def load_model(self):
print("Loading model with an input size of: [" +
str(self.input_width) + "," + str(self.input_height) + "]")
graph = tf.Graph()
with graph.as_default():
model = inference_wrapper.InferenceWrapper()
restore_fn = model.build_graph_from_config(
configuration.ModelConfig(),
os.path.join(self.model_dir,
"model.ckpt-" + str(self.checkpoint)))
graph.finalize()
# Create the vocabulary.
vocab = vocabulary.Vocabulary(
os.path.join(self.model_dir, "word_counts.txt"))
sess = tf.Session(graph=graph)
restore_fn(sess)
generator = caption_generator.CaptionGenerator(model, vocab)
self._sess = sess
self._generator = generator
self._vocab = vocab
def inference():
# build the inference graph
g = tf.Graph()
with g.as_default():
model = inference_wrapper.InferenceWrapper(FLAGS.rnn_type)
restore_fn = model.build_graph_from_config(configuration.ModelConfig(),
FLAGS.checkpoint_path)
g.finalize()
# Create the vocabulary.
vocab = vocabulary.Vocabulary(FLAGS.vocab_file)
filename = path
with tf.Session(graph=g) as sess:
# Load the model from checkpoint.
restore_fn(sess)
# Prepare the caption generator. Here we are implicitly using the default
# beam search parameters. See caption_generator.py for a description of the
# available beam search parameters.
generator = caption_generator.CaptionGenerator(model, vocab)
with tf.gfile.GFile(filename, "rb") as f:
image = f.read()
captions = generator.beam_search(sess, image)
print("Captions for image %s:" % os.path.basename(filename))
global sentences
sentences = []
for i, caption in enumerate(captions):
# Ignore begin and end words.
sentence = [vocab.id_to_word(w) for w in caption.sentence[1:-1]]
sentence = " ".join(sentence)
sentences.append(sentence)
print(" %d) %s (p=%f)" % (i, sentence, math.exp(caption.logprob)))
def main(_):
with open(FLAGS.keyword_pickle_file,'r')as f:
keyword_data=cPickle.load(f)
with open(FLAGS.test_json_path)as f:
test_json=json.load(f)
id_to_filename=test_json['images']
id_to_path=[{'path':os.path.join(FLAGS.image_path,x['file_name']),'id':x['id']}for x in id_to_filename]
result_json=[]
g=tf.Graph()
with g.as_default():
model=inference_wrapper.InferenceWrapper()
restore_fn=model.build_graph_from_config(configuration.ModelConfig(),FLAGS.checkpoint_path)
g.finalize()
vocab=vocabulary.Vocabulary(FLAGS.vocab_file)
with tf.Session(graph=g)as sess:
restore_fn(sess)
generator=caption_generator.CaptionGenerator(model,vocab)
for data in id_to_path:
filename=data['path']
with tf.gfile.GFile(filename,"r")as f:
image=f.read()
captions=generator.beam_search(sess,image,keyword_data[os.path.basename(filename)])
print("Captions for image %s:"%os.path.basename(filename))
result={'image_id':data['id'],'caption':(" ".join([vocab.id_to_word(w)for w in captions[0].sentence[1:-1]])).decode('utf-8')}
print(result)
result_json.append(result)
with open(os.path.join(FLAGS.temp_path,"result.json"),'w')as f:
json.dump(result_json,f)
coco=COCO(FLAGS.test_json_path)
cocoRes=coco.loadRes(os.path.join(FLAGS.temp_path,"result.json"))
cocoEval=COCOEvalCap(coco,cocoRes)
cocoEval.evaluate()
def predict(args_):
checkpoint_path = args_.checkpoint_path
words_file = args_.words_file
image_file = args_.path
if not os.path.exists(checkpoint_path):
print('checkpoint path is not exist.')
exit(0)
if not os.path.exists(words_file):
print('words file not found.')
exit(0)
g = tf.Graph()
with g.as_default():
model = inference_wrapper.InferenceWrapper()
restore_fn = model.build_graph_from_config(configuration.ModelConfig(), checkpoint_path)
g.finalize()
vocab = vocabulary.Vocabulary(words_file)
if os.path.isdir(image_file):
with tf.Session(graph=g) as sess:
restore_fn(sess)
generator = caption_generator.CaptionGenerator(model, vocab)
# sent a directory contains images
file_names = [os.path.join(image_file, i) for i in os.listdir(image_file) if i.lower().endswith('.jpg')
or i.lower().endswith('jpeg') or i.lower().endswith('png')]
file_names = [i for i in file_names if os.path.isfile(i)]
for f in file_names:
with tf.gfile.GFile(f, "rb") as img_file:
image = img_file.read()
captions = generator.beam_search(sess, image)
print("Captions for image %s:" % os.path.basename(f))
for i, caption in enumerate(captions):
# Ignore begin and end words.
sentence = [vocab.id_to_word(w) for w in caption.sentence[1:-1]]
sentence = " ".join(sentence)
print(" %d) %s (p=%f)" % (i, sentence, math.exp(caption.logprob)))
# cv2 show image
image_array = cv2.imread(f, cv2.COLOR_BGR2RGB)
cv2.imshow('image', image_array)
cv2.waitKey(0)
elif os.path.isfile(image_file):
# sent a single image file
with tf.Session(graph=g) as sess:
restore_fn(sess)
generator = caption_generator.CaptionGenerator(model, vocab)
# sent a directory contains images
with tf.gfile.GFile(image_file, "rb") as f:
image = f.read()
captions = generator.beam_search(sess, image)
print("Captions for image %s:" % os.path.basename(f))
for i, caption in enumerate(captions):
# Ignore begin and end words.
sentence = [vocab.id_to_word(w) for w in caption.sentence[1:-1]]
sentence = " ".join(sentence)
print(" %d) %s (p=%f)" % (i, sentence, math.exp(caption.logprob)))
else:
print('image path: {} not found.'.format(image_file))
exit(0)
def main(_):
