def main(argv):
# Utility main to load flags
try:
argv = FLAGS(argv) # parse flags
except gflags.FlagsError:
print('Usage: %s ARGS\\n%s' % (sys.argv[0], FLAGS))
sys.exit(1)
_main()
def main(argv):
try:
argv = FLAGS(argv)
except gflags.FlagsError as e:
print ('FlagsError: ', e)
sys.exit(1)
else:
tf.compat.v1.set_random_seed(479)
random.seed(101)
complete_FLAGS(FLAGS)
pp = pprint.PrettyPrinter()
print_flags_dict = {}
for key in FLAGS.__flags.keys():
print_flags_dict[key] = getattr(FLAGS, key)
pp.pprint(print_flags_dict)
myinput("Press enter to continue")
repo = Repo()
sha = repo.head.object.hexsha
FLAGS.checkpoint_dir = FLAGS.checkpoint_dir[:-1] if FLAGS.checkpoint_dir[-1]=='/' else FLAGS.checkpoint_dir
if os.path.exists(FLAGS.checkpoint_dir):
I = myinput(FLAGS.checkpoint_dir+' already exists. \n Are you sure to'
' place the outputs in the same dir? Y or Y! or N\n'
'Y: resume training, save previous outputs in the dir and continue saving outputs in it\n'
'Y!: restart training, delete previous outputs in the dir\n'
'N to quit \n')
if I in ['Y','y']:
save_log(FLAGS.sh_path, FLAGS.checkpoint_dir, print_flags_dict, sha)
import time
tf.compat.v1.set_random_seed(time.localtime()[5]*10)
random.seed(time.localtime()[4]*10) #new random seed
elif I in ['N','n']:
sys.exit(1)
else:
os.system('rm -f -r '+FLAGS.checkpoint_dir+'/*')
save_log(FLAGS.sh_path, FLAGS.checkpoint_dir, print_flags_dict, sha)
else:
os.makedirs(FLAGS.checkpoint_dir)
save_log(FLAGS.sh_path, FLAGS.checkpoint_dir, print_flags_dict, sha)
assert FLAGS.mode in ['pretrain_inpainter','train_CIS', 'train_VAE', 'train_PC', 'train_end2end'
'eval_CIS', 'eval_VAE']
if FLAGS.mode == 'pretrain_inpainter':
train_inpainter.train(FLAGS)
elif FLAGS.mode == 'train_CIS':
train_CIS.train(FLAGS)
elif FLAGS.mode == 'eval_VAE':
eval_VAE.eval(FLAGS)
# elif FLAGS.mode == 'train_end2end':
# train_end2end.train(FLAGS)
elif FLAGS.mode == 'train_PC':
train_PC.train(FLAGS)
elif FLAGS.mode == 'train_VAE':
train_VAE.train(FLAGS)
else:
pass
def main(argv):
# Utility main to load flags
try:
argv = FLAGS(argv) # parse flags
except gflags.FlagsError:
print('Usage: %s ARGS\\n%s' % (sys.argv[0], FLAGS))
sys.exit(1)
steering_dict, collision_dict = _main()
return steering_dict, collision_dict
def main(argv):
# Utility main to load flags
print('started')
try:
argv = FLAGS(argv) # parse flags
except gflags.FlagsError:
print ('Usage: %s ARGS\\n%s' % (sys.argv[0], FLAGS))
sys.exit(1)
print('reached here')
_main()
def main(argv):
# Utility main to load flags
try:
FLAGS(argv) # parse flags
except gflags.Error:
print('Usage: %s ARGS\\n%s' % (sys.argv[0], FLAGS))
sys.exit(1)
s = misc.load_settings_or_use_args(FLAGS)
# path = os.path.join(outfolder + "models2/*")
# for item in sorted(glob.glob(path)):
#for x in range(0, 5, 1):
for x in ["rgb", "flow", "flow_as_rgb", "flow_as_mag"]:
s['name'] = "final_" + x
s['settings_fname'] = "settings_" + s['name'] + ".json"
s['test_dir'] = "test/rgb"
