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74 rader
2.3 KiB
74 rader
2.3 KiB
from scipy.misc import imread,imshow
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import os
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from PIL import Image
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import PIL
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from model.img2seq import Img2SeqModel
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from model.utils.general import Config, run
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from model.utils.text import Vocab
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from model.utils.image import greyscale, crop_image, pad_image,predictsize ,\
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downsample_image, TIMEOUT
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def interactive_shell(model):
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"""Creates interactive shell to play with model
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"""
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model.logger.info("""
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This is an interactive mode.
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To exit, enter 'exit'.
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Enter a path to a file
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input> data/images_test/0.png""")
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while True:
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img_path = input("input> ")
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if img_path == "exit":
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break
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if img_path[-3:] == "png":
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img = imread(img_path)
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elif img_path[-3:] == "pdf":
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# call magick to convert the pdf into a png file
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buckets = [
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[240, 100], [320, 80], [400, 80], [400, 100], [480, 80], [480, 100],
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[560, 80], [560, 100], [640, 80], [640, 100], [720, 80], [720, 100],
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[720, 120], [720, 200], [800, 100], [800, 320], [1000, 200],
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[1000, 400], [1200, 200], [1600, 200], [1600, 1600]
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]
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dir_output = "tmp/"
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name = img_path.split('/')[-1].split('.')[0]
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run("magick convert -density {} -quality {} {} {}".format(200, 100,
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img_path, dir_output+"{}.png".format(name)), TIMEOUT)
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img_path = dir_output + "{}.png".format(name)
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crop_image(img_path, img_path)
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pad_image(img_path, img_path, buckets=buckets)
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downsample_image(img_path, img_path, 2)
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img = imread(img_path)
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img = predictsize(img)
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im_converted = PIL.Image.fromarray(img)
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im_converted.show()
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img = greyscale(img)
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hyps = model.predict(img)
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with open("norm_formula_val.txt", "w") as f:
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f.write(hyps[0])
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model.logger.info(hyps[0])
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if __name__ == "__main__":
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# restore config and model
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dir_output = "results/full/"
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config_vocab = Config(dir_output + "vocab.json")
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config_model = Config(dir_output + "model.json")
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vocab = Vocab(config_vocab)
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model = Img2SeqModel(config_model, dir_output, vocab)
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model.build_pred()
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model.restore_session(dir_output + "model.weights4/test-model.ckpt")
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interactive_shell(model)
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