def _mouse_pressed_manual_detection(self, point):
success = False
self.manual_points.append(point)
self.exam.image.draw_corner(point)
self.interface.display_capture(self.exam.image.image_drawn)
if len(self.manual_points) == 4 * len(self.exam.image.boxes_dim):
corner_matrixes = imageproc.process_box_corners(
self.manual_points, self.exam.image.boxes_dim)
if corner_matrixes != []:
self.exam.image.detect_manual(corner_matrixes)
if self.exam.image.status['infobits']:
self.exam.model = utils.decode_model(self.exam.image.bits)
if self.exam.model is not None:
self.exam.solutions = self._solutions(self.exam.model)
if self.exam.solutions is None:
msg = 'There are no solutions for model {0}.'\
.format(self.exam.model)
self.interface.show_error(msg)
else:
self.exam.grade()
self.exam.draw_answers()
self.interface.update_status( \
self.exam.score, self.exam.model,
self.exam.im_id,
survey_mode=self.exam_data.survey_mode)
success = True
if not success:
self.exam.image.clean_drawn_image()
self.interface.show_error('Manual detection failed!')
self.from_manual_detection = True
self._start_review_mode()
def _action_snapshot(self):
"""Callback for the snapshot action."""
enable_manual_detection = False
if self.latest_graded_exam is None:
if self.latest_image is None:
return
image = self.latest_image
image.clean_drawn_image()
if image.status['infobits']:
model = utils.decode_model(image.bits)
else:
model = None
self.exam = utils.Exam(image, model, {}, self.valid_student_ids,
self.image_id, self.exam_data.score_weights,
save_image_func=self.interface.save_capture)
self.exam.grade()
self.exam.lock_capture()
else:
self.exam = self.latest_graded_exam
self.exam.image.clean_drawn_image()
self.exam.lock_capture()
self.exam.draw_answers()
enable_manual_detection = True
self._start_review_mode()
if enable_manual_detection:
self.interface.enable_manual_detect(True)
def __init__(self, success, answers, detected_id, id_scores, model=None,
infobits=None):
self.success = success
self.answers = answers
self.detected_id = detected_id
self.id_scores = id_scores
if model is not None:
self.model = model
elif infobits:
self.model = utils.decode_model(infobits)
else:
self.model = None
self.student = None
self.students_rank = []
def _process_capture(self, image):
"""Processes a captured image."""
exam = None
if image.status['infobits']:
model = utils.decode_model(image.bits)
if model is not None:
if (model in self.exam_data.solutions
or self.exam_data.survey_mode):
exam = utils.Exam(image, model, self._solutions(model),
self.valid_student_ids,
self.image_id, self.exam_data.score_weights,
save_image_func=self.interface.save_capture)
exam.grade()
self.latest_graded_exam = exam
elif model not in self.exam_data.solutions:
msg = 'There are no solutions for model {0}.'.format(model)
self.interface.show_error(msg)
return exam
def __init__(self,
success,
answers,
detected_id,
id_scores,
model=None,
infobits=None):
self.success = success
self.answers = answers
self.detected_id = detected_id
self.id_scores = id_scores
if model is not None:
self.model = model
elif infobits:
self.model = utils.decode_model(infobits)
else:
self.model = None
self.student = None
self.students_rank = []
def main():
options = read_cmd_options()
config = utils.read_config()
save_pattern = config['save-filename-pattern']
exam_data = utils.ExamConfig(options.ex_data_filename)
solutions = exam_data.solutions
if options.accept_model_0:
solutions['0'] = exam_data.num_questions * [1]
dimensions = exam_data.dimensions
id_num_digits = exam_data.id_num_digits
read_id = (id_num_digits > 0)
save_pattern = os.path.join(options.output_dir, save_pattern)
if options.answers_filename is not None:
answers_file = options.answers_filename
else:
answers_file = 'eyegrade-answers.csv'
answers_file = os.path.join(options.output_dir, answers_file)
im_id = options.start_id
valid_student_ids = None
if options.ids_file is not None:
valid_student_ids = utils.read_student_ids(filename=options.ids_file,
with_names=True)
interface = gui.PygameInterface((640, 480), read_id, options.ids_file)
profiler = PerformanceProfiler()
# Initialize options
imageproc_options = imageproc.ExamCapture.get_default_options()
imageproc_options['logging-dir'] = options.output_dir
if read_id:
imageproc_options['read-id'] = True
imageproc_options['id-num-digits'] = id_num_digits
if options.debug:
imageproc_options['show-status'] = True
imageproc_options['error-logging'] = True
if config['error-logging']:
imageproc_options['error-logging'] = True
if not options.fixed_hough:
imageproc_context = imageproc.ExamCaptureContext()
else:
imageproc_context = imageproc.ExamCaptureContext(options.fixed_hough)
# Initialize capture source
if options.proc_file is not None:
imageproc_options['capture-from-file'] = True
imageproc_options['capture-proc-file'] = options.proc_file
elif options.raw_file is not None:
imageproc_options['capture-from-file'] = True
imageproc_options['capture-raw-file'] = options.raw_file
else:
imageproc_context.init_camera(select_camera(options, config))
if imageproc_context.camera is None:
print >> sys.stderr, 'ERROR: No camera found!'
