'Bolt2', 'Boy', 'Car2', 'Car24', 'Coke', 'Coupon', 'Crossing', 'Dancer',
'Dancer2', 'David2', 'David3', 'Dog', 'Dog1', 'Doll', 'FaceOcc1',
'FaceOcc2', 'Fish', 'FleetFace', 'Football1', 'Freeman1', 'Freeman3',
'Girl2', 'Gym', 'Human2', 'Human5', 'Human7', 'Human8', 'Jogging',
'KiteSurf', 'Lemming', 'Man', 'Mhyang', 'MountainBike', 'Rubik', 'Singer1',
'Skater', 'Skater2', 'Subway', 'Suv', 'Tiger1', 'Toy', 'Trans',
'Twinnings', 'Vase'
]
if __name__ == '__main__':
nbins_iou = 21
nbins_ce = 51
video_path = 'E:\\xxx\\OTB2015\\Bolt'
img_files = sorted(glob.glob(os.path.join(video_path, 'img/*.jpg')))
anno_files = glob.glob(os.path.join(video_path, 'groundtruth_rect*.txt'))
with open(anno_files[0], 'r') as f:
anno = np.loadtxt(io.StringIO(f.read().replace(',', ' ')))
net_path = './pretrained/model.pth'
tracker = TrackerSiamFC(net_path=net_path)
boxes, _, fps = tracker.track(img_files,
anno[0, :],
visualize=True,
debug=False,
gt=anno)
ious, center_errors = _calc_metrics(boxes, anno)
succ_curve, prec_curve = _calc_curves(ious, center_errors)
print('OP is {:.3f},DP is {:.3f},AUC is {:.3f},fps is {:.3f}'.format(
len(ious[ious > 0.5]) / len(ious), prec_curve[20], np.mean(succ_curve),
fps))
from __future__ import absolute_import
import os
import glob
import numpy as np
from siamfc import TrackerSiamFC
if __name__ == '__main__':
seq_dir = os.path.expanduser('E:/Datasets/OTB100/hongwai/')
img_files = sorted(glob.glob(seq_dir + 'img/*.jpg'))
anno = np.loadtxt(seq_dir + 'groundtruth_rect.txt', delimiter=',')
net_path = 'C:/Users/however/Desktop/siamfc-pytorch-master/pretrained/siamfc_alexnet_e49.pth'
tracker = TrackerSiamFC(net_path=net_path)
tracker.track(img_files, anno[0], visualize=True)
def main(dataset, data_time, detector):
path_result = os.path.join('results', data_time + '_' + detector, dataset)
os.makedirs(path_result, exist_ok=True)
# initialize detector
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = torchvision.models.detection.fasterrcnn_resnet50_fpn(
pretrained=True)
# model = torchvision.models.detection.maskrcnn_resnet50_fpn(pretrained=True)
model.to(device=device)
model.eval()
# load background
img_bkgd_bev = cv2.imread('calibration/' + dataset +
'_background_calibrated.png')
# load transformation matrix
transform_cam2world = np.loadtxt('calibration/' + dataset +
'_matrix_cam2world.txt')
# open video of dataset
if dataset == 'oxford_town':
cap = cv2.VideoCapture(os.path.join('datasets', 'TownCentreXVID.avi'))
frame_skip = 10 # oxford town dataset has fps of 25
thr_score = 0.9
elif dataset == 'oxford_town_group':
cap = cv2.VideoCapture(os.path.join('datasets', 'TownCentreXVID.avi'))
path_track_frames = os.path.join(os.getcwd(), 'datasets',
'dataset_tracks', 'TownCentre',
'img1')
frame_images = sorted(glob.glob(path_track_frames + '\****.jpg'))
net_path = os.path.join(os.getcwd(), 'tracker', 'siamfc_pytorch',
'tools', 'pretrained\siamfc_alexnet_e50.pth')
tracker = TrackerSiamFC(net_path=net_path)
frame_skip = 10 # oxford town dataset has fps of 25
thr_score = 0.9
elif dataset == 'mall':
cap = cv2.VideoCapture(os.path.join('datasets', 'mall.mp4'))
frame_skip = 1
thr_score = 0.9
elif dataset == 'grand_central':
cap = cv2.VideoCapture(os.path.join('datasets', 'grandcentral.avi'))
frame_skip = 25 # grand central dataset has fps of 25
thr_score = 0.5
else:
raise Exception('Invalid Dataset')
# f = open(os.path.join(path_result, 'statistics.txt'), 'w')
statistic_data = []
i_frame = 0
# while cap.isOpened() and i_frame < 5000:
while cap.isOpened() and i_frame <= 7450:
ret, img = cap.read()
print("at frame " + str(i_frame) + "------")
if ret is False:
break
if i_frame % frame_skip == 0: #only run the social distancing system every 10 frames.