# Build the inference graph.
g = tf.Graph()
model_path = '/Users/harshpyati/personal/fyp/text_gen/model.ckpt-2000000'
vocab_path = '/Users/harshpyati/personal/fyp/text_gen/word_counts.txt'
with g.as_default():
model = inference_wrapper.InferenceWrapper()
restore_fn = model.build_graph_from_config(configuration.ModelConfig(),
model_path)
g.finalize()
# Create the vocabulary.
vocab = vocabulary.Vocabulary(vocab_path)
all_files = FLAGS.input_files.split(',')
files = []
for fil in all_files:
word = None
if '[' in fil:
word = fil.replace('[', '')
if ']' in fil:
word = fil.replace(']', '')
if ' ' in fil:
word = fil.replace(' ', '')
if "u'" in fil:
word = fil.replace("u'", '')
if '\'' in fil:
word = fil.replace("'", '')
if "'" in fil:
word = fil.replace("'", '')
if "[u" in fil:
word = fil.replace("[u", '')
if " u" in fil:
word = fil.replace(" u", '')
word = word.split('\'')[1]
files.append(word)
filenames = []
with tf.Session(graph=g) as sess:
generator = caption_generator.CaptionGenerator(model, vocab)
# Load the model from checkpoint.
restore_fn(sess)
all_captions = []
for file_pattern in files:
filenames.extend(tf.gfile.Glob(file_pattern))
tf.logging.info("Running caption generation on %d files matching %s",
len(filenames), file_pattern)
with tf.gfile.GFile(file_pattern, "rb") as f:
image = f.read()
captions = generator.beam_search(sess, image)
for index, caption in enumerate(captions):
sentence = [vocab.id_to_word(w) for w in caption.sentence[1:-1]]
sentence = " ".join(sentence)
data = {
"name": file_pattern,
"caption": sentence
}
all_captions.append(data)
break
print(all_captions)
def main(_):
assert FLAGS.checkpoint_path, "--checkpoint_path is required"
assert FLAGS.vocab_file, "--vocab_file is required"
assert FLAGS.input_file_pattern, "--input_file_pattern is required"
assert FLAGS.output, "--output is required"
# Build the inference graph.
g = tf.Graph()
with g.as_default():
model = inference_wrapper.InferenceWrapper()
restore_fn = model.build_graph(FLAGS.checkpoint_path)
g.finalize()
# Create the vocabulary.
vocab = vocabulary.Vocabulary(FLAGS.vocab_file)
results = []
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=FLAGS.gpu_memory_fraction)
with tf.Session(graph=g,
config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
# Load the model from checkpoint.
restore_fn(sess)
# Prepare the caption generator. Here we are implicitly using the default
# beam search parameters. See caption_generator.py for a description of the
# available beam search parameters.
generator = caption_generator.CaptionGenerator(model, vocab)
t_start = time.time()
files = tf.gfile.Glob(FLAGS.input_file_pattern)
for i, filename in enumerate(files):
if i % 100 == 0:
print(i)
with tf.gfile.GFile(filename, "r") as f:
image = f.read()
image_id = filename.split('.')[0]
if "/" in image_id:
image_id = image_id.split("/")[-1]
result = {}
result['image_id'] = image_id
if FLAGS.predict_attributes_only:
attributes_ids, attributes_probs = generator.predict_attributes(
sess, image)
attributes = [vocab.id_to_word(w) for w in attributes_ids]
result['attributes'] = " ".join(attributes)
result['probabilities'] = " ".join(
[str(prob) for prob in attributes_probs])
else:
captions = generator.beam_search(sess, image)
sent = [vocab.id_to_word(w) for w in captions[0]]
result['caption'] = "".join(sent)
results.append(result)
t_end = time.time()
print("time: %f" % (t_end - t_start))
output = open(FLAGS.output, 'w')
json.dump(results, output, ensure_ascii=False, indent=4)
output.close()
def main(_):
assert FLAGS.checkpoint_path, "--checkpoint_path is required"
assert FLAGS.vocab_file, "--vocab_file is required"
assert FLAGS.input_file_pattern, "--input_file_pattern is required"
assert FLAGS.output, "--output is required"
# Build the inference graph.
g = tf.Graph()
with g.as_default():
model = inference_wrapper.InferenceWrapper()
restore_fn = model.build_graph(FLAGS.checkpoint_path)
init_fn = tf.local_variables_initializer()
g.finalize()
# Create the vocabulary.
vocab = vocabulary.Vocabulary(FLAGS.vocab_file)
results = []
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=FLAGS.gpu_memory_fraction)
with tf.Session(graph=g,
config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
# Load the model from checkpoint.
restore_fn(sess)
sess.run(init_fn)
tf.train.start_queue_runners()
# Prepare the caption generator. Here we are implicitly using the default
# beam search parameters. See caption_generator.py for a description of the
# available beam search parameters.
generator = caption_generator.CaptionGenerator(model, vocab)
t_start = time.time()
try:
i = 0
while True:
if i % 10 == 0:
print(i * FLAGS.batch_size)
i += 1
image_names, final_captions = generator.batched_beam_search(
sess)
sents = [
"".join([vocab.id_to_word(w) for w in captions[0]])
for captions in final_captions
]
image_names = image_names.tolist()
for name, sent in zip(image_names, sents):
result = {}
result['image_id'] = name
result['caption'] = "".join(sent)
results.append(result)
except Exception as e:
print(e)
t_end = time.time()
print("time: %f" % (t_end - t_start))
output = open(FLAGS.output, 'w')
json.dump(results, output, ensure_ascii=False, indent=4)
output.close()
def main(_):
# Build the inference graph.
g = tf.Graph()
with g.as_default():
model = inference_wrapper.InferenceWrapper()
restore_fn = model.build_graph_from_config(configuration.ModelConfig(),
FLAGS.checkpoint_path)
g.finalize()
# Create the vocabulary.
vocab = vocabulary.Vocabulary(FLAGS.vocab_file)
filenames = []
for file_pattern in FLAGS.input_files.split(","):
filenames.extend(tf.gfile.Glob(file_pattern))
tf.logging.info("Running caption generation on %d files matching %s",
len(filenames), FLAGS.input_files)
with tf.Session(graph=g) as sess:
# Load the model from checkpoint.
restore_fn(sess)
# Prepare the caption generator. Here we are implicitly using the default
# beam search parameters. See caption_generator.py for a description of the
# available beam search parameters.
generator = caption_generator.CaptionGenerator(model, vocab)
image_id_caption = []
j = 0
for filename in filenames:
with tf.gfile.GFile(filename, "rb") as f:
image = f.read()
captions = generator.beam_search(sess, image)
j += 1
print(j)
print("Captions for image %s:" % os.path.basename(filename))
for i, caption in enumerate(captions):