sys.exit(1)
# Program main loop
lock_mode = not options.adjust
last_time = time.time()
interface.update_text('Searching...', False)
interface.set_statusbar_message(utils.program_name + ' ' + utils.version
+ ' - ' + utils.web_location)
interface.set_search_toolbar(True)
latest_graded_exam = None
while True:
override_id_mode = False
exam = None
model = None
manual_detection = False
profiler.count_capture()
image = imageproc.ExamCapture(dimensions, imageproc_context,
imageproc_options)
image.detect_safe()
success = image.success
if image.status['infobits']:
model = utils.decode_model(image.bits,
accept_model_0=options.accept_model_0)
if model is not None and model in solutions:
exam = utils.Exam(image, model, solutions[model],
valid_student_ids, im_id, options.save_stats,
exam_data.score_weights,
interface.save_capture)
exam.grade()
latest_graded_exam = exam
else:
success = False
interface.set_manual_detect_enabled(False)
events = interface.events_search_mode()
for event, event_info in events:
if event == gui.event_quit:
sys.exit(0)
elif event == gui.event_debug_proc and options.debug:
imageproc_options['show-image-proc'] = \
not imageproc_options['show-image-proc']
elif event == gui.event_debug_lines and options.debug:
imageproc_options['show-lines'] = \
not imageproc_options['show-lines']
elif event == gui.event_snapshot:
if latest_graded_exam is None:
exam = utils.Exam(image, model, {}, valid_student_ids,
im_id, options.save_stats,
exam_data.score_weights,
interface.save_capture)
exam.grade()
interface.set_manual_detect_enabled(True)
else:
exam = latest_graded_exam
success = True
if options.ids_file is not None:
exam.reset_student_id_filter(False)
else:
exam.reset_student_id_editor()
override_id_mode = True
elif event == gui.event_manual_detection:
exam = utils.Exam(image, model, {}, valid_student_ids, im_id,
options.save_stats, exam_data.score_weights,
interface.save_capture)
exam.grade()
interface.set_manual_detect_enabled(True)
# Set the event to repeat again in lock_mode
interface.enqueue_event((gui.event_manual_detection, None))
success = True
if options.ids_file is not None:
exam.reset_student_id_filter(False)
else:
exam.reset_student_id_editor()
override_id_mode = True
elif event == gui.event_lock:
lock_mode = True
# Enter review mode if the capture was succesfully read or the
# image was locked by the user
if success and lock_mode:
continue_waiting = True
manual_detection_mode = False
manual_points = []
exam.lock_capture()
exam.draw_answers()
interface.show_capture(exam.image.image_drawn, False)
interface.update_text(exam.get_student_id_and_name(), False)
if exam.score is not None:
interface.update_status(exam.score, exam.model, exam.im_id,
flip=False)
interface.set_review_toolbar(True)
while continue_waiting:
event, event_info = interface.wait_event_review_mode()
if event == gui.event_quit:
sys.exit(0)
elif event == gui.event_cancel_frame:
continue_waiting = False
elif event == gui.event_save:
stats = profiler.finish_exam(exam)
exam.save_image(save_pattern)
exam.save_answers(answers_file, config['csv-dialect'],
stats)
if options.debug:
exam.save_debug_images(save_pattern)
im_id += 1
continue_waiting = False
elif event == gui.event_manual_id:
override_id_mode = True
exam.reset_student_id_editor()
exam.draw_answers()
