def main():
common.create_directories()
print(" Q = ignore image")
print(" 1 = label as round")
print(" 2 = label as wide")
print(" 3 = label as narrow")
print("ARROW KEYS = label directions\n")
global type_label
global direction_label
global plt_text
unlabeled_imgs = common.get_files(common.SCREENSHOTS_DIR)
num_labeled = 0
for path, filename in unlabeled_imgs:
print("Processing {}...".format(filename))
img = plt.imread(path)
ax = plt.gca()
fig = plt.gcf()
plot = ax.imshow(img)
plt.axis('off')
plt.tight_layout()
plt_text = plt.text(0, 0, "")
fig.canvas.mpl_connect('key_press_event', on_press)
mng = plt.get_current_fig_manager()
mng.window.state('zoomed')
plt.show()
if type_label and direction_label:
dst_filename = "{}_{}_{}.png".format(
type_dictionary[type_label], direction_label, time.strftime("%Y%m%d-%H%M%S"))
os.rename(path, common.LABELED_DIR + dst_filename)
direction_label = ''
type_label = None
num_labeled += 1
if len(unlabeled_imgs) > 0:
print("\nLabeled {} out of {} images ({}%).".format(
num_labeled, len(unlabeled_imgs), 100 * num_labeled // len(unlabeled_imgs)))
print("Finished!")
else:
print("\nThere are no images to label.")
def main(training_set_ratio):
common.create_directories()
arrows = pd.DataFrame(np.zeros((3, 4), dtype=np.int32),
index=('round', 'wide', 'narrow'),
columns=('down', 'left', 'right', 'up'))
images = [(p, f) for p, f in common.get_files(common.SAMPLES_DIR)
if f[-5] != 'F']
if images:
for _, filename in images:
arrow_direction, arrow_type = common.arrow_labels(filename)
arrows[arrow_direction][arrow_type] += 1
num_samples = int(arrows.min().min() * training_set_ratio)
print("Samples per type: {}".format(num_samples * 4))
for t, _ in arrows.iterrows():
print("\nProcessing {} arrows...".format(t))
for direction in arrows:
candidates = [(p, f) for p, f in images
if common.arrow_labels(f) == (direction, t)]
print("{}: {}".format(direction, len(candidates)))
training = random.sample(candidates, num_samples)
for path, filename in training:
dst_dir = common.TRAINING_DIR + direction + '/'
os.rename(path, dst_dir + filename)
os.rename(flipped(path), dst_dir + flipped(filename))
candidates = [c for c in candidates if c not in training]
validation = random.sample(
candidates, int(len(candidates) * VALIDATION_SET_RATIO))
for path, filename in validation:
dst_dir = common.VALIDATION_DIR + direction + '/'
os.rename(path, dst_dir + filename)
os.rename(flipped(path), dst_dir + flipped(filename))
testing = [c for c in candidates if c not in validation]
for path, filename in testing:
dst_dir = common.TESTING_DIR + direction + '/'
os.rename(path, dst_dir + filename)
os.rename(flipped(path), dst_dir + flipped(filename))
show_summary()
print("\nFinished!")
def main():
common.create_directories()
print("Reverting images from the training directory...")
revert_files(common.TRAINING_DIR)
print("Reverting images from the validation directory...")
revert_files(common.VALIDATION_DIR)
print("Reverting images from the testing directory...")
revert_files(common.TESTING_DIR)
print("Finished!")
def main():
json_message = json.loads(get_message_from_stdin())
try:
# just testing connectivity
if json_message["native_messaging_host_accessible"]:
return
except Exception as e:
pass
output_archive_content = base64.b64decode(json_message["output_archive"])
output_archive_extension = json_message["output_archive_extension"]
board = json_message["board"]
application_name = json_message["application_name"]
try:
temporary_directory = os.path.join("tmp", application_name)
common.create_directories(temporary_directory)
archive_file_path = os.path.join(
temporary_directory,
application_name + "." + output_archive_extension)
with open(archive_file_path, "wb") as archive:
archive.write(output_archive_content)
dest_path = os.path.join(temporary_directory, application_name)
common.create_directories(dest_path)
tar = tarfile.open(archive_file_path, "r:gz")
for tarinfo in tar:
tar.extract(tarinfo, dest_path)
tar.close()
path_to_makefile = os.path.join(dest_path, "generated_by_riotam",
application_name)
#open standard terminal and execute shell script "flash"
Popen([
"x-terminal-emulator", "-e",
"./flash {0} {1}".format(board, path_to_makefile)
])
#rmtree(temporary_directory)
except Exception as e:
logging.error(str(e), exc_info=True)
def cache(self, path, board, name):
create_directories(self._cache_dir)
# only store if not in cache already
if self.get_entry(board, name) is None:
dest_in_cache = os.path.join(self._cache_dir, board, name)
copytree(path, dest_in_cache)
# show that cached application/module is now ready to use
ready_file_path = os.path.join(dest_in_cache, ".ready_to_use")
open(ready_file_path, "a").close()
def cache(self, src_path, board, app_dir_name, file_name):
create_directories(self._cache_dir)
# only store if not in cache already
if self.get_entry(board, app_dir_name, file_name) is None:
dest_in_cache = os.path.join(self._cache_dir, board, app_dir_name)
create_directories(dest_in_cache)
logging.debug("CACHING: %s" % file_name)
copyfile(src_path, os.path.join(dest_in_cache, file_name))
# show that cached application/module is now ready to use
ready_file_path = os.path.join(dest_in_cache, ".ready_to_use")
open(ready_file_path, "a").close()
def main(batch_size, model_name):
common.create_directories()
show_settings(batch_size)
model = make_model()
training, validation = make_generators(batch_size)
fit(model, training, validation, batch_size)
save(model, model_name)
print("\nFinished!")