s['epochs'] = 100
s['img_mode'] = x
s['model_dir'] = "models_" + x
s['eval_dir'] = "eval"
settings_out_filename = os.path.join(outfolder, s['settings_fname'])
misc.write_to_file(s, settings_out_filename, beautify=True)
{'flow':inference['gt_flow'][batch_num],
'img1':cv2.cvtColor(preprocessed_bgr, cv2.COLOR_BGR2RGB),
'pred_mask': out_mask, #inference['gen_masks'][batch_num],
'gt_mask': inference['gt_masks'][batch_num]} )
i+=1
progbar.update(step)
tot_ious = 0
tot_maes = 0
per_cat_iou = []
for cat, list_iou in CategoryIou.items():
print("Category {}: IoU is {} and MAE is {}".format(cat, np.mean(list_iou), np.mean(CategoryMae[cat])))
tot_ious += np.sum(list_iou)
tot_maes += np.sum(CategoryMae[cat])
per_cat_iou.append(np.mean(list_iou))
print("The Average over the dataset: IoU is {} and MAE is {}".format(tot_ious/float(i), tot_maes/float(i)))
print("The Average over sequences IoU is {}".format(np.mean(per_cat_iou)))
print("Success: Processed {} frames".format(i))
if __name__ == "__main__":
try:
argv = FLAGS(sys.argv)
except gflags.FlagsError:
print ('Usage: %s ARGS\\n%s' % (sys.argv[0], FLAGS))
sys.exit(1)
_test_masks()
def main():
FLAGS(sys.argv)
# print(sys.path[0])
# file_name(sys.path[0])
#json_model_path = sys.path[0] + '/model/mytest_29_net_changed/model_struct.json'
weights_path = sys.path[0] + '/model/model-TrailNet/model_weights_249.h5'
# Set keras utils
K.set_learning_phase(TEST_PHASE)
# Load json and create model
#model = utils.jsonToModel(json_model_path)
# Input image dimensions
img_width, img_height = FLAGS.img_width, FLAGS.img_height
# Cropped image dimensions
crop_img_width, crop_img_height = FLAGS.crop_img_width_res18, FLAGS.crop_img_height_res18
target_size = (img_height, img_width)
crop_size = (crop_img_height, crop_img_width)
model = cnn_models.s_Resnet_18(crop_img_width, crop_img_height, 3, 1)
# Load weights
model.load_weights(weights_path, by_name=True)
# model.compile(loss='mse', optimizer='sgd')
model.compile(loss='mse', optimizer='adam')
#print("json_model_path: {}".format(json_model_path))
print("Loaded model from {}".format(weights_path))
# print("[INFO]")
# model.summary()
cv2.namedWindow("img", 0)
cv2.resizeWindow("img", 960, 540)
dataset_dir = '/home/rikka/uav-project/UAVPatrol-test/UAVPatrol-test/'
img_mode = FLAGS.img_mode
for dirs in os.listdir(dataset_dir):
foldername = dirs
if (foldername[0] != 't' and foldername[len(foldername) - 1] != 'p'):
print(dirs)
pics_path = dataset_dir + foldername + '/images' # sys.path[0] + '/pics'
dirct_label_exist = 1
trans_label_exist = 1
direct_label_path = pics_path + '/../direction_n_filted.txt'
trans_label_path = pics_path + '/../../' + 'translation' + foldername + '/translation.txt'
try:
direct_label = np.loadtxt(direct_label_path, usecols=0)
except OSError as e:
dirct_label_exist = 0
print('No direction labels.')
try:
trans_label = np.loadtxt(trans_label_path, usecols=0)
except OSError as e:
trans_label_exist = 0
print('No translation labels.')
