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Retrieval_mysemantic_ui.py
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Retrieval_mysemantic_ui.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 20 11:01:07 2019
similar to perform_test_2.py
for the new rico split sets..
add argparse...
@author: dipu
"""
import os
import torch
from torchvision import transforms
import glob
import json
from collections import defaultdict
from PIL import Image
import pickle
from scipy.spatial.distance import cdist
import numpy as np
import argparse
import _init_paths
from BoundingBox import BoundingBox
from BoundingBoxes import BoundingBoxes
from eval_metrics.get_overall_IOU import get_overall_IOU
from eval_metrics.get_overall_Classwise_IOU import get_overall_Classwise_IOU
from eval_metrics.get_overall_pix_acc import get_overall_pix_acc
from RICO_Dataset import RICO_Dataset
import models
from utils import mkdir_if_missing, load_checkpoint
def extract_features(data_loader, model):
model.eval()
torch.set_grad_enabled(False)
features = []
labels = []
for i, (imgs, im_fn) in enumerate(data_loader):
#for i, (imgs, im_fn, img25Chan) in enumerate(data_loader):
imgs = imgs.cuda()
x_enc, out = model(imgs)
outputs = x_enc.detach().cpu().numpy()
features.append(outputs)
labels += list(im_fn)
# print(i)
return features, labels
data_dir = '/mnt/amber/scratch/Dipu/RICO/MySemanticUI/'
UI_data = pickle.load(open('/mnt/amber/scratch/Dipu/RICO/UI_data.p', 'rb'))
train_uis = UI_data['train_uis']
UI_test_data = pickle.load(open("/mnt/amber/scratch/Dipu/RICO/UI_test_data.p", "rb"))
query_uis = UI_test_data['query_uis']
gallery_uis = UI_test_data['gallery_uis']
rico_info = pickle.load(open('/home/dipu/codes/GraphEncoding-RICO/data/rico_box_info.pkl', 'rb'))
rico_ids = list(rico_info.keys())
rico_ids = [x+'.png' for x in rico_ids]
gallery_uis = list(set(rico_ids) & set(gallery_uis))
#data_dir = '/mnt/amber/scratch/Dipu/RICO/semantic_annotations'
#split_set_file = '/mnt/amber/scratch/Dipu/RICO/rico_split_set2.pkl'
#rico_split_set2 = pickle.load(open(split_set_file, 'rb'))
#
#train_uis = rico_split_set2['train_uis']
#query_uis = rico_split_set2['query_uis']
#gallery_uis = rico_split_set2['gallery_uis']
#
#train_uis = [x + '.png' for x in train_uis]
#query_uis = [x + '.png' for x in query_uis]
#gallery_uis = [x + '.png' for x in gallery_uis]
def main(args):
BATCH_SIZE = args.batch_size
if args.model_name == 'strided_512' or args.model_name == 'strided':
resize_shape = [255,127]
else:
resize_shape = [254,126]
data_transform = transforms.Compose([
transforms.Resize(resize_shape), #transforms.Resize([255,127]) # transforms.Resize([254,126])
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
# train_dataset = RICO_Dataset(train_uis, data_dir, transform= data_transform)
# train_loader = torch.utils.data.DataLoader(train_dataset, batch_size= BATCH_SIZE,
# drop_last = True, pin_memory=True, num_workers=16)
query_dataset = RICO_Dataset(query_uis, data_dir, transform= data_transform)
query_loader = torch.utils.data.DataLoader(query_dataset, batch_size= BATCH_SIZE, shuffle=False,
