Initial Notebook with RetinaNet Object Detection

README.md

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+# Trajectory Prediction
+
+Use object detector (e.g. RetinaNET, YOLO), and multi-object-tracker (e.g. SORT, DeepSORT) to capture trajectories. Then tinker with Weighted Networks (Hunger 2023) to create predictions of new movements.
+
+## Initiate
+
+The project uses poetry. So to set it up:
+
+```bash
+poetry install
+```
+
+If using python notebook with Jupyter it might be useful to do:
+
+```bash
+poetry run python -m ipykernel install --user --name trajpred 
+```

pyproject.toml

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+[tool.poetry]
+name = "trajpred"
+version = "0.1.0"
+description = "Predict trajectories for anomaly detection"
+authors = ["Ruben van de Ven <git@rubenvandeven.com>"]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = "^3.9"
+numpy = "^1.24.3"
+opencv-python = "^4.7.0.72"
+ipykernel = "^6.22.0"
+torch = "^2.0.0"
+torchvision = "^0.15.1"
+av = "^10.0.0"
+matplotlib = "^3.7.1"
+numba = "^0.57.0"
+
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

sort_cfotache.py

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+"""
+    from: https://github.com/cfotache/pytorch_objectdetecttrack/blob/master/sort.py
+    
+    SORT: A Simple, Online and Realtime Tracker
+    Copyright (C) 2016 Alex Bewley alex@dynamicdetection.com
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+"""
+from __future__ import print_function
+
+from numba import jit
+import os.path
+import numpy as np
+##import matplotlib.pyplot as plt
+##import matplotlib.patches as patches
+from skimage import io
+from sklearn.utils.linear_assignment_ import linear_assignment
+import glob
+import time
+import argparse
+from filterpy.kalman import KalmanFilter
+
+@jit
+def iou(bb_test,bb_gt):
+  """
+  Computes IUO between two bboxes in the form [x1,y1,x2,y2]
+  """
+  xx1 = np.maximum(bb_test[0], bb_gt[0])
+  yy1 = np.maximum(bb_test[1], bb_gt[1])
+  xx2 = np.minimum(bb_test[2], bb_gt[2])
+  yy2 = np.minimum(bb_test[3], bb_gt[3])
+  w = np.maximum(0., xx2 - xx1)
+  h = np.maximum(0., yy2 - yy1)
+  wh = w * h
+  o = wh / ((bb_test[2]-bb_test[0])*(bb_test[3]-bb_test[1])
+    + (bb_gt[2]-bb_gt[0])*(bb_gt[3]-bb_gt[1]) - wh)
+  return(o)
+
+def convert_bbox_to_z(bbox):
+  """
+  Takes a bounding box in the form [x1,y1,x2,y2] and returns z in the form
+    [x,y,s,r] where x,y is the centre of the box and s is the scale/area and r is
+    the aspect ratio
+  """
+  w = bbox[2]-bbox[0]
+  h = bbox[3]-bbox[1]
+  x = bbox[0]+w/2.
+  y = bbox[1]+h/2.
+  s = w*h    #scale is just area
+  r = w/float(h)
+  return np.array([x,y,s,r]).reshape((4,1))
+
+def convert_x_to_bbox(x,score=None):
+  """
+  Takes a bounding box in the centre form [x,y,s,r] and returns it in the form
+    [x1,y1,x2,y2] where x1,y1 is the top left and x2,y2 is the bottom right
+  """
+  w = np.sqrt(x[2]*x[3])
+  h = x[2]/w
+  if(score==None):
+    return np.array([x[0]-w/2.,x[1]-h/2.,x[0]+w/2.,x[1]+h/2.]).reshape((1,4))
+  else:
+    return np.array([x[0]-w/2.,x[1]-h/2.,x[0]+w/2.,x[1]+h/2.,score]).reshape((1,5))
+
+
+class KalmanBoxTracker(object):
+  """
+  This class represents the internel state of individual tracked objects observed as bbox.
+  """
+  count = 0
+  def __init__(self,bbox):
+    """
+    Initialises a tracker using initial bounding box.
+    """
+    #define constant velocity model
+    self.kf = KalmanFilter(dim_x=7, dim_z=4)
+    self.kf.F = np.array([[1,0,0,0,1,0,0],[0,1,0,0,0,1,0],[0,0,1,0,0,0,1],[0,0,0,1,0,0,0],  [0,0,0,0,1,0,0],[0,0,0,0,0,1,0],[0,0,0,0,0,0,1]])
+    self.kf.H = np.array([[1,0,0,0,0,0,0],[0,1,0,0,0,0,0],[0,0,1,0,0,0,0],[0,0,0,1,0,0,0]])
+
+    self.kf.R[2:,2:] *= 10.