l2_set = []
dril2_set = []
tral2_set = []
avg_l2 = 0
correct_dirct_num = 0
correct_trans_num = 0
count = 0
pic_list = os.listdir(pics_path)
pic_list.sort()
for count, pic in enumerate(pic_list):
# select pic
# for file in pic_list:
# print("{0}, {1}".format(count, file))
# count = count + 1
# pic_index = input("Input the number of the pic:")
#pic = pic_list[int(pic_index)]
print(pic)
img_origi = cv2.imread(os.path.join(pics_path, pic),
cv2.IMREAD_COLOR)
# run predict
if img_mode == 'grayscale':
img = cv2.cvtColor(img_origi, cv2.COLOR_BGR2GRAY)
img = cv2.resize(img, (target_size[1], target_size[0]))
else:
img = cv2.resize(img_origi,
(target_size[1], target_size[0]))
img = central_image_crop(img, crop_size[0], crop_size[1])
if img_mode == 'grayscale':
img = img.reshape((img.shape[0], img.shape[1], 1))
cv_image = np.asarray(img, dtype=np.float32) * np.float32(
1.0 / 255.0)
# print(cv_image)
outs = model.predict_on_batch(cv_image[None])
oriten, trans = outs[0][0], outs[1][0]
direct = (oriten[2] - oriten[0]) * 0.5
translation = (trans[2] - trans[0]) * 0.2
out_mu = np.array([[0, 0, direct]])
out_pi = np.array([[0, 0, 1]])
pi = np.split(out_pi, 3, axis=1)
# component_splits = [1, 1, 1]
mus = np.split(out_mu, 3, axis=1)
out_sigma = np.array([[0.05, 0.05, 0.05]], dtype='float32')
sigs = np.split(out_sigma, 3, axis=1)
x = np.linspace(-1, 1, 100)
y = np.array([])
for x_ in x:
y = np.append(y, gaussian(sigs, mus, pi, x_))
orient = np.array([])
possible_direct = []
start = 0
continue_flag = 0
for x_, y_ in zip(x, y):
if (y_ > 1.):
if continue_flag == 0:
continue_flag = 1
start = x_
y_ = (img_origi.shape[0] - y_ * 200 - 80).astype(
np.int32)
x_ = ((x_ + 1) / 2 * img_origi.shape[1]).astype(
np.int32)
x_ = img_origi.shape[1] - x_
cv2.circle(img_origi, (x_, y_), 3, (0, 255, 0), 4)
else:
if (continue_flag == 1):
continue_flag = 0
possible_direct.append((x_ + start) / 2)
y_ = (img_origi.shape[0] - y_ * 200 - 80).astype(
np.int32)
x_ = ((x_ + 1) / 2 * img_origi.shape[1]).astype(
np.int32)
x_ = img_origi.shape[1] - x_
cv2.circle(img_origi, (x_, y_), 1, (255, 0, 255), 4)
# cat = tfd.Categorical(logits=out_pi)
# coll = [tfd.MultivariateNormalDiag(loc=loc, scale_diag=scale) for loc, scale
# in zip(mus, sigs)]
# mixture = tfd.Mixture(cat=cat, components=coll)
# with tf.Session() as sess:
# xx = tf.expand_dims(tf.linspace(-1., 1., int(1e2)), 1)
# yy = mixture.prob(xx).eval()
# xx = tf.cast(((xx+1)/2*img_origi.shape[1]), dtype=tf.int32).eval()
# yy = tf.cast(img_origi.shape[0]-yy*200-80, dtype=tf.int32).eval()
# for point in zip(xx, yy):
# # print(point)
# cv2.circle(img_origi, point, 1, (0, 0, 255), 4)
# # plt.plot(x, mixture.prob(x).eval());
# # plt.savefig("abc.png")
#
steer = 0
if (dirct_label_exist):
steer = direct_label[count]
#print('direction label: {}'.format(steer))
cv2.line(img_origi, (int(
img_origi.shape[1] / 2), img_origi.shape[0] - 150),
(int(img_origi.shape[1] / 2 -
math.tan(steer * 3.14 / 2) * 100),