drop_last = False, pin_memory=True, num_workers=16)
gallery_dataset = RICO_Dataset(gallery_uis, data_dir, transform= data_transform)
gallery_loader = torch.utils.data.DataLoader(gallery_dataset, batch_size= BATCH_SIZE, shuffle=False,
drop_last = False, pin_memory=True, num_workers=16)
#Create boundingboxes class instance and intialize with all the bboxes in rico
#boundingBoxes = getBoundingBoxes()
#Model
model = models.create(args.model_name)
model_path = 'runs_MySemanticUI/%s/ckp_ep20.pth.tar'%(args.model_name)
resume = load_checkpoint(model_path)
model.load_state_dict(resume['state_dict'])
model = model.cuda()
model.eval()
#onlyGallery = True
q_feat, q_fnames = extract_features(query_loader, model)
g_feat, g_fnames = extract_features(gallery_loader, model)
#t_feat, t_fnames = extract_features(train_loader, model)
q_feat = np.concatenate(q_feat)
g_feat = np.concatenate(g_feat)
#t_feat = np.concatenate(t_feat)
print('extracted features from query images with shape {}'.format( q_feat.shape))
print('extracted features from gallery images with shape {}'.format(g_feat.shape))
# if not(onlyGallery):
# g_feat = np.vstack((g_feat,t_feat))
# g_fnames = g_fnames + t_fnames
distances = cdist(q_feat, g_feat, metric= 'euclidean')
sort_inds = np.argsort(distances)
plot_retrieved_images_and_uis(sort_inds, query_uis, gallery_uis, args.model_name)
#%% Preparing the dataset
def parse_ui_elements(sui):
"""
Parse the json file iteratively using recursion,, un winding all the nested chilfre
returns the dictionay of elements
"""
global counter
counter = 0
elements = defaultdict(dict)
def recurse(sui):
global counter
n_uis = len(sui['children'])
for i in range(n_uis):
[x1, y1, x2, y2] = sui['children'][i]['bounds']
elements[counter]['component_Label'] = sui['children'][i]['componentLabel']
elements[counter]['x'] = x1
elements[counter]['y'] = y1
elements[counter]['w'] = x2-x1
elements[counter]['h'] = y2-y1
elements[counter]['iconClass'] = sui['children'][i].get('iconClass')
elements[counter]['textButtonClass'] = sui['children'][i].get('textButtonClass')
counter +=1
if sui['children'][i].get('children') != None:
recurse(sui['children'][i])
recurse(sui)
return elements, counter
def getBoundingBoxes(data_dir = '/mnt/amber/scratch/Dipu/RICO/semantic_annotations/'):
allBoundingBoxes = BoundingBoxes()
files = glob.glob(data_dir+ "*.json")
for file in files:
imageName = os.path.split(file)[1]
imageName = imageName.replace(".json", "")
# print(imageName)
with open(file, "r") as f:
sui = json.load(f) # sui = semantic ui annotation.
elements, count = parse_ui_elements(sui)
for i in range(count):
box = elements[i]
bb = BoundingBox(
imageName,
box['component_Label'],
box['x'],
box['y'],
box['w'],
box['h'],
iconClass=box['iconClass'],
textButtonClass=box['textButtonClass'])
allBoundingBoxes.addBoundingBox(bb)
# testBoundingBoxes(allBoundingBoxes)
print('Collected {} bounding boxes from {} images'. format(allBoundingBoxes.count(), len(files) ))
return allBoundingBoxes
def testBoundingBoxes(boundingBoxes, samples = ['28970', '62918']):
#Visualize if every colored element is plotted or not.
