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VisualizeOnImage.py
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VisualizeOnImage.py
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'''By He Wei Oct23 2017'''
import pickle
import argparse
import os
from utils import DataLoader
import numpy as np
import cv2
import random
green = (0,255,0)
red = (0,0,255)
parser = argparse.ArgumentParser()
# Observed length of the trajectory parameter
parser.add_argument('--obs_length', type=int, default=8,
help='Observed length of the trajectory')
# Predicted length of the trajectory parameter
parser.add_argument('--pred_length', type=int, default=12,
help='Predicted length of the trajectory')
# Test dataset
parser.add_argument('--load_dataset', type=int, default=1,
help='Dataset to be loaded on')
parser.add_argument('--visual_dataset', type=int, default=3,
help='Dataset to be tested on')
# Model to be loaded
parser.add_argument('--epoch', type=int, default=148,
help='Epoch of model to be loaded')
#[146, 148, 132,137,134]
# Parse the parameters
sample_args = parser.parse_args()
'''KITTI Training Setting'''
#save_directory = '/home/hesl/PycharmProjects/social-lstm-tf-HW/ResultofTrainingKITTI-13NTestonKITTI-17/save'
save_directory = '/home/hesl/PycharmProjects/srnn-pytorch/save/FixedPixel_150epochs_batchsize24/'+str(sample_args.load_dataset)+'/'
save_directory += 'save_attention'
#save_directory ='/home/hesl/PycharmProjects/social-lstm-tf-HW/ResultofTrainingETH1TestETH0/save/'
with open(os.path.join(save_directory, 'config.pkl'), 'rb') as f:
saved_args = pickle.load(f)
# f = open('/home/hesl/PycharmProjects/srnn-pytorch/save/FixedPixel_150epochs_batchsize24/'+str(sample_args.visual_dataset)+'/save_attention/results.pkl', 'rb')
# results = pickle.load(f)
f = open('/home/hesl/PycharmProjects/srnn-pytorch/plot/KITTI/0016-5-by-5-batchsize24/results.pkl', 'rb')
results = pickle.load(f)
f = open('/home/hesl/PycharmProjects/srnn-pytorch/plot/KITTI/0016-5-by-5-batchsize24/extract_y/results.pkl', 'rb')
append = pickle.load(f)
dataset = [sample_args.visual_dataset]
data_loader = DataLoader(1, sample_args.pred_length + sample_args.obs_length, dataset, True, infer=True)
videopath=['/media/hesl/OS/Documents and Settings/N1701420F/De4sktop/video/eth/eth/',
'/media/hesl/OS/Documents and Settings/N1701420F/Desktop/video/eth/hotel/',
'/media/hesl/OS/Documents and Settings/N1701420F/Desktop/video/ucy/zara01/',
'/media/hesl/OS/Documents and Settings/N1701420F/Desktop/video/ucy/zara02/',
'/media/hesl/OS/Documents and Settings/N1701420F/Desktop/video/ucy/univ/']
H_path=['/media/hesl/OS/Documents and Settings/N1701420F/Desktop/pedestrians/ewap_dataset/seq_eth/H.txt',
'/media/hesl/OS/Documents and Settings/N1701420F/Desktop/pedestrians/ewap_dataset/seq_hotel/H.txt',
'/media/hesl/OS/Documents and Settings/N1701420F/Desktop/pedestrians/ucy_crowd/data_zara01/H.txt',
