470 lines
18 KiB
Plaintext
470 lines
18 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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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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"from pathlib import Path\n",
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"import numpy as np\n",
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"# from PIL import Image\n",
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"import torch\n",
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"from torchvision.io.video import read_video\n",
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"import matplotlib.pyplot as plt\n",
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"from torchvision.utils import draw_bounding_boxes\n",
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"from torchvision.transforms.functional import to_pil_image\n",
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"from torchvision.models.detection import retinanet_resnet50_fpn_v2, RetinaNet_ResNet50_FPN_V2_Weights\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": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"source = Path('../DATASETS/VIRAT_subset_0102x')\n",
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"videos = source.glob('*.mp4')\n",
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"homography = list(source.glob('*img2world.txt'))[0]\n",
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"H = np.loadtxt(homography, delimiter=',')\n",
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"\n"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The homography matrix helps to transform points from image space to a flat world plane. The `README_homography.txt` from VIRAT describes:\n",
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"\n",
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"> Roughly estimated 3-by-3 homographies are included for convenience. \n",
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"> Each homography H provides a mapping from image coordinate to scene-dependent world coordinate.\n",
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"> \n",
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"> [xw,yw,zw]' = H*[xi,yi,1]'\n",
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"> \n",
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"> xi: horizontal axis on image with left top corner as origin, increases right.\n",
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"> yi: vertical axis on image with left top corner as origin, increases downward.\n",
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"> \n",
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"> xw/zw: world x coordinate\n",
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"> yw/zw: world y coordiante"
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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": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"# H.dot(np.array([20,300, 1]))"
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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": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"video_path = list(videos)[0]\n",
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"video_path = Path(\"../DATASETS/VIRAT_subset_0102x/VIRAT_S_010200_00_000060_000218.mp4\")"
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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": 5,
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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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"PosixPath('../DATASETS/VIRAT_subset_0102x/VIRAT_S_010200_00_000060_000218.mp4')"
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]
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},
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"execution_count": 5,
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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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"video_path"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Suggestions from: https://stackabuse.com/retinanet-object-detection-with-pytorch-and-torchvision/"
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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": 34,
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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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"device(type='cuda')"
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]
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},
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"execution_count": 34,
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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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"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
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"device"
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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": 37,
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"metadata": {},
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"outputs": [],
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"source": [
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"weights = RetinaNet_ResNet50_FPN_V2_Weights.DEFAULT\n",
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"model = retinanet_resnet50_fpn_v2(weights=weights, score_thresh=0.35)\n",
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"model.to(device)\n",
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"# Put the model in inference mode\n",
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"model.eval()\n",
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"# Get the transforms for the model's weights\n",
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"preprocess = weights.transforms().to(device)"
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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": 38,
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"metadata": {},
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"outputs": [],
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"source": [
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"# hub.set_dir()"
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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": 39,
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"metadata": {},
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"outputs": [],
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"source": [
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"video = cv2.VideoCapture(str(video_path))"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"> The score_thresh argument defines the threshold at which an object is detected as an object of a class. Intuitively, it's the confidence threshold, and we won't classify an object to belong to a class if the model is less than 35% confident that it belongs to a class."
