image_height = int(config_parser.get('training', 'image_height'))
# Load images for training.
logger = Logger()
logger.log(Logger.INFO, "Loading images to train SVM classifier.")
positive_images = Files(path_pre_processed_positive)
negative_images = Files(path_pre_processed_negative)
samples = []
responses = []
for file_path in positive_images.paths:
# Load image.
image = Image(file_path)
# Convert and equalize image.
image.convert_to_gray()
# Add image for training.
samples.append(image)
responses.append(1)
for file_path in negative_images.paths:
# Load image.
image = Image(file_path)
image.convert_to_gray()
# Add negative images for training.
for i in range(1):
x = int(np.random.random() * (image.width - image_width - 1))
y = int(np.random.random() * (image.height - image_height - 1))
negative = image.crop(Point(x, y),
character_validator = CharacterValidator()
number_labels = []
number_images = []
letter_labels = []
letter_images = []
for index in range(len(positive_images.paths)):
file_path = positive_images.paths[index]
# Load image and configuration data.
image_original = Image(file_path)
config_data = ConfigData(Quadrilaterals(), file_path)
config_data.read_data()
# Convert and equalize image.
image_original.convert_to_gray()
for quadrilateral in config_data.quadrilaterals.data:
# Crop image using configuration data.
image = crop_image(image_original, quadrilateral, int(image_width), int(image_height))
image = pre_process_license_plate_image(image, adaptative=True)
point1 = quadrilateral.points[0]
point2 = quadrilateral.points[2]
width = point2.x -point1.x
height = point2.y -point1.y
license_plate = Rect([point1.x, point1.y, width, height])
license_plate.image = image
license_plate.subrects = image.compute_rectangles_for_characters()
character_validator.remove_wrong_characters(license_plate)
license_plate = character_validator.adjust_characters(license_plate, image_original, image_width)
def pre_processing(argv):
'''
Pre process image files to train detector.
'''
# Parses args.
arg_parser = ArgumentParser(description='Load and plot image.')
arg_parser.add_argument('-c',
'--config',
dest='config_file',
default='config.ini',
help='Configuration file')
args = vars(arg_parser.parse_args())
# Parses configuration file.
config_parser = SafeConfigParser()
config_parser.read(args['config_file'])
path_original_positive = config_parser.get('data',
'path_original_positive')
path_original_negative = config_parser.get('data',
'path_original_negative')
path_pre_processed_positive = config_parser.get(
'data', 'path_pre_processed_positive')
path_pre_processed_negative = config_parser.get(
'data', 'path_pre_processed_negative')
path_training_vec = config_parser.get('data', 'path_training_vec')
total_positive_files = config_parser.get('training',
'total_positive_files')
image_width = config_parser.get('training', 'image_width')
image_height = config_parser.get('training', 'image_height')
training_width = config_parser.get('training', 'training_width')
training_height = config_parser.get('training', 'training_height')
# Removing old files.
logger = Logger()
logger.log(Logger.INFO, "Removing old files.")
files = Files(path_pre_processed_positive, True)
files.remove()
files = Files(path_pre_processed_negative, True)
files.remove()
files = Files(path_training_vec, True)
files.remove()
# Pre process positive images.
logger.log(Logger.INFO, "Pre-processing positive images.")
positive_images = Files(path_original_positive)
for file_path in positive_images.paths:
# Load image and configuration data.
image = Image(file_path)
config_data = ConfigData(Quadrilaterals(), file_path)
config_data.read_data()
# Convert and equalize image.
image.convert_to_gray()
image.equalize()
# Get file name and extension.
base_name = os.path.splitext(ntpath.basename(file_path))
file_name = base_name[0]
extension = base_name[1]
index_quadrilateral = 0
for quadrilateral in config_data.quadrilaterals.data:
# Crop image using configuration data.
license_plate = crop_image(image, quadrilateral, int(image_width),
int(image_height))
# Save image.
save_image_path = os.path.abspath(path_pre_processed_positive +
file_name + "_" +
str(index_quadrilateral) +
extension)
license_plate.save(save_image_path)
index_quadrilateral += 1
# Pre process negative images.
logger.log(Logger.INFO, "Pre-processing negative images.")
negative_images = Files(path_original_negative)
for file_path in negative_images.paths:
# Load image.
image = Image(file_path)
# Convert and equalize image.
image.convert_to_gray()
image.equalize()
# Get base name.
base_name = ntpath.basename(file_path)
# Save image.
save_image_path = os.path.abspath(path_pre_processed_negative +
base_name)
image.save(save_image_path)
# Save detector_training image paths in txt files.
save_training_images_paths(path_pre_processed_positive,
path_pre_processed_negative)
# Generating samples.
logger.log(Logger.INFO, "Generating samples.")
call([
"perl", 'lib/generate_samples.pl', 'positive.txt', 'negative.txt',
path_training_vec,
str(int(total_positive_files) + 500),
'opencv_createsamples -bgcolor 0 -bgthresh 0 -maxxangle 1.1\ -maxyangle 1.1 maxzangle 0.5 -maxidev 40 -w '
+ str(training_width) + ' -h ' + str(training_height)
])
# Merging data for detector_training.
path_training_vec_file = "positive.vec"
logger.log(
Logger.INFO,
"Merging data for detector_training in: " + path_training_vec_file)
merge_vec_files(path_training_vec, path_training_vec_file)