# Ignore begin and end words.
# print(caption.sentence[1:-1])
sentence = [
vocab.id_to_word(w) for w in caption.sentence[1:-1]
]
# print(sentence)
sentence = "".join(sentence)
print(" %d) %s (p=%f)" %
(i, sentence, math.exp(caption.logprob)))
if not i:
image_id_caption.append(
{
"image_id": filename.split('/')[-1].replace(
".jpg", ""),
"caption": sentence
}, )
image_id_caption = json.dumps(image_id_caption).encode('utf-8')
data = json.loads(image_id_caption)
with open(FLAGS.captions_file, 'w') as f:
json.dump(data, f)
print("Saving captions file to path %s" % FLAGS.captions_file)
def main(_):
# Build the inference graph.
g = tf.Graph()
with g.as_default():
model = inference_wrapper.InferenceWrapper()
restore_fn = model.build_graph_from_config(configuration.ModelConfig(),
FLAGS.checkpoint_path)
g.finalize()
# Create the vocabulary.
vocab = vocabulary.Vocabulary(FLAGS.vocab_file)
# q&a: understand follow snippets
# filenames = []
# for file_pattern in FLAGS.input_files.split(","):
# # tf.gfile.Glob(pattern) Returns a list of files that match the given pattern(s)
# filenames.extend(tf.gfile.Glob(file_pattern))
# note: assert FLAGS.input_files == 'utils/test_file_abspath_flickr8k'
with open(FLAGS.input_files, 'r') as f:
filenames = f.readlines()
filenames = [filename.strip() for filename in filenames]
tf.logging.info("Running caption generation on %d files matching %s",
len(filenames), FLAGS.input_files)
session_config = tf.ConfigProto()
session_config.gpu_options.allow_growth = True
with tf.Session(graph=g, config=session_config) as sess:
# Load the model from checkpoint.
restore_fn(sess)
# Prepare the caption generator. Here we are implicitly using the default
# beam search parameters. See caption_generator.py for a description of the
# available beam search parameters.
generator = caption_generator.CaptionGenerator(model, vocab)
json_file = list()
for count, filename in enumerate(filenames):
with tf.gfile.GFile(filename, "rb") as f:
image = f.read()
captions = generator.beam_search(sess,
image) # 返回的是beam_size个caption
# print("Captions for image %s:" % os.path.basename(filename))
for i, caption in enumerate(captions):
img_caption_dict = {}
img_caption_dict['filename'] = os.path.basename(filename)
# Ignore begin and end words.
sentence = [
vocab.id_to_word(w) for w in caption.sentence[1:-1]
]
sentence = " ".join(sentence)
img_caption_dict['caption'] = sentence
json_file.append(img_caption_dict)
if count % 50 == 0:
print("counter: %d" % count)
store_json_file("im2txt_flickr8k_cap_google.json", json_file)
def main(_):
# Build the inference graph.
g = tf.Graph()
with g.as_default():
model = inference_wrapper.InferenceWrapper()
restore_fn = model.build_graph_from_config(configuration.ModelConfig(),
FLAGS.checkpoint_path)
g.finalize()
# Create the vocabulary.
vocab = vocabulary.Vocabulary(FLAGS.vocab_file)
filenames = []
dirs = os.walk(FLAGS.image_dir)
for a, _, filelist in dirs:
for filename in filelist:
origin_name = a + filename
if origin_name.endswith('.jpg'):
filenames.append(origin_name)
with tf.Session(graph=g) as sess:
# Load the model from checkpoint.
restore_fn(sess)
# Prepare the caption generator. Here we are implicitly using the default
# beam search parameters. See caption_generator.py for a description of the
# available beam search parameters.
generator = caption_generator.CaptionGenerator(model, vocab)
res = []
num = 1
for filename in filenames:
imgid_sentence = {}
with tf.gfile.GFile(filename, "r") as f:
image = f.read()
captions = generator.beam_search(sess, image)
# print("Captions for image %s:" % os.path.basename(filename))
for i, caption in enumerate(captions):
# Ignore begin and end words.
sentence = [vocab.id_to_word(w) for w in caption.sentence[1:-1]]
sentence = "".join(sentence)
if i == 0:
if num % 100 ==0 :
print("Captions for image %s:" % os.path.basename(filename))
print("%d) %s (p=%f)" % (num,sentence, math.exp(caption.logprob)))
imgid_sentence['image_id'] = os.path.basename(filename).split('.')[0]
imgid_sentence['caption'] = sentence
res.append(imgid_sentence)
num = num + 1
with io.open(FLAGS.out_predict_json, 'w', encoding='utf-8') as fd:
fd.write(unicode(json.dumps(res,
ensure_ascii=False, sort_keys=True, indent=2, separators=(',', ': '))))
assert len(filenames) == len(res)
print("Finished process %d images!"%len(filenames))
def main(_):
# Build the inference graph.
g = tf.Graph()
with g.as_default():
model = inference_wrapper.InferenceWrapper()
restore_fn = model.build_graph_from_config(configuration.ModelConfig(),
FLAGS.checkpoint_path)
g.finalize()
# Create the vocabulary.
vocab = vocabulary.Vocabulary(FLAGS.vocab_file)
filenames = []
# print("FLAGS.input_files", FLAGS.input_files)
for file_pattern in FLAGS.input_files.split(","): # original might be right?
