def show_sentence_attention(img, attention_array, cap, batch_index):
# ==== folder to store the sentence attention ====
folder = 'attentions/{}/'.format(batch_index)
if not os.path.exists(folder):
os.makedirs(folder)
# ==== write the cap ====
with open(os.path.join(folder, 'cap.txt'), 'wb') as f:
pickle.dump(cap, f)
# ==== write origin image ====
cv2.imwrite(os.path.join(folder, 'origin.jpg'), img)
assert attention_array.shape[0] + attention_array.shape[1] == 20 + 196
attention_array = attention_array.reshape((20, 14, 14))
attention = Attention()
# ==== for every word in the sentence ====
# ==== draw the attention ====
threshold = 1e-3
for i in range(20):
attention.build(img)
for j in range(14):
for k in range(14):
if attention_array[i][j][k] > threshold:
attention.add_spot((j + 0.5) * 16, (k + 0.5) * 16, strength = attention_array[i][j][k])
cv2.imwrite(folder + 'step {}.jpg'.format(i), attention.get_image())
pickle.dump(cap, f)
# ==== write origin image ====
cv2.imwrite(os.path.join(folder, 'origin.jpg'), img)
assert attention_array.shape[0] + attention_array.shape[1] == 20 + 196
attention_array = attention_array.reshape((20, 14, 14))
attention = Attention()
# ==== for every word in the sentence ====
# ==== draw the attention ====
threshold = 1e-3
for i in range(20):
attention.build(img)
for j in range(14):
for k in range(14):
if attention_array[i][j][k] > threshold:
attention.add_spot((j + 0.5) * 16, (k + 0.5) * 16, strength = attention_array[i][j][k])
cv2.imwrite(folder + 'step {}.jpg'.format(i), attention.get_image())
if __name__ == '__main__':
img_path = './figure/COCO_train2014_000000000025.jpg'
img = load_img(img_path)
attention = Attention()
attention.build(img, 0.3)
attention.add_spot(50, 50, strength = 0.5, dev = 25)
attention.add_spot(150, 150, strength = 0.5)
attention.show()
