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draw_heat_new.py

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+import numpy as np
+from tqdm import tqdm
+from sklearn.metrics.pairwise import cosine_similarity
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+import os
+from PIL import Image
+from matplotlib.image import imread
+# import cv2
+import random
+from nltk.tokenize import word_tokenize
+from nltk import pos_tag
+
+tag_dict = {}
+tag_dict['CD'] = 1
+tag_dict['JJ'] = 2
+tag_dict['JJR'] = 2
+tag_dict['JJS'] = 2
+tag_dict['NN'] = 3
+tag_dict['NNS'] = 3
+tag_dict['NNP'] = 3
+tag_dict['NNPS'] = 3
+tag_dict['VB'] = 4
+tag_dict['VBD'] = 4
+tag_dict['VBG'] = 4
+tag_dict['VBN'] = 4
+tag_dict['VBP'] = 4
+tag_dict['VBZ'] = 4
+tag_dict['VBZ'] = 4
+tag_dict['WP'] = 5
+tag_dict['WP$'] = 5
+tag_dict['WRB'] = 6
+w = [1.41417003,0.65907812,1.98009944,1.50878048,0.68756366,1.72526932,1.37473035]
+
+def sent_pos(sent):
+	# sent is a list of words
+	return [tag_dict[x[1]] if x[1] in tag_dict else 7 for x in pos_tag(sent)]
+
+def nltk_tokenize(sent):
+	sent = str(sent).lower()
+	sent = sent.replace("-", " ")
+	sent = sent.replace("/", " ")
+	sent = sent.replace("`", " ")
+	token = word_tokenize(sent)
+
+	for i in range(len(token)):
+		if token[i].isalpha():
+			token[i] = token[i].lower()
+	return token
+prefix = './'
+file_list = sorted(os.listdir(prefix + 'att'))
+# print file_list 
+
+xtmp = 0
+# f, ax = plt.subplots(2,3,sharex=False,sharey=False)
+# f, ax = plt.subplots(0,0,sharex=False,sharey=False)
+def Generate_Heat(att):
+	x = np.zeros((224,224))
+	for idx in range(7):
+		for idy in range(7):
+			if att[idx][idy] > 0.7:
+				att[idx][idy] = 1
+	for idx in range(7):
+		for idy in range(7):
+			for i in range(224):
+				for j in range(224):
+					xx = idx * 32 + 16
+					yy = idy * 32 + 16
+					d = ((i - xx) ** 2 + (j - yy) ** 2) ** 0.5
+					x[i][j] += max(0,att[idx][idy] - d * 0.01)
+
+	return x 
+def Get_graph(ax, aword, gid):
+	xori = 1
+	for file in file_list:
+		qid,img,ques,ans = file.split('*')
+		qid = int(qid)
+		if qid != gid:
+			continue
+		graph_path = './graph/'+str(gid)
+		if os.path.exists(graph_path):
+			x = np.load(graph_path)
+		else:
+			att = np.load(prefix + 'att/' + file)
+			att = np.array(map(lambda x: ((x-x.min())/(x.max()-x.min())), att)).reshape(7,7)			
+			x = Generate_Heat(att)
+			x.dump(graph_path)
+
+		xori = Image.open(prefix + 'images/' + img + '.jpg')
+		ori = xori.resize((224,224))
+		ax.imshow(ori)
+		ax.imshow(x, cmap=plt.cm.jet, alpha=0.9, interpolation='gaussian' )
+		ax.get_xaxis().set_visible(False)
+		ax.get_yaxis().set_visible(False)
+
+		ques = nltk_tokenize(str(ques).lower())[:-1]
+		ans = nltk_tokenize(str(ans).lower())[:-1]
+		ques_pos = sent_pos(ques)
+		ans_pos = sent_pos(ans)
+		quesx = np.arange(len(ques + ans))
+		quesy = [w[x - 1] for x in (ques_pos + ans_pos)]
+		print (ques + ans)
+
+		aword.plot(quesx, quesy,'ro-')
+		aword.set_xticks(quesx)
+		aword.set_xticklabels(ques + ans,rotation=30, fontsize = 10)
+		#aword.set_size_inches((3,3))
+	return xori
+
+
+def Pic(ax, graph_id, i):
+	Get_graph(ax[1], ax[2], graph_id[1])
+	ori = Get_graph(ax[3], ax[4], graph_id[0])
+
+	ax[0].get_xaxis().set_visible(False)
+	ax[0].get_yaxis().set_visible(False)
+	if i==2 or i==4:
+		ax[0].plot(np.random.random(size=30))
+	ax[0].imshow( ori )
+
+#graph_id =[[179456, 179457],[335448, 335449],[128800, 128801],[94908, 94909]]
+good_id = [[100,101], [10052, 10053], [297766,297764], [32968, 32969], [202652, 202653]]#, [159696, 159697]] # [297740,297741],
+bad_id  = [[179456, 179457], [335448, 335449],[128800, 128801],[94908, 94909],[383988, 383989]]#, [324, 325]]
+graph_id = good_id
+
+# plt.tight_layout()
+# plt.savefig(prefix + '2.eps',foramt = 'eps', dpi = 100, bbox_inches='tight')
+
+
+from random import shuffle
+file_list = os.listdir(prefix + 'att')
+shuffle(file_list)
+
+f, ax = plt.subplots(4,5, sharex=False,sharey=False)	
+plt.subplots_adjust(left=None, bottom=None, right=None, top=None,
+                wspace=-0.1, hspace=0.5)
+f.set_size_inches(15.5, 10.5)
+
+# for xid, file in enumerate(file_list[:2]):
+# 	qid = int(file.split('*')[0])
+# 	graph_id[xid] = [qid / 4 * 4, qid / 4 * 4 + 1]
+
+print graph_id
+i = 0
+for tmp_ax, gid in zip(ax, graph_id):
+	Pic(tmp_ax, gid, i)
+	i=i+1
+
+# plt.tight_layout()
+plt.savefig(prefix + '2_4.eps',foramt = 'eps', dpi = 200, bbox_inches='tight')
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+#for num, gid in enumerate(graph_id):
+# 	f, ax = plt.subplots(1,5,sharex=False,sharey=False)
+# 	f.set_size_inches(18.5, 10.5)
+# 	Pic(ax, gid)
+# 	plt.tight_layout()
+# 	plt.savefig(prefix + str(num) + '.eps',foramt = 'eps', dpi = 200)