img_origi.shape[0] - 250), (255, 0, 0), 3)
steer_x = ((steer + 1) / 2 * img_origi.shape[1]).astype(
np.int32)
steer_x = img_origi.shape[1] - steer_x
steer_y = gaussian(sigs, mus, pi, steer)
steer_y = (img_origi.shape[0] - steer_y * 200 - 80).astype(
np.int32)
steer_x_left = ((steer + 0.1 + 1) / 2 *
img_origi.shape[1]).astype(np.int32)
steer_x_left = img_origi.shape[1] - steer_x_left
steer_y_left = gaussian(sigs, mus, pi, steer + 0.1)
steer_y_left = (img_origi.shape[0] - steer_y_left * 200 -
80).astype(np.int32)
steer_x_right = ((steer - 0.1 + 1) / 2 *
img_origi.shape[1]).astype(np.int32)
steer_x_right = img_origi.shape[1] - steer_x_right
steer_y_right = gaussian(sigs, mus, pi, steer - 0.1)
steer_y_right = (img_origi.shape[0] - steer_y_right * 200 -
80).astype(np.int32)
# print('x:{}, y:{}'.format(steer_x, steer_y))
if (steer_y < 1 * img_origi.shape[0] / 3):
cv2.circle(img_origi, (steer_x, steer_y), 6,
(255, 0, 0), 6)
cv2.circle(img_origi, (steer_x_left, steer_y_left), 6,
(255, 0, 0), 6)
cv2.circle(img_origi, (steer_x_right, steer_y_right),
6, (255, 0, 0), 6)
else:
cv2.circle(img_origi, (steer_x, steer_y), 3,
(0, 0, 255), 6)
cv2.circle(img_origi, (steer_x_left, steer_y_left), 3,
(0, 0, 255), 6)
cv2.circle(img_origi, (steer_x_right, steer_y_right),
3, (0, 0, 255), 6)
direct_l2_min = 180 * 180
direct_diff_min = 2
trans_l2 = 0
if (trans_label_exist):
trans = trans_label[count]
tral2_set.append(trans - translation)
# random
# translation = random.randint(0,10000)/5000 - 1
trans_l2 = (trans - translation)**2
if (math.fabs(translation - trans) < 0.2):
correct_trans_num = correct_trans_num + 1
#print('translation label: {}'.format(trans))
cv2.line(img_origi, (int(
img_origi.shape[1] / 2), img_origi.shape[0] - 150),
(int((trans + 1) / 2 * img_origi.shape[1]),
img_origi.shape[0] - 150), (255, 0, 0), 8)
cv2.line(img_origi,
(int(img_origi.shape[1] / 2), img_origi.shape[0]),
(int(img_origi.shape[1] / 2), 50), (0, 255, 0), 1)
cv2.line(
img_origi,
(int(img_origi.shape[1] / 2), img_origi.shape[0] - 150),
(int((translation + 1) / 2 * img_origi.shape[1]),
img_origi.shape[0] - 150), (0, 255, 0), 8)
for possible_direct_ in possible_direct:
if (dirct_label_exist):
steer = direct_label[count]
l2_direct = (steer - possible_direct_)**2
if (l2_direct < direct_l2_min):
direct_l2_min = l2_direct
if abs(steer -
possible_direct_) < abs(direct_diff_min):
direct_diff_min = (steer - possible_direct_)
# random
# possible_direct_ = random.randint(0,10000)/5000 - 1
if (abs(steer - possible_direct_) < (2 / (180 / 15))):
correct_dirct_num = correct_dirct_num + 1
print(correct_dirct_num)
break
#print("predicted: {}".format(possible_direct_))
cv2.line(img_origi, (int(
img_origi.shape[1] / 2), img_origi.shape[0] - 150),
(int(img_origi.shape[1] / 2 -
math.tan(possible_direct_ * 3.14 / 2) * 100),
img_origi.shape[0] - 250), (0, 255, 0), 3)
l2 = (trans_l2 + direct_l2_min)**0.5
l2_set.append(l2)
