def main_coco():
#val_path = "data/Definitive/Hexagon/val"
#val_path = "data/Definitive/Cube/val"
#val_path = "data/Definitive/Octahedron/val"
#val_path = "data/Definitive/Needle/val"
val_path = "data/Random/Cube/val"
#weights = "logs/Final/Hexagon/orientations_2900.h5"
#weights = "logs/Final/Cube/orientations_2900.h5"
#weights = "logs/Final/Needle/orientations_3000.h5"
weights = "logs/Random/Cube/orientations_1200.h5"
#weights = "logs/Final/Octahedron/orientations_2900.h5"
evaluation_dir = "evaluation"
config = Config()
dataset_val = FiguresDataset()
dataset_val.load_figures(val_path, "val_annotations.json")
dataset_val.prepare()
val_images, val_orientations, _ = load_figures_data(dataset_val, config, mask=False)
# Loading model and weights
or_model = model.OrientationModel("logs", config)
or_model.compile(weights)
# Inference
predictions = detect.detect(or_model, val_images)
gt_orientations = R.from_euler('ZYX', val_orientations, degrees=True).as_matrix()
utils.evaluate(gt_orientations, predictions, dataset_val, evaluation_dir)
coco_data.save_pred_annotations(predictions, dataset_val, val_path, evaluation_dir)
visualize.show_results(val_images, predictions, evaluation_dir)
def on_data(self, raw_data):
'''
:param raw_data: JSON format text corresponding to a tweet object
:return: (bool) True. Prints tweet id and tweet text if the tweet is found offensive,
"not offensive" if not.
'''
#the following condition may only occur if stop_stream has been executed
if s.stream_tab.switch_stream["text"] == "":
s.stream_tab.switch_stream.configure(text="Start stream")
streamer.disconnect()
try:
data = json.loads(raw_data)
print(data["text"])
insulting, words = detect(data["text"])
if insulting: # A word in the tweet was found in dict
s.stream_tab.tweets.config(state='normal')
output = "Offensive words: \"{}\" found in: \n[{}] {}".format(', '.join(words),
data["user"]["screen_name"], strip_smileys(data["text"])) + "\n" \
+ "https://twitter.com/{}/status/{}".format(data["user"]["screen_name"], data["id"]) \
+ "\n_____________________________________________________\n"
s.stream_tab.tweets.insert(index='end', chars=output)
s.stream_tab.tweets.config(state='disabled')
return True
except KeyError as error:
print("oops! Key error encoutered in on_data: {}".format(error))
return True
finally:
#Controlling the rate of streaming.
sleep(1)
return False
def main_or():
IMAGES_DIR = "data/Cube_2.0/Images"
FILE_PATH = "data/Cube_2.0/cube_quat_angles.csv"
config = Config()
weights = "logs/orientations_2300.h5"
train_images, train_angles, val_images, val_angles = \
orientation_data.load_dataset(FILE_PATH, IMAGES_DIR, (128, 128), train_per=0.5)
# Loading model and weights
or_model = model.OrientationModel("logs", config)
if weights is not None:
or_model.compile(weights)
# Inference
predictions = detect.detect(or_model, val_images)
visualize.show_results(val_images, predictions)
def main_coco():
#dataset_path = "data/Detections/Cube_0.9"
dataset_path = "data/Detections/Final/Cube"
#dataset_path = "data/Detections/Final/Octahedron"
#dataset_path = "data/Detections/Final/Needle"
#dataset_path = "data/Detections/Final/Hexagon_lat"
#weights = "logs/Final/Cube/orientations_2900.h5"
#weights = "logs/Final/Cube/orientations_2900.h5"
#weights = "logs/Final/Needle/orientations_3000.h5"
#weights = "logs/Final/Octahedron/orientations_2900.h5"
weights = "logs/Random/Cube/orientations_1200.h5"
annotations = "prediction_annotations_90.json"
pred_dir = "predictions/Final"
config = Config()
dataset = FiguresDataset()
dataset.load_figures(dataset_path, annotations)
dataset.prepare()
images, orientations, masks = load_figures_data(dataset, config, mask=True)
#images = utils.apply_mask(images, masks, extra=3)
# Loading model and weights
or_model = model.OrientationModel("logs", config)
or_model.compile(weights)
# Inference
start = time.time()
predictions = detect.detect(or_model, images)
end = time.time()
print(end - start)
coco_data.save_pred_annotations(predictions, dataset, dataset_path, pred_dir)
visualize.show_results(images, predictions, pred_dir)
def detect_test(self):
text = "F**k you, c**t!"
assert detect(text)[0] == True
text = "No way he gets back from this."
assert detect(text)[0] == False