# for file_pattern in FLAGS.input_files.split(","):
print("HIIII", file_pattern)
# # filenames.extend(tf.gfile.Glob(file_pattern))
# filenames.extend(tf.gfile.Glob("*.jpg"))
# print("filenames list", filenames)
filenames.extend(tf.gfile.Glob(file_pattern))
print("filenames", filenames)
tf.logging.info("Running caption generation on %d files matching %s",
len(filenames), FLAGS.input_files)
with tf.Session(graph=g) as sess:
# Load the model from checkpoint.
restore_fn(sess)
# Prepare the caption generator. Here we are implicitly using the default
# beam search parameters. See caption_generator.py for a description of the
# available beam search parameters.
generator = caption_generator.CaptionGenerator(model, vocab)
print("generator")
for filename in filenames:
# print("*" * 10)
# print("\n" * 5)
# print("FILENAME", filename)
# print("\n" * 5)
# print("*" * 10)
# with tf.gfile.GFile(filename, "r") as f:
with tf.gfile.GFile(filename, 'rb') as f:
# https://github.com/tensorflow/tensorflow/issues/11312
image = f.read()
captions = generator.beam_search(sess, image)
print("Captions for image %s:" % os.path.basename(filename))
for i, caption in enumerate(captions):
# Ignore begin and end words.
sentence = [vocab.id_to_word(w) for w in caption.sentence[1:-1]]
sentence = " ".join(sentence)
print(" %d) %s (p=%f)" % (i, sentence, math.exp(caption.logprob)))
print("DONE :)")
def main(_):
# Build the inference graph.
g = tf.Graph()
with g.as_default():
infer = inference_wrapper.InferenceWrapper()
restore_fn = infer.build_graph_from_config(configuration.ModelConfig(),
FLAGS.checkpoint_path)
g.finalize()
# Load file in the provied directory
filenames = []
for file_pattern in FLAGS.input_files.split(","):
filenames.extend(tf.gfile.Glob(file_pattern))
tf.logging.info("Running text detection on %d files matching %s",
len(filenames), FLAGS.input_files)
with tf.Session(graph=g) as sess:
# Load the model from checkpoint.
restore_fn(sess)
filenames.sort()
# Predict
for filename in filenames:
with tf.gfile.GFile(filename, "r") as f:
# Read image
cv_img = cv2.imread(filename)
image = f.read()
# Make prediction
tic = time.time()
text_bboxes = infer.inference_step(sess, image)
toc = time.time()
print("Prediction for image %s in %.3f ms" %
(os.path.basename(filename), (toc - tic) * 1000))
# Show the result
for i in range(len(text_bboxes)):
text = "{}: {:.3f}".format(i, float(text_bboxes[i][4]))
cv2.putText(cv_img, text, (int(text_bboxes[i][0]) + 5, int(text_bboxes[i][1]) + 16), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,0), 2)
cv2.rectangle(cv_img,
(int(text_bboxes[i][0]), int(text_bboxes[i][1])),
(int(text_bboxes[i][2]), int(text_bboxes[i][3])),
(0,0,255), 2)
cv2.namedWindow('image', cv2.WND_PROP_FULLSCREEN)
cv2.resizeWindow('image', 1500, 900);
cv2.imshow('image', cv_img)
k = cv2.waitKey(0)
if k == ord('n'):
cv2.destroyAllWindows()
def main(_):
# Build the inference graph.
g = tf.Graph()
with g.as_default():
model = inference_wrapper.InferenceWrapper()
restore_fn = model.build_graph_from_config(configuration.ModelConfig(),
FLAGS.checkpoint_path)
g.finalize()
# Create the vocabulary.
vocab = vocabulary.Vocabulary(FLAGS.vocab_file)
filenames = []
#for file_pattern in FLAGS.input_files.split(","):
# filenames.extend(tf.gfile.Glob(file_pattern))
tf.logging.info("Running caption generation on %d files matching %s",
len(filenames), FLAGS.input_files)
config_sess = tf.ConfigProto()
config_sess.gpu_options.allow_growth = True
with tf.Session(graph=g, config=config_sess) as sess:
# Load the model from checkpoint.
restore_fn(sess)
# Prepare the caption generator. Here we are implicitly using the default
# beam search parameters. See caption_generator.py for a description of the
# available beam search parameters.
generator = caption_generator.CaptionGenerator(model, vocab)
test_path = r'C:\Users\PSIML-1.PSIML-1\Desktop\projekti\Image-Captioning\test_data'
filenames = os.listdir(test_path)
#captions_index = preprocess_captions()
j = 0
for filename in filenames:
full_fname = os.path.join(test_path, filename)
with tf.gfile.GFile(full_fname, "rb") as f:
image = f.read()
captions = generator.beam_search(sess, image)
best_captions = []
for i, caption in enumerate(captions):
