491 lines
68 KiB
Plaintext
491 lines
68 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 64,
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"id": "d1489f9f-328c-4812-9cdb-0a2dee44ae88",
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"metadata": {},
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"outputs": [],
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"source": [
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"import cv2\n",
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"import argparse\n",
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"import logging\n",
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"import os\n",
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"from tqdm.auto import tqdm\n",
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"from pathlib import Path\n",
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"\n",
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"import numpy as np\n",
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"from tracker.multitracker import JDETracker\n",
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"from utils.timer import Timer\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"from tracker.basetrack import TrackState\n",
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"import torch\n",
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"import pickle\n",
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"\n",
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"from utils import visualization as vis"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 65,
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"id": "edec1b34-64ad-4610-856a-68d886a45142",
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"metadata": {},
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"outputs": [],
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"source": [
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"logger = logging.getLogger(__name__)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 66,
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"id": "010bf567-8845-46d4-8500-883efce2d010",
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"metadata": {},
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"outputs": [],
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"source": [
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"# path of video to use. Replace with integer to use /dev/video device\n",
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"# that is, the webcam.\n",
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"video_path = Path('OUT/embedding_test/V003.seq')\n",
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"projector_pcl = Path('OUT/embedding_test/reducer_pca.pcl')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 67,
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"id": "8a413424-13c4-4bdc-825a-0aa6164e89e2",
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"metadata": {},
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"outputs": [],
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"source": [
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"from utils.parse_config import parse_model_cfg\n",
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"from utils.utils import mkdir_if_missing\n",
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"import utils.datasets as datasets\n",
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"from utils.log import logger as trmlog # we need to override this...\n",
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"\n",
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"\n",
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"trmlog.setLevel(logging.INFO)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 68,
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"id": "7b291b67-93ad-4b51-934a-dbaf095f7704",
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"metadata": {},
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"outputs": [],
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"source": [
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"# load options; quick'n'dirty copy from track.py (as the Namespace object is used in the multitracker)\n",
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"parser = argparse.ArgumentParser(prog='visualise_embeddings.py')\n",
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"parser.add_argument('--cfg', type=str, default='cfg/yolov3_1088x608.cfg', help='cfg file path')\n",
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"parser.add_argument('--weights', type=str, default='jde.1088x608.uncertainty.pt', help='path to weights file')\n",
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"parser.add_argument('--iou-thres', type=float, default=0.5, help='iou threshold required to qualify as detected')\n",
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"parser.add_argument('--conf-thres', type=float, default=0.5, help='object confidence threshold')\n",
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"parser.add_argument('--nms-thres', type=float, default=0.4, help='iou threshold for non-maximum suppression')\n",
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"parser.add_argument('--min-box-area', type=float, default=200, help='filter out tiny boxes')\n",
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"parser.add_argument('--track-buffer', type=int, default=30, help='tracking buffer')\n",
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"parser.add_argument('--dataset-dir', type=str, default=\"/datasets\", help='Path to directory with datasets')\n",
