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demo_4K.py
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demo_4K.py
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import argparse
import scipy.io
import torch
import numpy as np
import os
from torchvision import datasets
#import matplotlib
#matplotlib.use('agg')
import matplotlib.pyplot as plt
#######################################################################
# Evaluate
parser = argparse.ArgumentParser(description='Demo')
parser.add_argument('--query_index', default=0, type=int, help='test_image_index')
parser.add_argument('--test_dir',default='./data/test',type=str, help='./test_data')
opts = parser.parse_args()
gallery_name = 'gallery_satellite'
query_name = '4K_drone'
data_dir = opts.test_dir
image_datasets = {x: datasets.ImageFolder( os.path.join(data_dir,x) ) for x in [gallery_name, query_name]}
#####################################################################
#Show result
def imshow(path, title=None):
"""Imshow for Tensor."""
im = plt.imread(path)
plt.imshow(im)
if title is not None:
plt.title(title)
plt.pause(0.1) # pause a bit so that plots are updated
######################################################################
result = scipy.io.loadmat('4K_result.mat')
query_feature = torch.FloatTensor(result['query_f'])
query_label = result['query_label'][0]
result = scipy.io.loadmat('pytorch_result.mat')
gallery_feature = torch.FloatTensor(result['gallery_f'])
gallery_label = result['gallery_label'][0]
multi = os.path.isfile('multi_query.mat')
if multi:
m_result = scipy.io.loadmat('multi_query.mat')
mquery_feature = torch.FloatTensor(m_result['mquery_f'])
mquery_cam = m_result['mquery_cam'][0]
mquery_label = m_result['mquery_label'][0]
mquery_feature = mquery_feature.cuda()
query_feature = query_feature.cuda()
gallery_feature = gallery_feature.cuda()
print(query_feature)
#######################################################################
# sort the images
def sort_img(qf, ql, gf, gl):
query = qf.view(-1,1)
# print(query.shape)
score = torch.mm(gf,query)
score = score.squeeze(1).cpu()
score = score.numpy()
# predict index
index = np.argsort(score) #from small to large
index = index[::-1]
# index = index[0:2000]
# good index
query_index = np.argwhere(gl==ql)
#good_index = np.setdiff1d(query_index, camera_index, assume_unique=True)
junk_index = np.argwhere(gl==-100)
mask = np.in1d(index, junk_index, invert=True)
index = index[mask]
return index
i = opts.query_index
index = sort_img(query_feature[i],query_label[i],gallery_feature,gallery_label)
########################################################################
# Visualize the rank result
query_path, _ = image_datasets[query_name].imgs[i]
query_label = query_label[i]
print(query_path)
print('Top 10 images are as follow:')
save_folder = 'image_4K/%02d'%opts.query_index
if not os.path.isdir(save_folder):
os.mkdir(save_folder)
os.system('cp %s %s/query.png'%(query_path, save_folder))
try: # Visualize Ranking Result
# Graphical User Interface is needed
fig = plt.figure(figsize=(16,4))
ax = plt.subplot(1,11,1)
ax.axis('off')
imshow(query_path,'query')
for i in range(10):
ax = plt.subplot(1,11,i+2)
ax.axis('off')
img_path, _ = image_datasets[gallery_name].imgs[index[i]]
label = gallery_label[index[i]]
imshow(img_path)
os.system('cp %s %s/s%02d.jpg'%(img_path, save_folder, i))
if label == query_label:
ax.set_title('%d'%(i+1), color='green')
else:
ax.set_title('%d'%(i+1), color='red')
print(img_path)
#plt.pause(100) # pause a bit so that plots are updated
except RuntimeError:
for i in range(10):
img_path = image_datasets.imgs[index[i]]
print(img_path[0])
print('If you want to see the visualization of the ranking result, graphical user interface is needed.')
fig.savefig("show.png")