#ret, img = cap.read()
# print('Frame %d - ' % i_frame)
# if i_frame > 50:
# break
# skip frames to achieve 1hz detection
# if not i_frame % frame_skip == 0: # conduct detection per second
# i_frame += 1
# continue
#vis = True
if i_frame <= 3000:
# if i_frame / frame_skip < 20:
vis = True
else:
vis = False
# counting process time
t0 = time.time()
# convert image from OpenCV format to PyTorch tensor format
img_t = np.moveaxis(img, -1, 0) / 255
img_t = torch.tensor(img_t, device=device).float()
# pedestrian detection
predictions = model([img_t])
boxes = predictions[0]['boxes'].cpu().data.numpy()
classIDs = predictions[0]['labels'].cpu().data.numpy()
scores = predictions[0]['scores'].cpu().data.numpy()
box_id = [0] * len(boxes)
# array to hold box ids for tracking
#box 1 at (x1,y1), (x2,y2)
#box 2 at (x1,y1), (x2,y2)
#reg box_1 array - box_2 array
# get positions and plot on raw image
pts_world = []
iter_tracks = []
for i in range(len(boxes)):
##if class is a person and threshold is met
if classIDs[i] == 1 and scores[i] > thr_score:
# extract the bounding box coordinates
(x1, y1) = (boxes[i][0], boxes[i][1])
(x2, y2) = (boxes[i][2], boxes[i][3])
#detector gives coords x1 ,y1, x2, y2
#convert these coords to tracker input
#input for tracker is a bounding box [x1,y1, width, height]
track_box_in = [
boxes[i][0], boxes[i][1], boxes[i][2] - boxes[i][0],
boxes[i][3] - boxes[i][1]
]
#adjust so input images are 3 frames at i, i +5, i +10 rather than the whole set
track_images = []
for z in range(4): #number of frames to prepare
if i + 5 * z < len(frame_images):
track_images.append(frame_images[i_frame + 5 * z])
##step 1 tracker
curr_track = tracker.track(track_images, track_box_in)
##assign labels to the bounding boxes
##label box = x
box_id[i] = i + 1
iter_tracks.append(curr_track)
#run tracker on each box
################################################
#(if box_id > 0 run tracker)
#takes box (pixel coord)
#10 frames skip
#output is a tracklet area of the coord in each frame
#if ( box_id[i] > 0):
#convert coord of tracket to real
#run regression on coord of traklet if their is a violation
#regression confidence is high that difference is low that means no violation
#regress(y1,y2 -> x1,x2 from the difference array
##############################################################################
if vis:
# draw a bounding box rectangle and label on the image
cv2.rectangle(img, (x1, y1), (x2, y2), [0, 0, 255], 2)
text = "{}: {:.2f}".format(LABELS[classIDs[i]],
scores[i], box_id[i])
cv2.putText(img, text, (int(x1), int(y1) - 5),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, [0, 255, 0],
2)
# find the bottom center position and convert it to world coordinate
p_c = np.array([[(x1 + x2) / 2], [y2], [1]])
p_w = transform_cam2world @ p_c
p_w = p_w / p_w[2]
pts_world.append([p_w[0][0], p_w[1][0]])
## convert all tracks coords to real world
track_btm_cntr = np.zeros(
(len(iter_tracks), 4,
3)) ##to hold bounding boxes adjusted to bottom center coord
track_world = np.zeros(
(len(iter_tracks), 4, 3)) ##to hold real world coord
## for each track iterate through each bounding box in a track and convert it to realworld coord
## foll steps above in which p_c and p_w are calculated
for w in range(len(iter_tracks)):
for u in range(
4
): #add each of the boxes from the 4 frame of the track
row_converted = np.array([[
(iter_tracks[w][0][u][0] + iter_tracks[w][0][u][0] +
iter_tracks[w][0][u][2]) / 2
], [iter_tracks[w][0][u][1] + iter_tracks[w][0][u][3]],
[1]])
track_btm_cntr[w][u] = [
row_converted[0], row_converted[1], row_converted[2]
]
track_world[w][
u] = transform_cam2world @ track_btm_cntr[w][u]
track_world[w][
u] = track_world[w][u] / track_world[w][u][2]
#get every combination of difference between each track
#because difference between track i and track j is the just the negative of the difference of track j and i
#only store i - j
track_differences = {(w, u): 0
for w in range(len(track_world) - 1)
for u in range(1 + w, len(track_world))}
for w in range(len(track_differences)):
for u in range(w + 1, len(track_world)):
track_diff_w = track_world[w, :, :2]
track_diff_u = track_world[u, :, :2]
track_diff = track_diff_w - track_diff_u
track_differences[w, u] = track_diff
