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process_clip.py
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process_clip.py
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import csv
import json
import logging
import multiprocessing as mp
import os
import subprocess as sp
import sys
import click
import cv2
import numpy as np
sys.path.append("/workspace/frigate")
from frigate.config import FrigateConfig # noqa: E402
from frigate.motion import MotionDetector # noqa: E402
from frigate.object_detection import LocalObjectDetector # noqa: E402
from frigate.object_processing import CameraState # noqa: E402
from frigate.track.centroid_tracker import CentroidTracker # noqa: E402
from frigate.util import ( # noqa: E402
EventsPerSecond,
SharedMemoryFrameManager,
draw_box_with_label,
)
from frigate.video import ( # noqa: E402
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 didn't 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 = CentroidTracker(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)
motion_enabled = mp.Value("d", True)
stop_event = mp.Event()
process_frames(
self.camera_name,
self.frame_queue,
self.frame_shape,
self.config.model,
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,
motion_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": {"enabled": False},
"detectors": {"coral": {"type": "edgetpu", "device": "usb"}},
"cameras": {
"camera": {
"ffmpeg": {
"inputs": [
{
"path": "path.mp4",
"global_args": "-hide_banner",
"input_args": "-loglevel info",
"roles": ["detect"],
}
]
},
"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()