def index():
amongus_str, res = "", ""
if request.method == "POST" and "amongus_str" in request.form:
amongus_str = request.form.get("amongus_str")
if len(amongus_str) > 10000:
res["error"] = "input string is too long"
else:
res = infer(amongus_str)
return render_template("inference.html", amongus_str=amongus_str, res=res)
def upload_image():
if request.method == "POST":
if request.files:
image = request.files["image"]
image.save(os.path.join(app.config["IMAGE_UPLOADS"], 'test.png'))
decoderType = DecoderType.BeamSearch
model = Model(open('../data/charList.txt').read(),
decoderType,
mustRestore=True)
text = infer(model, '../data/test.png')
return make_response(jsonify(text), 200)
def recognize_button(): # submit photo for handwriting to be recognized in
rec_word = infer(model, filename)
rec_label.configure(text="Recognized text is: " + rec_word,
font=("Times", 32))
if len(rec_word) > 10:
rec_label.place_configure(x=60, y=325)
elif len(rec_word) < 5:
rec_label.place_configure(x=135, y=325)
elif len(rec_word) < 5:
rec_label.place_configure(x=115, y=325)
else:
rec_label.place_configure(x=90, y=325)
afterRecognitionImageCanvas.place_configure(x=75, y=20)
recognizeButton.place_forget()
tryAgainRecognize.place_configure(x=300, y=400)
recognitionImageCanvas.place_forget()
# -*- coding: utf-8 -*-
"""
Created on Mon Feb 3 01:05:28 2020
@author: Tanmay Thakur
"""
import cv2
import os, sys
from DataLoader import DataLoader, Batch
from Model import Model, DecoderType
from SamplePreprocessor import preprocess
from autocorrect import Speller
from main import infer, FilePaths
spell = Speller(lang="en")
decoderType = DecoderType.BestPath
loader = DataLoader(FilePaths.fnTrain, Model.batchSize, Model.imgSize,
Model.maxTextLen)
model = Model(loader.charList, decoderType)
imgFiles = os.listdir('data_/')
for i in imgFiles:
images = os.listdir('out/%s' % i)
for j in images:
if (j != "summary.png"):
infer(model, "out/" + i + "/" + j)
help='file name')
args = parser.parse_args()
print(args)
checkpoint = torch.load(os.path.join(args.dir, args.name))
model, final, words, word2int, emb = checkpoint['model'], checkpoint[
'final'], checkpoint['words'], checkpoint['word2int'], checkpoint[
'emb']
print('Finish Loading')
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
final.to(device)
start_words, start_freq = calc_word_freq('./data/poems.txt')
while True:
start = input()
try:
if len(start) == 0:
start = start_words[prob_sample(start_freq)]
poems = infer(model,
final,
words,
word2int,
emb,
hidden_size=model.hidden_size,
start=start,
n=20,
num=5 if random.random() < 0.5 else 7)
print(evaluate(poems))
except KeyError:
print(u'此字在语料库中未出现过，请更换首字')