'/media/hesl/OS/Documents and Settings/N1701420F/Desktop/pedestrians/ucy_crowd/data_zara02/H.txt',
'/media/hesl/OS/Documents and Settings/N1701420F/Desktop/pedestrians/ucy_crowd/data_students03/H.txt']
'''KITTI Homography and Video'''
H_path=['/home/hesl/Desktop/KITTITrackinData/GT/0012/H.txt',
'/home/hesl/Desktop/KITTITrackinData/GT/0013/H.txt',
'/home/hesl/Desktop/KITTITrackinData/GT/0015/H.txt',
'/home/hesl/Desktop/KITTITrackinData/GT/0016/H.txt',
'/home/hesl/Desktop/KITTITrackinData/GT/0017/H.txt',
'/home/hesl/Desktop/KITTITrackinData/GT/0019/H.txt']
videopath=['/home/hesl/Desktop/KITTITrackinData/0012',
'/home/hesl/Desktop/KITTITrackinData/0013',
'/home/hesl/Desktop/KITTITrackinData/0015',
'/home/hesl/Desktop/KITTITrackinData/0016',
'/home/hesl/Desktop/KITTITrackinData/0017',
'/home/hesl/Desktop/KITTITrackinData/0019']
R_path=['/home/hesl/Desktop/KITTITrackinData/GT/0012/R.txt',
'/home/hesl/Desktop/KITTITrackinData/GT/0013/R.txt',
'/home/hesl/Desktop/KITTITrackinData/GT/0015/R.txt',
'/home/hesl/Desktop/KITTITrackinData/GT/0016/R.txt',
'/home/hesl/Desktop/KITTITrackinData/GT/0017/R.txt',
'/home/hesl/Desktop/KITTITrackinData/GT/0019/R.txt']
other=[[[0,0],[0,0]],[[0,0],[0,0]],[[0,0],[0,0]],[[24.6152,-17.5380],[40.0037,3.4277]],[[14.2367,- 9.4110],[28.3571,2.6694]],[[0,0],[0,0]]]
H=np.loadtxt(H_path[sample_args.visual_dataset])
R=np.loadtxt(R_path[sample_args.visual_dataset])
skip_frame=5
#[7,16]
#print(data_loader.data[0][0].shape)
#
'''Visualize Ground Truth (u,v)'''
# for j in range(len(data_loader.frameList[0])):
#
# #sourceFileName = "/home/hesl/PycharmProjects/social-lstm-tf-HW/data/KITTI-17/img1/" + str(j + 1).zfill(6) + ".jpg"
# #Visualize ETH/hotel
# #sourceFileName = "/media/hesl/OS/Documents and Settings/N1701420F/Desktop/video/eth/hotel/frame-" + str(int(data_loader.frameList[0][j])).zfill(3) + ".jpg"
# #Eth/eth
# #sourceFileName = "/media/hesl/OS/Documents and Settings/N1701420F/Desktop/video/eth/eth/frame-" + str(int(data_loader.frameList[0][j])).zfill(3)+ ".jpeg"
# #UCY/univ
# #sourceFileName = "/media/hesl/OS/Documents and Settings/N1701420F/Desktop/video/ucy/univ/frame-" + str(int(data_loader.frameList[0][j])+1).zfill(3) + ".jpg"
# # UCY/zara01
# sourceFileName = "/media/hesl/OS/Documents and Settings/N1701420F/Desktop/video/ucy/zara01/frame-" + str(int(data_loader.frameList[0][j])+1).zfill(3) + ".jpg"
#
# # UCY/zara02
# #sourceFileName = "/media/hesl/OS/Documents and Settings/N1701420F/Desktop/video/ucy/zara02/frame-" + str(int(data_loader.frameList[0][j])).zfill(3) + ".jpg"
#
# print(sourceFileName)
#
# avatar= cv2.imread(sourceFileName)
# #drawAvatar= ImageDraw.Draw(avatar)
# #print(avatar.shape)
# xSize = avatar.shape[1]
# ySize = avatar.shape[0]
# #print(data_loader.data[0][0][0])
# for i in range(data_loader.maxNumPeds):
# #print(i)
#
# y=int(data_loader.data[0][j][i][2])
# x=int(data_loader.data[0][j][i][1])
# if x!=0 and y!=0:
# print(x, y)
#
# cv2.rectangle(avatar, (x - 2, y - 2), (x + 2, y + 2), green,thickness=-1)
# #drawAvatar.rectangle([(x - 2, y - 2), (x + 2, y + 2)], fill=(255, 100, 0))
#
# #drawAvatar.rectangle([(466, 139), (91 + 466, 139 + 193.68)])
# #avatar.show()
# cv2.imshow("avatar", avatar)
# cv2.waitKey(0)
'''Visualize Ground Truth (x,y)'''