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"parser.add_argument('--experiment-name', type=str, default=\"embedding_test\", help=\"name to prefix output directory with\")\n",
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"parser.add_argument('--output-dir', type=str, default=\"./OUT\", help=\"directory for results\")\n",
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" \n",
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"# we're running in notebook, so default to empty\n",
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"opt = parser.parse_args([])\n",
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"\n",
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"logger.setLevel(logging.INFO)\n",
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"result_path = os.path.join(opt.output_dir, opt.experiment_name)\n",
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"result_frame_path = os.path.join(result_path, 'track-test')\n",
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"mkdir_if_missing(result_path)\n",
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"mkdir_if_missing(result_frame_path)\n",
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"data_type = 'mot'\n",
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"\n",
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"# Read config\n",
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"cfg_dict = parse_model_cfg(opt.cfg)\n",
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"# set img_size in opt, so it is passed on to JDETracker\n",
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"opt.img_size = [int(cfg_dict[0]['width']), int(cfg_dict[0]['height'])]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 77,
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"id": "c87f2b73-6109-4509-a0ce-62a690c44030",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'./OUT/embedding_test/track-test'"
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]
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},
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"execution_count": 77,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"result_frame_path"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 69,
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"id": "648faf4b-d692-473a-a99d-06b50a2e2261",
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"metadata": {},
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"outputs": [],
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"source": [
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"import queue, threading\n",
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"\n",
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"# bufferless VideoCapture (by Ulrich Stern: https://stackoverflow.com/a/54577746)\n",
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"# Usefull when using Webcam with higher fps than the script can process it.\n",
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"class BufferlessVideoCapture:\n",
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"\n",
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" def __init__(self, name):\n",
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" self.cap = cv2.VideoCapture(name)\n",
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" self.q = queue.Queue()\n",
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" t = threading.Thread(target=self._reader)\n",
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" t.daemon = True\n",
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" t.start()\n",
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"\n",
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" # read frames as soon as they are available, keeping only most recent one\n",
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" def _reader(self):\n",
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" while True:\n",
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" ret, frame = self.cap.read()\n",
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" if not ret:\n",
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" break\n",
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" if not self.q.empty():\n",
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" try:\n",
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" self.q.get_nowait() # discard previous (unprocessed) frame\n",
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" except queue.Empty:\n",
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" pass\n",
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" self.q.put(frame)\n",
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"\n",
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" def read(self):\n",
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" return self.q.get()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 70,
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"id": "a28ef404-2031-43cf-aeb1-357aa1be0934",
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"with projector_pcl.open('rb') as fp:\n",
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" reducer = pickle.load(fp)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c433bdc3-e3bb-4d53-8e41-6e11791d8ac2",
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"metadata": {},
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"source": [
