:
# now we will open a file for writing
data_file = open(output, "w")
# create the csv writer object
csv_writer = csv.writer(data_file)
# Counter variable used for writing
# headers to the CSV file
count = 0
for result in results:
if count == 0:
# Writing headers of CSV file
header = ["file"] + list(result[1].keys())
csv_writer.writerow(header)
count += 1
# Writing data of CSV file
csv_writer.writerow([result[0]] + list(result[1].values()))
data_file.close()
if __name__ == "__main__":
process()