# for j in range(len(data_loader.frameList[0])):
#
# #sourceFileName = "/home/hesl/PycharmProjects/social-lstm-tf-HW/data/KITTI-17/img1/" + str(j + 1).zfill(6) + ".jpg"
# #Visualize ETH/hotel
# #sourceFileName = "/media/hesl/OS/Documents and Settings/N1701420F/Desktop/video/eth/hotel/frame-" + str(int(data_loader.frameList[0][j])).zfill(3) + ".jpg"
# #Eth/eth
# #sourceFileName = "/media/hesl/OS/Documents and Settings/N1701420F/Desktop/video/eth/eth/frame-" + str(int(data_loader.frameList[0][j])).zfill(3)+ ".jpeg"
# #UCY/univ
# #sourceFileName = "/media/hesl/OS/Documents and Settings/N1701420F/Desktop/video/ucy/univ/frame-" + str(int(data_loader.frameList[0][j])+1).zfill(3) + ".jpg"
# # UCY/zara01
# sourceFileName = "/media/hesl/OS/Documents and Settings/N1701420F/Desktop/video/ucy/zara01/frame-" + str(int(data_loader.frameList[0][j])+1).zfill(3) + ".jpg"
#
# # UCY/zara02
# #sourceFileName = "/media/hesl/OS/Documents and Settings/N1701420F/Desktop/video/ucy/zara02/frame-" + str(int(data_loader.frameList[0][j])).zfill(3) + ".jpg"
#
# print(sourceFileName)
#
# avatar= cv2.imread(sourceFileName)
#
# xSize = avatar.shape[1]
# ySize = avatar.shape[0]
#
# for i in range(data_loader.maxNumPeds):
#
# pos = np.ones(3)
# y=data_loader.data[0][j][i][2]
# x=data_loader.data[0][j][i][1]
#
# pos[0] = x
# pos[1] = y
#
# pos = np.dot(pos, np.linalg.inv(H.transpose()))
#
# print(pos[0] ,pos[1] )
# u=int(np.around(pos[0] / pos[2]))
# v=int(np.around(pos[1] / pos[2]))
#
# if data_loader.data[0][j][i][0]!=0:lengthy
# print(u, v)
# cv2.rectangle(avatar, (u - 2, v - 2), (u + 2, v + 2), green, thickness=-1)
#
#
# cv2.imshow("avatar", avatar)
# cv2.waitKey(0)
#
print(results[0][1][0][2])
'''Visualize result of image coordinate data'''
#Each Frame
# for k in range(int(len(data_loader.frameList[0])/(sample_args.obs_length+sample_args.pred_length))):
# #Each
# for j in range(sample_args.obs_length+sample_args.pred_length):
#
# sourceFileName = "/media/hesl/OS/Documents and Settings/N1701420F/Desktop/video/eth/eth/frame-" + str(int(data_loader.frameList[0][j+k*(sample_args.obs_length+sample_args.pred_length)])).zfill(3) + ".jpeg"
# print(sourceFileName)
#
# avatar= cv2.imread(sourceFileName)
#
#
# #width
# xSize = avatar.shape[1]
# #height
# ySize = avatar.shape[0]
#
#
# for i in range(data_loader.maxNumPeds):
# if results[k][1][j][i][0] != 0:
# # Predicted
# yp = int(np.round(results[k][1][j][i][2] * ySize))
# xp = int(np.round(results[k][1][j][i][1] * xSize))
# cv2.rectangle(avatar, (xp - 2, yp - 2), (xp + 2, yp + 2), red, thickness=-1)
#
# if results[k][0][j][i][0]!