2.4 MiB
2.4 MiB
Use DeepSORT instead of SORT for tracking¶
Based on ZQPei's repository, I replace SORT with DeepSort:
Deep sort is basicly the same with sort but added a CNN model to extract features in image of human part bounded by a detector. ZQPei
Other additions:
- Use generator for for video analysis and detection per frame.
- This also allows for caching of intermediate steps
In [2]:
import cv2
from pathlib import Path
import numpy as np
from PIL import Image
import torch
from torchvision.io.video import read_video
import matplotlib.pyplot as plt
from torchvision.utils import draw_bounding_boxes
from torchvision.transforms.functional import to_pil_image
from torchvision.models.detection import retinanet_resnet50_fpn_v2, RetinaNet_ResNet50_FPN_V2_Weights
import tempfile
In [3]:
source = Path('../DATASETS/VIRAT_subset_0102x')
videos = list(source.glob('*.mp4'))
tmpdir = Path(tempfile.gettempdir()) / 'trajpred'
tmpdir.mkdir(exist_ok=True)
In [4]:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device
Out[4]:
Based on code from: https://stackabuse.com/retinanet-object-detection-with-pytorch-and-torchvision/
In [5]:
weights = RetinaNet_ResNet50_FPN_V2_Weights.DEFAULT
model = retinanet_resnet50_fpn_v2(weights=weights, score_thresh=0.35)
model.to(device)
# Put the model in inference mode
model.eval()
# Get the transforms for the model's weights
preprocess = weights.transforms().to(device)
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.
The result from a single prediction coming from model(batch) looks like:
{'boxes': tensor([[5.7001e+02, 2.5786e+02, 6.3138e+02, 3.6970e+02],
[5.0109e+02, 2.4508e+02, 5.5308e+02, 3.4852e+02],
[3.4096e+02, 2.7015e+02, 3.6156e+02, 3.1857e+02],
[5.0219e-01, 3.7588e+02, 9.7911e+01, 7.2000e+02],
[3.4096e+02, 2.7015e+02, 3.6156e+02, 3.1857e+02],
[8.3241e+01, 5.8410e+02, 1.7502e+02, 7.1743e+02]]),
'scores': tensor([0.8525, 0.6491, 0.5985, 0.4999, 0.3753, 0.3746]),
'labels': tensor([64, 64, 1, 64, 18, 86])}
In [26]:
%matplotlib inline
import pylab as pl
from IPython import display
from utils.timer import Timer
from sort_cfotache import Sort
import pickle
def detect_persons(video_path: Path):
video = cv2.VideoCapture(str(video_path))
cachefile = tmpdir / f"detections-{video_path.name}.pcl"
if cachefile.exists():
with cachefile.open('rb') as fp:
all_detections = pickle.load(fp)
for detections in all_detections:
yield detections
else:
all_detections = []
while True:
ret, frame = video.read()
if not ret:
# print("Can't receive frame (stream end?). Exiting ...")
break
t = torch.from_numpy(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
# change axes of image loaded image to be compatilbe with torch.io.read_image (which has C,W,H format instead of W,H,C)
t = t.permute(2, 0, 1)
batch = preprocess(t)[None, :].to(device)
# no_grad can be used on inference, should be slightly faster
with torch.no_grad():
predictions = model(batch)
prediction = predictions[0] # we feed only one frame at the once
mask = prediction['labels'] == 1 # if we want more than one: np.isin(prediction['labels'], [1,86])
scores = prediction['scores'][mask]
labels = prediction['labels'][mask]
boxes = prediction['boxes'][mask]
# TODO: introduce confidence and NMS supression: https://github.com/cfotache/pytorch_objectdetecttrack/blob/master/PyTorch_Object_Tracking.ipynb
# (which I _think_ we better do after filtering)
# alternatively look at Soft-NMS https://towardsdatascience.com/non-maximum-suppression-nms-93ce178e177c
# dets - a numpy array of detections in the format [[x1,y1,x2,y2,score],[x1,y1,x2,y2,score],...]
detections = np.array([np.append(bbox, [score, label]) for bbox, score, label in zip(boxes.cpu(), scores.cpu(), labels.cpu())])
all_detections.append(detections)
yield detections
with cachefile.open('wb') as fp:
pickle.dump(all_detections, fp)
def track_video(video_path: Path) -> dict:
tracked_instances = {}
mot_tracker = Sort()
# timer = Timer()
for detections in detect_persons(video_path):
# timer.tic()
tracks = mot_tracker.update(detections)
# now convert back to boxes and labels
# print(tracks)
boxes = np.array([t[:4] for t in tracks])
# initialize empty with the necesserary dimensions for drawing_bounding_boxes glitch
t_boxes = torch.from_numpy(boxes) if len(boxes) else torch.Tensor().new_empty([0, 6])
labels = [str(int(t[4])) for t in tracks]
# print(t_boxes, boxes, labels)
for track in tracks:
yield track
# print("time for frame: ", timer.toc(), ", avg:", 1/timer.average_time, "fps")
# display.clear_output(wait=True)
# return tracked_instances
In [27]:
def track_videos(video_paths: list[Path]) -> dict:
# collect instances of all videos with unique key
video_paths = list(video_paths)
tracked_instances = {}
timer = Timer()
for i, p in enumerate(video_paths):
print(f"{i}/{len(video_paths)}: {p}")
cachefile = tmpdir / (p.name + '.pcl')
if cachefile.exists() and False:
print('\tLoad pickle')
with cachefile.open('rb') as fp:
new_instances = pickle.load(fp)
else:
#continue # to quickly test from cache
new_instances = {}
timer.tic()
for track in track_video(p):
track_id = f"{i}_{str(int(track[4]))}"
if track_id not in new_instances:
new_instances[track_id] = []
new_instances[track_id].append(track)
with cachefile.open('wb') as fp:
pickle.dump(new_instances, fp)
print(" time for video: ", timer.toc())
tracked_instances.update(new_instances)
return tracked_instances
In [28]:
tracked_instances = track_videos(videos)
len(tracked_instances)
Out[28]:
Project / Homography¶
Now that all trajectories are captured (for a single video), these can then be projected onto a flat surface by homography). The necessary $H$ matrix is already provided by VIRAT in the homographies folder of their online data repository.
