person_monitor/dependence/ByteTrack/tools/txt2video.py

import os
import sys
import json
import cv2
import glob as gb
import numpy as np


def colormap(rgb=False):
    color_list = np.array(
        [
            0.000, 0.447, 0.741,
            0.850, 0.325, 0.098,
            0.929, 0.694, 0.125,
            0.494, 0.184, 0.556,
            0.466, 0.674, 0.188,
            0.301, 0.745, 0.933,
            0.635, 0.078, 0.184,
            0.300, 0.300, 0.300,
            0.600, 0.600, 0.600,
            1.000, 0.000, 0.000,
            1.000, 0.500, 0.000,
            0.749, 0.749, 0.000,
            0.000, 1.000, 0.000,
            0.000, 0.000, 1.000,
            0.667, 0.000, 1.000,
            0.333, 0.333, 0.000,
            0.333, 0.667, 0.000,
            0.333, 1.000, 0.000,
            0.667, 0.333, 0.000,
            0.667, 0.667, 0.000,
            0.667, 1.000, 0.000,
            1.000, 0.333, 0.000,
            1.000, 0.667, 0.000,
            1.000, 1.000, 0.000,
            0.000, 0.333, 0.500,
            0.000, 0.667, 0.500,
            0.000, 1.000, 0.500,
            0.333, 0.000, 0.500,
            0.333, 0.333, 0.500,
            0.333, 0.667, 0.500,
            0.333, 1.000, 0.500,
            0.667, 0.000, 0.500,
            0.667, 0.333, 0.500,
            0.667, 0.667, 0.500,
            0.667, 1.000, 0.500,
            1.000, 0.000, 0.500,
            1.000, 0.333, 0.500,
            1.000, 0.667, 0.500,
            1.000, 1.000, 0.500,
            0.000, 0.333, 1.000,
            0.000, 0.667, 1.000,
            0.000, 1.000, 1.000,
            0.333, 0.000, 1.000,
            0.333, 0.333, 1.000,
            0.333, 0.667, 1.000,
            0.333, 1.000, 1.000,
            0.667, 0.000, 1.000,
            0.667, 0.333, 1.000,
            0.667, 0.667, 1.000,
            0.667, 1.000, 1.000,
            1.000, 0.000, 1.000,
            1.000, 0.333, 1.000,
            1.000, 0.667, 1.000,
            0.167, 0.000, 0.000,
            0.333, 0.000, 0.000,
            0.500, 0.000, 0.000,
            0.667, 0.000, 0.000,
            0.833, 0.000, 0.000,
            1.000, 0.000, 0.000,
            0.000, 0.167, 0.000,
            0.000, 0.333, 0.000,
            0.000, 0.500, 0.000,
            0.000, 0.667, 0.000,
            0.000, 0.833, 0.000,
            0.000, 1.000, 0.000,
            0.000, 0.000, 0.167,
            0.000, 0.000, 0.333,
            0.000, 0.000, 0.500,
            0.000, 0.000, 0.667,
            0.000, 0.000, 0.833,
            0.000, 0.000, 1.000,
            0.000, 0.000, 0.000,
            0.143, 0.143, 0.143,
            0.286, 0.286, 0.286,
            0.429, 0.429, 0.429,
            0.571, 0.571, 0.571,
            0.714, 0.714, 0.714,
            0.857, 0.857, 0.857,
            1.000, 1.000, 1.000
        ]
    ).astype(np.float32)
    color_list = color_list.reshape((-1, 3)) * 255
    if not rgb:
        color_list = color_list[:, ::-1]
    return color_list


def txt2img(visual_path="visual_val_gt"):
    print("Starting txt2img")

    valid_labels = {1}
    ignore_labels = {2, 7, 8, 12}

    if not os.path.exists(visual_path):
        os.makedirs(visual_path)
    color_list = colormap()