interface.show_capture(exam.image.image_drawn, False)
interface.update_text(exam.get_student_id_and_name())
elif (event == gui.event_next_id
or event == gui.event_previous_id):
if not override_id_mode and options.ids_file is not None:
if len(exam.student_id_filter) == 0:
if event == gui.event_next_id:
exam.try_next_student_id()
else:
exam.try_previous_student_id()
else:
exam.reset_student_id_filter()
profiler.count_student_id_change()
exam.draw_answers()
interface.show_capture(exam.image.image_drawn, False)
interface.update_text(exam.get_student_id_and_name())
elif event == gui.event_id_digit:
if override_id_mode:
exam.student_id_editor(event_info)
elif options.ids_file is not None:
exam.filter_student_id(event_info)
profiler.count_student_id_change()
exam.draw_answers()
interface.show_capture(exam.image.image_drawn, False)
interface.update_text(exam.get_student_id_and_name())
elif event == gui.event_debug_proc and options.debug:
imageproc_options['show-image-proc'] = \
not imageproc_options['show-image-proc']
continue_waiting = False
elif event == gui.event_debug_lines and options.debug:
imageproc_options['show-lines'] = \
not imageproc_options['show-lines']
continue_waiting = False
elif event == gui.event_manual_detection:
exam.image.clean_drawn_image()
interface.show_capture(exam.image.image_drawn, False)
interface.update_text(('Manual detection: click on the '
'outer corners of the answer '
'box(es)'))
manual_detection_mode = True
manual_points = []
elif event == gui.event_click:
if not manual_detection_mode:
cell = cell_clicked(exam.image, event_info)
if cell is not None:
question, answer = cell
exam.toggle_answer(question, answer)
interface.show_capture(exam.image.image_drawn,
False)
interface.update_status(exam.score, exam.model,
exam.im_id, flip=True)
else:
manual_points.append(event_info)
exam.image.draw_corner(event_info)
interface.show_capture(exam.image.image_drawn, True)
if len(manual_points) == 4 * len(exam.image.boxes_dim):
corner_matrixes = imageproc.process_box_corners(
manual_points, exam.image.boxes_dim)
if corner_matrixes != []:
exam.image.detect_manual(corner_matrixes)
if exam.image.status['infobits']:
exam.model = utils.decode_model(image.bits)
if exam.model is not None:
exam.solutions = solutions[exam.model]
exam.grade()
interface.update_status(exam.score,
exam.model,
exam.im_id,
flip=False)
else:
exam.image.clean_drawn_image()
interface.set_statusbar_message(('Manual '
'detection '
'failed!'),
False)
exam.draw_answers()
interface.show_capture(exam.image.image_drawn,
False)
interface.update_text('', True)
manual_detection_mode = False
dump_camera_buffer(imageproc_context.camera)
interface.update_text('Searching...', False)
interface.update_status(None, flip=False)
interface.set_search_toolbar(True)
latest_graded_exam = None
if imageproc_options['capture-from-file']:
sys.exit(0)
else:
if exam is not None:
exam.draw_answers()
else:
image.draw_status()
interface.show_capture(image.image_drawn)
current_time = time.time()
diff = current_time - last_time
if current_time > last_time and diff < param_max_wait_time:
interface.delay(param_max_wait_time - diff)
last_time += 1
else:
if diff > 3 * param_max_wait_time:
dump_camera_buffer(imageproc_context.camera)
last_time = current_time
if imageproc_options['capture-from-file']:
interface.wait_key()
sys.exit(1)
