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
import click
import csv
import cv2
import numpy as np
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 (
EventsPerSecond,
SharedMemoryFrameManager,
draw_box_with_label,
)
from frigate.video import capture_frames, process_frames, start_or_restart_ffmpeg
logging.basicConfig()
logging.root.setLevel(logging.DEBUG)
logger = logging.getLogger(__name__)
def get_frame_shape(source):
ffprobe_cmd = [
"ffprobe",
"-v",
"panic",
"-show_error",
"-show_streams",
"-of",
"json",
source,
]
p = sp.run(ffprobe_cmd, capture_output=True)
info = json.loads(p.stdout)
video_info = [s for s in info["streams"] if s["codec_type"] == "video"][0]
if video_info["height"] != 0 and video_info["width"] != 0:
return (video_info["height"], video_info["width"], 3)
# fallback to using opencv if ffprobe didnt succeed
video = cv2.VideoCapture(source)
ret, frame = video.read()
frame_shape = frame.shape
video.release()
return frame_shape
class ProcessClip:
def __init__(self, clip_path, frame_shape, config: FrigateConfig):
self.clip_path = clip_path
self.camera_name = "camera"
self.config = config
self.camera_config = self.config.cameras["camera"]
self.frame_shape = self.camera_config.frame_shape
self.ffmpeg_cmd = [
c["cmd"] for c in self.camera_config.ffmpeg_cmds if "detect" in c["roles"]
][0]
self.frame_manager = SharedMemoryFrameManager()
self.frame_queue = mp.Queue()
self.detected_objects_queue = mp.Queue()
self.camera_state = CameraState(self.camera_name, config, self.frame_manager)
def load_frames(self):
fps = EventsPerSecond()
skipped_fps = EventsPerSecond()
current_frame = mp.Value("d", 0.0)
frame_size = (
self.camera_config.frame_shape_yuv[0]
* self.camera_config.frame_shape_yuv[1]
)
ffmpeg_process = start_or_restart_ffmpeg(
self.ffmpeg_cmd, logger, sp.DEVNULL, frame_size
)
capture_frames(
ffmpeg_process,
self.camera_name,
self.camera_config.frame_shape_yuv,
self.frame_manager,
self.frame_queue,
fps,
skipped_fps,
current_frame,
)
ffmpeg_process.wait()
ffmpeg_process.communicate()
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, self.camera_config.motion)
motion_detector.save_images = False
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)
process_frames(
self.camera_name,
self.frame_queue,
self.frame_shape,
model_shape,
self.camera_config.detect,
self.frame_manager,
motion_detector,
object_detector,
object_tracker,
self.detected_objects_queue,
process_info,
objects_to_track,
object_filters,
detection_enabled,
stop_event,
exit_on_empty=True,
)
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():
(
camera_name,
frame_time,
current_tracked_objects,
motion_boxes,
regions,
) = self.detected_objects_queue.get()
if debug_path:
self.save_debug_frame(
debug_path, frame_time, current_tracked_objects.values()
)
self.camera_state.update(
frame_time, current_tracked_objects, motion_boxes, regions
)
total_regions += len(regions)
total_motion_boxes += len(motion_boxes)
top_score = 0
for id, obj in self.camera_state.tracked_objects.items():
if not obj.false_positive:
object_ids.add(id)
if obj.top_score > top_score:
top_score = obj.top_score
total_frames += 1
self.frame_manager.delete(self.camera_state.previous_frame_id)
return {
"total_regions": total_regions,
"total_motion_boxes": total_motion_boxes,
"true_positive_objects": len(object_ids),
"total_frames": total_frames,
"top_score": top_score,
}
def save_debug_frame(self, debug_path, frame_time, tracked_objects):
current_frame = cv2.cvtColor(
self.frame_manager.get(
f"{self.camera_name}{frame_time}", self.camera_config.frame_shape_yuv
),
cv2.COLOR_YUV2BGR_I420,
)
# draw the bounding boxes on the frame
for obj in tracked_objects:
thickness = 2
color = (0, 0, 175)
if obj["frame_time"] != frame_time:
thickness = 1
color = (255, 0, 0)
else:
color = (255, 255, 0)
# draw the bounding boxes on the frame
box = obj["box"]
draw_box_with_label(
current_frame,
box[0],
box[1],
box[2],
box[3],
obj["id"],
f"{int(obj['score']*100)}% {int(obj['area'])}",
thickness=thickness,
color=color,
)
# draw the regions on the frame
region = obj["region"]
draw_box_with_label(
current_frame,
region[0],
region[1],
region[2],
region[3],
"region",
"",
thickness=1,
color=(0, 255, 0),
)
cv2.imwrite(
f"{os.path.join(debug_path, os.path.basename(self.clip_path))}.{int(frame_time*1000000)}.jpg",
current_frame,
)
@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("-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, output, debug_path):
clips = []
if os.path.isdir(path):
files = os.listdir(path)
files.sort()
clips = [os.path.join(path, file) for file in files]
elif os.path.isfile(path):
clips.append(path)
json_config = {
"mqtt": {"host": "mqtt"},
"detectors": {"coral": {"type": "edgetpu", "device": "usb"}},
"cameras": {
"camera": {
"ffmpeg": {
"inputs": [
{
"path": "path.mp4",
"global_args": "-hide_banner",
"input_args": "-loglevel info",
"roles": ["detect"],
}
]
},
"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"]["detect"] = {
"height": frame_shape[0],
"width": frame_shape[1],
}
json_config["cameras"]["camera"]["ffmpeg"]["inputs"][0]["path"] = c
frigate_config = FrigateConfig(**json_config)
runtime_config = frigate_config.runtime_config
runtime_config.cameras["camera"].create_ffmpeg_cmds()
process_clip = ProcessClip(c, frame_shape, runtime_config)
process_clip.load_frames()
process_clip.process_frames(object_detector, objects_to_track=[label])
results.append((c, process_clip.stats(debug_path)))
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()