+    self.kf.P[4:,4:] *= 1000. #give high uncertainty to the unobservable initial velocities
+    self.kf.P *= 10.
+    self.kf.Q[-1,-1] *= 0.01
+    self.kf.Q[4:,4:] *= 0.01
+
+    self.kf.x[:4] = convert_bbox_to_z(bbox)
+    self.time_since_update = 0
+    self.id = KalmanBoxTracker.count
+    KalmanBoxTracker.count += 1
+    self.history = []
+    self.hits = 0
+    self.hit_streak = 0
+    self.age = 0
+    self.objclass = bbox[6]
+
+  def update(self,bbox):
+    """
+    Updates the state vector with observed bbox.
+    """
+    self.time_since_update = 0
+    self.history = []
+    self.hits += 1
+    self.hit_streak += 1
+    self.kf.update(convert_bbox_to_z(bbox))
+
+  def predict(self):
+    """
+    Advances the state vector and returns the predicted bounding box estimate.
+    """
+    if((self.kf.x[6]+self.kf.x[2])<=0):
+      self.kf.x[6] *= 0.0
+    self.kf.predict()
+    self.age += 1
+    if(self.time_since_update>0):
+      self.hit_streak = 0
+    self.time_since_update += 1
+    self.history.append(convert_x_to_bbox(self.kf.x))
+    return self.history[-1]
+
+  def get_state(self):
+    """
+    Returns the current bounding box estimate.
+    """
+    return convert_x_to_bbox(self.kf.x)
+
+def associate_detections_to_trackers(detections,trackers,iou_threshold = 0.3):
+  """
+  Assigns detections to tracked object (both represented as bounding boxes)
+
+  Returns 3 lists of matches, unmatched_detections and unmatched_trackers
+  """
+  if(len(trackers)==0):
+    return np.empty((0,2),dtype=int), np.arange(len(detections)), np.empty((0,5),dtype=int)
+  iou_matrix = np.zeros((len(detections),len(trackers)),dtype=np.float32)
+
+  for d,det in enumerate(detections):
+    for t,trk in enumerate(trackers):
+      iou_matrix[d,t] = iou(det,trk)
+  matched_indices = linear_assignment(-iou_matrix)
+
+  unmatched_detections = []
+  for d,det in enumerate(detections):
+    if(d not in matched_indices[:,0]):
+      unmatched_detections.append(d)
+  unmatched_trackers = []
+  for t,trk in enumerate(trackers):
+    if(t not in matched_indices[:,1]):
+      unmatched_trackers.append(t)
+
+  #filter out matched with low IOU
+  matches = []
+  for m in matched_indices:
+    if(iou_matrix[m[0],m[1]]<iou_threshold):
+      unmatched_detections.append(m[0])
+      unmatched_trackers.append(m[1])
+    else:
+      matches.append(m.reshape(1,2))
+  if(len(matches)==0):
+    matches = np.empty((0,2),dtype=int)
+  else:
+    matches = np.concatenate(matches,axis=0)
+
+  return matches, np.array(unmatched_detections), np.array(unmatched_trackers)
+
+
+
+class Sort(object):
+  def __init__(self,max_age=1,min_hits=3):
+    """
+    Sets key parameters for SORT
+    """
+    self.max_age = max_age
+    self.min_hits = min_hits
+    self.trackers = []
+    self.frame_count = 0
+
+  def update(self,dets):
+    """
+    Params:
+      dets - a numpy array of detections in the format [[x1,y1,x2,y2,score],[x1,y1,x2,y2,score],...]
+    Requires: this method must be called once for each frame even with empty detections.
+    Returns the a similar array, where the last column is the object ID.
+
+    NOTE: The number of objects returned may differ from the number of detections provided.
+    """
+    self.frame_count += 1
+    #get predicted locations from existing trackers.
+    trks = np.zeros((len(self.trackers),5))
+    to_del = []
+    ret = []
+    for t,trk in enumerate(trks):
+      pos = self.trackers[t].predict()[0]
+      trk[:] = [pos[0], pos[1], pos[2], pos[3], 0]
+      if(np.any(np.isnan(pos))):
+        to_del.append(t)
+    trks = np.ma.compress_rows(np.ma.masked_invalid(trks))
+    for t in reversed(to_del):
+      self.trackers.pop(t)