print("Run " + cf.skyBlue("classify.py") +
" to test the model and get information about its performance.")
print("More information available with " + cf.orange("Tensorboard") + ".")
def main(argv):
parser = init_argparse()
try:
args = parser.parse_args(argv)
except Exception as e:
print(str(e))
return
home_dir = expanduser("~")
try:
target_dir = common.get_target_dir(home_dir, args.browser)
except common.BrowserNotSupportedException as e:
print(str(e))
return
# create directory to store native messaging host
common.create_directories(target_dir)
# copy native messaging host manifest
json_manifest_name = "%s.json" % common.HOST_NAME
copyfile(os.path.join(CUR_DIR, json_manifest_name),
os.path.join(target_dir, json_manifest_name))
# replace HOST_PATH placeholder in the manifest
host_path = "%s/riot_app_market.py" % CUR_DIR
replace_host_path(os.path.join(target_dir, json_manifest_name), host_path)
# set permissions for the manifest so that all users can read it
json_manifest = "{0}/{1}".format(target_dir, json_manifest_name)
st = os.stat(json_manifest)
os.chmod(json_manifest, st.st_mode | stat.S_IROTH)
print("Native messaging host {0} has been installed for {1}".format(
common.HOST_NAME, args.browser))
def main(src_subdir, verbose, model_name):
common.create_directories()
src_dir = common.DATA_DIR + src_subdir + "/"
model = tensorflow.keras.models.load_model(common.MODEL_DIR + model_name)
# real (index) x predicted (column)
confusion_matrix = pd.DataFrame(np.zeros((4, 4), dtype=np.int32),
index=('down', 'left', 'right', 'up'),
columns=('down', 'left', 'right', 'up'))
classification_matrix = pd.DataFrame(np.zeros((4, 3)),
index=('down', 'left', 'right', 'up'),
columns=('precision', 'recall', 'f1'))
type_matrix = pd.DataFrame(np.zeros((4, 2), dtype=np.int32),
index=('round', 'wide', 'narrow', 'total'),
columns=('correct', 'incorrect'))
images = common.get_files(src_dir)
print("Processing {} file(s) in {}/...\n".format(len(images), src_subdir))
for path, filename in images:
img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
data = np.reshape(img, (1, ) + common.INPUT_SHAPE)
prediction = model.predict(data)
class_index = np.argmax(prediction)
predicted_class = common.CLASSES[class_index]
real_class, arrow_type = common.arrow_labels(filename)
if verbose and real_class != predicted_class:
print(path)
print("Expected {} but got {}: {}\n".format(
cf.lightGreen(real_class), cf.lightCoral(predicted_class),
str(prediction[0])))
confusion_matrix[predicted_class][real_class] += 1
if real_class == predicted_class:
type_matrix['correct'][arrow_type] += 1
type_matrix['correct']['total'] += 1
else:
type_matrix['incorrect'][arrow_type] += 1
type_matrix['incorrect']['total'] += 1
print("\n" + cf.sandyBrown("Confusion matrix"))
print(confusion_matrix)
classification_matrix['precision'] = confusion_matrix.apply(precision)
classification_matrix['recall'] = confusion_matrix.apply(recall, axis=1)
classification_matrix['f1'] = classification_matrix.apply(f1, axis=1)
print("\n" + cf.skyBlue("Classification summary"))
print(classification_matrix)
type_matrix['accuracy'] = type_matrix.apply(type_accuracy, axis=1)
print("\n" + cf.plum("Accuracy by type"))
print(type_matrix)
print("\nFinished!")
def main(inspection, mode, automatic):
common.create_directories()
print(" SPACE = approve")
print("OTHER KEYS = skip")
print(" Q = quit\n")
labeled_imgs = common.get_files(common.LABELED_DIR)
approved = 0
for path, filename in labeled_imgs:
print("Processing " + cf.skyBlue(path))
arrows = []
display = cv2.imread(path)
height, width, _ = display.shape
# manually tuned values
search_x, search_y = width // 5 + 35, height // 4
search_width, search_height = SEARCH_REGION_WIDTH, height // 2 - search_y
for _ in range(4):
x0 = search_x
x1 = x0 + search_width
y0 = search_y
y1 = y0 + search_height
img = display[y0:y1, x0:x1]
(cx, cy), arrow_box = process_arrow(img, mode)
search_x += int(cx + ARROW_BOX_DIST - SEARCH_REGION_WIDTH / 2)
search_y += int(cy - SEARCH_REGION_HEIGHT / 2)
search_width = SEARCH_REGION_WIDTH
search_height = SEARCH_REGION_HEIGHT
arrows.append(arrow_box)
if not automatic:
arrow_type, directions, _ = re.split('_', filename)
reference = get_reference_arrows(directions, arrows[0].shape)
cv2.imshow(arrow_type, np.vstack([np.hstack(arrows), reference]))
key = cv2.waitKey()
cv2.destroyAllWindows()
else:
key = APPROVE_KEY
if key == APPROVE_KEY:
if not inspection:
save_arrow_imgs(arrows, filename)
approved += 1
elif key == EXIT_KEY:
break
else:
print("Skipped!")
if len(labeled_imgs) > 0:
print("\nApproved {} out of {} images ({}%).\n".format(
approved, len(labeled_imgs), 100 * approved // len(labeled_imgs)))
else:
print("There are no images to preprocess.\n")
show_summary()
print("Finished!")