from matplotlib import pyplot as plt
from PIL import Image
import matplotlib.patches as patches
samples = ['28970', '62918']
base_ui_path = '/mnt/amber/scratch/Dipu/RICO/semantic_annotations/'
base_im_path = '/mnt/amber/scratch/Dipu/RICO/combined/'
for sample in samples:
img = base_ui_path + sample + '.png'
img = Image.open(img).convert('RGB')
img2 = base_im_path + sample + '.jpg'
img2 = Image.open(img2).convert('RGB')
fig, ax = plt.subplots(1,2)
plt.setp(ax, xticklabels=[], yticklabels=[])
ax[0].imshow(img2)
ax[1].imshow(img)
bbs = boundingBoxes.getBoundingBoxesByImageName(sample)
for bb in bbs:
bb_cordinates = bb.getBoundingBox()
bb_class = bb.classId
# if bb_cordinates[2] < 0:
rect = patches.Rectangle((bb_cordinates[0], bb_cordinates[1]), bb_cordinates[2], bb_cordinates[3], linewidth=2, edgecolor='r', facecolor= 'none')
ax[1].add_patch(rect)
ax[1].text(bb_cordinates[0], bb_cordinates[1], bb_class, fontsize=8, color= 'r', verticalalignment='top')
plt.show()
def plot_classwiseResults(save_dir, classwiseResult, name):
from matplotlib import pyplot as plt
import collections
D = classwiseResult
for k, v in D.items():
if D[k] == []:
D[k]= [0,0]
else:
D[k] = [np.mean(v), len(v)]
D = sorted(D.items(), key=lambda kv: kv[1][0], reverse=False)
D = collections.OrderedDict(D)
fig, ax = plt.subplots()
for i, (k,v) in enumerate(D.items()):
ax.text( v[0]+0.001, i+0.25 , '{:.2f} ({})'.format(v[0], v[1]), fontsize=10, fontweight='bold', color= 'b', verticalalignment='top')
# print (i ,k ,v)
# fig.title(name)
fig.set_size_inches(7, 5)
ax.barh(range(len(D)), [x[0] for x in D.values()], align='center')
plt.yticks(range(len(D)), list(D.keys()), rotation='horizontal')
plt.subplots_adjust(bottom=0.3)
plt.tight_layout(h_pad=1)
plt.show()
plt.savefig('{}/{}.png'.format(save_dir, name), dpi = 500)
def plot_retrieved_images_and_uis(sort_inds, query_uis, gallery_uis, model_name):
from matplotlib import pyplot as plt
base_im_path = '/mnt/amber/scratch/Dipu/RICO/combined/'
base_ui_path = '/mnt/amber/scratch/Dipu/RICO/semantic_annotations/'
query_uis = [x.replace('.png', '') for x in query_uis]
gallery_uis = [x.replace('.png', '') for x in gallery_uis]
for i in range((sort_inds.shape[0])): #range(1):
q_path = base_im_path + query_uis[i] + '.jpg'
q_img = Image.open(q_path).convert('RGB')
q_ui_path = base_ui_path + query_uis[i] + '.png'
q_ui = Image.open(q_ui_path).convert('RGB')
fig, ax = plt.subplots(2,6)
plt.setp(ax, xticklabels=[], yticklabels=[])
fig.suptitle('Query-%s, %s (Gallery_Only-Set)'%(i, model_name), fontsize=20)
#fig = plt.figure(1)
fig.set_size_inches(30, 10)
#f1 = fig.add_subplot(2,6,1)
ax[0,0].imshow(q_ui)
ax[0,0].axis('off')
ax[0,0].set_title('Query: %s '%(i) + query_uis[i] + '.png')
ax[1,0].imshow(q_img)
ax[1,0].axis('off')
ax[1,0].set_title('Query: %s '%(i) + query_uis[i] + '.jpg')
#plt.pause(0.1)
for j in range(5):
path = base_im_path + gallery_uis[sort_inds[i][j]] + '.jpg'
# print(gallery_uis[sort_inds[i][j]] )
im = Image.open(path).convert('RGB')
ui_path = base_ui_path + gallery_uis[sort_inds[i][j]] + '.png'
#print(gallery_uis[sort_inds[i][j]])
ui = Image.open(ui_path).convert('RGB')
ax[0,j+1].imshow(ui)
ax[0,j+1].axis('off')
ax[0,j+1].set_title('Rank: %s '%(j+1) + gallery_uis[sort_inds[i][j]] + '.png')
ax[1,j+1].imshow(im)
ax[1,j+1].axis('off')
ax[1,j+1].set_title('Rank: %s '%(j+1) + gallery_uis[sort_inds[i][j]] + '.jpg')
directory = 'Retrieved_Images_MysemanticUI/{}/Gallery_Only/'.format(model_name)
if not os.path.exists(directory):
os.makedirs(directory)
plt.savefig( directory + str(i) + '.png')
# plt.pause(0.1)
plt.close()
#print('Wait')
print(i)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
# optimization
parser.add_argument('--batch_size', default = 128, type=int, metavar='N',
help='mini-batch size (1 = pure stochastic) Default: 256')
# model
parser.add_argument('--model_name', default = 'upsample_512', type = str,
help = 'which CNN autoencoder: upsample or strided or strided_512 or upsample_512')
parser.add_argument('--gpu_id', type=str, default = '3', help = 'GPU ID')
args = parser.parse_args()
main(args)