openremote
no Run !!
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Python 3.8.3 (tags/v3.8.3:6f8c832, May 13 2020, 22:37:02) [MSC v.1924 64 bit (AMD64)] on win32
runfile('C:/Users/usuario/Documents/Dev/Python/or-objectdetection/TESTING/Webcamtest.py', wdir='C:/Users/usuario/Documents/Dev/Python/or-objectdetection/TESTING')
Traceback (most recent call last):
File "C:\Python38\lib\site-packages\IPython\core\interactiveshell.py", line 3319, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "
@GCPBigData This project is not supported on windows. Try to run it on ubuntu 18.04.
The Project Runs on windows, I'm running it on Windows 10
I suggest the following Setup
- pip install torch===1.5.0 torchvision===0.6.0 -f https://download.pytorch.org/whl/torch_stable.html
- conda 4.9.2 /Anaconda
I encountered a Few issues I'm willing to help if needed
@Code-King I am facing this problem , i am running this on windows 10 please help me
C:\Users\Acer\Desktop\or-objectdetection-master\or-objectdetection-master\YOLO_DETECTION\sort.py:32: NumbaWarning: �[1m Compilation is falling back to object mode WITH looplifting enabled because Function "iou" failed type inference due to: �[1m�[1mnon-precise type pyobject�[0m �[0m�[1mDuring: typing of argument at C:\Users\Acer\Desktop\or-objectdetection-master\or-objectdetection-master\YOLO_DETECTION\sort.py (37)�[0m �[1m File "sort.py", line 37:�[0m �[1mdef iou(bb_test,bb_gt):
""" �[1m xx1 = np.maximum(bb_test[0], bb_gt[0]) �[0m �[1m^�[0m�[0m �[0m @jit
Please note my IUO Method below, please send me your IUO model in the Sort.py Class
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)
@Code-King Its same, i clone from the repo
""" SORT: A Simple, Online and Realtime Tracker Copyright (C) 2016 Alex Bewley [email protected]
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[4]
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/train/%s/det/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")
Code-King please reply if you got some time
On Mon, Feb 15, 2021 at 9:10 AM Code-King [email protected] wrote:
Please note my IUO Method below, please send me your IUO model in the Sort.py Class
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)
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As in this we are required to count only humans but it is counted dog also How to fix that
The Project Runs on windows, I'm running it on Windows 10
I suggest the following Setup
- pip install torch===1.5.0 torchvision===0.6.0 -f https://download.pytorch.org/whl/torch_stable.html
- conda 4.9.2 /Anaconda
I encountered a Few issues I'm willing to help if needed
i am facing rthis error when i run this code. plz help me. code-king how i contact you?