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The result from a single prediction coming from `model(batch)` looks like:\n",
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"\n",
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"```python\n",
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"{'boxes': tensor([[5.7001e+02, 2.5786e+02, 6.3138e+02, 3.6970e+02],\n",
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" [5.0109e+02, 2.4508e+02, 5.5308e+02, 3.4852e+02],\n",
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" [3.4096e+02, 2.7015e+02, 3.6156e+02, 3.1857e+02],\n",
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" [5.0219e-01, 3.7588e+02, 9.7911e+01, 7.2000e+02],\n",
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" [3.4096e+02, 2.7015e+02, 3.6156e+02, 3.1857e+02],\n",
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" [8.3241e+01, 5.8410e+02, 1.7502e+02, 7.1743e+02]]),\n",
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" 'scores': tensor([0.8525, 0.6491, 0.5985, 0.4999, 0.3753, 0.3746]),\n",
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" 'labels': tensor([64, 64, 1, 64, 18, 86])}\n",
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"```"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Now with SORT tracking\n",
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"\n",
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"Using a sort implementation originally by Alex Bewley, but adapted by [Chris Fotache](https://github.com/cfotache/pytorch_objectdetecttrack/blob/master/README.md). For an example implementation, see [his notebook](https://github.com/cfotache/pytorch_objectdetecttrack/blob/master/PyTorch_Object_Tracking.ipynb).\n",
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"\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": 56,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sort_cfotache import Sort\n",
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"\n",
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"mot_tracker = Sort()\n",
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"\n",
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"display_image = True"
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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": 57,
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"metadata": {},
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"outputs": [],
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"source": [
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"tracked_instances = {}"
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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": 58,
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"metadata": {},
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"outputs": [
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{
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"ename": "KeyboardInterrupt",
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"evalue": "",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[58], line 29\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[39m# no_grad can be used on inference, should be slightly faster\u001b[39;00m\n\u001b[1;32m 28\u001b[0m \u001b[39mwith\u001b[39;00m torch\u001b[39m.\u001b[39mno_grad():\n\u001b[0;32m---> 29\u001b[0m predictions \u001b[39m=\u001b[39m model(batch)\n\u001b[1;32m 30\u001b[0m prediction \u001b[39m=\u001b[39m predictions[\u001b[39m0\u001b[39m] \u001b[39m# we feed only one frame at the once\u001b[39;00m\n\u001b[1;32m 32\u001b[0m mask \u001b[39m=\u001b[39m prediction[\u001b[39m'\u001b[39m\u001b[39mlabels\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m==\u001b[39m \u001b[39m1\u001b[39m \u001b[39m# if we want more than one: np.isin(prediction['labels'], [1,86])\u001b[39;00m\n",
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"File \u001b[0;32m~/suspicion/trajpred/.venv/lib/python3.9/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1497\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1499\u001b[0m \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 1502\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n",
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"File \u001b[0;32m~/suspicion/trajpred/.venv/lib/python3.9/site-packages/torchvision/models/detection/retinanet.py:663\u001b[0m, in \u001b[0;36mRetinaNet.forward\u001b[0;34m(self, images, targets)\u001b[0m\n\u001b[1;32m 660\u001b[0m split_anchors \u001b[39m=\u001b[39m [\u001b[39mlist\u001b[39m(a\u001b[39m.\u001b[39msplit(num_anchors_per_level)) \u001b[39mfor\u001b[39;00m a \u001b[39min\u001b[39;00m anchors]\n\u001b[1;32m 662\u001b[0m \u001b[39m# compute the detections\u001b[39;00m\n\u001b[0;32m--> 663\u001b[0m detections \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mpostprocess_detections(split_head_outputs, split_anchors, images\u001b[39m.\u001b[39;49mimage_sizes)\n\u001b[1;32m 664\u001b[0m detections \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtransform\u001b[39m.\u001b[39mpostprocess(detections, images\u001b[39m.\u001b[39mimage_sizes, original_image_sizes)\n\u001b[1;32m 666\u001b[0m \u001b[39mif\u001b[39;00m torch\u001b[39m.\u001b[39mjit\u001b[39m.\u001b[39mis_scripting():\n",