+    matched, unmatched_dets, unmatched_trks = associate_detections_to_trackers(dets,trks)
+
+    #update matched trackers with assigned detections
+    for t,trk in enumerate(self.trackers):
+      if(t not in unmatched_trks):
+        d = matched[np.where(matched[:,1]==t)[0],0]
+        trk.update(dets[d,:][0])
+
+    #create and initialise new trackers for unmatched detections
+    for i in unmatched_dets:
+        trk = KalmanBoxTracker(dets[i,:])
+        self.trackers.append(trk)
+    i = len(self.trackers)
+    for trk in reversed(self.trackers):
+        d = trk.get_state()[0]
+        if((trk.time_since_update < 1) and (trk.hit_streak >= self.min_hits or self.frame_count <= self.min_hits)):
+          ret.append(np.concatenate((d,[trk.id+1], [trk.objclass])).reshape(1,-1)) # +1 as MOT benchmark requires positive
+        i -= 1
+        #remove dead tracklet
+        if(trk.time_since_update > self.max_age):
+          self.trackers.pop(i)
+    if(len(ret)>0):
+      return np.concatenate(ret)
+    return np.empty((0,5))
+    
+def parse_args():
+    """Parse input arguments."""
+    parser = argparse.ArgumentParser(description='SORT demo')
+    parser.add_argument('--display', dest='display', help='Display online tracker output (slow) [False]',action='store_true')
+    args = parser.parse_args()
+    return args
+
+if __name__ == '__main__':
+  # all train
+  sequences = ['PETS09-S2L1','TUD-Campus','TUD-Stadtmitte','ETH-Bahnhof','ETH-Sunnyday','ETH-Pedcross2','KITTI-13','KITTI-17','ADL-Rundle-6','ADL-Rundle-8','Venice-2']
+  args = parse_args()
+  display = args.display
+  phase = 'train'
+  total_time = 0.0
+  total_frames = 0
+  colours = np.random.rand(32,3) #used only for display
+  if(display):
+    if not os.path.exists('mot_benchmark'):
+      print('\n\tERROR: mot_benchmark link not found!\n\n    Create a symbolic link to the MOT benchmark\n    (https://motchallenge.net/data/2D_MOT_2015/#download). E.g.:\n\n    $ ln -s /path/to/MOT2015_challenge/2DMOT2015 mot_benchmark\n\n')
+      exit()
+    plt.ion()
+    fig = plt.figure() 
+  
+  if not os.path.exists('output'):
+    os.makedirs('output')
+  
+  for seq in sequences:
+    mot_tracker = Sort() #create instance of the SORT tracker
+    seq_dets = np.loadtxt('data/%s/det.txt'%(seq),delimiter=',') #load detections
+    with open('output/%s.txt'%(seq),'w') as out_file:
+      print("Processing %s."%(seq))
+      for frame in range(int(seq_dets[:,0].max())):
+        frame += 1 #detection and frame numbers begin at 1
+        dets = seq_dets[seq_dets[:,0]==frame,2:7]
+        dets[:,2:4] += dets[:,0:2] #convert to [x1,y1,w,h] to [x1,y1,x2,y2]
+        total_frames += 1
+
+        if(display):
+          ax1 = fig.add_subplot(111, aspect='equal')
+          fn = 'mot_benchmark/%s/%s/img1/%06d.jpg'%(phase,seq,frame)
+          im =io.imread(fn)
+          ax1.imshow(im)
+          plt.title(seq+' Tracked Targets')
+
+        start_time = time.time()
+        trackers = mot_tracker.update(dets)
+        cycle_time = time.time() - start_time
+        total_time += cycle_time
+
+        for d in trackers:
+          print('%d,%d,%.2f,%.2f,%.2f,%.2f,1,-1,-1,-1'%(frame,d[4],d[0],d[1],d[2]-d[0],d[3]-d[1]),file=out_file)
+          if(display):
+            d = d.astype(np.int32)
+            ax1.add_patch(patches.Rectangle((d[0],d[1]),d[2]-d[0],d[3]-d[1],fill=False,lw=3,ec=colours[d[4]%32,:]))
+            ax1.set_adjustable('box-forced')
+
+        if(display):
+          fig.canvas.flush_events()
+          plt.draw()
+          ax1.cla()
+
+  print("Total Tracking took: %.3f for %d frames or %.1f FPS"%(total_time,total_frames,total_frames/total_time))
+  if(display):
+    print("Note: to get real runtime results run without the option: --display")
+