=0:
# # GT
# y = int(np.round(results[k][0][j][i][2] * ySize))
# x = int(np.round(results[k][0][j][i][1] * xSize))
# cv2.rectangle(avatar, (x - 2, y - 2), (x + 2, y + 2), green, thickness=-1)
#
# print('x,y')
# print(x,y)
# if results[k][1][j][i][0] != 0 and results[k][0][j][i][0]!=0 and results[k][0][j][i][0]==results[k][1][j][i][0]:
# cv2.line(avatar, (x, y), (xp, yp), (255,0,0),1)
#
# cv2.imshow("avatar", avatar)
# #imagename='/home/hesl/PycharmProjects/social-lstm-tf-HW/plot/visualize-'+str(int(data_loader.frameList[0][j+k*(sample_args.obs_length+sample_args.pred_length)])).zfill(3)+'.png'
# #cv2.imwrite(imagename, avatar)
# cv2.waitKey(0)
print(int((len(data_loader.frameList[0])/10)/(sample_args.obs_length+sample_args.pred_length)))
'''Visualize result of unnormalized world coordinate data 10frame-by-10frame'''
# #Each Trajectory
# for k in range(int((len(data_loader.frameList[0])/10)/(sample_args.obs_length+sample_args.pred_length))):
# #Each frame
# for j in range(sample_args.obs_length+sample_args.pred_length):
#
# sourceFileName = videopath[sample_args.visual_dataset]+"frame-" + str(int(k*(10*(sample_args.obs_length+sample_args.pred_length))+j*10+data_loader.frameList[0][0])).zfill(3) + ".jpg"
#
# avatar= cv2.imread(sourceFileName)
#
#
# xSize = avatar.shape[1]
# ySize = avatar.shape[0]
# print(sourceFileName)
#
# for i in range(len(results[k][2][j])):
#
# x=results[k][0][j][results[k][2][j][i]][0]
# y=results[k][0][j][results[k][2][j][i]][1]
#
# xp=results[k][1][j][results[k][2][j][i]][0]
# yp=results[k][1][j][results[k][2][j][i]][1]
#
# pos = np.ones(3)
# posp = np.ones(3)
#
#
# pos[0] = x
# pos[1] = y
#
# posp[0] = xp
# posp[1] = yp
#
# pos = np.dot(pos, np.linalg.inv(H.transpose()))
#
# v = int(np.around(pos[1] / pos[2]))
# u = int(np.around(pos[0] / pos[2]))
#
# posp = np.dot(posp, np.linalg.inv(H.transpose()))
#
# vp = int(np.around(posp[1] / posp[2]))
# up = int(np.around(posp[0] / posp[2]))
#
# cv2.rectangle(avatar, (up - 2, vp - 2), (up + 2, vp + 2), red, thickness=-1)
# cv2.rectangle(avatar, (u - 2, v - 2), (u + 2, v + 2), green, thickness=-1)
#
# # print('uv:')
# # print(u, v)
#
# #
# # if results[k][1][j][i][0] != 0 and results[k][0][j][i][0]!=0 and results[k][0][j][i][0]==results[k][1][j][i][0]:
# # cv2.line(avatar, (u, v), (up, vp), (255,0,0),1)
#
# cv2.imshow("avatar", avatar)
# #imagename='/home/hesl/PycharmProjects/social-lstm-pytorch/plot/'+str(sample_args.visual_dataset)+'/visualize-'+ str(int(k*(10*(sample_args.obs_length+sample_args.pred_length))+j*10+data_loader.frameList[0][0])).zfill(3) +'.png'
# imagename = '/home/hesl/PycharmProjects/social-lstm-pytorch/plot'+'/visualize-' + str(int(
# k * (10 * (sample_args.obs_length + sample_args.pred_length)) + j * 10 + data_loader.frameList[0][
# 0])).zfill(3) + '.png'
#
# #print(imagename)
#
# cv2.imwrite(imagename, avatar)
# #print(t)
# #cv2.waitKey(0)
#
'''Visualize result of normalized world coordinate data 10frame-by-10frame'''
#Each Trajectory
