def _load_batch_data(self, indices):
filenames = [
os.path.join(self.celeba_path, f'{ind+1:06}.jpg')
for ind in indices
]
images = [
image_utils.get_image(name,
input_height=178,
input_width=218,
resize_height=self.res,
resize_width=self.res,
is_crop=True) / 255. for name in filenames
]
images = np.asarray(images)
return images
def _load_data(self):
TRAIN_STOP = self.num_samples
self._TRAIN_STOP = TRAIN_STOP
print(TRAIN_STOP)
celebA = [
os.path.join(root, filename)
for root, dirnames, filenames in os.walk(self.celeba_path)
for filename in filenames if filename.endswith('.jpg')
]
celebA = celebA[:TRAIN_STOP]
images = [
image_utils.get_image(name,
input_height=512,
input_width=512,
resize_height=self.res,
resize_width=self.res,
is_crop=False) / 255. for name in celebA
]
print('finish reading face images')
images = np.asarray(images)
print(images.shape)
return images
debug_dir = output_folder / (
data_exporting.format_timestamp_for_file(files_timestamp) + "__debug")
if debug_mode_on:
data_exporting.make_dir_if_not_exists(debug_dir)
try:
for image_path in image_paths:
if debug_mode_on:
debug_path = debug_dir / image_path.stem
data_exporting.make_dir_if_not_exists(debug_path)
else:
debug_path = None
progress.set_status(f"Processing '{image_path.name}'.")
image = image_utils.get_image(image_path, save_path=debug_path)
prepared_image = image_utils.prepare_scan_for_processing(
image, save_path=debug_path)
# Find the corners, skipping the image on failure
try:
corners = corner_finding.find_corner_marks(prepared_image,
save_path=debug_path)
except corner_finding.CornerFindingError:
progress.set_status(
f"Error with '{image_path.name}': couldn't find corners. Skipping..."
)
time.sleep(1)
continue
# Dilates the image - removes black pixels from edges, which preserves
def process_input(image_paths: tp.List[Path], output_folder: Path,
multi_answers_as_f: bool, empty_answers_as_g: bool,
keys_file: tp.Optional[Path],
arrangement_file: tp.Optional[Path], sort_results: bool,
output_mcta: bool, debug_mode_on: bool,
form_variant: grid_i.FormVariant,
progress_tracker: tp.Optional[ProgressTrackerWidget],
files_timestamp: tp.Optional[datetime]):
"""Takes input as parameters and process it for either gui or cli.
Parameter progress_tracker determines whith interface in use.
If progress_tracker is given, function runs in gui mode.
If progress_tracker parameter is None, prints all progress statuses to stdout.
"""
answers_results = data_exporting.OutputSheet([x for x in grid_i.Field],
form_variant.num_questions)
keys_results = data_exporting.OutputSheet(
[grid_i.Field.TEST_FORM_CODE, grid_i.Field.IMAGE_FILE],
form_variant.num_questions)
rejected_files = data_exporting.OutputSheet([grid_i.Field.IMAGE_FILE], 0)
debug_dir = output_folder / (
data_exporting.format_timestamp_for_file(files_timestamp) + "debug")
if debug_mode_on:
data_exporting.make_dir_if_not_exists(debug_dir)
try:
for image_path in image_paths:
if debug_mode_on:
debug_path = debug_dir / image_path.stem
data_exporting.make_dir_if_not_exists(debug_path)
else:
debug_path = None
if progress_tracker:
progress_tracker.set_status(f"Processing '{image_path.name}'.")
else:
print(f"Processing '{image_path.name}'.")
image = image_utils.get_image(image_path, save_path=debug_path)
prepared_image = image_utils.prepare_scan_for_processing(
image, save_path=debug_path)
try:
corners = corner_finding.find_corner_marks(
prepared_image, save_path=debug_path)
except corner_finding.CornerFindingError:
rejected_files.add({grid_i.Field.IMAGE_FILE: image_path.name},
[])
continue
# Dilates the image - removes black pixels from edges, which preserves
# solid shapes while destroying nonsolid ones. By doing this after noise
# removal and thresholding, it eliminates irregular things like W and M
morphed_image = image_utils.dilate(prepared_image,
save_path=debug_path)
# Establish a grid
grid = grid_r.Grid(corners,
grid_i.GRID_HORIZONTAL_CELLS,
grid_i.GRID_VERTICAL_CELLS,
morphed_image,
save_path=debug_path)
# Calculate fill percent for every bubble
field_fill_percents = {
key: grid_r.get_group_from_info(value,
grid).get_all_fill_percents()
for key, value in form_variant.fields.items()
if value is not None
}
answer_fill_percents = [
grid_r.get_group_from_info(question,
grid).get_all_fill_percents()
for question in form_variant.questions
]
# Calculate the fill threshold
threshold = grid_r.calculate_bubble_fill_threshold(
field_fill_percents,
answer_fill_percents,
save_path=debug_path,
form_variant=form_variant)
# Get the answers for questions
answers = [
grid_r.read_answer_as_string(i, grid, multi_answers_as_f,
threshold, form_variant,
answer_fill_percents[i])
for i in range(form_variant.num_questions)
]
field_data: tp.Dict[grid_i.RealOrVirtualField, str] = {
grid_i.Field.IMAGE_FILE: image_path.name,
}
# Read the Student ID. If it indicates this exam is a key, treat it as such
student_id = grid_r.read_field_as_string(
grid_i.Field.STUDENT_ID, grid, threshold, form_variant,
field_fill_percents[grid_i.Field.STUDENT_ID])
if student_id == grid_i.KEY_STUDENT_ID:
form_code_field = grid_i.Field.TEST_FORM_CODE
field_data[form_code_field] = grid_r.read_field_as_string(
form_code_field, grid, threshold, form_variant,
field_fill_percents[form_code_field]) or ""
keys_results.add(field_data, answers)
else:
for field in form_variant.fields.keys():
field_value = grid_r.read_field_as_string(
field, grid, threshold, form_variant,
field_fill_percents[field])
if field_value is not None:
field_data[field] = field_value
answers_results.add(field_data, answers)
if progress_tracker:
progress_tracker.step_progress()
answers_results.clean_up(
replace_empty_with="G" if empty_answers_as_g else "")
answers_results.save(output_folder,
"results",
sort_results,
timestamp=files_timestamp)
if rejected_files.row_count == 0:
success_string = "✔️ All exams processed and saved.\n"
else:
success_string = "❗ Some files could not be processed (see rejected_files output).\nAll other exams were processed and saved.\n"
rejected_files.save(output_folder,
"rejected_files",
sort=False,
timestamp=files_timestamp)
if keys_file:
keys_results.add_file(keys_file)
if (keys_results.row_count == 0):
success_string += "No exam keys were found, so no scoring was performed."