dril2_set.append(direct_diff_min)
avg_l2 = avg_l2 + l2
#print(l2)
cv2.imshow("img", img_origi)
cv2.imshow('crop', img)
cv2.waitKey(10)
print('==================================')
print('==================================')
print('==================================')
print('direct_accuracy = {}'.format(correct_dirct_num /
len(pic_list)))
print('trans_accuracy = {}'.format(correct_trans_num /
len(pic_list)))
# print('avg_l2 = {}'.format(avg_l2/len(pic_list)))
# print('avg_l2 = {}'.format(np.mean(l2_set)))
print('direct_SD_l2 = {}'.format(np.std(dril2_set, ddof=0)))
print('trans_SD_l2 = {}'.format(np.std(tral2_set, ddof=0)))
# Serialize model into json
json_model_path = os.path.join(FLAGS.experiment_rootdir,
FLAGS.json_model_fname)
model_to_json(model, json_model_path)
# Train model
train_model(train_generator, val_generator, model, initial_epoch)
# Plot training and validation losses
utils.plot_loss(FLAGS.experiment_rootdir)
if __name__ == "__main__":
try:
argv = FLAGS(sys.argv) # parse flags
except gflags.FlagsError:
print('Usage: %s ARGS\\n%s' % (sys.argv[0], FLAGS))
sys.exit(1)
_main()
'''
plt.figure(figsize=(10, 4))
librosa.display.specshow(librosa.power_to_db(ps, ref=np.max), y_axis='mel', x_axis='time')
plt.colorbar(format='%+2.0f dB')
plt.title('Mel spectrogram')
plt.tight_layout()
img = image.load_img(ps, target_size=(96, 431))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
def main():
FLAGS(sys.argv)
weights_path = FLAGS.model_dir
# Set keras utils
K.set_learning_phase(TEST_PHASE)
# Load json and create model
#model = utils.jsonToModel(json_model_path)
# Input image dimensions
img_width, img_height = FLAGS.img_width, FLAGS.img_height
# Cropped image dimensions
crop_img_width, crop_img_height = FLAGS.crop_img_width_dronet, FLAGS.crop_img_height_dronet
target_size = (img_height, img_width)
crop_size = (crop_img_height, crop_img_width)
model = cnn_models.resnet8(crop_img_width, crop_img_height, 1, 1)
# Load weights
model.load_weights(weights_path)
# model.compile(loss='mse', optimizer='sgd')
model.compile(loss='mse', optimizer='adam')
print("Loaded model from {}".format(weights_path))
# print("[INFO]")
# model.summary()
cv2.namedWindow("img", 0)
cv2.resizeWindow("img", 960, 540)
dataset_dir = FLAGS.test_dir
for dirs in os.listdir(dataset_dir):
foldername = dirs
if (foldername[0] != 't' and foldername[len(foldername) - 1] != 'p'):
print(dirs)
pics_path = dataset_dir + foldername + '/images' # sys.path[0] + '/pics'
dirct_label_exist = 1
trans_label_exist = 0
direct_label_path = pics_path + '/../direction_n_filted.txt'
trans_label_path = pics_path + '/../../' + 'translation' + foldername + '/translation.txt'
try:
direct_label = np.loadtxt(direct_label_path, usecols=0)
except OSError as e:
dirct_label_exist = 0
print('No direction labels.')
try:
trans_label = np.loadtxt(trans_label_path, usecols=0)
except OSError as e:
trans_label_exist = 0
print('No translation labels.')