# for k in range(int((len(data_loader.frameList[0])/10)/(sample_args.obs_length+sample_args.pred_length))):
# #Each frame
# for j in range(sample_args.obs_length+sample_args.pred_length):
#
# sourceFileName = videopath[sample_args.visual_dataset]+"frame-" + str(int(k*(10*(sample_args.obs_length+sample_args.pred_length))+j*10+data_loader.frameList[0][0])).zfill(3) + ".jpg"
#
# avatar= cv2.imread(sourceFileName)
#
#
# xSize = avatar.shape[1]
# ySize = avatar.shape[0]
# print(sourceFileName)
#
# for i in range(len(results[k][2][j])):
#
# x=results[k][0][j][results[k][2][j][i]][0]
# y=results[k][0][j][results[k][2][j][i]][1]
#
# xp=results[k][1][j][results[k][2][j][i]][0]
# yp=results[k][1][j][results[k][2][j][i]][1]
#
# # y=(((y)*(11.8703)))-9.4262
# # x=(((x)*(18.6887)))-10.8239
# #
# # yp = (((yp ) * (11.8703)) ) - 9.4262
# # xp = (((xp ) * (18.6887)) ) - 10.8239
#
# pos = np.ones(3)
# posp = np.ones(3)
#
#
# pos[0] = x
# pos[1] = y
#
# posp[0] = xp
# posp[1] = yp
#
# pos = np.dot(pos, np.linalg.inv(H.transpose()))
#
# # v = int(np.around(pos[1] / pos[2]))
# # u = int(np.around(pos[0] / pos[2]))
#
# u = int(np.round((x+1)*(xSize /2)))
# v = int(np.round((y+1)*(ySize /2)))
#
# posp = np.dot(posp, np.linalg.inv(H.transpose()))
# #
# # vp = int(np.around(posp[1] / posp[2]))
# # up = int(np.around(posp[0] / posp[2]))
#
# up = int(np.round((xp + 1) * (xSize / 2)))
# vp = int(np.round((yp + 1) * (ySize / 2)))
#
# cv2.rectangle(avatar, (up - 2, vp - 2), (up + 2, vp + 2), red, thickness=-1)
# cv2.rectangle(avatar, (u - 2, v - 2), (u + 2, v + 2), green, thickness=-1)
# cv2.line(avatar, (u, v), (up, vp), (255, 0, 0), 1)
# print('uv:')
# print(u, v)
# print('upvp:')
# print(up, vp)
#
# #
# # if results[k][1][j][i][0] != 0 and results[k][0][j][i][0]!=0 and results[k][0][j][i][0]==results[k][1][j][i][0]:
# # cv2.line(avatar, (u, v), (up, vp), (255,0,0),1)
#
# cv2.imshow("avatar", avatar)
# imagename='/home/hesl/PycharmProjects/social-lstm-pytorch/plot/'+str(sample_args.visual_dataset)+'/visualize-'+ str(int(k*(10*(sample_args.obs_length+sample_args.pred_length))+j*10+data_loader.frameList[0][0])).zfill(3) +'.png'
# #print(imagename)
#
# #cv2.imwrite(imagename, avatar)
# #print(t)
# cv2.waitKey(0)
'''Visualize result of normalized pixel coordinate data 10frame-by-10frame'''
# count=0
# #print(results[0][0])
# #Each Trajectory (20 frames)
# for k in range(int((len(data_loader.frameList[0])/skip_frame)/(sample_args.obs_length+sample_args.pred_length))):
#
# gt_trajectory=[]
# pred_trajectory=[]
#
# #Each frame
# Max_num_ped = max([val for sublist in results[k][2] for val in sublist])
# #print(Max_num_ped)
#
# for j in range(sample_args.obs_length+sample_args.pred_length):
# #print(str(int(data_loader.frameList[0][count])).zfill(3))
# print('j = {}'.format(j))
# print('k = {}'.format(k))
# sourceFileName = videopath[sample_args.visual_dataset]+"frame-" + str(int(data_loader.frameList[0][count+j*skip_frame+k*(sample_args.obs_length+sample_args.pred_length)*skip_frame])).zfill(3) + ".jpg"
