def test_filename(self):
predictor = LinePredictor(IamLinesDataset)
for filename in SUPPORT_DIRNAME.glob('*.png'):
pred, conf = predictor.predict(str(filename))
true = filename.stem
edit_distance = editdistance.eval(pred, true) / len(pred)
print(f'Pred: "{pred}" | Confidence: {conf} | True: {true} | Edit distance: {edit_distance}')
def test_filename(self):
predictor = LinePredictor()
for filename in (SUPPORT_DIRNAME / 'emnist_lines').glob('*.png'):
pred, conf = predictor.predict(str(filename))
true = str(filename.stem)
edit_distance = editdistance.eval(pred, true) / len(pred)
print(f'Pred: "{pred}" | Confidence: {conf} | True: {true} | Edit distance: {edit_distance}')
self.assertLess(edit_distance, 0.2)
def predict():
"""Provide main prediction API route. Responds to both GET and POST requests."""
K.clear_session()
predictor = LinePredictor()
image = _load_image()
pred, conf = predictor.predict(image)
print("METRIC confidence {}".format(conf))
print("METRIC mean_intensity {}".format(image.mean()))
print("INFO pred {}".format(pred))
return jsonify({"pred": str(pred), "conf": float(conf)})
def test_filename(self): # pylint: disable=R0201
predictor = LinePredictor(IamLinesDataset)
for filename in (SUPPORT_DIRNAME / 'iam_lines').glob('*.png'):
pred, conf = predictor.predict(str(filename))
true = filename.stem
if pred:
edit_distance = editdistance.eval(pred, true) / len(pred)
else:
edit_distance = 0
print(f'Pred: "{pred}" | Confidence: {conf} | True: {true} | Edit distance: {edit_distance}')
def test_filename(self):
"""Test that LinePredictor correctly predicts on single images, for several test images."""
predictor = LinePredictor()
for filename in (SUPPORT_DIRNAME / "emnist_lines").glob("*.png"):
pred, conf = predictor.predict(str(filename))
true = str(filename.stem)
edit_distance = editdistance.eval(pred, true) / len(pred)
print(
f'Pred: "{pred}" | Confidence: {conf} | True: {true} | Edit distance: {edit_distance}'
)
self.assertLess(edit_distance, 0.2)
def test_filename(self):
predictor = LinePredictor()
for filename in SUPPORT_DIRNAME.glob('*.png'):
image = util.read_image(str(filename), grayscale=True)
print('Saved image shape:', image.shape)
image = image[:, :-np.random.randint(1, 150)] # pylint: disable=invalid-unary-operand-type
print('Randomly cropped image shape:', image.shape)
pred, conf = predictor.predict(image)
true = str(filename.stem)
edit_distance = editdistance.eval(pred, true) / len(pred)
print(f'Pred: "{pred}" | Confidence: {conf} | True: {true} | Edit distance: {edit_distance}')
self.assertLess(edit_distance, 0.2)
def test_evaluate(self):
predictor = LinePredictor(IamLinesDataset)
dataset = IamLinesDataset()
dataset.load_or_generate_data()
t = time()
metric = predictor.evaluate(dataset)
time_taken = time() - t
print(f'acc: {metric}, time_taken: {time_taken}')
self.assertGreater(metric, 0.6)
self.assertLess(time_taken, 90)
def test_filename(self): # pylint: disable=R0201
"""Test that LinePredictor correctly predicts on single images, for several test images."""
predictor = LinePredictor(IamLinesDataset)
for filename in (SUPPORT_DIRNAME / "iam_lines").glob("*.png"):
pred, conf = predictor.predict(str(filename))
true = filename.stem
if pred:
edit_distance = editdistance.eval(pred, true) / len(pred)
else:
edit_distance = 0
print(
f'Pred: "{pred}" | Confidence: {conf} | True: {true} | Edit distance: {edit_distance}'
)
def load_model_to_app():
"""Instantiate tensorflow session and load model."""
# NOTE: following https://github.com/keras-team/keras/issues/2397#issuecomment-519128406
app.session = tf.Session(graph=tf.Graph())
with app.session.graph.as_default():
backend.set_session(app.session)
app.predictor = LinePredictor()
def test_evaluate(self):
# predictor = LinePredictor(IamLinesDataset)
# dataset = IamLinesDataset()
#instantiates the line predictor
predictor = LinePredictor(EmnistLinesDataset)
#load dataset
dataset = EmnistLinesDataset()
dataset.load_or_generate_data()
t = time()
#evaluate on the dataset
metric = predictor.evaluate(dataset)
time_taken = time() - t
print(f'acc: {metric}, time_taken: {time_taken}')
#threshold values for expected performance
self.assertGreater(metric, 0.8)
self.assertLess(time_taken, 60)
import tempfile
import flask
from flask import Flask, request, jsonify
import numpy as np
from tensorflow.keras import backend
from text_recognizer.line_predictor import LinePredictor
from text_recognizer.datasets import IamLinesDataset
import text_recognizer.util as util
app = Flask(__name__)
# Tensorflow bug: https://github.com/keras-team/keras/issues/2397
with backend.get_session().graph.as_default() as g:
predictor = LinePredictor()
# predictor = LinePredictor(dataset_cls=IamLinesDataset)
@app.route('/')
def index():
return 'Hello, world!'
@app.route('/v1/predict', methods=['GET', 'POST'])
def predict():
image = _load_image()
with backend.get_session().graph.as_default() as g:
pred, conf = predictor.predict(image)
# LOG SOME IMAGE STATISTIC
# LOG CONFIDENCE
import unzip_requirements # pylint: disable=unused-import
except ImportError:
pass
from flask import Flask, request, jsonify
from tensorflow.keras import backend
from text_recognizer.line_predictor import LinePredictor
# from text_recognizer.datasets import IamLinesDataset
import text_recognizer.util as util
app = Flask(__name__) # pylint: disable=invalid-name
# Tensorflow bug: https://github.com/keras-team/keras/issues/2397
with backend.get_session().graph.as_default() as _:
predictor = LinePredictor() # pylint: disable=invalid-name
# predictor = LinePredictor(dataset_cls=IamLinesDataset)
@app.route('/')
def index():
return 'Hello, world!'
@app.route('/v1/predict', methods=['GET', 'POST'])
def predict():
image = _load_image()
with backend.get_session().graph.as_default() as _:
pred, conf = predictor.predict(image)
print("METRIC confidence {}".format(conf))
print("METRIC mean_intensity {}".format(image.mean()))
def test_evaluate_accuracy(self, set_leaderboard_value=None):
predictor = LinePredictor()
dataset = EmnistLinesDataset()
dataset.load_or_generate_data()
metric = predictor.evaluate(dataset)
set_leaderboard_value(metric)
