def apply_model_to_image_raw_bytes(raw):
img = utils.decode_image_from_buf(raw)
fig = plt.figure(figsize=(7, 7))
plt.grid('off')
plt.axis('off')
plt.imshow(img)
img = utils.crop_and_preprocess(img, (IMG_SIZE, IMG_SIZE), final_model.preprocess_for_model)
print(' '.join(generate_caption(img)[1:-1]))
plt.show()
def apply_model_to_image_raw_bytes(raw, fname=None, do_save=False):
img = utils.decode_image_from_buf(raw)
fig = plt.figure(figsize=(7, 7))
plt.grid('off')
plt.axis('off')
plt.imshow(img)
img = utils.crop_and_preprocess(img, (IMG_SIZE, IMG_SIZE),
final_model.preprocess_for_model)
plt.title(' '.join(generate_caption(img)[1:-1]))
if do_save:
plt.savefig(fname)
plt.show()
def apply_model_to_image_raw_bytes(raw):
img = utils.decode_image_from_buf(raw)
plt.figure(figsize=(7, 7))
plt.grid('off')
plt.axis('off')
plt.imshow(img)
img = utils.crop_and_preprocess(img, (IMG_SIZE, IMG_SIZE),
generator_model.process_for_model)
img = np.expand_dims(img, axis=0)
title = generate_caption(img)[1:-1]
plt.title(' '.join(title))
plt.show()
def show_trainig_example(train_img_fns, train_captions, example_idx=0):
"""
You can change example_idx and see different images
"""
zf = zipfile.ZipFile("train2014_sample.zip")
captions_by_file = dict(zip(train_img_fns, train_captions))
all_files = set(train_img_fns)
found_files = list(filter(lambda x: x.filename.rsplit("/")[-1] in all_files, zf.filelist))
example = found_files[example_idx]
img = utils.decode_image_from_buf(zf.read(example))
plt.imshow(utils.image_center_crop(img))
plt.title("\n".join(captions_by_file[example.filename.rsplit("/")[-1]]))
plt.show()
def show_training_example(train_img_fns, train_captions, example_idx=0):
"""
showing images with their image captions
"""
zf = zipfile.ZipFile("train2014_sample.zip")
captions_by_file = dict(zip(train_img_fns, train_captions))
all_files = set(train_img_fns)
found_files = list(
filter(lambda x: x.filename.rsplit("/")[-1] in all_files,
zf.filelist)) # the last word i.e. file name
example = found_files[example_idx]
img = utils.decode_image_from_buf(zf.read(
example)) # example is ZipInfo necessary for zf.read() function
plt.imshow(utils.image_center_crop(img))
plt.title("\n".join(captions_by_file[example.filename.rsplit("/")[-1]]))
plt.show()
def show_valid_example_series(val_img_fns, index_array):
all_files = set(val_img_fns)
found_files = list(
filter(lambda x: x.filename.rsplit("/")[-1] in all_files, zf.filelist))
#plt.figure()
index = 1
for row in range(index_array.shape[0]):
for col in range(index_array.shape[1]):
fn = found_files[index_array[row, col]]
img = utils.decode_image_from_buf(zf.read(fn))
subplot = plt.subplot(index_array.shape[0], index_array.shape[1],
index)
subplot.axis('off')
subplot.grid('off')
plt.imshow(img)
img = utils.crop_and_preprocess(img, (IMG_SIZE, IMG_SIZE),
generator_model.process_for_model)
img_s = np.expand_dims(img, axis=0)
title = generate_caption(img_s, sample=False)[1:-1]
subplot.set_title(" ".join(title))
index += 1
plt.show()
def apply_model_to_image_raw_bytes(raw):
img = utils.decode_image_from_buf(raw)
img = utils.crop_and_preprocess(img, (IMG_SIZE, IMG_SIZE),
final_model.preprocess_for_model)
print(' '.join(generate_caption(img)[1:-1]))
def apply_model_to_image_raw_bytes_return_next_word_probs(raw, par_caption):
img = utils.decode_image_from_buf(raw)
img = utils.crop_and_preprocess(img, (IMG_SIZE, IMG_SIZE),
final_model.preprocess_for_model)
return generate_next_word_probs(img, par_caption)