elif (arrangement_file and keys_results.row_count == 1):
answers_results.reorder(arrangement_file)
keys_results.data[1][keys_results.field_columns.index(
grid_i.Field.TEST_FORM_CODE)] = ""
answers_results.save(output_folder,
"rearranged_results",
sort_results,
timestamp=files_timestamp)
success_string += "✔️ Results rearranged based on arrangement file.\n"
keys_results.delete_field_column(grid_i.Field.TEST_FORM_CODE)
keys_results.save(output_folder,
"key",
sort_results,
timestamp=files_timestamp,
transpose=True)
success_string += "✔️ Key processed and saved.\n"
scores = scoring.score_results(answers_results, keys_results,
form_variant.num_questions)
scores.save(output_folder,
"rearranged_scores",
sort_results,
timestamp=files_timestamp)
success_string += "✔️ Scored results processed and saved."
elif (arrangement_file):
success_string += "❌ Arrangement file and keys were ignored because more than one key was found."
else:
keys_results.save(output_folder,
"keys",
sort_results,
timestamp=files_timestamp)
success_string += "✔️ All keys processed and saved.\n"
scores = scoring.score_results(answers_results, keys_results,
form_variant.num_questions)
scores.save(output_folder,
"scores",
sort_results,
timestamp=files_timestamp)
success_string += "✔️ All scored results processed and saved."
if (output_mcta):
transform_and_save_mcta_output(answers_results, keys_results,
files_timestamp, output_folder)
if progress_tracker:
progress_tracker.set_status(success_string, False)
else:
print(success_string)
except (RuntimeError, ValueError) as e:
wrapped_err = "\n".join(textwrap.wrap(str(e), 70))
if progress_tracker:
progress_tracker.set_status(f"Error: {wrapped_err}", False)
else:
print(f'Error: {wrapped_err}')
if debug_mode_on:
raise
if progress_tracker:
progress_tracker.show_exit_button_and_wait()
g = FollowJointTrajectoryGoal()
g.trajectory = JointTrajectory()
joint_states = rospy.wait_for_message("joint_states", JointState)
joints_pos = joint_states.position
g.trajectory.joint_names = JOINT_NAMES
g.trajectory.points = [JointTrajectoryPoint(positions=joints_pos, velocities=[0]*6, time_from_start=rospy.Duration(0))]
g.trajectory.points.append(
JointTrajectoryPoint(positions=Q1, velocities=[0]*6, time_from_start=rospy.Duration(wait)))
client.send_goal(g)
print("waiting for finish")
client.wait_for_result()
print("finished")
time.sleep(5)
centroid = get_cork_centroid(get_image())
#x_adjust = (308-centroid[0])/3
x_adjust = 0
x_original = centroid[0]
x_goal = 308+x_adjust
x_error = centroid[0] - x_goal
print("x_goal is {0}".format(x_goal))
while abs(x_error) > 1:
print(centroid)
pose_goal = group.get_current_pose().pose
#pose_goal.position.x -= 0.005
#pose_goal.position.y += 0.005
#x_delta = x_error/3500.0
x_delta = copysign(0.003, x_error) + x_error/4500.0
#x_delta = copysign(0.002, x_error) + x_error/5000.0
print(x_delta)
save_file = SAVER_FORMAT.format(epoch, index)
saver.restore(session, save_path=str(path.join(SAVE_DIR, save_file)))
print('Loaded', epoch, 'epochs of training at', index)
else:
epoch = 1
width = len(str(index + N_TARGET_IMAGES))
image_files = listdir(TRAIN_COLOR_DIR)
length = len(image_files)
for i in range(index, min(index + N_TARGET_IMAGES, length)):
image_file = image_files[i]
print(str(i + 1).zfill(width) + '/' + str(index + N_TARGET_IMAGES),
image_file + ': ',
end='',
flush=True)
grayscale_image = get_image(
str(path.join(TRAIN_COLOR_DIR, image_file)), cv2.IMREAD_GRAYSCALE)
color_image = get_image(str(path.join(TRAIN_COLOR_DIR, image_file)),
cv2.IMREAD_COLOR, cv2.COLOR_BGR2Lab)[0]
color_image_ab = cv2.merge(
[color_image[:, :, 1], color_image[:, :, 2]])
color_image_ab = np.expand_dims(color_image_ab, axis=0)
c, image_loss, _ = session.run([colorizer_out, loss, optimizer],
feed_dict={
grayscale_in: grayscale_image,
color_in: color_image_ab
})
print(image_loss)
save_params = get_save_params(epoch, i, length)
saver.save(session,