def image_grab(id):
try:
new_image = get_image(id,path=coco_dir+'/processed_flatten/')
except IOError:
new_image = predict_image(id)[1]
try:
new_class = get_image(id,path=coco_dir+'/processed_predictions/')
except IOError:
new_class = predict_image(id)[2]
def image_grab(id):
try:
new_image = get_image(id, path='/extra' + '/processed_flatten/')
except IOError:
new_image = predict_image(id)[1]
try:
new_class = get_image(id,
path='/extra' + '/processed_predictions/')
except IOError:
new_class = predict_image(id)[2]
return new_class, new_image
def image_grab(id):
#word_to_idx, idx_to_word = load_refexp_dicts()
#classes, images, alt_classes,alt_images, partial_captions, next_words_one_hot, \
# vocab_size, idx_to_word, word_to_idx = load_stream(stream_num=cur_stream_num, stream_size=stream_size, preprocess=preproc,
# max_caption_len=max_caption_len, word_to_idx=word_to_idx)
#return images[:1],classes[:1],alt_images[:1],alt_classes[:1]
try:
new_image = get_image(id, path='/extra' + '/processed_flatten/')
except IOError:
new_image = predict_image(id)[1]
try:
new_class = get_image(id,
path='/extra' + '/processed_predictions/')
except IOError:
new_class = predict_image(id)[2]
return new_class, new_image
def load_stream(stream_num, stream_size, preprocess, max_caption_len,
word_to_idx):
# Preprocess the data if necessary
if preprocess:
preprocess_refexp_images(stream_num=stream_num,
stream_size=stream_size,
word_to_idx=word_to_idx,
max_cap_len=max_caption_len,
coco_dir=coco_dir,
out_file=data_path)
with open(data_path, 'rb') as handle:
X, next_words = pickle.load(handle)
if False:
new_X1 = []
new_X0 = []
new_y = []
for i, x in enumerate(X[1]):
if x[-5] == 0 and x[-6] != 0:
new_X1.append(x[:-5])
new_X0.append(X[0][i])
new_y.append(next_words[i])
next_words = np.asarray(new_y)
X[0] = np.asarray(new_X0)
X[1] = np.asarray(new_X1)
print([([idx_to_word[x] for x in X[1][p]], idx_to_word[next_words[p]])
for (p, q) in enumerate(X[1][:50])])
image_ids = X[0]
#bounding_boxes = [x[1] for x in image_ids]
#image_ids = [x[0] for x in image_ids]
partial_captions = X[1]
vocab_size = len(word_to_idx)
# Load the CNN feature representation of each image
images = []
classes = []
alt_images = []
alt_classes = []
for image_id in image_ids:
number = str(('_0000000000000' + str(image_id[0]))[-12:])
try:
x_whole = get_image(number, path='/extra' + '/processed_flatten/')
class_whole = get_image(number,
path='/extra' + '/processed_predictions/')
x_region = get_image(number,
path='/extra' + '/processed_flatten_b/')
class_region = get_image(number,
path='/extra' +
'/processed_predictions_b/')
images.append(x_region)
classes.append(class_region)
alt_images.append(x_whole)
alt_classes.append(class_whole)
except IOError:
x = predict_image(image_id[0], image_id[1])
images.append(x[4])
classes.append(x[5])
alt_images.append(x[1])
alt_classes.append(x[2])
images = np.asarray(images).transpose((1, 0, 2))[0]
alt_images = np.asarray(alt_images).transpose((1, 0, 2))[0]
classes = np.asarray(classes).transpose((1, 0, 2))[0]
alt_classes = np.asarray(alt_classes).transpose((1, 0, 2))[0]
# Convert next words to one hot vectors
new_next_words = []
for x in next_words:
a = np.zeros(vocab_size)
a[x] = 1
new_next_words.append(a)
next_words_one_hot = np.asarray(new_next_words)
return classes, images, alt_classes, alt_images, partial_captions, next_words_one_hot, \
vocab_size, idx_to_word, word_to_idx
# print([idx_to_word[n] if n!=0 else "null" for n in y],"NEXT_WORDS")
# print("SANITY CHECK",idx_to_word[word_to_idx['the']],word_to_idx[idx_to_word[1]])
# print([idx_to_word[x] if x!=0 else "null" for x in X[1][0]])
# print(idx_to_word[y[0]])
images = []
for image_id in image_ids:
number = str(('_0000000000000' + str(image_id))[-12:])
try:
x = get_image(number, path='../external/coco/processed/')
except IOError:
x = predict_image(str(image_id))
images.append(x)
X[0] = np.asarray(images).transpose((1, 0, 2))[0]
partial_captions = X[1] # (batch_size,16)
max_caption_len = partial_captions.shape[1]
next_words = y # vocab_size
new_next_words = []
for x in next_words:
# print x
a = np.zeros(vocab_size)
a[x] = 1
new_next_words.append(a)
next_words = np.asarray(new_next_words)
cap = ['$START$']
while len(cap) < max_caption_len:
result = model.predict([new_class,new_image, words_to_caption(cap,word_to_idx,max_caption_len)])
out = idx_to_word[np.argmax(result[0])]
cap.append(out)
