inference.py

import os
import tempfile

import numpy as np
from norfair import AbsolutePaths, Paths, Tracker, Video
from norfair.camera_motion import HomographyTransformationGetter, MotionEstimator

from custom_models import YOLO, yolo_detections_to_norfair_detections
from demo_utils.configuration import (
    DISTANCE_THRESHOLD_BBOX,
    DISTANCE_THRESHOLD_CENTROID,
    models_path,
    style,
)
from demo_utils.distance_function import euclidean_distance, iou
from demo_utils.draw import center, draw
from demo_utils.files import get_files


def inference(
    input_video: str,
    cb_cache_output: bool,
    model: str,
    features: str,
    track_points: str,
    model_threshold: str,
):
    # temp_dir = tempfile.TemporaryDirectory()
    # output_path = temp_dir.name

    if cb_cache_output:
        for cache_video in get_files("examples"):
            if input_video.find(cache_video) == -1:
                continue

            cache_video_name = f"outputs/{cache_video}_{model}_out.mp4"
            return cache_video_name
        
    coord_transformations = None
    paths_drawer = None
    fix_paths = False
    track_points = style[track_points]
    model = YOLO(models_path[model])
    video = Video(input_path=input_video)

    motion_estimation = len(features) > 0 and (
        features[0] == 0 or (len(features) > 1 and features[1] == 0)
    )

    drawing_paths = len(features) > 0 and (
        features[0] == 1 or (len(features) > 1 and features[1] == 1)
    )

    if motion_estimation and drawing_paths:
        fix_paths = True

    if motion_estimation:
        transformations_getter = HomographyTransformationGetter()

        motion_estimator = MotionEstimator(
            max_points=500, min_distance=7, transformations_getter=transformations_getter
        )

    distance_function = iou if track_points == "bbox" else euclidean_distance
    distance_threshold = (
        DISTANCE_THRESHOLD_BBOX if track_points == "bbox" else DISTANCE_THRESHOLD_CENTROID
    )

    tracker = Tracker(
        distance_function=distance_function,
        distance_threshold=distance_threshold,
    )

    if drawing_paths:
        paths_drawer = Paths(center, attenuation=0.01)

    if fix_paths:
        paths_drawer = AbsolutePaths(max_history=15, thickness=2)

    for frame in video:
        yolo_detections = model(
            frame, conf_threshold=model_threshold, iou_threshold=0.45, image_size=720
        )

        mask = np.ones(frame.shape[:2], frame.dtype)

        if motion_estimation:
            coord_transformations = motion_estimator.update(frame, mask)

        detections = yolo_detections_to_norfair_detections(
            yolo_detections, track_points=track_points
        )

        tracked_objects = tracker.update(
            detections=detections, coord_transformations=coord_transformations
        )

        frame = draw(
            paths_drawer,
            track_points,
            frame,
            detections,
            tracked_objects,
            coord_transformations,
            fix_paths,
        )
        video.write(frame)

    base_file_name = input_video.split("/")[-1].split(".")[0]
    file_name = base_file_name + "_out.mp4"
    
    # return os.path.join(output_path, file_name)

    return file_name