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_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}'
)