return cap
print(unroll('000000000431'))
p = '000000000431'
try:
new_image = get_image(p,path=coco_dir+'/processed_flatten/')
except IOError:
new_image = predict_image(p)[1]
try:
new_class = get_image(p,path=coco_dir+'/processed_predictions/')
except IOError:
new_class = predict_image(p)[2]
#new_image = np.zeros((1,num_img_features))
cap = ['$START$']
while len(cap) < max_caption_len:
result = model.predict([new_class,new_image, words_to_caption(cap,word_to_idx,max_caption_len)])
#m = max(result[0])
# print(result)
#out = idx_to_word[[i for i, j in enumerate(result[0]) if j == m][0]]
out = idx_to_word[np.argmax(result[0])]
cap.append(out)
print(cap)
# # # cap =
# # # print(words_to_caption(cap,word_to_idx))
# # result = model.predict([new_image, inp])
# result2 = model.predict([new_image, words_to_caption(cap,word_to_idx, max_caption_len)])
# # print("EQUAL?",np.array_equal(result,result2))
# print(result[0][np.argmax(result[0])],"PROB DIST")
# result = idx_to_word[np.argmax(result[0])]
# # print(result)
#new_image = images[0].reshape((1, num_img_features))
try:
new_image = get_image('000000000036',
path=coco_dir + '/processed_flatten/')
except IOError:
new_image = predict_image('000000000036')[1]
#new_image = np.zeros((1,num_img_features))
cap = ['$START$']
while len(cap) < max_caption_len:
result = model.predict(
[new_image,
words_to_caption(cap, word_to_idx, max_caption_len)])
m = max(result[0])
# print(result)
out = idx_to_word[[i for i, j in enumerate(result[0]) if j == m][0]]
# out = idx_to_word[sample(result[0])]
cap.append(out)
print(cap)
# if out == STOP_TOKEN:
# break
def load_stream(stream_num, stream_size, preprocess, max_caption_len, word_to_idx):
# Preprocess the data if necessary
if preprocess:
preprocess_captioned_images(stream_num=stream_num, stream_size=stream_size, word_to_idx=word_to_idx,
max_cap_len=max_caption_len, coco_dir=coco_dir, category_name='person',
out_file=data_path)
with open(data_path, 'rb') as handle:
X, next_words = pickle.load(handle)
if False:
new_X1 = []
new_X0 = []
new_y = []
for i,x in enumerate(X[1]):
if x[-5] == 0 and x[-6] != 0:
new_X1.append(x[:-5])
new_X0.append(X[0][i])
new_y.append(next_words[i])
next_words = np.asarray(new_y)
X[0] = np.asarray(new_X0)
X[1] = np.asarray(new_X1)
print([([idx_to_word[x] for x in X[1][p]],idx_to_word[next_words[p]]) for (p,q) in enumerate(X[1][:50])])
image_ids = X[0]
partial_captions = X[1]
vocab_size = len(word_to_idx)
# Load the CNN feature representation of each image
images = []
for image_id in image_ids:
number = str(('_0000000000000'+str(image_id))[-12:])
try:
x = get_image(number,path=coco_dir+'/processed/')
except IOError:
x = predict_image(str(image_id))[0]
images.append(x)
images = np.asarray(images).transpose((1,0,2))[0]
classes = []
for image_id in image_ids:
number = str(('_0000000000000'+str(image_id))[-12:])
try:
x = get_image(number,path='../external/coco/processed_predictions/')
except IOError:
x = predict_image(str(image_id))[2]
classes.append(x)
classes = np.asarray(classes).transpose((1,0,2))[0]
# Convert next words to one hot vectors
new_next_words = []
for x in next_words:
a = np.zeros(vocab_size)
a[x] = 1
new_next_words.append(a)
next_words_one_hot = np.asarray(new_next_words)
return classes, images, partial_captions, next_words_one_hot, \
vocab_size, idx_to_word, word_to_idx
# # # cap =
# # # print(words_to_caption(cap,word_to_idx))
# # result = model.predict([new_image, inp])
# result2 = model.predict([new_image, words_to_caption(cap,word_to_idx, max_caption_len)])
# # print("EQUAL?",np.array_equal(result,result2))
# print(result[0][np.argmax(result[0])],"PROB DIST")
# result = idx_to_word[np.argmax(result[0])]
# # print(result)
#new_image = images[0].reshape((1, num_img_features))
p = '000000000094'
try:
new_image = get_image(p,path=coco_dir+'/processed/')
except IOError:
new_image = predict_image(p)[0]
try:
new_class = get_image(p,path=coco_dir+'/processed_predictions/')
except IOError:
new_class = predict_image(p)[2]
#new_image = np.zeros((1,num_img_features))
cap = ['$START$']
while len(cap) < max_caption_len:
result = model.predict([new_class,new_image, words_to_caption(cap,word_to_idx,max_caption_len)])
m = max(result[0])
# print(result)
out = idx_to_word[[i for i, j in enumerate(result[0]) if j == m][0]]
# out = idx_to_word[sample(result[0])]
cap.append(out)
print(cap)
# if out == STOP_TOKEN:
