def predict(
model: tf.keras.Model,
results_dir: str,
name: str,
) -> None:
time_measurement = tf.keras.metrics.Mean(name='time_measurement')
# time_measurement.reset_states()
experiment = choose_one_from_dir(results_dir)
model_dir = os.path.join(experiment, 'model')
model_path = choose_model(model_dir)
model.load_weights(model_path)
inputs = tf.keras.Input(shape=(512, 512, 3), dtype=tf.float32)
model._set_inputs(inputs)
loader = DataLoader(data_path='/media/david/A/Datasets/PlayHippo/images')
for x in loader.yield_annotations_from_path(
loader.get_data_as_list(shuffle=True)):
path = x['path']
cls = x['cls']
im_name = x['im_name']
input_device = x['input_device']
original_image = cv2.imread(path)
original_image = cv2.resize(original_image, (512, 512))
image = np.expand_dims(cv2.cvtColor(original_image, cv2.COLOR_RGB2BGR),
axis=0) / 255
start = time.time()
image_tf = tf.convert_to_tensor(image)
prediction_tf = model(image_tf)
end = time.time()
time_measurement(end - start)
print(end - start)
prediction_ = prediction_tf.numpy()[0]
prediction_rgb = cv2.cvtColor(prediction_, cv2.COLOR_GRAY2RGB)
merge_images = np.hstack(
(original_image, (prediction_rgb * 255).astype(np.uint8)))
show(merge_images)
def eval(
model: tf.keras.Model,
loader: RecordReader,
results_dir: str,
save_images: bool,
) -> None:
experiment = choose_one_from_dir(results_dir)
eval_train_dir = os.path.join(experiment, 'eval_train')
eval_test_dir = os.path.join(experiment, 'eval_test')
mkdir(eval_train_dir)
mkdir(eval_test_dir)
model_dir = os.path.join(experiment, 'model')
model_path = choose_model(model_dir)
model.load_weights(model_path)
inputs = tf.keras.Input(shape=(512, 512, 3), dtype=tf.float32)
model._set_inputs(inputs)
model.save(os.path.join(experiment, 'test_model'))
loader._count = 1
loader._batch_size = 1
def process_set(
source: str,
save_dir: str,
save_images: bool = True,
max_save_images: int = 200,
max_processed_images: int = 600,
):
assert source in ['train', 'test']
print('Evaluating {}...'.format(source))
conf_matrix = np.zeros((len(classes_encode), len(classes_encode)))
br = 0
wrong_images_count = 0
for name, cls, cls_name, image in loader.read_record(source):
br += 1
if br > max_processed_images:
break
print(' [ {} ]'.format(br), end='\r')
prediction = model(image, training=False)
prediction = softmax(prediction.numpy())
pred_cls = np.argmax(prediction)
label_cls = np.argmax(cls.numpy())
conf_matrix[pred_cls][label_cls] += 1
if pred_cls != label_cls and save_images:
wrong_images_count += 1
if wrong_images_count < max_save_images:
save_figure(image=image.numpy()[0],
gt=cls.numpy()[0],
pred=prediction[0],
name=name.numpy()[0].decode('utf8'),
destination=os.path.join(
save_dir,
name.numpy()[0].decode('utf8') + '.png'))
print('{} conf_matrix\n{}'.format(source, conf_matrix))
F1_list = []
for x in range(len(classes_decode)):
P = conf_matrix[x][x] / np.sum(conf_matrix, axis=1)[x]
R = conf_matrix[x][x] / np.sum(conf_matrix, axis=0)[x]
F1 = 2 * P * R / (P + R)
F1_list.append(F1)
print('{} Precision {} Recall {} F1 {}'.format(
classes_decode[x],
P,
R,
F1,
))
print('Macro F1 {}'.format(sum(F1_list) / len(F1_list)))
TP = np.sum([conf_matrix[x][x] for x in range(len(classes_decode))])
print('Accuracy {}'.format(TP / np.sum(conf_matrix)))
process_set('test', eval_test_dir, save_images)
process_set('train', eval_train_dir, save_images)