# # print('k range:',int((len(data_loader.frameList[0])/10)/(sample_args.obs_length+sample_args.pred_length)))
# # print('j range:',sample_args.obs_length+sample_args.pred_length)
# avatar= cv2.imread(sourceFileName)
#
# width = avatar.shape[1]
# height = avatar.shape[0]
# print(sourceFileName)
#
# current_frame_gt=[]
# current_frame_pred=[]
#
#
# print(len(results[k][2][j]))
# #Ped
# for i in range(Max_num_ped):
#
# u=results[k][0][j][i][0]
# v=results[k][0][j][i][1]
#
# up=results[k][1][j][i][0]
# vp=results[k][1][j][i][1]
#
# if u!=0 or v!=0:
# u = int(np.round((u + 1) * (width / 2)))
# v = int(np.round((v + 1) * (height / 2)))
#
# if up != 0 or vp != 0:
# up = int(np.round((up + 1) * (width / 2)))
# vp = int(np.round((vp + 1) * (height / 2)))
#
# gt_tmp=[u,v]
# pred_tmp=[up,vp]
#
# current_frame_gt.append(gt_tmp)
# current_frame_pred.append(pred_tmp)
#
# # cv2.rectangle(avatar, (up - 2, vp - 2), (up + 2, vp + 2), red, thickness=-1)
# # cv2.rectangle(avatar, (u - 2, v - 2), (u + 2, v + 2), green, thickness=-1)
# # cv2.line(avatar, (u, v), (up, vp), (255, 0, 0), 1)
# # # print('uv:')
# # print(u, v)
# # print('upvp:')
# # print(up, vp)
#
# #
# # if results[k][1][j][i][0] != 0 and results[k][0][j][i][0]!=0 and results[k][0][j][i][0]==results[k][1][j][i][0]:
# # cv2.line(avatar, (u, vpixel_coordinate_inter_normalized.csv), (up, vp), (255,0,0),1)
#
# gt_trajectory.append(current_frame_gt)
# pred_trajectory.append(current_frame_pred)
#
# #print('current length',len(gt_trajectory))
# for ped in range(Max_num_ped):
# random.seed(ped)
# current_color=(random.randint(0,256),random.randint(0,256),random.randint(0,256))
# #print(type(11))
#
# for instance in range(len(gt_trajectory)):
# if gt_trajectory[instance][ped][0]!=0 or gt_trajectory[instance][ped][1]!=0:
#
# #cv2.drawMarker(avatar, (gt_trajectory[instance][ped][0], gt_trajectory[instance][ped][1]), (0, 0, 255),markerType=cv2.MARKER_TILTED_CROSS, markerSize=10, thickness=1, line_type=cv2.LINE_AA)
# cv2.rectangle(avatar, (gt_trajectory[instance][ped][0] - 1, gt_trajectory[instance][ped][1] - 1), (gt_trajectory[instance][ped][0] + 1, gt_trajectory[instance][ped][1] + 1), color=current_color, thickness=-1)
# if instance !=0 and (gt_trajectory[instance-1][ped][0]!=0 or gt_trajectory[instance-1][ped][1]!=0):
# cv2.line(avatar, (gt_trajectory[instance-1][ped][0], gt_trajectory[instance-1][ped][1]), (gt_trajectory[instance][ped][0], gt_trajectory[instance][ped][1]),color=current_color,thickness= 1)
#
# if instance >sample_args.obs_length-1 and (pred_trajectory[instance][ped][0]!=0 or pred_trajectory[instance][ped][1]!