# Ignore begin and end words.
sentence = [
vocab.id_to_word(w) for w in caption.sentence[1:-1]
]
sentence = " ".join(sentence)
best_captions.append(" %d) %s\n" % (i, sentence))
#image_idx = int(filename.split('.')[0].split('_')[2])
#true_captions = captions_index[image_idx]
plot_image(full_fname, None, best_captions, j)
j += 1
def main(argv):
inputfile = ' '
outputfile = ' '
try:
opts, args = getopt.getopt(argv,"hi:o",["ifile=","ofile="])
except getopt.GetoptError:
print("input/output error ")
sys.exit(2)
for opt, arg in opts:
if opt =='-h':
print ('usage: python run_inference.py -i <inputfile> -o <outuptfile>')
sys.exit()
elif opt in ('-i','--input'):
inputfile = arg
elif opt in ('-o','--output'):
outputfile = arg
g = tf.Graph()
with g.as_default():
model = inference_wrapper.InferenceWrapper()
restore_fn = model.build_graph_from_config(configuration.ModelConfig(),
FLAGS.checkpoint_path)
g.finalize()
vocab = vocabulary.Vocabulary(FLAGS.vocab_file)
filenames = []
if inputfile == ' ':
for file_pattern in FLAGS.input_files.split(","):
filenames.extend(tf.gfile.Glob(file_pattern))
else:
for file_pattern in inputfile.split(","):
filenames.extend(tf.gfile.Glob(file_pattern))
with tf.Session(graph=g) as sess:
restore_fn(sess)
generator = caption_generator.CaptionGenerator(model, vocab)
for filename in filenames:
with tf.gfile.FastGFile(filename, "rb") as f:
image = f.read()
captions = generator.beam_search(sess, image)
# print("Captions for image %s using NIC model:" % os.path.basename(filename))
prob = []
for i, caption in enumerate(captions):
sentence = [vocab.id_to_word(w) for w in caption.sentence[1:-1]]
sentence = " ".join(sentence)
prob.append(caption.logprob)
# In this case, only the one with the largetst logprob is left for futher operation
for caption in captions:
sentence = [vocab.id_to_word(w) for w in caption.sentence[1:-1]]
sentence = " ".join(sentence)
if 'UNK' in sentence:# if luckily the model recognized the text information itself
final = sentence
break
if caption.logprob == max(prob):
final = [vocab.id_to_word(w) for w in caption.sentence[1:-1]]
final = ' '.join(final)
img = Image.open(FLAGS.input_files)
def main(_):
# Build the inference graph.
g = tf.Graph()
with g.as_default():
model = inference_wrapper.InferenceWrapper()
restore_fn = model.build_graph_from_config(configuration.ModelConfig(),
FLAGS.checkpoint_path)
g.finalize()
# Create the vocabulary.
vocab = vocabulary.Vocabulary(FLAGS.vocab_file)
filenames = []
for file_pattern in FLAGS.input_files.split(","):
filenames.extend(tf.gfile.Glob(file_pattern))
tf.logging.info("Running caption generation on %d files matching %s",
len(filenames), FLAGS.input_files)
with tf.Session(graph=g) as sess:
# Load the model from checkpoint.
restore_fn(sess)
# Prepare the caption generator. Here we are implicitly using the default
# beam search parameters. See caption_generator.py for a description of the
# available beam search parameters.
generator = caption_generator.CaptionGenerator(model, vocab)
out = []
for filename in filenames:
with tf.gfile.FastGFile(filename, "rb") as f:
image = f.read()
captions = generator.beam_search(sess, image)
print("Captions for image %s:" % os.path.basename(filename))
for i, caption in enumerate(captions):
# Ignore begin and end words.
# print(caption.sentence[1:-1])
sentence = [
vocab.id_to_word(w) for w in caption.sentence[1:-1]
]
sentence = " ".join(sentence)
print(" %d) %s (p=%f)" %
(i, sentence, math.exp(caption.logprob)))
if (i == 0):
out = sentence
img = Image.open(FLAGS.input_files)
plt.imshow(img)
plt.axis('off')
plt.title(str(out))
plt.show()
def model_predict(img_path):
# Build the inference graph.
g = tf.Graph()
with g.as_default():
model = inference_wrapper.InferenceWrapper()
checkpoint_path = os.path.join(
os.path.dirname(os.path.abspath('__file__')), 'models')
print(checkpoint_path)
restore_fn = model.build_graph_from_config(configuration.ModelConfig(),
checkpoint_path)
g.finalize()
# Create the vocabulary.
vocab = vocabulary.Vocabulary("./data/word_counts.txt")
filenames = []
for file_pattern in img_path.split(","):
filenames.extend(tf.gfile.Glob(file_pattern))
tf.logging.info("Running caption generation on %d files matching %s",
len(filenames), img_path)
with tf.Session(graph=g) as sess:
# Load the model from checkpoint.
restore_fn(sess)
# Prepare the caption generator. Here we are implicitly using the default
# beam search parameters. See caption_generator.py for a description of the
# available beam search parameters.
generator = caption_generator.CaptionGenerator(model, vocab)
preds = ''
out_data = []
for filename in filenames:
with tf.gfile.GFile(filename, "rb") as f:
image = f.read()
captions = generator.beam_search(sess, image)
print("Captions for image %s:" % os.path.basename(filename))
for i, caption in enumerate(captions):
# Ignore begin and end words.
sentence = [
vocab.id_to_word(w) for w in caption.sentence[1:-1]
]
sentence = " ".join(sentence)
preds = str(i + 1) + ") " + sentence + "(p=" + str(
round(math.exp(caption.logprob), 6)) + ")"
out_data.append(preds)
print(" %d) %s (p=%f)" %
(i, sentence, math.exp(caption.logprob)))
out_json = json.dumps(out_data)
#print(out_json)
return out_json
def main(_):
# Change tensor name
#rename_model_ckpt()
# Build the inference graph.
g = tf.Graph()
with g.as_default():
model = inference_wrapper.InferenceWrapper()
restore_fn = model.build_graph_from_config(configuration.ModelConfig(),
FLAGS.checkpoint_path)
g.finalize()
# Create the vocabulary.
vocab = vocabulary.Vocabulary(FLAGS.vocab_file)
filenames = []
for file_pattern in FLAGS.input_files.split(","):
filenames.extend(tf.gfile.Glob(file_pattern))
tf.logging.info("Running caption generation on %d files matching %s",
len(filenames), FLAGS.input_files)
with tf.Session(graph=g) as sess:
# Load the model from checkpoint.
restore_fn(sess)
# Prepare the caption generator. Here we are implicitly using the default
# beam search parameters. See caption_generator.py for a description of the
# available beam search parameters.
generator = caption_generator.CaptionGenerator(model, vocab)
df = pd.DataFrame(columns=["id", "caption"])
for idx, filename in enumerate(filenames):
df.loc[idx, "id"] = filename.split('/')[-1]
with tf.gfile.GFile(filename, "rb") as f:
image = f.read()
captions = generator.beam_search(sess, image)
print("Captions for image %s:" % os.path.basename(filename))
for i, caption in enumerate(captions):