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"File \u001b[0;32m~/suspicion/trajpred/.venv/lib/python3.9/site-packages/torchvision/models/detection/retinanet.py:531\u001b[0m, in \u001b[0;36mRetinaNet.postprocess_detections\u001b[0;34m(self, head_outputs, anchors, image_shapes)\u001b[0m\n\u001b[1;32m 529\u001b[0m scores_per_level \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39msigmoid(logits_per_level)\u001b[39m.\u001b[39mflatten()\n\u001b[1;32m 530\u001b[0m keep_idxs \u001b[39m=\u001b[39m scores_per_level \u001b[39m>\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mscore_thresh\n\u001b[0;32m--> 531\u001b[0m scores_per_level \u001b[39m=\u001b[39m scores_per_level[keep_idxs]\n\u001b[1;32m 532\u001b[0m topk_idxs \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mwhere(keep_idxs)[\u001b[39m0\u001b[39m]\n\u001b[1;32m 534\u001b[0m \u001b[39m# keep only topk scoring predictions\u001b[39;00m\n",
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"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
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]
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}
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],
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"source": [
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"# TODO make into loop\n",
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"%matplotlib inline\n",
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"\n",
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"\n",
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"import pylab as pl\n",
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"from IPython import display\n",
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"from utils.timer import Timer\n",
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"\n",
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"i=0\n",
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"timer = Timer()\n",
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"while True:\n",
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" timer.tic()\n",
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" ret, frame = video.read()\n",
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" i+=1\n",
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" \n",
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" if not ret:\n",
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" print(\"Can't receive frame (stream end?). Exiting ...\")\n",
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" break\n",
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"\n",
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" t = torch.from_numpy(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))\n",
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" t.shape\n",
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" # image = image[np.newaxis, :] \n",
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" t = t.permute(2, 0, 1)\n",
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" t.shape\n",
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"\n",
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" batch = preprocess(t)[None, :].to(device)\n",
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" # no_grad can be used on inference, should be slightly faster\n",
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" with torch.no_grad():\n",
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" predictions = model(batch)\n",
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" prediction = predictions[0] # we feed only one frame at the once\n",
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"\n",
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" mask = prediction['labels'] == 1 # if we want more than one: np.isin(prediction['labels'], [1,86])\n",
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"\n",
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" scores = prediction['scores'][mask]\n",
|
||
|
|
" labels = prediction['labels'][mask]\n",
|
||
|
|
" boxes = prediction['boxes'][mask]\n",
|
||
|
|
" \n",
|
||
|
|
" # TODO: introduce confidence and NMS supression: https://github.com/cfotache/pytorch_objectdetecttrack/blob/master/PyTorch_Object_Tracking.ipynb\n",
|
||
|
|
" # (which I _think_ we better do after filtering)\n",
|
||
|
|
" # alternatively look at Soft-NMS https://towardsdatascience.com/non-maximum-suppression-nms-93ce178e177c\n",
|
||
|
|
"\n",
|
||
|
|
" \n",
|
||
|
|
" # dets - a numpy array of detections in the format [[x1,y1,x2,y2,score],[x1,y1,x2,y2,score],...]\n",