correct_dirct_num = 0
correct_trans_num = 0
l2_set = []
dril2_set = []
tral2_set = []
avg_l2 = 0
count = 0
pic_list = os.listdir(pics_path)
pic_list.sort()
for count, pic in enumerate(pic_list):
# select pic
# for file in pic_list:
# print("{0}, {1}".format(count, file))
# count = count + 1
# pic_index = input("Input the number of the pic:")
#pic = pic_list[int(pic_index)]
print(pic)
img_origi = cv2.imread(os.path.join(pics_path, pic),
cv2.IMREAD_COLOR)
# run predict
if FLAGS.img_mode == 'grayscale':
img = cv2.cvtColor(img_origi, cv2.COLOR_BGR2GRAY)
img = cv2.resize(img, (target_size[1], target_size[0]))
else:
img = cv2.resize(img_origi,
(target_size[1], target_size[0]))
img = central_image_crop(img, crop_size[0], crop_size[1])
if FLAGS.img_mode == 'grayscale':
img = img.reshape((img.shape[0], img.shape[1], 1))
cv_image = np.asarray(img, dtype=np.float32) * np.float32(
1.0 / 255.0)
# print(cv_image)
outs = model.predict_on_batch(cv_image[None])
orient, coll = outs[0][0], outs[1][0]
translation = 0
orient = np.max(orient)
coll = 0 * coll
out_mu = np.array([[0, 0, orient]])
out_pi = np.array([[0, 0, 1]])
# print(out_pi)
# pi = sum_exp(out_pi, 1)
pi = np.split(out_pi, 3, axis=1)
# component_splits = [1, 1, 1]
mus = np.split(out_mu, 3, axis=1)
out_sigma = np.array([[0.05, 0.05, 0.05]], dtype='float32')
sigs = np.split(out_sigma, 3, axis=1)
x = np.linspace(-1, 1, 100)
y = np.array([])
for x_ in x:
y = np.append(y, gaussian(sigs, mus, pi, x_))
possible_direct = []
start = 0
continue_flag = 0
for x_, y_ in zip(x, y):
# print(point)
if (y_ > 1.):
if (continue_flag == 0):
continue_flag = 1
start = x_
y_ = (img_origi.shape[0] - y_ * 200 - 80).astype(
np.int32)
x_ = ((x_ + 1) / 2 * img_origi.shape[1]).astype(
np.int32)
x_ = img_origi.shape[1] - x_
cv2.circle(img_origi, (x_, y_), 3, (0, 255, 0), 4)
else:
if (continue_flag == 1):
continue_flag = 0
possible_direct.append((x_ + start) / 2)
y_ = (img_origi.shape[0] - y_ * 200 - 80).astype(
np.int32)
x_ = ((x_ + 1) / 2 * img_origi.shape[1]).astype(
np.int32)
x_ = img_origi.shape[1] - x_
cv2.circle(img_origi, (x_, y_), 1, (255, 0, 255), 4)
steer = 0
if (dirct_label_exist):
steer = direct_label[count]
#print('direction label: {}'.format(steer))
cv2.line(img_origi, (int(
img_origi.shape[1] / 2), img_origi.shape[0] - 150),
(int(img_origi.shape[1] / 2 -
math.tan(steer * 3.14 / 2) * 100),
img_origi.shape[0] - 250), (255, 0, 0), 3)
steer_x = ((steer + 1) / 2 * img_origi.shape[1]).astype(
np.int32)
steer_x = img_origi.shape[1] - steer_x
steer_y = gaussian(sigs, mus, pi, steer)
steer_y = (img_origi.shape[0] - steer_y * 200 - 80).astype(
np.int32)
steer_x_left = ((steer + 0.1 + 1) / 2 *
img_origi.shape[1]).astype(np.int32)
steer_x_left = img_origi.shape[1] - steer_x_left
steer_y_left = gaussian(sigs, mus, pi, steer + 0.1)
steer_y_left = (img_origi.shape[0] - steer_y_left * 200 -
80).astype(np.int32)
steer_x_right = ((steer - 0.1 + 1) / 2 *
img_origi.shape[1]).astype(np.int32)
steer_x_right = img_origi.shape[1] - steer_x_right
steer_y_right = gaussian(sigs, mus, pi, steer - 0.1)
steer_y_right = (img_origi.shape[0] - steer_y_right * 200 -
80).astype(np.int32)
# print('x:{}, y:{}'.format(steer_x, steer_y))
if (steer_y < 1 * img_origi.shape[0] / 3):
cv2.circle(img_origi, (steer_x, steer_y), 6,
(255, 0, 0), 6)
cv2.circle(img_origi, (steer_x_left, steer_y_left), 6,
(255, 0, 0), 6)
cv2.circle(img_origi, (steer_x_right, steer_y_right),
6, (255, 0, 0), 6)
else:
cv2.circle(img_origi, (steer_x, steer_y), 3,
(0, 0, 255), 6)
cv2.circle(img_origi, (steer_x_left, steer_y_left), 3,
(0, 0, 255), 6)
cv2.circle(img_origi, (steer_x_right, steer_y_right),
3, (0, 0, 255), 6)
