blakeblackshear.frigate/process_clip.py

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(level=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)
        process_clip = ProcessClip(c, frame_shape, frigate_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()