|
||
|
|
" detections = np.array([np.append(bbox, [score, label]) for bbox, score, label in zip(boxes.cpu(), scores.cpu(), labels.cpu())])\n",
|
||
|
|
" # print(detections)\n",
|
||
|
|
" tracks = mot_tracker.update(detections)\n",
|
||
|
|
"\n",
|
||
|
|
" # now convert back to boxes and labels\n",
|
||
|
|
" # print(tracks)\n",
|
||
|
|
" boxes = np.array([t[:4] for t in tracks])\n",
|
||
|
|
" # initialize empty with the necesserary dimensions for drawing_bounding_boxes glitch\n",
|
||
|
|
" t_boxes = torch.from_numpy(boxes) if len(boxes) else torch.Tensor().new_empty([0, 6])\n",
|
||
|
|
" labels = [str(int(t[4])) for t in tracks]\n",
|
||
|
|
" # print(t_boxes, boxes, labels)\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
" for track in tracks:\n",
|
||
|
|
" # TODO add to tracked_instances\n",
|
||
|
|
" track_id = str(int(track[4]))\n",
|
||
|
|
" if track_id not in tracked_instances:\n",
|
||
|
|
" tracked_instances[track_id] = []\n",
|
||
|
|
" tracked_instances[track_id].append(track)\n",
|
||
|
|
"\n",
|
||
|
|
" \n",
|
||
|
|
" # labels = [weights.meta[\"categories\"][i] for i in labels]\n",
|
||
|
|
"\n",
|
||
|
|
" if display_image:\n",
|
||
|
|
" box = draw_bounding_boxes(t, boxes=t_boxes,\n",
|
||
|
|
" labels=labels,\n",
|
||
|
|
" colors=\"cyan\",\n",
|
||
|
|
" width=2, \n",
|
||
|
|
" font_size=30,\n",
|
||
|
|
" # font='Arial'\n",
|
||
|
|
" )\n",
|
||
|
|
"\n",
|
||
|
|
" im = to_pil_image(box.detach())\n",
|
||
|
|
"\n",
|
||
|
|
" display.display(im, f\"frame {i}\")\n",
|
||
|
|
" print(prediction)\n",
|
||
|
|
" print(\"time for frame: \", timer.toc(), \", avg:\", 1/timer.average_time, \"fps\")\n",
|
||
|
|
"\n",
|
||
|
|
" display.clear_output(wait=True)\n",
|
||
|
|
"\n",
|
||
|
|
" # break # for now\n",
|
||
|
|
" # pl.clf()\n",
|
||
|
|
" # # pl.plot(pl.randn(100))\n",
|
||
|
|
" # pl.figure(figsize=(24,50))\n",
|
||
|
|
" # # fig.axes[0].imshow(img)\n",
|
||
|
|
" # pl.imshow(im)\n",
|
||
|
|
" # display.display(pl.gcf(), f\"frame {i}\")\n",
|
||
|
|
" # display.clear_output(wait=True)\n",
|
||
|
|
" # time.sleep(1.0)\n",
|
||
|
|
"\n",
|
||
|
|
" # fig, ax = plt.subplots(figsize=(16, 12))\n",
|
||
|
|
" # ax.imshow(im)\n",
|
||
|
|
" # plt.show()\n",
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 55,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"dict_keys(['22', '24', '26', '27', '30', '31', '32', '33', '37'])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 55,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"(array([[5.30405334e+02, 5.34641296e+02, 6.03237061e+02, 7.18612122e+02,\n",
|
||
|
|
" 9.42070127e-01, 1.00000000e+00],\n",
|
||
|
|
" [4.61479340e+02, 5.49811340e+02, 5.34607056e+02, 7.17237122e+02,\n",
|
||
|
|
" 9.26090062e-01, 1.00000000e+00],\n",
|
||
|
|
" [3.38673218e+02, 2.55078461e+02, 3.57062561e+02, 2.95217896e+02,\n",
|
||
|
|
" 6.61470771e-01, 1.00000000e+00]]),)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 23,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 24,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"{'17': [array([573.00909697, 551.76122438, 657.56378982, 720.05069192,\n",
|
||
|
|
" 17. , 1. ]),\n",
|
||
|
|
" array([570.16715738, 550.85464258, 652.59986304, 719.88004284,\n",
|
||
|
|
" 17. , 1. ]),\n",
|
||
|
|
" array([568.02909891, 550.10706805, 649.96206622, 720.03113806,\n",
|
||
|
|
" 17. , 1. ]),\n",
|
||
|
|
" array([562.49451695, 549.06638446, 644.29895964, 720.04103925,\n",
|
||
|
|
" 17. , 1. ])],\n",
|
||
|
|
" '13': [array([337.63475088, 255.66774475, 355.97561492, 296.69147428,\n",
|
||
|
|
" 13. , 1. ]),\n",
|
||
|
|
" array([337.77042983, 255.72223676, 356.05113319, 296.63698388,\n",
|
||
|
|
" 13. , 1. ]),\n",
|
||
|
|
" array([338.02427059, 255.89595935, 356.25536645, 296.58306741,\n",
|
||
|
|
" 13. , 1. ]),\n",
|
||
|
|
" array([338.1632419 , 255.82719651, 356.27227032, 296.33234513,\n",
|
||
|
|
" 13. , 1. ])],\n",
|
||
|
|
" '12': [array([481.57704931, 568.79192296, 570.79284909, 718.23349465,\n",
|
||
|
|
" 12. , 1. ]),\n",
|
||
|
|
" array([479.96268827, 569.31456975, 567.89464999, 718.91657277,\n",
|
||
|
|
" 12. , 1. ]),\n",
|
||
|
|
" array([478.23383288, 568.93539717, 565.05653529, 718.92571522,\n",
|
||
|
|
" 12. , 1. ]),\n",
|
||
|
|
" array([475.43950486, 567.4295262 , 561.46362594, 718.3620136 ,\n",
|
||
|
|
" 12. , 1. ])]}"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 24,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 25,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"True"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 25,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": ".venv",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.9.2"
|
||
|
|
},
|
||
|
|
"orig_nbformat": 4,
|
||
|
|
"vscode": {
|
||
|
|
"interpreter": {
|
||
|
|
"hash": "1135f674f58caf91385e41dd32dc418daf761a3c5d4526b1ac3bad0b893c2eb5"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 2
|
||
|
|
}
|