##regress each item in the difference dictionary against 0. If the p > 0.05 we fail to reject that their
##there is a difference between two tracks (that is to say they are walking together)
#holds the outcome for a track pair (i,j) if they are a group or not
track_regression_out = {(w, u): 0
for w in range(len(track_world) - 1)
for u in range(1 + w, len(track_world))}
for pair in track_differences:
pair_x = track_differences[pair][:, 0].reshape(-1, 1)
pair_y = track_differences[pair][:, 1].reshape(-1, 1)
pair_norm = [0, 0, 0, 0]
x_sample = [1, 2, 3, 4]
for j in range(4):
if j == 0:
pair_norm[j] = np.linalg.norm([pair_x[j], pair_y[j]
]) * 0.001
else:
pair_norm[j] = np.linalg.norm([
pair_x[j], pair_y[j]
]) - np.linalg.norm([pair_x[0], pair_y[0]])
reg_pair = sm.OLS(pair_norm, x_sample)
#reg_pair = sm.OLS(pair_y, pair_x)
reg_pair = reg_pair.fit()
p_value = reg_pair.pvalues
if pair == (5, 6):
x = "test"
#if pvalue is less than 0.05 we reject null in favour that is there is a difference between track i and j
#so they are not a group and set track_regression_out to false
if p_value < 0.05:
track_regression_out[pair] = False
#else set track regression out to true because we fail to reject the null and therefore conclude that
#the two tracks are a group
else:
track_regression_out[pair] = True
t1 = time.time()
pts_world = np.array(pts_world)
if dataset == 'oxford_town':
pts_world[:, [0, 1]] = pts_world[:, [1, 0]]
pass
elif dataset == 'oxford_town_group':
pts_world[:, [0, 1]] = pts_world[:, [1, 0]]
pass
elif dataset == 'mall':
# pts_world[:, [0, 1]] = pts_world[:, [1, 0]]
pass
elif dataset == 'grand_central':
# pts_world[:, [0, 1]] = pts_world[:, [1, 0]]
pass
statistic_data.append(
(i_frame, t1 - t0, pts_world, track_regression_out))
# visualize
if vis:
violation_pairs = find_violation(pts_world,
track_regression_out)
pts_roi_world, pts_roi_cam = get_roi_pts(
dataset=dataset,
roi_raw=ROIs[dataset],
matrix_c2w=transform_cam2world)
fig = plot_frame_one_row(dataset=dataset,
img_raw=img,
pts_roi_cam=pts_roi_cam,
pts_roi_world=pts_roi_world,
pts_w=pts_world,
pairs=violation_pairs)
# fig = plot_frame(
# dataset=dataset,
# img_raw=img,
# img_bev_bkgd_10x=img_bkgd_bev,
# pts_roi_cam=pts_roi_cam,
# pts_roi_world=pts_roi_world,
# pts_w=pts_world,
# pairs=violation_pairs
# )
fig.savefig(
os.path.join(path_result, 'frame%04d.png' % i_frame))
plt.close(fig)
# update loop info
print('Frame %d - Inference Time: %.2f' % (i_frame, t1 - t0))
print('=======================')
i_frame += 1
if cap.isOpened():
cap.release()
# save statistics
# f.close()
pickle.dump(statistic_data,
open(os.path.join(path_result, 'statistic_data.p'), 'wb'))
from __future__ import absolute_import
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
import glob
import numpy as np
import torch
from siamfc import TrackerSiamFC
if __name__ == '__main__':
print(torch.cuda.is_available())
seq_dir = os.path.expanduser('D:\Dataset\OTB100\Crossing\\') #seq_dir='F:\\data\\OTB100\\Crossing\img'
img_files = sorted(glob.glob(seq_dir + 'img/*.jpg')) #img_files是视频序列 glob.glob获取指定目录下的所有jpg文件,再进行排序
#img_files[0]='F:\\data\\OTB100\\Crossing\\img\\0001.jpg' 依次类推
# img_files = sorted(glob.glob('F:\data\OTB100\Crossing\img/*.jpg')) #这个跟上面一样
anno = np.loadtxt(seq_dir + 'groundtruth_rect.txt', delimiter=',') #anno[0]=array([205.,151.,17.,50.])就是第一帧中的groundtruth,依此类推
net_path = 'pretrained/siamfc_alexnet_e42.pth'
tracker = TrackerSiamFC(net_path=net_path)
tracker.track(img_files, anno[0], visualize=True) #传入120帧图片以及第一个groundtruth，进行可视化
from __future__ import absolute_import
import os
import glob
import numpy as np
from siamfc import TrackerSiamFC
if __name__ == '__main__':
currentDir = os.getcwd()
parentDir = os.path.abspath( os.path.join(currentDir, os.pardir ) )
print(parentDir)
seq_dir = os.path.join(parentDir, 'data', 'OTB', 'Crossing')
print(seq_dir)
#seq_dir = os.path.expanduser('~/data/OTB/Crossing/')
img_files = sorted(glob.glob(seq_dir + '\img\****.jpg'))
print(glob.glob(seq_dir + '\img\****.jpg'))
anno = np.loadtxt(seq_dir + '\Crossing_groundtruth_rect2.txt')
print(seq_dir + '\Crossing_groundtruth_rect.txt2')
net_path = 'pretrained\siamfc_alexnet_e50.pth'
tracker = TrackerSiamFC(net_path=net_path)
x = tracker.track(img_files, anno, visualize=True)
print(x)