    gt_json_path = 'datasets/mot/annotations/val_half.json'
    img_path = 'datasets/mot/train/'
    show_video_names = ['MOT17-02-FRCNN', 
                    'MOT17-04-FRCNN',
                    'MOT17-05-FRCNN',
                    'MOT17-09-FRCNN',
                    'MOT17-10-FRCNN',        
                    'MOT17-11-FRCNN',
                    'MOT17-13-FRCNN']


    test_json_path = 'datasets/mot/annotations/test.json'
    test_img_path = 'datasets/mot/test/'
    test_show_video_names = ['MOT17-01-FRCNN', 
                    'MOT17-03-FRCNN',
                    'MOT17-06-FRCNN',
                    'MOT17-07-FRCNN',
                    'MOT17-08-FRCNN',        
                    'MOT17-12-FRCNN',
                    'MOT17-14-FRCNN']
    if visual_path == "visual_test_predict":
        show_video_names = test_show_video_names
        img_path = test_img_path
        gt_json_path = test_json_path
    for show_video_name in show_video_names:
        img_dict = dict()
        
        if visual_path == "visual_val_gt":
            txt_path = 'datasets/mot/train/' + show_video_name + '/gt/gt_val_half.txt'
        elif visual_path == "visual_yolox_x":
            txt_path = 'YOLOX_outputs/yolox_mot_x_1088/track_results/'+ show_video_name + '.txt'
        elif visual_path == "visual_test_predict":
            txt_path = 'test/tracks/'+ show_video_name + '.txt'
        else:
            raise NotImplementedError
        
        with open(gt_json_path, 'r') as f:
            gt_json = json.load(f)

        for ann in gt_json["images"]:
            file_name = ann['file_name']
            video_name = file_name.split('/')[0]
            if video_name == show_video_name:
                img_dict[ann['frame_id']] = img_path + file_name


        txt_dict = dict()    
        with open(txt_path, 'r') as f:
            for line in f.readlines():
                linelist = line.split(',')

                mark = int(float(linelist[6]))
                label = int(float(linelist[7]))
                vis_ratio = float(linelist[8])
                
                if visual_path == "visual_val_gt":
                    if mark == 0 or label not in valid_labels or label in ignore_labels or vis_ratio <= 0:
                        continue

                img_id = linelist[0]
                obj_id = linelist[1]
                bbox = [float(linelist[2]), float(linelist[3]), 
                        float(linelist[2]) + float(linelist[4]), 
                        float(linelist[3]) + float(linelist[5]), int(obj_id)]
                if int(img_id) in txt_dict:
                    txt_dict[int(img_id)].append(bbox)
                else:
                    txt_dict[int(img_id)] = list()
                    txt_dict[int(img_id)].append(bbox)

        for img_id in sorted(txt_dict.keys()):
            img = cv2.imread(img_dict[img_id])
            for bbox in txt_dict[img_id]:
                cv2.rectangle(img, (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])), color_list[bbox[4]%79].tolist(), thickness=2)
                cv2.putText(img, "{}".format(int(bbox[4])), (int(bbox[0]), int(bbox[1])), cv2.FONT_HERSHEY_SIMPLEX, 0.8, color_list[bbox[4]%79].tolist(), 2)
            cv2.imwrite(visual_path + "/" + show_video_name + "{:0>6d}.png".format(img_id), img)
        print(show_video_name, "Done")
    print("txt2img Done")

        
def img2video(visual_path="visual_val_gt"):
    print("Starting img2video")

    img_paths = gb.glob(visual_path + "/*.png") 
    fps = 16 
    size = (1920,1080) 
    videowriter = cv2.VideoWriter(visual_path + "_video.avi",cv2.VideoWriter_fourcc('M','J','P','G'), fps, size)

    for img_path in sorted(img_paths):
        img = cv2.imread(img_path)
        img = cv2.resize(img, size)
        videowriter.write(img)

    videowriter.release()
    print("img2video Done")


if __name__ == '__main__':
    visual_path="visual_yolox_x"
    if len(sys.argv) > 1:
        visual_path =sys.argv[1]
    txt2img(visual_path)
    #img2video(visual_path)