# cv2.line(img_origi, (int(steer), img_origi.shape[0] - 150),
# (int(steer), 50), (255, 0, 0), 4)
# pics in /translation*/images are not the same as those pics in direction dataset.
# (if they are same, following code can be used)
# if(trans_label_exist):
# trans = trans_label[count]
# if(math.fabs(translation - trans)<0.2):
# correct_trans_num = correct_trans_num+1
# print('translation label: {}'.format(trans))
# cv2.line(img_origi, (int(img_origi.shape[1] / 2), img_origi.shape[0] - 150),
# (int((trans + 1) / 2 * img_origi.shape[1]), img_origi.shape[0] - 150), (255, 0, 0), 8)
direct_l2_min = 180 * 180
direct_diff_min = 2
trans_l2 = 0
if (trans_label_exist):
trans = trans_label[count]
tral2_set.append(0)
# random
# translation = random.randint(0,10000)/5000 - 1
trans_l2 = (trans - translation)**2
if (math.fabs(translation - trans) < 0.2):
correct_trans_num = correct_trans_num + 1
#print('translation label: {}'.format(trans))
cv2.line(img_origi, (int(
img_origi.shape[1] / 2), img_origi.shape[0] - 150),
(int((trans + 1) / 2 * img_origi.shape[1]),
img_origi.shape[0] - 150), (255, 0, 0), 8)
cv2.line(img_origi,
(int(img_origi.shape[1] / 2), img_origi.shape[0]),
(int(img_origi.shape[1] / 2), 50), (0, 255, 0), 1)
cv2.line(
img_origi,
(int(img_origi.shape[1] / 2), img_origi.shape[0] - 150),
(int((translation + 1) / 2 * img_origi.shape[1]),
img_origi.shape[0] - 150), (0, 255, 0), 8)
for possible_direct_ in possible_direct:
if (dirct_label_exist):
steer = direct_label[count]
l2_direct = (steer - possible_direct_)**2
if (l2_direct < direct_l2_min):
direct_l2_min = l2_direct
if abs(steer -
possible_direct_) < abs(direct_diff_min):
direct_diff_min = (steer - possible_direct_)
# random
# possible_direct_ = random.randint(0,10000)/5000 - 1
if (abs(steer - possible_direct_) < (2 / (180 / 15))):
correct_dirct_num = correct_dirct_num + 1
print(correct_dirct_num)
break
#print("predicted: {}".format(possible_direct_))
cv2.line(img_origi, (int(
img_origi.shape[1] / 2), img_origi.shape[0] - 150),
(int(img_origi.shape[1] / 2 -
math.tan(possible_direct_ * 3.14 / 2) * 100),
img_origi.shape[0] - 250), (0, 255, 0), 3)
l2 = (trans_l2 + direct_l2_min)**0.5
l2_set.append(l2)
avg_l2 = avg_l2 + l2
dril2_set.append(direct_diff_min)
#print(l2)
cv2.imshow("img", img_origi)
cv2.imshow('crop', img)
cv2.waitKey(10)
print('==================================')
print('==================================')
print('==================================')
print('direct_accuracy = {}'.format(correct_dirct_num /
len(pic_list)))
print('trans_accuracy = {}'.format(correct_trans_num /
len(pic_list)))
# print('avg_l2 = {}'.format(avg_l2/len(pic_list)))
# print('avg_l2 = {}'.format(np.mean(l2_set)))
print('direct_SD_l2 = {}'.format(np.std(dril2_set, ddof=0)))
print('trans_SD_l2 = {}'.format(np.std(tral2_set, ddof=0)))
