def main():
config = Config()
image_process = ImageProcess()
try:
if (len(sys.argv) != 3):
print('Use model_path image_file_name.')
return
image = cv2.imread(sys.argv[2])
image = cv2.resize(image, (config.image_size[0], config.image_size[1]))
image = image_process.equalize_histogram(image)
if (len(image) == 0):
print('File open error: ', sys.argv[2])
return
drive_run = DriveRun(sys.argv[1])
measurments = drive_run.run(image)
print(measurments)
except KeyboardInterrupt:
print('\nShutdown requested. Exiting...')
class DriveBatch:
###########################################################################
# model_path = 'path_to_pretrained_model_name' excluding '.h5' or 'json'
# data_path = 'path_to_drive_data' e.g. ../data/2017-09-22-10-12-34-56'
def __init__(self, model_path):
self.model = None
self.num_test_samples = 0
self.config = Config()
self.net_model = NetModel(model_path)
self.net_model.load()
self.image_process = ImageProcess()
###########################################################################
#
def _prepare_data(self, data_path):
folder_name = data_path[data_path.rfind('/'):] # get folder name
folder_name = folder_name.strip('/')
csv_path = data_path + '/' + folder_name + '.csv' # use it for csv file name
self.drive = DriveData(csv_path)
self.drive.read()
self.test_data = list(
zip(self.drive.image_names, self.drive.measurements))
self.num_test_samples = len(self.test_data)
print('\nTest samples: ', self.num_test_samples)
###########################################################################
#
def run(self, data_path):
self._prepare_data(data_path)
fname = data_path + '_log.csv'
file = open(fname, 'w')
#print('image_name', 'label', 'predict', 'abs_error')
bar = ProgressBar()
file.write('image_name, label, predict, abs_error\n')
for image_name, measurement in bar(self.test_data):
image_fname = data_path + '/' + image_name + self.config.fname_ext
image = cv2.imread(image_fname)
image = cv2.resize(
image, (self.config.image_size[0], self.config.image_size[1]))
image = self.image_process.equalize_histogram(image)
npimg = np.expand_dims(image, axis=0)
predict = self.net_model.model.predict(npimg)
#print(image_name, measurement[0], predict[0][0],\
# abs(measurement[0]-predict[0][0]))
log = image_name+','+str(measurement[0])+','+str(predict[0][0])\
+','+str(abs(measurement[0]-predict[0][0]))
file.write(log + '\n')
file.close()
print(fname, 'created.')