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"Load video file and get it's properties. Use that to calculate the dimension to fit the loaded model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 74,
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"id": "eb63b1c4-fdde-48e9-977e-21ceb10c5316",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(2, 512)"
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]
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},
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"execution_count": 74,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# array with shape n-components, n-features\n",
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"reducer.components_.shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 59,
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"id": "642c1953-6f33-4a22-a223-797755ece204",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"video 640 x 480\n",
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"Scale to 810 x 608\n"
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]
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}
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],
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"source": [
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"stream = cv2.VideoCapture(str(video_path))\n",
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"frame_rate = stream.get(cv2.CAP_PROP_FPS)\n",
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"vw = int(stream.get(cv2.CAP_PROP_FRAME_WIDTH))\n",
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"vh = int(stream.get(cv2.CAP_PROP_FRAME_HEIGHT))\n",
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"\n",
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"\n",
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"print(\"video\", vw, \"x\", vh)\n",
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"\n",
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"# get_size (from datasets.py)\n",
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"aspect_w, aspect_h = float(opt.img_size[0]) / vw, float(opt.img_size[1]) / vh\n",
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"aspect = min(aspect_w, aspect_h)\n",
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"w, h = int(vw *aspect), int(vh*aspect)\n",
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"print(\"Scale to\", w, \"x\", h)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 60,
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"id": "be84553e-4309-46e5-9d9a-3075c20c0463",
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"metadata": {},
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"outputs": [],
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"source": [
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"umap_pcl = os.path.join(result_path, 'reducer_umap.pcl')\n",
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"pca_pcl = os.path.join(result_path, 'reducer_pca.pcl')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 75,
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"id": "c623aa17-5ce2-4948-9adf-d4c9a6d1ccd2",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(-1.0, 1.0)"
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]
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},
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"execution_count": 75,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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\n",
|
|
"text/plain": [
|
|
"<Figure size 2000x1000 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# matplotlib in interactive mode so the scatter can be updated.\n",
|
|
"\n",
|
|
"plt.style.use('dark_background')\n",
|
|
"\n",
|
|
"fig, ax = plt.subplots(1, 2, figsize=(10, 5), dpi=200)\n",
|
|
"x, y = [],[]\n",
|
|
"scatter = ax[1].scatter(x,y)\n",
|
|
"# scatter = ax.plot([],[],label='toto',ms=10,color='k',marker='o',ls='')\n",
|
|
"plt.xlim(-1,1)\n",
|
|
"plt.ylim(-1,1)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 76,
|
|
"id": "e8952235-7e56-4606-858a-a9165b967726",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# init tracker\n",
|
|
"tracker = JDETracker(opt, frame_rate=frame_rate)\n",
|
|
"timer = Timer()\n",
|
|
"results = []\n",
|
|
"frame_id = -1\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 63,
|
|
"id": "f9cc6fd1-b9c2-4303-a21c-a193c6045526",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"application/vnd.jupyter.widget-view+json": {
|
|
"model_id": "3ec1f9b647cf40edbf662db14ac36bcb",
|
|
"version_major": 2,
|
|
"version_minor": 0
|
|
},
|
|
"text/plain": [
|
|
"0it [00:00, ?it/s]"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"ename": "error",
|
|
"evalue": "OpenCV(4.7.0) /io/opencv/modules/imgproc/src/resize.cpp:4062: error: (-215:Assertion failed) !ssize.empty() in function 'resize'\n",
|
|
"output_type": "error",
|
|
"traceback": [
|
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
|
"\u001b[0;31merror\u001b[0m Traceback (most recent call last)",
|
|
"Cell \u001b[0;32mIn[63], line 10\u001b[0m\n\u001b[1;32m 8\u001b[0m ret, frame \u001b[38;5;241m=\u001b[39m stream\u001b[38;5;241m.\u001b[39mread()\n\u001b[1;32m 9\u001b[0m \u001b[38;5;66;03m# scale down/up frame to fit tracker\u001b[39;00m\n\u001b[0;32m---> 10\u001b[0m frame \u001b[38;5;241m=\u001b[39m \u001b[43mcv2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresize\u001b[49m\u001b[43m(\u001b[49m\u001b[43mframe\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mw\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mh\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;66;03m# letterbox as to have a constant size for the model\u001b[39;00m\n\u001b[1;32m 14\u001b[0m img, _, _, _ \u001b[38;5;241m=\u001b[39m datasets\u001b[38;5;241m.\u001b[39mletterbox(frame, height\u001b[38;5;241m=\u001b[39mopt\u001b[38;5;241m.\u001b[39mimg_size[\u001b[38;5;241m1\u001b[39m], width\u001b[38;5;241m=\u001b[39mopt\u001b[38;5;241m.\u001b[39mimg_size[\u001b[38;5;241m0\u001b[39m])\n",