# Ignore begin and end words.
sentence = [
vocab.id_to_word(w) for w in caption.sentence[1:-1]
]
sentence = " ".join(sentence)
print(" %d) %s (p=%f)" %
(i, sentence, math.exp(caption.logprob)))
if i == 0:
df.loc[idx, "caption"] = sentence
df.to_csv("df_ph2.csv")
def main(_):
# Build the inference graph
g = tf.Graph()
with g.as_default():
model = inference_wrapper.InferenceWrapper()
restore_fn = model.build_graph_from_config(ModelConfig(),
FLAGS.checkpoint_path)
g.finalize()
filenames = list(
filter(lambda x: x.endswith('.jpg'),
os.listdir(FLAGS.input_files_dir)))
filenames = [
os.path.join(FLAGS.input_files_dir, filename) for filename in filenames
]
print("Running de-rain infer on %d files from directory: %s" %
(len(filenames), FLAGS.input_files_dir))
print(filenames)
index_word = model.index_word
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(graph=g, config=config) as sess:
# Load the model from checkpoint
restore_fn(sess)
# Prepare the caption generator. Here we are implicitly using the default parameters.
generator = CaptionGenerator(model)
if not os.path.exists("plot_attention"):
os.makedirs("plot_attention")
for i, filename in tqdm(enumerate(filenames)):
# with tf.gfile.GFile(filename, "rb") as f:
# image = f.read()
captions = generator.beam_search(
sess, filename) # return beam_size captions
print("Captions for image %s:" % os.path.basename(filename))
for j, caption in enumerate(captions):
# Ignore begin and end words.
sentence_list = [index_word[w] for w in caption.sentence[1:-1]]
sentence = " ".join(sentence_list)
print(" %d) %s (p=%f)" %
(j, sentence, math.exp(caption.logprob)))
if j == 1:
print(len(caption.attenplot))
plot_attention(filename, sentence_list, caption.attenplot)
def main(_):
start_time = time.time()
# Create the vocabulary.
vocab = vocabulary.Vocabulary(FLAGS.vocab_file)
# remove Thumbs.db from files
filenames = [
f for f in os.listdir(FLAGS.test_img_dir) if f.endswith('png')
]
print('There are totally {0} images.....'.format(len(filenames)))
checkpoint_file = FLAGS.checkpoint_path + 'model.ckpt-1783119'
submit_json_file = '{0}submit_{1}_inception.json'.format(
FLAGS.submit_json_dir,
checkpoint_file.split('/')[-1])
g = tf.Graph()
with g.as_default():
model = inference_wrapper.InferenceWrapper(cnn_model='InceptionV3')
restore_fn = model.build_graph_from_config(configuration.ModelConfig(),
checkpoint_file)
g.finalize()
with tf.Session(graph=g) as sess:
# Load the model from checkpoint.
restore_fn(sess)
# Prepare the caption generator. Here we are implicitly using the default
# beam search parameters. See caption_generator.py for a description of the
# available beam search parameters.
count, result = 0, []
generator = caption_generator.CaptionGenerator(model, vocab)
for filename in filenames:
count += 1
with open(FLAGS.test_img_dir + filename, "rb") as f:
image = f.read()
captions = generator.beam_search(sess, image)
sentence = [
vocab.id_to_word(w) for w in captions[0].sentence[1:-1]
]
sentence = ''.join(sentence)
image_id = filename.split('.')[0]
result.append({'caption': sentence, 'image_id': image_id})
if count % 500 == 0:
print('finish generating caption for {0} images'.format(count))
print('finish totally {0} images'.format(count))
with open(submit_json_file, encoding='utf8', mode='w') as f:
json.dump(result, f, ensure_ascii=False)
print('time consuming: {0}s'.format(time.time() - start_time))
def main(_):
# Build the inference graph.
g = tf.Graph()
with g.as_default():
infer = inference_wrapper.InferenceWrapper()
restore_fn = infer.build_graph_from_config(configuration.ModelConfig(),
FLAGS.checkpoint_path)
g.finalize()
filenames = []
for file_pattern in FLAGS.input_files.split(","):
filenames.extend(tf.gfile.Glob(file_pattern))
tf.logging.info("Running caption generation on %d files matching %s",
len(filenames), FLAGS.input_files)
with tf.Session(graph=g) as sess:
# Load the model from checkpoint.
restore_fn(sess)
# Initialize the vocabulary lookup table
infer.model.vocab_table.init.run(session=sess)
filenames.sort()
# Predict
for filename in filenames:
with tf.gfile.GFile(filename, "r") as f:
# Predict transcription
tic = time.time()
image = f.read()
pred_chars = infer.inference_step(sess, image)[0][0]
pred_word = "".join([item for item in pred_chars])
auto_correct_word = spell(pred_word)
toc = time.time()
# Print out the result
print("Prediction for image %s in %.3f ms" %
(os.path.basename(filename), (toc - tic) * 1000))
print("predicted word: %s" % pred_word)
print("auto correct word: %s" % auto_correct_word)
print("*" * 50)
# Show image
cv_img = cv2.imread(filename)
cv2.imshow('image', cv_img)
k = cv2.waitKey(0)
if k == ord('n'):
cv2.destroyAllWindows()
def mainfunction(currentimagename):
# Change tensor name
#rename_model_ckpt()
# Build the inference graph.
g = tf.Graph()
with g.as_default():
model = inference_wrapper.InferenceWrapper()
restore_fn = model.build_graph_from_config(configuration.ModelConfig(),
checkpoint_path)
g.finalize()
# Create the vocabulary.
vocab = vocabulary.Vocabulary(vocab_file)
imageName = input_files + currentimagename
filenames = []
for file_pattern in imageName.split(","):
filenames.extend(tf.gfile.Glob(file_pattern))
tf.logging.info("Running caption generation on %d files matching %s",
len(filenames), imageName)
with tf.Session(graph=g) as sess:
# Load the model from checkpoint.
restore_fn(sess)