=0):
# cv2.drawMarker(avatar, (pred_trajectory[instance][ped][0], pred_trajectory[instance][ped][1]), color=current_color,markerType=cv2.MARKER_TILTED_CROSS, markerSize=5, thickness=1, line_type=cv2.LINE_AA)
#
# #cv2.imshow("avatar", avatar)
# imagename='/home/hesl/PycharmProjects/srnn-pytorch/plot/FixedPixel_150epochs_batchsize24/'+str(sample_args.visual_dataset)+'/visualize-'+ str(int(data_loader.frameList[0][count+j*skip_frame+k*(sample_args.obs_length+sample_args.pred_length)*skip_frame])).zfill(3) +'.jpg'
# #print(imagename)
#
# cv2.imwrite(imagename, avatar)
# #print(t)
# #cv2.waitKey(0)
#
# print(len(results))
'''Visualize KITTI (Result of Normalized world coordinate) 12 13 15 19'''
for k in range(int((len(data_loader.frameList[0])/skip_frame)/(sample_args.obs_length+sample_args.pred_length))):
print('k range {}'.format(int((len(data_loader.frameList[0])/skip_frame)/(sample_args.obs_length+sample_args.pred_length))))
#Each frame
Max = max([val for sublist in results[k][2] for val in sublist])
#Max=max([len(sublist) for sublist in results[k][2] ])
print([val for sublist in results[k][2] for val in sublist])
#print(Max_num_ped)
gt_trajectory = np.zeros((20, Max + 1,2))
pred_trajectory = np.zeros((20, Max + 1,2))
gt_trajectory.dtype=int
pred_trajectory.dtype=int
for j in range(sample_args.obs_length+sample_args.pred_length):
#print(str(int(data_loader.frameList[0][count])).zfill(3))
print('j = {}'.format(j))
print('k = {}'.format(k))
#print('filename:',)
sourceFileName = videopath[sample_args.visual_dataset] + "/" + str(int(data_loader.frameList[0][
j * skip_frame + k * (
sample_args.obs_length + sample_args.pred_length) * skip_frame])).zfill(
6) + ".png"
# print('k range:',int((len(data_loader.frameList[0])/10)/(sample_args.obs_length+sample_args.pred_length)))
# print('j range:',sample_args.obs_length+sample_args.pred_length)
print('result on {}'.format(sourceFileName))
avatar= cv2.imread(sourceFileName)
width = avatar.shape[1]
height = avatar.shape[0]
current_frame_gt=[]
current_frame_pred=[]
#Max_num_ped = data_loader.numPedsList[0][ j * skip_frame + k * (sample_args.obs_length + sample_args.pred_length) * skip_frame]
#print(len(results[k][2][j]))
#Ped
for i in range(len(results[k][2][j])):
print('i={}'.format(i))
pos=np.ones(4)
posp=np.ones(4)
print('index:{}'.format(results[k][2][j][i]))
x=results[k][0][j][results[k][2][j][i]][0]
z=results[k][0][j][results[k][2][j][i]][1]
y=append[k][0][j][results[k][2][j][i]][1]
print('x={}'.format(x))
xp=results[k][1][j][results[k][2][j][i]][0]
zp=results[k][1][j][results[k][2][j][i]][1]
yp=append[k][0][j][results[k][2][j][i]][1]
if x!=0 or z!=0:
z = (z + 1) *(other[sample_args.visual_dataset][1][0] * 0.5) + other[sample_args.visual_dataset][1][1]
x = (x + 1) *(other[sample_args.visual_dataset][0][0] * 0.5) + other[sample_args.visual_dataset][0][1]
# y = data_loader.data[0][j * skip_frame + k * (sample_args.obs_length + sample_args.pred_length) * skip_frame][i][2]
pos[0]=x
pos[1]=y
pos[2]=z
uvtemp=np.dot(H,np.dot(R, pos))
u=int(round(uvtemp[0]/uvtemp[2]))
v=int(round(uvtemp[1]/uvtemp[2]))
else:
u=0
v=0
if xp != 0 or zp != 0:
zp = (zp + 1)*(other[sample_args.visual_dataset][1][0] * 0.5) + other[sample_args.visual_dataset][1][1]
xp = (xp + 1)*(other[sample_args.visual_dataset][0][0] * 0.5) + other[sample_args.visual_dataset][0][1]
#yp = data_loader.data[0][j * skip_frame + k * (sample_args.obs_length + sample_args.pred_length) * skip_frame][i][2]
posp[0] = xp
posp[1] = yp
posp[2] = zp
uvptemp =np.dot(H,np.dot(R, posp))
up = int(round(uvptemp[0] / uvptemp[2]))
vp = int(round(uvptemp[1] / uvptemp[2]))
else:
up=0
vp=0
gt_tmp=[u,v]
pred_tmp=[up,vp]
gt_trajectory[j][results[k][2][j][i]][0] = gt_tmp[0]
gt_trajectory[j][results[k][2][j][i]][1] = gt_tmp[1]
pred_trajectory[j][results[k][2][j][i]][0]= pred_tmp[0]
pred_trajectory[j][results[k][2][j][i]][1] = pred_tmp[1]
'''Visualize each Point'''
# cv2.rectangle(avatar, (up - 2, vp - 2), (up + 2, vp + 2), red, thickness=-1)
# cv2.rectangle(avatar, (u - 2, v - 2), (u + 2, v + 2), green, thickness=-1)
#cv2.line(avatar, (u, v), (up, vp), (255, 0, 0), 1)
# print('uv:')
# print(u, v)
# print('upvp:')
# print(up, vp)
# if results[k][1][j][i][0] != 0 and results[k][0][j][i][0]!=0 and results[k][0][j][i][0]==results[k][1][j][i][0]:
# cv2.line(avatar, (u, v), (up, vp), (255,0,0),1)
# '''Visualize trajectory'''
#print('current length',len(gt_trajectory))
for ped in range(Max):
random.seed(ped)
current_color=(random.randint(0,256),random.randint(0,256),random.randint(0,256))
#print(type(11))
if ped ==17:
for instance in range(j+1):
if gt_trajectory[instance][ped][0]!=0 or gt_trajectory[instance][ped][1]!=0:
print('gt_trajectory[instance][ped][0]:',gt_trajectory[instance][ped][0])
#cv2.drawMarker(avatar, (gt_trajectory[instance][ped][0], gt_trajectory[instance][ped][1]), (0, 0, 255),markerType=cv2.MARKER_TILTED_CROSS, markerSize=10, thickness=1, line_type=cv2.LINE_AA)
cv2.rectangle(avatar, (gt_trajectory[instance][ped][0] - 2, gt_trajectory[instance][ped][1] - 2), (gt_trajectory[instance][ped][0] + 2, gt_trajectory[instance][ped][1] + 2), color=current_color, thickness=-1)
if instance !=0 and (gt_trajectory[instance-1][ped][0]!=0 or gt_trajectory[instance-1][ped][1]!=0):
cv2.line(avatar, (gt_trajectory[instance-1][ped][0], gt_trajectory[instance-1][ped][1]), (gt_trajectory[instance][ped][0], gt_trajectory[instance][ped][1]),color=current_color,thickness= 1)
if instance >sample_args.obs_length-1 and (pred_trajectory[instance][ped][0]!=0 or pred_trajectory[instance][ped][1]!=0):
cv2.drawMarker(avatar, (pred_trajectory[instance][ped][0], pred_trajectory[instance][ped][1]), color=current_color,markerType=cv2.MARKER_TILTED_CROSS, markerSize=6, thickness=2, line_type=cv2.LINE_AA)
#cv2.imshow("avatar", avatar)
imagename='/home/hesl/PycharmProjects/srnn-pytorch/plot/KITTI/0016-specific-ped/visualize-'+ str(int(data_loader.frameList[0][j*skip_frame+k*(sample_args.obs_length+sample_args.pred_length)*skip_frame])).zfill(6) + ".jpg"
#print(imagename)
cv2.imwrite(imagename, avatar)
#print(t)
#cv2.waitKey(0)