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)