# Prepare the caption generator. Here we are implicitly using the default
# beam search parameters. See caption_generator.py for a description of the
# available beam search parameters.
generator = caption_generator.CaptionGenerator(model, vocab)
for filename in filenames:
with tf.gfile.GFile(filename, "rb") as f:
image = f.read()
captions = generator.beam_search(sess, image)
print("Captions for image %s:" % os.path.basename(filename))
for i, caption in enumerate(captions):
# Ignore begin and end words.
sentence = [vocab.id_to_word(w) for w in caption.sentence[1:-1]]
sentence = " ".join(sentence)
translator = Translator()
result = translator.translate(sentence, src='en', dest='ja')
#print(" %d) %s (p=%f)" % (i, result, math.exp(caption.logprob)))
if i==0:
displayCaption = result.text
print(displayCaption)
return displayCaption
return displayCaption
def main(_):
#####
#model_config.input_file_pattern = FLAGS.input_file_pattern
#####
# Build the inference graph.
g = tf.Graph()
with g.as_default():
model = inference_wrapper.InferenceWrapper()
restore_fn = model.build_graph_from_config(configuration.ModelConfig(),
FLAGS.checkpoint_path)
g.finalize()
# Create the vocabulary.
vocab = vocabulary.Vocabulary(FLAGS.vocab_file)
#40.000 100
filenames = []
for file_pattern in FLAGS.input_files.split(","):
filenames.extend(tf.gfile.Glob(file_pattern))
tf.logging.info("Running caption generation on %d files matching %s",
len(filenames), FLAGS.input_files)
config = tf.ConfigProto()
config.gpu_options.visible_device_list = "0"
with tf.Session(config = config,graph=g) as sess:
# Load the model from checkpoint.
restore_fn(sess)
# Prepare the caption generator. Here we are implicitly using the default
# beam search parameters. See caption_generator.py for a description of the
# available beam search parameters.
generator = caption_generator.CaptionGenerator(model, vocab)
for filename in filenames:
with tf.gfile.GFile(filename, "rb") as f:
image = f.read()
captions = generator.beam_search(sess, image)
print("Captions for image %s:" % os.path.basename(filename))
for i, caption in enumerate(captions):
# Ignore begin and end words.
sentence = [vocab.id_to_word(w) for w in caption.sentence[1:-1]]
sentence = " ".join(sentence)
print(" %d) %s (p=%f)" % (i, sentence, math.exp(caption.logprob)))
def load_model_im2txt(self, checkpoint_path, vocab_file):
global g
global model
global restore_fn
global vocab
g = tf.Graph()
with g.as_default():
model = inference_wrapper.InferenceWrapper()
restore_fn = model.build_graph_from_config(configuration.ModelConfig(),
checkpoint_path)
g.finalize()
# Create the vocabulary.
vocab = vocabulary.Vocabulary(vocab_file)
print('Model loaded.')
def main(_):
# Build the inference graph.
g = tf.Graph()
with g.as_default():
model = inference_wrapper.InferenceWrapper()
restore_fn = model.build_graph_from_config(configuration.ModelConfig(),
FLAGS.checkpoint_path)
#g.finalize()
# Create the vocabulary.
vocab = vocabulary.Vocabulary(FLAGS.vocab_file)
with tf.Session(graph=g) as sess:
# Load the model from checkpoint.
restore_fn(sess)
# Prepare the caption generator. Here we are implicitly using the default
# beam search parameters. See caption_generator.py for a description of the
# available beam search parameters.
generator = caption_generator.CaptionGenerator(model, vocab)
font = cv2.FONT_HERSHEY_SIMPLEX
cap = cv2.VideoCapture(0)
while (True):
ret, frame = cap.read()
#cap.set(cv2.CAP_PROP_FPS,1)
#cap.set(3,1000)
#cap.set(4,1000)
image = tf.image.encode_jpeg(frame)
image = sess.run(image)
captions = generator.beam_search(sess, image)
caption = captions[0]
#for i, caption in enumerate(captions):
# Ignore begin and end words.
sentence = [vocab.id_to_word(w) for w in caption.sentence[1:-1]]
sentence = " ".join(sentence)
print(" %s (p=%f)" % (sentence, math.exp(caption.logprob)))
cv2.putText(frame, sentence, (0, 20), font, 0.6, (255, 255, 255),
2)
cv2.imshow('frame', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
def analyze(FLAGS):
os.environ['CUDA_VISIBLE_DEVICES'] = ''
# Build the inference graph.
g = tf.Graph()
with g.as_default():
model = inference_wrapper.InferenceWrapper()
restore_fn = model.build_graph_from_config(configuration.ModelConfig(),
FLAGS["checkpoint_path"])
g.finalize()
# Create the vocabulary.
vocab = vocabulary.Vocabulary(FLAGS["vocab_file"])
filenames = []
for file_pattern in FLAGS["input_files"].split(","):
filenames.extend(tf.gfile.Glob(file_pattern))
tf.logging.info("Running caption generation on %d files matching %s",
len(filenames), FLAGS["input_files"])
with tf.Session(graph=g) as sess:
# Load the model from checkpoint.
restore_fn(sess)
# Prepare the caption generator. Here we are implicitly using the default
# beam search parameters. See caption_generator.py for a description of the
# available beam search parameters.
generator = caption_generator.CaptionGenerator(model, vocab)
caption_texts = []
for filename in filenames:
with tf.gfile.GFile(filename, "r") as f:
image = f.read()
captions = generator.beam_search(sess, image)
print("Captions for image %s:" % os.path.basename(filename))
for i, caption in enumerate(captions):
# Ignore begin and end words.
sentence = [vocab.id_to_word(w) for w in caption.sentence[1:-1]]
sentence = " ".join(sentence)
print(" %d) %s (p=%f)" % (i, sentence, math.exp(caption.logprob)))
text = "%s" % sentence
caption_texts += [text]
return caption_texts
def img_captions(file_inputs):
# Build the inference graph.
g = tf.Graph()
with g.as_default():
model = inference_wrapper.InferenceWrapper()
restore_fn = model.build_graph_from_config(configuration.ModelConfig(),
file_inputs[0])
g.finalize()