In [8]:
homography = list(source.glob('*img2world.txt'))[0]
H = np.loadtxt(homography, delimiter=',')
The homography matrix helps to transform points from image space to a flat world plane. The README_homography.txt from VIRAT describes:
Roughly estimated 3-by-3 homographies are included for convenience. Each homography H provides a mapping from image coordinate to scene-dependent world coordinate.
[xw,yw,zw]' = H*[xi,yi,1]'xi: horizontal axis on image with left top corner as origin, increases right. yi: vertical axis on image with left top corner as origin, increases downward.
xw/zw: world x coordinate yw/zw: world y coordiante
In [9]:
print(Image.open("../DATASETS/VIRAT_subset_0102x/VIRAT_0102_homography_img2world.png").size)
Image.open("../DATASETS/VIRAT_subset_0102x/VIRAT_0102_homography_img2world.png")
Out[9]:
In [10]:
from matplotlib import pyplot as plt
fig = plt.figure(figsize=(20,8))
ax1, ax2 = fig.subplots(1,2)
ax1.set_aspect(1)
ax2.imshow(Image.open("../DATASETS/VIRAT_subset_0102x/VIRAT_S_0102.jpg"))
for track_id in tracked_instances:
# print(track_id)
bboxes = tracked_instances[track_id]
traj = np.array([[[0.5 * (det[0]+det[2]), det[3]]] for det in bboxes])
projected_traj = cv2.perspectiveTransform(traj,H)
# plt.plot(projected_traj[:,0])
ax1.plot(projected_traj[:,:,0].reshape(-1), projected_traj[:,:,1].reshape(-1))
ax2.plot(traj[:,:,0].reshape(-1), traj[:,:,1].reshape(-1))
plt.show()
What if the projection is a heatmap of where people are (tracking would not really be necessary for this thoug). Using the above plot and some blurring effects of pyplot from their documentation
In [37]:
from matplotlib import gridspec
import matplotlib.cm as cm
import matplotlib.transforms as mtransforms
from matplotlib.colors import LightSource
from matplotlib.artist import Artist
def smooth1d(x, window_len):
# copied from https://scipy-cookbook.readthedocs.io/items/SignalSmooth.html
s = np.r_[2*x[0] - x[window_len:1:-1], x, 2*x[-1] - x[-1:-window_len:-1]]
w = np.hanning(window_len)
y = np.convolve(w/w.sum(), s, mode='same')
return y[window_len-1:-window_len+1]
def smooth2d(A, sigma=3):
window_len = max(int(sigma), 3) * 2 + 1
A = np.apply_along_axis(smooth1d, 0, A, window_len)
A = np.apply_along_axis(smooth1d, 1, A, window_len)
return A
class BaseFilter:
def get_pad(self, dpi):
return 0
def process_image(self, padded_src, dpi):
raise NotImplementedError("Should be overridden by subclasses")
def __call__(self, im, dpi):
pad = self.get_pad(dpi)
padded_src = np.pad(im, [(pad, pad), (pad, pad), (0, 0)], "constant")
tgt_image = self.process_image(padded_src, dpi)
return tgt_image, -pad, -pad
class GaussianFilter(BaseFilter):
"""Simple Gaussian filter."""
def __init__(self, sigma, alpha=0.5, color=(0, 0, 0)):
self.sigma = sigma
self.alpha = alpha
self.color = color
def get_pad(self, dpi):
return int(self.sigma*3 / 72 * dpi)
def process_image(self, padded_src, dpi):
tgt_image = np.empty_like(padded_src)
tgt_image[:, :, :3] = self.color
tgt_image[:, :, 3] = smooth2d(padded_src[:, :, 3] * self.alpha,
self.sigma / 72 * dpi)
return tgt_image
gauss = GaussianFilter(2)
fig = plt.figure(figsize=(20,12))
# Create 2x2 sub plots
gs = gridspec.GridSpec(2, 2)
# (ax1, ax2), (ax3, ax4) = fig.subplots(2,2)
ax1 = fig.add_subplot(gs[0,0])
ax3 = fig.add_subplot(gs[1,0])
ax2 = fig.add_subplot(gs[:,1])
ax1.set_aspect(1)
ax3.set_aspect(1)
ax2.imshow(Image.open("../DATASETS/VIRAT_subset_0102x/VIRAT_S_0102.jpg"))
for track_id in tracked_instances:
# print(track_id)
bboxes = tracked_instances[track_id]
traj = np.array([[[0.5 * (det[0]+det[2]), det[3]]] for det in bboxes])
projected_traj = cv2.perspectiveTransform(traj,H)
# plt.plot(projected_traj[:,0])
line, = ax1.plot(projected_traj[:,:,0].reshape(-1), projected_traj[:,:,1].reshape(-1), color=(0,0,0,0.05))
line.set_agg_filter(gauss)
line.set_rasterized(True) # "to suport mixed-mode renderers"
points = ax3.scatter(projected_traj[:,:,0].reshape(-1), projected_traj[:,:,1].reshape(-1), color=(0,0,0,0.01))
points.set_agg_filter(gauss)
points.set_rasterized(True) # "to suport mixed-mode renderers"
ax2.plot(traj[:,:,0].reshape(-1), traj[:,:,1].reshape(-1))
plt.show()