|
|
"\u001b[0;31merror\u001b[0m: OpenCV(4.7.0) /io/opencv/modules/imgproc/src/resize.cpp:4062: error: (-215:Assertion failed) !ssize.empty() in function 'resize'\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"with tqdm() as pbar:\n",
|
|
" while True:\n",
|
|
" timer.tic()\n",
|
|
" frame_id += 1\n",
|
|
"\n",
|
|
" pbar.update(1)\n",
|
|
"\n",
|
|
" ret, frame = stream.read()\n",
|
|
" # scale down/up frame to fit tracker\n",
|
|
" frame = cv2.resize(frame, (w, h))\n",
|
|
"\n",
|
|
"\n",
|
|
" # letterbox as to have a constant size for the model\n",
|
|
" img, _, _, _ = datasets.letterbox(frame, height=opt.img_size[1], width=opt.img_size[0])\n",
|
|
"\n",
|
|
" # Normalize RGB\n",
|
|
" img = img[:, :, ::-1].transpose(2, 0, 1)\n",
|
|
" img = np.ascontiguousarray(img, dtype=np.float32)\n",
|
|
" img /= 255.0\n",
|
|
"\n",
|
|
" # send frame to GPU\n",
|
|
" blob = torch.from_numpy(img).cuda().unsqueeze(0)\n",
|
|
"\n",
|
|
" # online targets: all targets that are not timed out\n",
|
|
" # frame_embeddings: the embeddings of objects visible only in the current frame\n",
|
|
" online_targets, frame_embeddings = tracker.update(blob, frame)\n",
|
|
"\n",
|
|
" # all relevant tracks from the tracker directly (excludes the 'removed' ones)\n",
|
|
" # thus also accessing its those that it has stored from previous frames\n",
|
|
" all_stracks = tracker.tracked_stracks + tracker.lost_stracks\n",
|
|
"\n",
|
|
" # project them to 2D using PCA (see visualise_embeddings.ipynb)\n",
|
|
" if len(all_stracks):\n",
|
|
" projections = reducer.transform([strack.smooth_feat for strack in all_stracks])\n",
|
|
" else:\n",
|
|
" projections = []\n",
|
|
"\n",
|
|
" timer.toc()\n",
|
|
"\n",
|
|
"\n",
|
|
" online_tlwhs = []\n",
|
|
" online_ids = []\n",
|
|
" for t in online_targets:\n",
|
|
" tlwh = t.tlwh\n",
|
|
" tid = t.track_id\n",
|
|
" vertical = tlwh[2] / tlwh[3] > 1.6\n",
|
|
" if tlwh[2] * tlwh[3] > opt.min_box_area and not vertical:\n",
|
|
" online_tlwhs.append(tlwh)\n",
|
|
" online_ids.append(tid)\n",
|
|
" # visualise results for single frame\n",
|
|
" online_im = vis.plot_tracking(frame, online_tlwhs, online_ids, frame_id=frame_id,\n",
|
|
" fps=1. / timer.average_time)\n",
|
|
" \n",
|
|
" # redraw the canvas\n",
|
|
" # fig.canvas.draw()\n",
|
|
" # ax[0].clear()\n",
|
|
" # ax[1].clear()\n",
|
|
" \n",
|
|
" ax[0].imshow(online_im)\n",
|
|
" # cv2.imwrite(result_frame_path + f\"/{frame_id:04d}-live.jpg\", online_im)\n",
|
|
"\n",
|
|
"\n",
|
|
" # scatter.set_data(projections[:,0],projections[:,1])\n",
|
|
" if len(projections):\n",
|
|
" scatter.set_offsets(projections)\n",
|
|
" scatter.set_color([(0,0,1,0.5) if strack.state == TrackState.Lost else (0,0,1,1) for strack in all_stracks])\n",
|
|
" else:\n",
|
|
" scatter = ax[1].scatter([],[])\n",
|
|
" pbar.set_description('todo: empty axes')\n",
|
|
" # print()\n",
|
|
"\n",
|
|
" # plot = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')\n",
|
|
" # plot = plot.reshape(fig.canvas.get_width_height()[::-1] + (3,))\n",
|
|
"\n",
|
|
" # # img is rgb, convert to opencv's default bgr\n",
|
|
" # plot = cv2.cvtColor(plot,cv2.COLOR_RGB2BGR)\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
" fig.savefig(result_frame_path + f\"/{frame_id:04d}.png\")\n",
|
|
"\n",
|
|
" # fig.canvas.draw_idle()\n",
|
|
" # plt.pause(0.1)\n",
|
|
" # if cv2.waitKey(1) & 0xFF == ord('q'): # wait for 1 millisecond\n",
|
|
" # break"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 78,
|
|
"id": "8f21e642-2320-4a7e-a0ea-af32e8d1a182",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"ffmpeg version 4.3 Copyright (c) 2000-2020 the FFmpeg developers\n",
|
|
" built with gcc 7.3.0 (crosstool-NG 1.23.0.449-a04d0)\n",
|
|
" configuration: --prefix=/opt/conda/conda-bld/ffmpeg_1597178665428/_h_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placeh --cc=/opt/conda/conda-bld/ffmpeg_1597178665428/_build_env/bin/x86_64-conda_cos6-linux-gnu-cc --disable-doc --disable-openssl --enable-avresample --enable-gnutls --enable-hardcoded-tables --enable-libfreetype --enable-libopenh264 --enable-pic --enable-pthreads --enable-shared --disable-static --enable-version3 --enable-zlib --enable-libmp3lame\n",
|
|
" libavutil 56. 51.100 / 56. 51.100\n",
|
|
" libavcodec 58. 91.100 / 58. 91.100\n",
|
|
" libavformat 58. 45.100 / 58. 45.100\n",
|
|
" libavdevice 58. 10.100 / 58. 10.100\n",
|
|
" libavfilter 7. 85.100 / 7. 85.100\n",
|
|
" libavresample 4. 0. 0 / 4. 0. 0\n",
|
|
" libswscale 5. 7.100 / 5. 7.100\n",
|
|
" libswresample 3. 7.100 / 3. 7.100\n",
|
|
"\u001b[1;31mUnrecognized option 'crf'.\n",
|
|
"\u001b[0m\u001b[4;31mError splitting the argument list: \u001b[0m\u001b[4;31mOption not found\n",
|
|
"\u001b[0m"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"! ffmpeg -i OUT/embedding_test/track-test/%04d.png -c:v libx264 -crf 10 OUT/embedding_test/track-test.mp4"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "94d6e0cb-4d32-4d18-8b18-4ecfcc67a634",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "30953716-c267-401f-ad30-51b2a139bfc2",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "trm",
|
|
"language": "python",
|
|
"name": "trm"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.10.8"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|