# Create the vocabulary.
vocab = vocabulary.Vocabulary(file_inputs[1])
filenames = []
for file_pattern in file_inputs[2].split(","):
filenames.extend(tf.gfile.Glob(file_pattern))
tf.logging.info("Running caption generation on %d files matching %s",
len(filenames), file_inputs[2])
with tf.Session(graph=g) as sess:
# Load the model from checkpoint.
restore_fn(sess)
# Prepare the caption generator. Here we are implicitly using the default
# beam search parameters. See caption_generator.py for a description of the
# available beam search parameters.
generator = caption_generator.CaptionGenerator(model, vocab)
caption_list = list()
prob_list = list()
for filename in filenames:
with tf.gfile.GFile(filename, "rb") as f:
image = f.read()
captions, probs = generator.beam_search(sess, image)
prob_list.append('['+", ".join(map(str, probs))+']')
loc_cap_list = list()
for i, caption in enumerate(captions):
# Ignore begin and end words.
sentence = [vocab.id_to_word(w) for w in caption.sentence[1:-1]]
sentence = " ".join(sentence).split('<S>')[0]
loc_cap_list.append([sentence, math.exp(caption.logprob)])
caption_list.append(loc_cap_list)
return prob_list, caption_list
def main(_):
# Build the inference graph.
g = tf.Graph()
with g.as_default():
model = inference_wrapper.InferenceWrapper()
restore_fn = model.build_graph_from_config(configuration.ModelConfig(),FLAGS.checkpoint_path)
vocab = vocabulary.Vocabulary(FLAGS.vocab_file)
filenames = []
data_dir= FLAGS.input_files
tf_flie_pattern = os.path.join(data_dir, '*.jpg' )
for file_pattern in tf_flie_pattern.split(","):
filenames.extend(tf.gfile.Glob(file_pattern))
tf.logging.info("Running caption generation on %d files matching %s",
len(filenames), FLAGS.input_files)
with tf.Session(graph=g) as sess:
restore_fn(sess)
generator = caption_generator.CaptionGenerator(model, vocab)
font=cv2.FONT_HERSHEY_SIMPLEX
for filename in filenames:
with tf.gfile.GFile(filename, "rb") as f:
image = f.read()
frame = tf.image.decode_jpeg(image)
frame = sess.run(frame)
print(type(frame))
captions = generator.beam_search(sess, image)
print("Captions for image %s:" % os.path.basename(filename))
for i, caption in enumerate(captions):
sentence = [vocab.id_to_word(w) for w in caption.sentence[1:-1]]
sentence = " ".join(sentence)
print("%d) %s (p=%f)" % (i, sentence, math.exp(caption.logprob)))
if i==0:
s=sentence
plt.axis('off')
plt.title(s)
plt.imshow(frame)
plt.show()
def mess(filename):
# Build the inference graph.
g = tf.Graph()
with g.as_default():
model = inference_wrapper.InferenceWrapper()
restore_fn = model.build_graph_from_config(configuration.ModelConfig(),
"model.ckpt-2000000")
g.finalize()
# Create the vocabulary.
vocab = vocabulary.Vocabulary("word_counts.txt")
filenames = [filename]
with tf.Session(graph=g) as sess:
# Load the model from checkpoint.
restore_fn(sess)
# Prepare the caption generator. Here we are implicitly using the default
# beam search parameters. See caption_generator.py for a description of the
# available beam search parameters.
generator = caption_generator.CaptionGenerator(model, vocab)
for filename in filenames:
with tf.gfile.GFile(filename, "rb") as f:
image = f.read()
captions = generator.beam_search(sess, image)
print("Captions for image %s:" % os.path.basename(filename))
allsen = ""
for i, caption in enumerate(captions):
# Ignore begin and end words.
sentence = [
vocab.id_to_word(w) for w in caption.sentence[1:-1]
]
sentence = " ".join(sentence)
print(" %d) %s (p=%f)" %
(i, sentence, math.exp(caption.logprob)))
allsen += sentence + "|"
return allsen
def initGraph():
tf.logging.set_verbosity(tf.logging.INFO)
displayconfig = configuration.DisplayConfig()
checkpoint_path = displayconfig.checkpoint_path
vocab_file = displayconfig.vocab_file
g = tf.Graph()
with g.as_default():
model = inference_wrapper.InferenceWrapper()
restore_fn = model.build_graph_from_config(configuration.ModelConfig(),
checkpoint_path)
g.finalize()
# Create the vocabulary.
v = vocabulary.Vocabulary(vocab_file)
s = tf.Session(graph=g)
# Load the model from checkpoint.
restore_fn(s)
# Prepare the caption generator. Here we are implicitly using the default
# beam search parameters. See caption_generator.py for a description of the
# available beam search parameters.
return s, v, caption_generator.CaptionGenerator(model, v)
def main(_):
# Build the inference graph.
g = tf.Graph()
with g.as_default():
model = inference_wrapper.InferenceWrapper()
restore_fn = model.build_graph_from_config(configuration.ModelConfig(),
FLAGS.checkpoint_path)
g.finalize()
# Create the vocabulary.
vocab = vocabulary.Vocabulary(FLAGS.vocab_file)
filenames = []
#for file_pattern in FLAGS.input_files.split(","):
# filenames.extend(tf.gfile.Glob(file_pattern))
tf.logging.info("Running caption generation on %d files matching %s",
len(filenames), FLAGS.input_files)
config_sess = tf.ConfigProto()
config_sess.gpu_options.allow_growth = True
with tf.Session(graph=g, config=config_sess) as sess:
# Load the model from checkpoint.
restore_fn(sess)
test_path = r'C:\Users\PSIML-1.PSIML-1\Desktop\projekti\Image-Captioning\test_gradient'
for filename in filenames:
full_fname = os.path.join(test_path, filename)
with tf.gfile.GFile(full_fname, "rb") as f:
image = f.read()
initial_state = model.feed_image(sess, image)
for i in range(20):
softmax, new_states, metadata = model.inference_step(
sess, input_feed, state_feed)
